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    Many thanks for all the feedback on the draft version of this post.

    Main updates since the draft:
    • Added DRIFTER to the list of de-noising methods
    • Added a reference for sex differences in hematocrit and the effects on BOLD
    • Added several medication classes, including statins, sedatives & anti-depressants
    • Added a few dietary supplements, under Food

    Please do continue to let me know about errors and omissions, especially new papers that get published. I'll gladly do future updates to this post.
      ______________________



      A recent conversation on Twitter led to the suggestion that someone compile a list of physiological effects of concern for BOLD. That is, a list of potentially confounding physiological changes that could arise sympathetically in an fMRI experiment, such as altered heart rate due to the stress of a task, or that could exist as a systematic difference between groups. What follows is the result of a PubMed literature search (mostly just the abstracts) where I have tried to identify either recent review articles or original research that can be used as starting points for learning more about candidate effects. Hopefully you can then determine whether a particular factor might be of concern for your experiment.

      This is definitely not a comprehensive list of all literature pertaining to all potential physiological confounds in fMRI, and I apologize if your very important contribution didn't make it into the post. Also, please note that I am not a physiologist so if I go seriously off piste in interpreting the literature, please forgive me and then correct my course. I would like to hear from you (comments below, or via Twitter) if I have omitted critical references or effects from the list, or if I have misinterpreted something. As far as possible I've tried to restrict the review to work in humans unless there was nothing appropriate, in which case I've included some animal studies if I think they are directly relevant. I'll try to keep this post up-to-date as new studies come out and as people let me know about papers I've missed.

      A final caution before we begin. It occurs to me that some people will take this list as (further) proof that all fMRI experiments are hopelessly flawed and will use it as ammunition. At the other extreme there will be people who see this list as baseless scare-mongering. How you use the list is entirely up to you, but my intent is to provide cautious fMRI scientists with a mechanism to (re)consider potential physiologic confounds in their experiments, and perhaps stimulate the collection of parallel data that might add power to those experiments.


      Getting into BOLD physiology


      There are some good recent articles that introduce the physiological artifacts of prime concern. Tom Liu has reviewed neurovascular factors in resting-state functional MRI and shows how detectable BOLD signals arise from physiological changes in the first place. Kevin Murphy et al. then review some of the most common confounds in resting-state fMRI and cover a few ways these spurious signal changes can be characterized and even removed from data. Finally, Dan Handwerker et al. consider some of the factors causing hemodynamic variations within and, in particular, between subjects.

      Once you start really looking into this stuff it can be hard not to get despondent. Think of the large number of potential manipulations as opportunities, not obstacles! Perhaps let The Magnetic Fields get you in the mood with their song, "I don't like your (vascular) tone." Then read on. It's a long list.


      Breathing and heart rates


      These are undoubtedly the two big issues and will be top-of-mind for most people. Murphy et al. do a good job introducing the topic of cleaning raw data, e.g. using independent respiration and heart rate data. There are numerous correction methods available today (see the section, "Time series cleanup with physiological recordings" in the Murphy paper) and these references are perhaps the best places to start if you want to dig into the origins of the problems. In reverse chronological order we have:

      PHYCAA+ by Churchill & Strother
      GLMdenoise by Kay, Rokem, Winawer, Dougherty & Wandell.
      RIPTiDe by Frederick, Nickerson & Tong (using near-infrared spectroscopy, NIRS)
      DRIFTER by Särkkä, Solin, Nummenmaa, Vehtari, Auranen, Vanni & Lin.
      RVT-HR by Chang, Cunningham & Glover
      RVT by Birn, Diamond, Smith & Bandettini
      RETROICOR by Glover, Li & Reese

      Then there are also numerous ICA-based methods, including multi-echo acquisition with a model fit. I won't comment on the relative or absolute performance of any of these schemes, just let you know you have options! But one important lesson seems to be this: measuring heart rate and respiration independently isn't likely to hurt.

      I'll also mention the inter-relatedness of HR and breathing, through vagal tone. This has implications for fMRI studies employing emotional stimuli.


      CO2: hypercapnia and hypocapnia


      Inspired CO2 is a well-known and powerful vasodilator. Similarly, breath holding is known to lead to hypercapnia (increases in arterial CO2) and thus vasodilation. Thus, under normal conditions (no external CO2 source) the arterial concentration of CO2 will be closely linked to the breathing rate, as demonstrated in this 2004 study. The quantitative relationship between breathing rate and end-tidal CO2 is further investigated in this 2009 study.

      As for dealing with the variation, in a 2012 study it was demonstrated that clamping arterial CO2 to a narrow range improved retrospective physiological artifact removal from resting-state and task-based data. I like this idea because I've often wondered whether not having the magnet bore properly ventilated could cause hypercapnia. I can't find any papers that have looked at this issue, however. The closest I've found is a study that used CO2 supplied artificially, clearly demonstrating the potential interaction of inspired CO2 with stimulus-driven BOLD. (There was an earlier study in rats that reached essentially the same conclusion.) It's probably best to keep the ventilation constant in the magnet bore for all subjects, just in case.

      It turns out there may be gender bias, too. A 2010 study found that men have higher cerebrovascular reactivity to a CO2 challenge. So, not only do men tend to exhibit more head movement than women on average, men also appear to be more sensitive to CO2.


      O2: hyperoxia and hypoxia


      While refluxed CO2 may be a legitimate concern, incidental hyperoxia seems unlikely unless your MRI facility has a very strange design indeed. But, if you happen to be doing fMRI of divers, high-altitude pilots or astronauts in a simulated environment, do bear in mind the possibility of arterial BOLD from dissolved O2 if your subjects are breathing 100% oxygen. I suppose this ought to be a concern in anesthetized fMRI subjects, too. (I'll consider the physiologic consequences of anesthetics below.) If you're comparing normal volunteers (awake) to anesthetized patients then you might want to consider giving the former group the same gas mixture (sans anesthetic) as the latter.

      What about hypoxia? If you have a well-designed MRI facility then ordinarily acute hypoxia shouldn't be an issue. But if your scanner is in a poorly ventilated basement you might want to consider an oxygen sensor, assuming you don't already have one, or improving your air conditioning. Otherwise you may be contributing to the variability in your subjects' BOLD responses.

      Chronic hypoxia may be an important issue to track, too. Yan et al. studied immigrants who had grown up at high altitude (HA, 2616–4200 m) and relocated to sea level (SL, < 400 m) to a group of sea-level controls, and found both anatomical and resting state fMRI differences associated with "chronic high altitude hypoxia." These changes were in spite of "...no significant differences in hemoglobin levels, circulating red blood cell count, blood pressure, and pulse rate between HA residents and SL controls." In a separate study, Yan et al. found persistent, correlated alterations in verbal working memory and BOLD responses for subjects who had relocated from HA (2,982.8 ± 478.7 m) to SL (< 400 m), even though they had been resident at sea level for at least one year and in spite of consistent hemoglobin levels.


      Vasomotion


      According to that great oracle, Wikipedia, "vasomotion is the spontaneous oscillation in tone of blood vessels, independent of heart beat, innervation or respiration." Fair enough. Our concern is whether vasomotion might differ across groups, or perhaps might even vary for an individual under different conditions. Again according to Wikipedia, "vasomotion has been shown to be altered in a variety of pathological situations, with vessels from both hypertensive and diabetic patients displaying altered flow patterns as compared to normotensive vessels."

      Murphy et al. say this: "If vasomotion is independent of cardiac, respiratory, arterial CO2 concentration and blood pressure fluctuations, its low-frequency characteristics will present another confound for resting-state fMRI BOLD oscillations."

      How serious a concern might spontaneous low-frequency variations be for your average BOLD study? A recent paper by Tong & Frederick claims that low frequency oscillations (LFOs) are a larger component of physiologic noise than respiration or heart rate, even when respiration and heart rate frequencies are aliased because of the relatively long TRs that are typical of most fMRI studies. There is also evidence from intraoperative recordings that vasomotion (at ~0.1 Hz) is "spatially localized to distinct regions of the cortex, exhibited wave-like propagation, and involved oscillations in the diameter of specific pial arterioles, indicating that the effect was not the result of systemic blood pressure oscillations."Not small potatoes, then.

      What do these LFOs tell us, and (how) do they relate to the ongoing intrinsic neural activity that we assume is driving resting-state fMRI? Methinks this might become the next fashionable study area, if it's not already.


      Blood pressure


      A good introduction to blood pressure (BP) and its relationship to CBF is given in the Murphy, Birn & Bandettini review. We might be interested in the subject's baseline (pre-scan) BP, but we might also be interested in the BP during a time series acquisition given that BP changes when laying supine versus standing, during stress, etc.

      I've been unable to locate any papers showing whether TR-to-TR (real-time) BP is of any use in explaining variance in BOLD time series data. Baseline BP (before MRI) was found to offer only a small normalizing effect on visual-evoked BOLD signals when tested across two conditions; baseline venous T2 was considerably better. A study by Lui et al. in cocaine-dependent (human) subjects found that dobutamine infusion raised mean arterial BP (MAP) but produced only localized BOLD signal changes (in anterior cingulate) that correlated with the BP rise. Gianaros et al. observed a correlation between mean arterial BP and BOLD activity in several brain regions of participants conducting a stressful Stroop task, with BP measured once for each block of sixteen 90-second task blocks.

      This is Murphy et al.'s take:
      "Evidence of the influence of blood pressure oscillations on resting-state fMRI fluctuations in humans is sparse. Blood pressure levels in rats have been shown to affect evoked fMRI responses, with transient hypertension increasing BOLD (Wang et al., 2006) and CBF (Qiao et al., 2007) signals. Under hypotension, neural activity-evoked CBV increases in visual cortex are negligible compared to ~ 10% at normal blood pressure levels (Nagaoka et al., 2006). Increases in the amplitude of low-frequency BOLD fluctuations have been demonstrated with a drop in mean arterial pressure (Biswal and Kannurpatti, 2009). As supporting evidence in humans, BOLD signal correlates of heart rate and pulse height in the low frequency range have been discovered with fluctuations in cardiac rate explaining up to 11% of the variance in the resting-state BOLD signal (Chang et al., 2009, de Munck et al., 2008 and Shmueli et al., 2007)."

      What about chronic hypertension? Another study from Gianaros et al. used the Stroop test to stress healthy young volunteers as a model for determining whether reactivity to psychological stressors might be used to assess risk from hypertension. They found: "Individuals exhibiting greater stressor-evoked MAP reactivity showed (1) greater amygdala activation, (2) lower amygdala gray matter volume, and (3) stronger positive functional connectivity between the amygdala and perigenual anterior cingulate cortex and brainstem pons."

      Hypotension may be a factor in cases of major blood loss, perhaps including recent blood donation. While I wasn't able to find any papers looking at recent blood loss (or donation) on BOLD fMRI, Kalisch et al. used a hemorrhage model in rats and observed heterogeneous correlations between BOLD and BP. They suggest that "...a BOLD decrease during a decrease in BP may result from an increase in mostly venous cerebral blood volume (CBV) as an autoregulatory response to maintain cerebral blood flow (CBF) during decreased perfusion pressure."


      Hematocrit level


      Hematocrit level is the percentage of red blood cells in the blood. And since red blood cells carry hemoglobin, which itself transports the usable oxygen in blood, a person's hematocrit level is an important parameter when considering BOLD signal variations. Hematocrit levels could be unusually high in endurance athletes or folks who have just returned from an extended time living at altitude, for example. At the other extreme, we would be concerned about fMRI subjects experiencing anemia.

      Levin et al. found a positive linear dependence of BOLD percent activation (BPA) on hematocrit level, the relationship being stronger in men than in women. Furthermore, "...9 men were studied before and following isotonic saline hemodilution, resulting in an average 6% reduction in hematocrit and an 8-31% reduction in BPA." This suggests checking whether your subject has just come off an i.v. saline drip because of food poisoning or flu.

      There are also well-known sex differences for hematocrit level, with men having an average value several percent (5% seems to be the consensus figure) higher than women. A poster by Yang et al. at this year's HBM conference showed how variation in hematocrit across individuals produced a systematic difference in several measures derived from resting-state fMRI.

      Most causes of anemia are chronic and presumably you would be aware of them, but there are forms of anemia that might be of issue for BOLD studies in so-called normal volunteers. Recent utilization of i.v. fluids has just been mentioned while recent blood loss, perhaps from major surgery or giving blood, were considered in a prior section. Are these important? Knowing about them might permit you to interpret an outlier. Low hematocrit could also be relevant to studies of certain patient groups, e.g. acute traumatic brain injury, if polytrauma could have resulted in recent loss of blood.

      Possible variations in hemoglobin suggests a role for normalization when comparing across groups (or even within subjects over an extended period of time). Lu, Yezhuvath & Xiao investigated the utility of baseline venous oxygenation as a normalizing parameter, finding that "...visual-evoked BOLD signal is significantly correlated with baseline venous T2 (P = 0.0003) and inclusion of physiologic modulator in the regression analysis can substantially reduce P values of group-level statistical tests."

      I'll include here a special case. Sickle cell anemia (SCA) has profound effects on the BOLD response because resting CBF is increased. Remarkably, Bob Ogg's group was able to get fMRI responses in SCA children but the BOLD signals were diminished in amplitude. What is especially intriguing is that "... blood hemoglobin concentration and resting CBF were not predictive of BOLD signal amplitude in the SCA patients."No attempt was made to measure venous blood T2 in this particular study. Definitely an area ripe for more research.


      Exercise


      The long-term effects of exercise, particularly in endurance athletes, should be reflected in hematocrit level. What about the short-term effects of recent exercise on BOLD? If your subjects arrive at the scanner having just come from a boot camp class at the gym, or they've ridden a bike twenty miles from home, do you need to know about it?

      A study by MacIntosh et al. showed that twenty minutes of aerobic exercise less than an hour before scanning decreased CBF in grey matter for up to 40 minutes. BOLD results from a go/no-go attention task were mostly consistent with the pre-exercise baseline. However, the same group used a similar exercise regimen prior to acquiring resting-state fMRI and "...observed a change in the resting-state BOLD functional connectivity of young healthy adults in three [resting state networks] RSNs, predominantly localized to cortical areas involved in sensorimotor activity." The latter study was limited in that "...at the time of the repeat rs-fMRI scan, the heart rate decreased after exercise but was nonetheless still significantly greater than the pre-exercise heart rate. Using heart rate as a covariate in the paired design group analysis did influence the session-related findings for two of the three significant RSNs. Blood pressure was not measured continuously so we cannot rule out the possibility that it too was elevated at the time of the repeat rs-fMRI scan."

      There are clear opportunities for more research in this direction, but it would appear that we should insist on baseline measures of hematocrit (or venous T2) and blood pressure in addition to heart rate and respiration, or we risk misinterpreting BOLD changes.


      (De)hydration


      Using thermal exercise so that subjects dehydrated via sweating, versus a non-thermal exercise control, Kempton et al. found an increased fronto-parietal BOLD response during an executive function task while cognitive performance and CBF were unchanged. The authors suggest: "This pattern indicates that participants exerted a higher level of neuronal activity in order to achieve the same performance level." 


      Caffeine


      The effect of this vasoconstrictor on BOLD seem to have been studied more than any other drug (or foodstuff), probably because it's the most widely used stimulant in the world. There are many references to choose from, and what emerges is a picture best described as "it's complicated."

      For a start it turns out that a subject's normal caffeine usage makes a difference if they're given a caffeine challenge. Laurienti et al. found "...that the BOLD signal change in visual cortex was significantly greater in high users than in low users in the presence of caffeine." Are your subjects high or low users? Would they even know? Here's a searchable website containing many of the common dietary sources of caffeine. Many sources are well-known and obvious, e.g. dark chocolate. Some, though, may surprise you.

      Next there is the dose response. The amount of caffeine alters BOLD magnitude non-linearly. According to Chen & Parrish, the greatest effects on BOLD are associated with an intermediate caffeine dose of 2.5 mg/kg. (It must only be the neural response that goes all jittery with high doses!) In the early 2000s it was even suggested to use caffeine as a BOLD signal booster, but the non-linear dose response seems to have nixed that idea. Laurienti et al. puts it this way: "It is not possible to consistently enhance BOLD signal intensity magnitude by decreasing resting perfusion with caffeine."

      In case the picture isn't sufficiently complicated already, it has been found that caffeine alters the temporal dynamics of visual BOLD responses. Intriguingly, administration of caffeine has also been found to enhance the linearity of the BOLD response to rapid visual stimuli. One assumes there must be a dose response that hasn't yet been investigated, however. What about resting-state fMRI and caffeine? For starters, caffeine changes resting-state connectivity for motor cortex. It also increases the variability of motor cortical connectivity.

      So it appears that we may have an additional, potentially large, source of inter- (and intra?) subject variability based on the pattern of normal caffeine use, right? Curiously, the answer is ambiguous. Addicott, Peiffer & Laurienti investigated the effects of a caffeine dose (or placebo) on subjects representing a range of usage levels (including abstinence). Acute caffeine administration did produce measurable effects in BOLD, consistent with prior reports. But the changes weren't moderated either by normal use or by abstinence in regular users. The authors conclude "...that dietary caffeine use does not produce a significant effect on task-related BOLD activation."

      Why might this be? Griffeth, Perthen & Buxton used a quantitative fMRI experiment to investigate caffeine's effects on BOLD and CBF simultaneously. They observed offsetting changes in baseline blood flow and oxygen metabolism with subsequent responses to visual stimuli, such that: "The combined effect was that BOLD responses pre- and post-caffeine were similar despite large underlying physiological changes, indicating that the magnitude of the BOLD response alone should not be interpreted as a direct measure of underlying neurophysiological changes."

      Finally, let's assume you're interested in the neural consequences of caffeine use. How to differentiate from physiology? A good place to start is this 2010 review of caffeine's effects on cognition as well as BOLD. Then check out these more recent studies using simultaneous EEG-fMRI, comparing MEG to fMRI in the same subjects across separate sessions, or comparing fMRI to PET.

      So, what's the bottom line regarding dietary caffeine as a potential BOLD confound? I don't know! But it sure looks like something that ought to be tracked, even if it's just self-reported accounts.

      Consuming a stimulant in the land of depressants.


      Alcohol


      Ahhh, the world's favorite depressant. (The bulk of this post is being written overlooking vineyards in the Bennett Valley of Sonoma county, CA. Yes, that is coffee on the table above. Wine comes later, once the shadows point to bottom-right.) Let's deal with what we do know first. Acute administration of alcohol reduces BOLD activation in response to a visual stimulus, suppresses BOLD activity during a goal-directed visuomotor task, changes CBF in a dose-dependent manner, and modulates neurovascular coupling. Furthermore, acute alcohol consumption changes significantly several cognitive and visual networks mapped with a resting fMRI paradigm. (Also this study, on resting networks.) Finally, there are studies assessing the chronic effects of alcohol abuse, but I don't think chronic alcohol consumption can be considered as a potential physiologic confound because the neural effects will likely dominate.

      Very surprisingly, nobody seems to have looked at the consequences of hangovers on fMRI. If acute alcohol administration causes changes to physiology and these changes are dose-dependent then at what point may we consider them negated? An hour? Eight hours? A night's sleep? Surely - surely? - someone must have looked at this. If not, it's a race to see who can get the first study reported.


      Nicotine


      The literature on nicotine, and cigarette smoking in particular, is a mixed bag as far as potential confounds go. In a study on nicotine-dependent smokers, Jacobsen et al. found no effect of intravenous nicotine on BOLD signals produced by visual stimulation. However, citing the "Considerable variability across individuals... in both the behavioral and fMRI blood oxygen level-dependent (BOLD) response to nicotine" that had been found in prior studies, Warbrick et al. observed: "...some participants showed an increase in activation while others showed a decrease in BOLD activation from the placebo to the [nasal] nicotine condition." Nineteen of the 41 total subjects were smokers.

      So much for the controlled administration of nicotine in an experiment. What about the effects of smoking itself? Smoking may be a much larger concern because cigarettes produce carbon monoxide (CO), and hemoglobin has a high affinity for CO. If hemoglobin is carrying CO then it can't be carrying O2. How much of a concern is this displacement for BOLD? The literature doesn't say. Inhaling cigarette smoke is associated with a high number of health issues, including reduced pulmonary function, but I didn't find any literature addressing these concerns for BOLD-based fMRI.

      One study from 2008 looked at heavy smoking as a potential confound in BOLD studies of schizophrenia, but found no difference with non-smokers using a sensorimotor task. However, another study from 2008 found significant differences in breath hold and visual activation tasks for heavy smokers versus controls. Why the contradiction? No idea I'm afraid.

      Given the well documented respiratory and cardiac issues associated with chronic cigarette smoking, it seems to me that there is a very strong likelihood of systematic bias in the physiology of any group of smokers versus non-smokers. Would we compare marathon runners to couch potatoes and not expect significant differences in physiology? Surely, then, we should insist on some baseline measures of physiology, including perhaps pulmonary function tests (e.g. spirometry), or we should expect high inter-subject variability as well as smoker versus non-smoker group differences.

      The physiologic effects produced by other nicotine delivery methods such as chewing tobacco, e-cigarettes (vaporizers) or patches may differ from the effects produced by cigarette smoking. Certainly that is my intuition but I wasn't able to locate any literature dealing with these issues. I think it suffices to say that we would want to uses caution when interpreting BOLD signals from smokers, and to predict systematic differences with non-smokers. That would just leave the dose effect (number of cigarettes/day, and perhaps depth and duration of smoke inhalation) to add a few more complications to the picture.


      Illicit drugs


      With the possible exception of cannabis (marijuana), we can probably discount most illegal (or partially legal) drugs as serious confounds for routine fMRI studies because they aren't in widespread use in the general population. (See Note 1.) But just in case, I did searches for several drugs, including ecstasy (MDMA), LSD and psychedelics in general, heroin, and methamphetamine, looking for direct investigations into possible confounding physiologic changes. Pattinson et al. studied the mu-opioid agonist remifentanil, finding only some small, regional modulations of the BOLD response to a hypercapnic challenge. Gollub et al.found that cocaine decreased CBF but BOLD responses to visual stimuli were normal. This doesn't imply that the underlying physiology is normal, however. As with caffeine, simultaneous changes in baseline CBF and oxygen metabolism could yield normal-looking responses in spite of different physiologic mechanisms. 

      Cannabis is now in widespread use in most western populations. There are numerous studies of the (neural and behavioral) effects of this compound, but only a small handful of investigations of the physiologic effects relevant to BOLD.

      There are a couple of literature reviews that have looked at neuroimaging and cannabis use. In a 2010 review the authors noted the general finding "...that resting global and prefrontal bloodflow are lower in cannabis users than in controls." An updated review, in 2013, assessed the findings as suggesting "...different patterns of resting global and brain activity during the performance of several cognitive tasks both in adolescents and adults, which may indicate compensatory effects in response to chronic cannabis exposure." Only neuroimaging studies involving chronic cannabis users were considered. Both reviews highlighted the methodological limitations of the work conducted to date and the considerable heterogeneity of results.

      What sort of physiologic confounds might be of concern in cannabis users, anyway? Let's look at the acute effects of smoking marijuana. O'Leary et al. used O-15 PET and found: "Smoking marijuana resulted in intoxication, as assessed by a behavioral rating scale, but did not significantly alter mean behavioral performance on the attention task. Heart rate and blood pressure increased dramatically following smoking of marijuana but not placebo cigarettes. However, mean global CBF did not change significantly. Increased rCBF was observed in orbital and mesial frontal lobes, insula, temporal poles, anterior cingulate, as well as in the cerebellum. The increases in rCBF in anterior brain regions were predominantly in "paralimbic" regions and may be related to marijuana's mood-related effects. Reduced rCBF was observed in temporal lobe auditory regions, in visual cortex, and in brain regions that may be part of an attentional network (parietal lobe, frontal lobe and thalamus)."

      That there are immediate effects on physiology following cannabis administration isn't all that surprising. And, as with acute alcohol administration, subjects smoking a joint immediately before doing an fMRI experiment probably aren't the prime concern. (If they are, you might want to check the effects of carbon monoxide, as for regular cigarettes.) What about the hangover effects or the chronic effects on pulmonary function?

      In a 2006 study, Sneider et al. obtained dynamic susceptibility contrast (DSC) MRI (i.e. gadolinium bolus) from twelve "current, long-term daily cannabis users between 6 and 36 hr after the subjects' last reported cannabis use. Cannabis users demonstrated significantly increased blood volumes in the right frontal area (p < .05), in the left temporal area (p < .005), and in the cerebellum (p < .005) relative to comparison subjects."They followed that study with a longer duration of abstinence, scanning subjects with DSC MRI at 7 and 28 days after last cannabis use: "The present findings demonstrate that at Day 7, cannabis users continued to display increased blood volumes in the right frontal region, the left and right temporal regions, and the cerebellum. However, after 28 days of abstinence, only the left temporal area and cerebellum showed significantly increased CBV values in cannabis users. These findings suggest that while CBV levels begin to normalize with continued abstinence from cannabis, specifically in frontal areas, other temporal and cerebellar brain regions show slower CBV decreases."

      Acute differences in regional CBF were also found in adolescent marijuana users, but the differences had resolved after four weeks of monitored abstinence. CBF wasn't measured at intermediate times, however, so we can't tell from this study how long any physiologic hangover effects might last. It also isn't clear whether the persistent effects are neural, physiologic or both, but you'd think that anything lasting more than a few days would have a strong neural basis. (This 2010 study observed BOLD signal changes in a spatial working memory task dependent on recency of use in adolescents.)

      But do changes in baseline CBV and CBF imply different neurovascular coupling or altered BOLD responses? These might be more problematic for interpretation of fMRI data than persistent (neural) effects. According to Murphy et al., perhaps not. They used a finger-tapping task and determined no differences between groups of users of cocaine, nicotine or cannabis and control subjects.


      Medications


      Many legal (prescription or over-the-counter) analgesic and anti-inflammatory drugs don't seem to have been studied for their effects on neurovascular coupling. I couldn't find anything on aspirin (acetylsalicylic acid), paracetamol (acetaminophen), ibuprofen, codeine or oxycodone (oxycontin).  I also couldn't find any references investigating the undesirable effects of Viagra (or its competitors) or antihistamines on BOLD physiology. (One group has even suggested using diphenhydramine, active ingredient in the US/Canadian version of the antihistamine drug, Benadryl as a way to combat nausea and vertigo in 7 T MRI.)

      Sedatives have been used in several fMRI studies, but to date there have been no investigations into concomitant physiologic effects for the popular benzodiazepines such as diazepam (Valium), lorazepam (Ativan) or midazolam.  The non-benzodiazepine sedative zolpidem (Ambien) has demonstrated clear effects on visual BOLD signals with no concomitant change in heart rate or oxygen saturation. The same drug was shown to alter resting connectivity but the accompanying heart rate data were deemed unreliable due to equipment failures. I was unable to find any studies using another common non-benzodiazepine, eszopiclone (Lunesta).

      Of those medications that have been considered for possible physiologic confounds, the non-steroidal anti-inflammatory indomethacin was studied a decade ago and found to change BOLD and CBF but not CMRO2 for subjects conducting a simple motor task.

      Medicines aimed specifically at increasing a patient's blood flow are of obvious concern for fMRI. Acetazolamide (Diamox) was shown to increase resting cortical perfusion by 20% and decrease primary motor cortical BOLD activation by 35%, whereas the CBF response in primary motor cortex was unchanged in normal volunteers. A similar pattern was observed in patients with steno-occlusive coronary disease. However, another common vasodilator, glyceryl trinitrate, which increases resting cerebral blood volume (CBV), had no demonstrable effect on the BOLD response. The authors suggest that the glyceryl trinitrate affects large arteries only and has negligible effect on the microvascular system driving BOLD.

      Blood pressure medications such as angiotensin, or anticoagulants such as warfarin, are presumably not something your subjects will use without telling you; your screening should catch these. Likewise asthma medications. I couldn't find any studies looking at potential physiologic confounds for these medications in any case. But Grichisch et al.investigated the potential effects of nasal insulin on CBF and BOLD and found no difference to a drug-free baseline. (Insulin and blood glucose level is considered again below, under Food.)

      Statins (Lipitor, Zocor, Pravachol, etc.) appear to be in widespread and increasing use in western populations. These medicines are designed to reduce cholesterol synthesis in the liver, but there appear to be secondary outcomes with implications for cerebral hemodynamics, such as "...upregulation of endothelial nitric oxide synthase (eNOS) with a subsequent increase in nitric oxide (NO) bioavailability." Xu et al. investigated the hemodynamic consequences of taking atorvastatin (Lipitor) over four months for asymptomatic middle-aged adults. They observed numerous regional changes in BOLD, including both greater and faster regional responses, compared to a group receiving placebo. They observed no change in baseline CBF nor any change in mean transit time (MTT) of cerebral perfusion, suggesting that the regional BOLD changes came about through altered (improved) small vessel vasoreactivity.

      Antidepressants, including several selective serotonin re-uptake inhibitors (SSRIs), serotonin-norepinephrine re-uptake inhibitors (SNRIs), and a host of compounds belonging to other four-letter acronyms have been given to subjects in dozens of fMRI studies. But I was unable to find any systematic investigation of concomitant physiology, such as measuring CBF before and after drug administration.

      And then there are the so-called "smart drugs" and other brain enhancers such as methylphenidate (Ritalin), amphetamine (Adderall) and their ilk. (If I'm not using the correct collective terms for these compounds, I apologize.) Rao et al. assessed BOLD and perfusion changes for subjects performing a finger tapping task before and after oral methylphenidate and found no changes in neurovascular coupling. Otherwise, I wasn't able to find much literature addressing the potential for physiologic confounds. Marquand et al. looked at CBF changes due to methylphenidate while Nordin et al.assessed CBF changes due to amphetamine, both groups finding regional differences compared to placebo. But any implications for BOLD fMRI are unclear.

      Literature coming out of the so-called pharmacological MRI (phMRI) field may provide the best clues for potential confounds to routine fMRI studies, should you have specific reason to be concerned about the use of medicines in your population of interest. Wang et al. reviewed the potential and challenges of using arterial spin labeling (ASL) in phMRI, "...with an emphasis on the methodologies used to control for potentially confounding vascular and systemic effects."


      Anesthetics


      It's unlikely you'll be surprised by use of anesthetics among your subjects. But there is certainly interest in scanning people who may have had recent surgery, e.g. traumatic brain-injured patients who may have required surgery to treat other injuries or even the TBI itself. You would presumably want to discount any residual neural and physiologic effects of anesthesia. For example, Qui et al. found "Low-dose sevoflurane significantly altered the task-induced CBF-BOLD coupling."Post surgery one expects altered blood pressure, heart rate and breathing rate, so there are plenty of options to measure and control for concomitant physiologic effects.


      Foods


      It seems that the biggest potential physiological confounds lie with those dietary substances known to alter neural activity, as already reviewed. In this section I'll try to deal with latent effects from things your subjects ingest for other reasons. For sustenance, say. Before that, though, what about the possible effects of restricted nutrition?

      There are a couple of fMRI studies on fasting, one that found an effect in a motor task and another that found altered functional connectivity of visual cortex. There are also several reports of altered BOLD signal in response to experimental hypoglycemia (induced via insulin administration), including cognitive, visual stimulation, and sentence comprehension fMRI tasks. All three studies observed regional differences when compared to normal glucose levels. (See Note 2.) Patients with type I diabetes also showed different BOLD activity compared to controls. Additionally, there is one study using CBF and measures of oxidative metabolism (CMRO2) to assess the effects of hypoglycemia. They found that "...metabolism and flow remained coupled. Elementary motor task activation was not associated with any consistent larger activated flows. Thus it remains that although mild hypoglycemia induced an increase in basal flow and metabolism, a similar increase was not seen in task activation."Finally, glucose and fructose infusions were compared to saline and found to alter BOLD signal magnitude in opposite directions.

      These are interesting studies, but you're probably more interested in any possible effect of blood glucose level on normal BOLD variability when your subjects aren't fasting intentionally or having their blood sugar levels manipulated artificially. I couldn't find any literature addressing this specific concern, however there is a very recent paper that compared an overnight fast with normal and hyperglycemic conditions. While the paper's abstract makes it sound like there were big changes in BOLD, in the Discussion they write: "These effects are comparatively small, yet may interfere with design sensitivity, when fasting status or blood glucose is not controlled in fMRI experiments."I only skimmed the paper but was left with the impression that the jury is still out on this one. Hyperglycemia, specifically, doesn't seem to be a concern for BOLD. Gruetter et al. observed no significant effects on BOLD for blood glucose of up to 300% of control levels.

      Now let's move to what your subjects might be eating before getting a scan. In 2003 it was reported that ingesting lipid - 50 ml of canola oil - decreased BOLD response in a finger tapping task. But the potential effect of triglyceride levels in blood was measured again in 2009 by another group, whereupon no significant effects on BOLD were observed. Why the contradiction? We can only speculate. The authors of the 2009 study offer several plausible explanations in their discussion, and I am inclined to go with the more recent result pending further experimental data.

      Then there is the antithesis to the high fat diet: salad. Aamand et al. used a nitrate challenge, "corresponding to the nitrate content of a large plate of salad," to trigger changes in BOLD reactivity without altering the baseline CBF. CBF (with arterial spin labeling) in response to dietary nitrate was also measured by Presley et al.They found no global CBF changes but did observe some regional changes, in frontal lobe. The apparent contradiction with the first report was addressed by Aamand et al. as perhaps due to differences in sensitivity. In Aamand et al. they used only the inferior half of a 32-channel head coil which reduced the SNR in frontal lobe for that study.

      Dietary supplements may deserve their own entire section at some point but for now I'll include them as food because they are generally unregulated, unlike FDA-controlled medications. Creatine, usually as the monohydrate, is a popular sports supplement that has been shown to decrease the magnitude of a visual BOLD response by 16%. I didn't find any literature assessing the effects of carnitine, calcium, glycerol, vitamin A (including retinol and beta-carotene), vitamin B (including folic acid and thiamine), vitamin C (ascorbic acid), vitamin D or vitamin E (including tocopherol) on fMRI signals or on cerebral blood flow.

      There was also no literature dealing with amino acids generally, but tryptophan has been investigated because of its role as a serotonin precursor. Acute tryptophan depletion, as a way to manipulate serotonin levels, was found to modify regional BOLD responses to a cognitive task, but global effects weren't assessed. I was unable to find any literature investigating dietary glutamate (including as monosodium glutamate) or arginine.

      Chronic ingestion of omega-3 fatty acids has been investigated using near-infrared spectroscopy, but only in frontal lobe where changes in both oxyhemoglobin and total hemoglobin were observed. Global measures of CBF weren't feasible. Taken together with prior fMRI results, which reported greater prefrontal BOLD signals in a sustained attention task without a change in task performance, it seems that some sort of vascular changes may be occurring. As with lipid ingestion generally, more work will be needed before we know whether there are general effects of concern for BOLD.



      Diurnal factors and sleep


      Those of you studying anxiety or the effects of sleep or sleep deprivation are presumably acutely aware of and controlling for potentially confounding physiologic changes in your study designs. The effects of intentional manipulation of cortisol and sleep deficiency on fMRI are well documented. What about the effects in "normal volunteers?"Stress (level of blood cortisol) and amount of sleep (drowsiness) may be factors for the time of day your study is conducted, and they may vary with the populations you sample from. (Ditto for caffeine use!) But I can't find any references specifically addressing these possibilities. There is a now classic study which showed how inter-subject variability is much larger than intra-subject variability, so perhaps it's all baked in. Still, you would want to avoid biasing groups or longitudinal studies by the time of day you scan.


      Hormones


      I was unable to find any papers dealing with concomitant physiological changes accompanying the menstrual cycle. It seems reasonable to expect that significant effects might be reflected in the heart rate, respiration rate/depth and blood pressure. Direct vasodilatory or vasoconstrictive effects seem unlikely to me, but I'm no endocrinologist. There are papers dealing with performance differences across menstrual cycle, but if these effects repeat they may have a neural basis rather than being physiological artifacts.

      Women (and soon men?) going on or off hormonal contraceptives during a study might be a bigger deal. A recent study found a marked change in BOLD responses for women using oral contraception. The effects could be neural or concomitant physiological changes, or a mixture of both.

      I couldn't leave this section without mentioning oxytocin. It's quite in vogue. I wasn't able to find any studies that had have investigated systemic physiological effects of this compound as they pertain to BOLD. But giving subjects oxytocin - or any other pharmacological agent come to that - without checking for concomitant physiological changes is taking a leap of faith that all effects are neural. At bare minimum I would expect blood pressure to be recorded with and without the challenge, and then I would want to see heart rate and respiration recorded during fMRI.


      Age and disease


      These variables should come as no major surprise if you are screening your subjects in any way at all. But in order to be complete and to give you a place to start if you're doing group studies across the lifespan, or comparing disease states to normal volunteers, I offer these papers:

      Alterations in the BOLD fMRI signal with aging and disease: a challenge for neuroimaging.

      Age-related differences in memory-encoding fMRI responses after accounting for decline in vascular reactivity.

      Evidence that neurovascular coupling underlying the BOLD effect increases with age during childhood.

      Age-related differences in cerebral blood flow underlie the BOLD fMRI signal in childhood.

      Age dependence of hemodynamic response characteristics in human functional magnetic resonance imaging. 

      Neural mechanisms of age-related slowing: the ΔCBF/ΔCMRO2 ratio mediates age-differences in BOLD signal and human performance.

      Calibrated fMRI during a cognitive Stroop task reveals reduced metabolic response with increasing age.

      Visualization of altered neurovascular coupling in chronic stroke patients using multimodal functional MRI.


      Acceleration


      If your subjects have just returned from a six-month stint on the International Space Station and you want to compare fMRI before and after the flight, you are no doubt fully aware of the many physiological factors to take into account where extended microgravity is concerned. Short duration microgravity - say, the few minutes to be experienced by lucky punters taking Virgin Galactic flights in the not-too-distant future - is probably not much of a concern, unless the flight happens the morning of their fMRI scan.

      At the other end of the scale are those people exposing themselves to high acceleration; much greater than 1 g. Presumably these exposures are seconds or at most minutes in duration, but if the exposure occurs frequently or happens in the hours before participating in an fMRI experiment, we should be sure to exclude delayed physiologic changes. To date, though, I can find no reports detailing possible confounding effects of aerobatic plane flights, gymnastics, fairground rides, trampolining and the like. I am assuming these aren't going to be common issues for the vast majority of fMRI studies, but concomitant physiologic changes would be worth consideration if your research happens to involve fighter pilots, race car drivers, etc. Presumably measuring blood pressure, respiration and heart rates would capture the main effects. 


      ________________________



      Notes:

      1.  The other day someone told me about a subject who had answered negative to a drug screen but who then got very concerned when, on the morning of the scan, she was asked to give a urine sample. (I don't recall why the urine sample was required, and presumably it was stated on the recruitment flier which I can only assume the subject didn't read fully before applying to do the experiment. In any event, it was a vital part of the experiment and not a law-enforcement action!) She apparently returned from the bathroom with a sample of tap water in her specimen tube. The point? People will lie on screening forms if they are trying to make the $20 an hour you're offering. So if your experiment is highly sensitive to illicit drug use then you may want to include the option (or the threat?) of a urine test to help filter those people out.

      2.  Hypoglycemia was also one of the conditions used (in rats) by Ogawa et al.in their preliminary demonstration of the BOLD effect. From the abstract of their 1990 paper: "This blood oxygenation level-dependent (BOLD) contrast follows blood oxygen changes induced by anesthetics, by insulin-induced hypoglycemia, and by inhaled gas mixtures that alter metabolic demand or blood flow."So we're not exactly covering new ground here, are we?



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      This post updates the checklist that was presented back in January, 2013. The updated checklist is denoted Version 1.2. The main update is to include reporting for simultaneous multi-slice (SMS) (a.k.a. multi-band, MB) EPI.

      Explanatory notes for parameter names appear in the lower portion of the post. Note that the present checklist was devised by considering typical fMRI experiments conducted on 1.5 T and 3 T scanners but the list should work reasonably well for 7 T sequences, too.

      Please keep the comments and feedback coming. This is an ongoing, iterative process.




      Release notes for Version 1.2

      All changes from Version 1.1 have been highlighted in yellow, both on the list PDF and on the explanatory notes (below).

      1. The "Spatial Encoding" parameter categories have been renamed "In-Plane Spatial Encoding" to better differentiate in-plane acceleration (e.g. GRAPPA) from slice dimension acceleration (SMS/MB).

      2. When using slice dimension acceleration (i.e. SMS/MB), certain parameters that are listed as Supplemental for other EPI variants should be considered Essential. Specifically, it is suggested to report:
      Matrix coil mode
      Coil combination method

      All the In-Plane Spatial Encoding parameters in the Supplemental category should be considered because there is a tendency to use SMS/MB to attain high spatial resolution, requiring long readout echo trains that can have higher distortion than found in typical EPI scans.

      The In-plane reconstructed matrix parameter should be reported whenever partial Fourier sampling is used, as it often is for SMS/MB EPI.

      All the RF & Contrast parameters in the Supplemental category should be reported because the shape, duration and amplitude of the excitation RF pulse are all integral components of the acceleration method.

      The Shim routine should be reported if a non-standard shim is performed before SMS/MB EPI.

      3. Pre-scan normalization has been added to the Supplemental section of RF & Contrast parameters. Large array coils produce strong receive field heterogeneity and the use of pre-scan normalization may improve the performance of post hoc motion correction.



      Explanatory notes


      Essential - Scanner

      Magnetic field strength: In lieu of magnetic field strength the scanner operating frequency (in MHz) might be considered acceptable. I'm assuming we're all doing 1-H fMRI. If not, chances are the methods section's going to be very detailed anyway.


      Essential - Hardware Options

      Rx coil type: For standard coils provided by the scanner vendor a simple description consisting of the number of independent elements or channels should suffice, e.g. a 12-channel phased array coil, a 16-leg birdcage coil. Custom or third-party coils might warrant more detailed information, including the manufacturer. Most head-sized coils are Rx-only these days, but specifying Rx-only doesn't hurt if there could be any ambiguity, e.g. a birdcage coil could quite easily be Tx/Rx.


      Essential - In-Plane Spatial Encoding

      Pulse sequence type: A generic name/descriptor is preferred, e.g. single-shot EPI, or spiral in/out.

      Number of shots (if > 1): Multi-shot EPI isn't a common pulse sequence; single-shot EPI is by far the most common variant, even if acquired using parallel imaging acceleration. I include this option to reinforce the importance of reporting the spatial encoding accurately.

      PE acceleration factor (if > 1): This is usually called the acceleration factor, R in the literature. Vendors use their own notation, e.g. iPAT factor, SENSE factor, etc.

      PE acceleration type (if > 1): It seems that most vendors use the generic, or published, names for parallel imaging methods such as SENSE, mSENSE and GRAPPA. I would think that trade names would also be acceptable provided that the actual (published) method can be deciphered from the scanner vendor and scanner type fields. But generic names/acroynms are to be preferred.

      PE partial Fourier scheme (if used): Convention suggests listing the acquired portion/fraction of k-space rather than the omitted fraction. Any fraction that makes sense could be used, e.g. 6/8 or 48/64 are clearly equivalent.


      Essential - Spatial Parameters

      In-plane matrix: This should be the acquired matrix. If partial Fourier is used then I would suggest reporting the corresponding full k-space matrix and giving the partial Fourier scheme as listed above. But I wouldn't object to you reporting that you acquired a 64x48 partial Fourier matrix and later reported the reconstructed matrix size as 64x64. So long as everything is consistent it's all interpretable by a reader. (But see also the In-plane reconstructed matrix parameter in the Supplemental section.)

      In-plane inline filtering (if any): Non-experts may be unaware that filtering might be applied to their "raw" images before they come off the scanner. It's imperative to check and report whether any spatial smoothing was applied on the scanner as well as during any "pre-processing" steps subsequent to porting the time series data offline.

      Slice thickness and Inter-slice gap: For now I would use the numbers reported by the scanner, even though there may be some small variation across scanner vendors and across pulse sequences. For example, some vendors may use full width at half slice height while others may use a definition of width at or near the base, there may be pulse sequence options for RF excitation pulse shape and duration, etc. I see these details as secondary to the essential reporting improvements we're aiming for.

      Slice acquisition order:Interleaved or contiguous would be sufficient, although explicit descending (e.g. head-to-foot) or ascending (e.g. foot-to-head) options for contiguous slices would be acceptable, too. Presumably, the subsequent use of slice timing correction will be reported under the post-processing steps (where most fMRIers call these "pre-processing" because they are applied before the statistical analysis).


      Essential - Timing Parameters

      TR: For single-shot EPI with no sparse sampling delay the TR also happens to be the acquisition time per volume of data. But if sparse sampling or multiple shot acquisition is being used then the TR should be clearly reported relative to these options. The conventional definition of TR is the time between successive RF excitations of the same slice. Thus, by this definition the reported TR would include any sparse sampling delay, but it would specify the time of each separate shot in a multi-shot acquisition and the per volume acquisition time would become TR x nshots.

      No. of volumes in time series: Dummy scans (not saved to disk) should be reported separately. Likewise, the use or rejection of the first n volumes for task-related reasons, e.g. to allow a subject to acclimatize to the scanner sounds, should also be reported separately in the post-processing segment of the experiment. In this field we are only interested in the total quantity of data available to the experimenter.

      No. of averages/volume (if > 1): I don't think I've ever seen anyone do anything but one average per TR for single-shot EPI/spiral (unless they've screwed something up) and I can't think of a reason why someone would want to do it for fMRI. But, if it happens for some reason then it's really, really important to specify it.


      Essential - RF & Contrast

      Fat suppression scheme: It's sufficient to state that fat saturation or fat suppression was used, for example. Further details aren't required unless the scheme was non-standard, e.g. a custom spatial-spectral excitation scheme.


      Essential - Slice Acceleration

      SMS/MB acceleration factor:  The number of slices excited simultaneously, equivalent to the spatial acceleration in the slice dimension. The total number of slices (Number of slices parameter) must be divisible by the SMS/MB acceleration factor. Note that the slice dimension acceleration (this parameter) is independent of any in-plane acceleration (e.g. SENSE, GRAPPA) and should be reported independently. For example, GRAPPA with R=2 should be reported as suggested in the In-Plane Spatial Encoding section, while slice acceleration should be reported here. It is not acceptable to combine the parameter reporting as one overall acceleration because the practical consequences of each are very different.

      Sequence/recon name &/or version:  Unlike many single-shot EPI sequences which are product, most SMS/MB sequences are being actively developed and change frequently. It is imperative to report version numbers for both the acquisition and, if they aren’t coupled, the reconstruction. If reconstruction is performed offline, report the method used, and any version number, in full.


      Essential - Customization

      Sparse sampling delay (if used): Sometimes called "Delay in TR" on the scanner interface. Used most often for auditory stimulus or auditory response fMRI.

      Prospective motion correction scheme (if used): PACE is one commercial option. These schemes fundamentally change the nature of the time series data that is available for subsequent processing and should be distinguished from retrospective (post-processing) corrections, e.g. affine registration such as MCFLIRT in FSL. It is also critical to know the difference between motion correction options on your scanner. On a Siemens Trio running VB15 or VB17, for instance, selecting the MoCo option enables PACE and a post hoc motion correction algorithm if you are using the sequence, ep2d_pace whereas only the post hoc motion correction algorithm - no PACE - is applied if you are using the ep2d_bold sequence. There's more detailed information on these options in my user training guide/FAQ.

      Cardiac gating (if used): This isn't a common procedure for fMRI, and recording of cardiac information, e.g. using a pulse oximeter, is radically different from controlling the scanner acquisition via the subject's physiology. The recording of physiological information doesn't usually alter the MRI data acquisition, whereas gating does. Reporting of physio information is tangential to the reporting structure here, but if you are recording (and using) physio data then presumably you will report it accordingly somewhere in the experiment description.


      Supplemental - Hardware Options

      Gradient set type: It should be possible to infer the gradient coil from the scanner model. If not, e.g. because of a custom upgrade or use of a gradient insert set, then the specifications of the actual gradient coil should be reported independently.

      Tx coil type (if non-standard): It should be possible to infer the Tx coil from the scanner model. If not, e.g. because of a custom upgrade or use of a combined Tx/Rx coil, then the new Tx coil should be reported independently. I would also advocate including the drive system used if the coil is used in anything but the typical quadrature mode.

      Matrix coil mode (if used): There are typically default modes set on the scanner when one is using un-accelerated or accelerated (e.g. GRAPPA, SENSE) imaging.  If a non-standard coil element combination is used, e.g. acquisition of individual coil elements followed by an offline reconstruction using custom software, then that should be stated.

      Coil combination method: Almost all fMRI studies using phased-array coils use root-sum-of-squares (rSOS) combination, but other methods exist. The image reconstruction is changed by the coil combination method (as for the matrix coil mode above), so anything non-standard should be reported.


      Supplemental - In-Plane Spatial Encoding

      PE direction: If you've shown any examples of EPI in your paper then the PE direction can usually be determined from the image. If N/2 ghosts or distortions aren't obvious, however, then it's rather important that the phase encode direction is stated, in concert with the readout echo spacing, so that a reader can infer your spatial accuracy.

      Phase oversampling (if used): There's no reason to use phase oversampling for EPI - you're wasting acquisition time - but if you are using it for some reason then it should be reported consistent with the acquired matrix, acquired FOV, echo spacing and associated parameters.

      Read gradient bandwith: Not an intrinsically useful parameter on its own, it does have value if reported in conjunction with the echo spacing. (An alternative to this single parameter would be the read gradient strength (in mT/m) and the digitizer bandwidth (in kHz).)

      Readout echo spacing: Rarely reported but really useful! This number, in conjunction with the FOV and acquired matrix size, allows a reader to estimate the likely distortion in the phase encode direction.

      Pulse sequence name: Could be invaluable for someone wanting to replicate a study. There may be multiple similar pulse sequences available, all capable of attaining the specifications given, but it is entirely feasible that only one of the sequences has a particular quirk in it!

      k-space scheme: Readers will assume linear (monotonic) k-space steps in the PE direction unless indicated to the contrary. Centric ordering or other atypical schemes should be indicated, especially in concert with multiple shots if the Number of shots parameter is greater than one.

      Read gradient strength: Could be useful in conjunction with information about the ramp sampling percentage and echo spacing time, otherwise probably of limited value to most readers.

      (Ramp sampling percentage:) Ramp sampling can increase the N/2 ghost level considerably if there is appreciable gradient and digitization (data readout) mismatch. But determining the percentage of readout data points that are acquired on the flat vs. the ramp portions of each readout gradient episode can be involved. And for routine studies, as opposed to method development studies, there's probably not a whole lot of value here. Maybe remove it?

      (Ghost correction method:) N/2 ghost correction usually happens invisibly to the user, but there are some options becoming available, especially useful for large array coils (e.g. 32-channel coils) where there may be local instabilities with some ghost correction methods. If known, and if non-standard, then it would be nice to report. But perhaps more overkill for fMRI methods?

      PE partial Fourier reconstruction method: If the scanner offers more than one reconstruction option then the chosen option should be reported.


      Supplemental - Spatial Parameters

      In-plane resolution: This field is redundant if the reconstructed matrix (In-plane matrix parameter) and FOV are reported, but I for one wouldn't object to seeing the nominal in-plane pixel size given anyway. It may make the paper a faster read. Probably not worth arguing about. (Cue extensive debate...!)

      In-plane reconstructed matrix: This is for reporting of zero filling (beyond the default zero filling that may have been done for a partial Fourier acquisition) to a larger matrix than acquired, prior to 2D FT. There may be modeling issues associated with the number of voxels in the image, not least of which is the size of the data set to be manipulated! It could save someone a lot of angst if she knows what you did to the data prior to uploading it to the public database.


      Supplemental - Timing Parameters

      No. of dummy scans: This is the number of dummy scans used to establish the T1 steady state. Many fMRI experiments also use acquired volumes subsequent to the (unsaved) dummy scans for neuroscientific reasons, e.g. to allow definition of a BOLD baseline or adjustment to the scanner noise. These two parameters should be reported separately.


      Supplemental - RF & Contrast

      Excitation RF pulse shape and Excitation RF pulse duration:  Not critical for standard pulse sequences on commercial scanners, but if atypical values are set in order to achieve very thin slices, for example, then reporting these parameters may be benenficial. These two parameters are essential, however, when reporting SMS/MB EPI.

      Pre-scan normalization: Typically achieved with a low-resolution gradient echo scan, but the actual process may be invisible to the user. Instead, activating pre-scan normalization may simply require selection of the appropriate option. (This is the case on Siemens scanners.)


      Supplemental - Slice Acceleration

      SMS/MB reconstruction type:  Report details of the reconstruction type if possible, e.g. the kernel size for a GRAPPA-style reconstruction.

      FOV shift:  The sequence may use blipped controlled aliasing along the slice direction to assist in the separation of the simultaneous slices. The blips are set to produce a fixed partial FOV shift, typically FOV/2, FOV/3 or FOV/4. The FOV shift may be user-controlled or set to a fixed default.

      SIR/SER factor:  If simultaneous image (or echo) refocusing is used in addition to SMS/MB (as in Feinberg et al., PLoS One, 2010), report the SIR/SER acceleration factor as well as the SMS/MB acceleration. 


      Supplemental - Customization

      Image reconstruction type: Unless specified to the contrary, most readers will assume that magnitude images were taken from the 2D Fourier transform that yielded each individual EPI. If you use complex data - magnitude and phase - then that option should be specified along with the particular processing pipeline used to accommodate the atypical data type.

      Shim routine: If manual shimming or an advanced phase map shimming routine is used, especially to improve the magnetic field over restricted brain volumes, then this information should be reported.

      Receiver gain: Most scanners use some form of autogain to ensure that the dynamic range at the receiver is acceptable. If manual control over receiver gain is an option and is used then it should be reported because a mis-set gain could lead to artifacts that aren't typically seen in EPI, and a reader could subsequently attribute certain image features to other artifact sources.


      _________________



      Abbreviations:

      FOV - Field-of-view
      MB - Multi-band
      N/2 - Half-FOV (for Nyquist ghosts)
      PE - Phase encode
      Rx - Radiofrequency (RF) receiver
      SER - Simultaneous echo refocusing
      SIR - Simultaeous image refocusing
      SMS - Simultaneous multi-slice
      Tx - Radiofrequency (RF) transmitter
      TE - Echo time
      TR - Repetition time



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      Crazy Scientist sent me a link to a paper, "Neuroimaging after mild traumatic brain injury: Review and meta-analysis," (doi:10.1016/j.nicl.2013.12.009) and it prompted me to do something with a brief review I wrote this time last year as a way to plan some research activities on MRI of mild traumatic brain injury (mTBI). By a remarkable coincidence the review paper was made available online four whole days before I completed my own review. I've yet to read the published review so I can't yet tell if I wasted my time. In any event, the document I wrote was for internal consumption (for my collaborators, and to clarify my own thoughts) and was never designed to become a public document. But since our research direction changed mid-year I figured I might as well stick my review out there in case anyone can make use of it.

      The title of my document is the same as the title of this post. You will find the contents pasted below, or if you prefer you can download a PDF from this Dropbox link. I have quickly re-read it to check for major bloopers, and I've added a couple of update notes highlighted in yellow. There may well be some direct copy-paste of parts of a few of the papers I reviewed, especially those with heavy neuroradiology content where I am generally a long way out of my depth and would prefer to accept charges of plagiarism than get the medical terminology wrong!

      For the record, we are still interested in mTBI but the logistics of studying acute brain injury in a non-hospital setting, using a home-made machine (the ULFMRI) sitting in a second basement lab in a physics department, made it all too hard to pursue right now. We have shifted instead to studying chronic conditions where we have a fighting chance of getting a few people scanned in our unorthodox facilities.


       ______________________________


       Document completed on 8th January 2014. A few updates highlighted in yellow, added on 2nd January 2015.


      Potential of ultralow field T1 and high field T1ρ in evaluating brain trauma


      Recent work at UC Berkeley has demonstrated that ultralow field (ULF) MRI of the human brain is readily achievable and provides a wide intrinsic contrast between brain tissue, CSF and blood (Inglis et al. 2013). The spin-lattice relaxation times (T1) observed at ULF are comparable to the relaxation times obtained at high fields using the so-called T1ρ contrast mechanism. (A brief review of the T1ρ mechanism appears below.) This similarity can be understood intuitively by recognizing that the static magnetic field strength B0 of the ULFMRI and the B1spin locking fields used for T1ρ contrast are comparable, typically in the range 5-200 μT. Thus, we may reasonably expect that the applications mooted for T1ρ imaging at high fields will extend naturally to applications for ULF T1 imaging.

      While T1ρimaging at fields of 1.5 T and above has shown great promise in a wide array of novel applications, it is in the arena of human brain trauma and stroke that the largest potential seems to reside. There is a major hurdle to clear, however. At high magnetic fields the amount of tissue heating generated by the T1ρ contrast module – generally called a spin locking pulse – can be prohibitive and leads to severe restrictions to the peak amplitude of B1 that can be used. At 1.5 T the limit is around 80 μT, whereas at 3 T the limit is around 20 μT due to the quadratic dependence of heating (as measured by specific absorption rate, SAR) on the operating frequency. The SAR concern has drastically limited the application of T1ρ to human brain, even though a number of exciting results in animal models have shown that T1ρ contrast contains novel information on cellular processes that is not reflected in standard contrast mechanisms – in isolation or taken together - for hours or even days.

      There are a number of “unmet needs” in the diagnosis and evaluation of such pathologies as mild traumatic brain injury (mTBI) and stroke where ULF T1 imaging holds promise. In the case of mTBI, for example, magnetic susceptibility weighted imaging (SWI) and diffusion-weighted imaging (DWI) have demonstrated their ability to characterize microscopic bleeding and white matter axonal damage, respectively. Yet subtle changes in brain tissue are often not detected on MRI scans in spite of cognitive deficits or psychiatric problems. And when lesions are detected, there is often limited correlation between acute measures derived from neuroimaging and the level of disability or eventual outcome. Predicting which patients are prone to cognitive or psychiatric disability is important since early therapeutic intervention has the potential to reduce the risk of long-term deficits.

      Imaging of acute stroke is another area where MRI has yet to make a major clinical impact. Computed tomography (CT) is used to differentiate ischemic from hemorrhagic stroke, after which many patients receive an MRI scan only days after the event. MRI may be able to show the long-term pathology caused by the stroke. However, an earlier MRI scan that could determine the time since an ischemic stroke could be invaluable for determining the most appropriate treatment, e.g. thrombolysis, hypothermia or administration of neuroprotective drugs. Combinations of MRI parameters, in particular quantitative spin-spin relaxation time (T2) imaging together with DWI-derived measures, have had modest success of determining at a single imaging time point the duration of ischemia, yet there remains considerable variability in the results and little clinical application of acute MRI to date.

      In this review the focus is on mild TBI. As will be shown, the reasons for this focus are four-fold: (1) there is a need to improve the detection of lesions in mTBI patients that have clinical symptoms but negative MRI scans; (2) there is a need to better differentiate lesions caused by mTBI from existing pathology or atrophy; (3) there is a need to develop biomarkers that can be used predictively in the acute and sub-acute stage of mTBI that might lead to improved treatment; and (4) the logistics of studying mTBI is amenable to the equipment and research subjects available at UC Berkeley. Other brain pathologies, most notably moderate to severe TBI, stroke and Alzheimer’s disease, are also of considerable interest to this proposal, but developing a research program tailored to mTBI should facilitate developments that can be redirected at these other conditions at the appropriate time.




      Glossary

      AD             Alzheimer’s disease
      ADC           Apparent diffusion coefficient
      BOLD         Blood oxygenation level dependent (contrast)
      CT              (X-ray) Computed tomography
      DAI            Diffuse axonal injury
      FA             Fractional anisotropy (http://en.wikipedia.org/wiki/Fractional_anisotropy)
      FLAIR        Fluid-attenuated inversion recovery
      FLASH       Fast, low-angle shot imaging: a low flip angle, short TR gradient echo method
      FSE            Fast spin echo: multiple spin echoes with separate phase encoding per echo
      GCS           Glasgow coma scale
      GRE           Gradient-recalled echo: a standard (spin warp) phase-encoded sequence
      MCAO        Middle cerebral artery occlusion (stroke model)
      mTBI          Mild TBI, synonymous with concussion.
      PTSD         Post traumatic stress disorder
      SAR           Specific absorption rate
                        (See http://en.wikipedia.org/wiki/Specific_absorption_rate for limits.)
      SWI            Susceptibility-weighted imaging
      TBI             Traumatic brain injury
      TBSS         Tract-based spatial statistics





      Traumatic brain injury in civilian and military populations

      In the US mild TBI accounts for 75% of the 1.7 million patients annually who seek medical attention for acute head injury. However, to date, there are no reliable markers in neuroimaging for mTBI. Instead, mTBI is diagnosed based on a loss of consciousness (LOC) for less than 30 minutes, post-traumatic amnesia under 24 hours duration and/or a transient focal seizure, and a Glasgow Coma Scale (GCS) score between 13 and 15. Based on this diagnosis, limited intervention may be prescribed but in general patients are discharged without follow-up care.

      In the last thirteen years more than 280,000 US service members have been diagnosed with TBI (http://www.dvbic.org/dod-worldwide-numbers-tbi). The rate of new cases now exceeds 30,000 a year. Most military or combat-related TBI occurs as a closed head injury as a result of an explosion (e.g.blast pressure wave) or from vehicle accidents, falls and athletic activities. Injuries during training are common, suggesting that the rate of mTBI will remain high in military populations even as deployments to Iraq and Afghanistan are reduced.

      The predominant pathological signs of TBI include diffuse axonal injury (DAI) and micro-hemorrhage, with patterns varying depending on the cause and severity of injury. Evaluating someone who has experienced head trauma to determine the extent of the injury is critical to limiting further brain damage. As with civilian mTBI, many cases probably go undiagnosed, while many other cases that are diagnosed based on clinical instruments such as the Glasgow Coma Scale have negative radiological scans. The vast majority (>80%) of military TBI cases are classified as mild TBI (mTBI) and, in the absence of other trauma, the soldier may be considered for return to active duty eventually. Rapid assessment is needed to determine if and when it is safe for the soldier to return to his or her unit. In the military theater, screening is complicated because TBI occurs in the context of sleep deprivation, nutritional changes, emotional stress, polytrauma, and difficult environmental factors. Mission pressure also plays a role in returning military personnel to duty when they may have residual pathology.

      Military brain injuries may be co-morbid with post-traumatic stress disorder (PTSD), and there is growing evidence that TBI and PTSD resulting from military exposures increases the risk of developing neurodegenerative diseases such as Alzheimer’s disease (AD) (Weiner et al. 2013), in addition to increased risk of depression, anxiety and substance abuse. Collectively, the prevalence and long-term consequences of mTBI in the military indicates the need to come up with better early detection procedures that rely on objective measures, and to be able to use them to develop improved interventions.


      MRI of mild TBI

      Emergency clinical neuroimaging is mostly conducted with CT because the procedure is sensitive to critical pathology such as intracranial bleeding, it readily detects skull fracture and it can be performed on patients with life support equipment or metallic fragments in the head or body. However, CT tends to be insensitive to many changes following mTBI and there is need for subsequent neuroimaging tests that could determine a baseline for evaluation and treatment. Between a quarter to a third of patients with negative CT findings are found to have pathology on subsequent MRI scans (Bigler, 2013).

      Hunter et al. (2012) recently published a review of emerging neuroimaging tools for TBI research. The predominant neuropathological signs of TBI include diffuse axonal injury (DAI) and microhemorrhage. In mTBI, as determined by the Glasgow Coma Scale or other clinical assessment, these pathologies may or may not be measurable on MRI scans. The heterogeneity and complexity of TBI are recognized factors in using neuroimaging for evaluation of injury severity. These limitations can be problematic because good evaluation of brain trauma can be critical to limiting further damage, either by prohibiting certain activities, including a return to active duty for military personnel, or by prescribing an appropriate course of therapy. Furthermore, many recent studies have shown that, even when focal traumatic lesions are detected on an acute scan, measures derived from the lesions often do not correlate with clinical outcome. The implication is that while MRI can detect numerous small lesions, including axonal injury and microscopic hemorrhages, these may be clinically irrelevant. Better specificity and sensitivity is required if MRI is to make a better impact in the evaluation and treatment of mTBI cases.

      Fluid-attenuated inversion recovery (FLAIR) imaging:

      Fluid-attenuated inversion recovery (FLAIR) imaging – more correctly, T2-weighted FLAIR - is useful for identifying edematous lesions. Its suppression of cerebrospinal fluid (CSF) signal allows better evaluation of lesions adjacent to CSF than standard T2-weighted imaging. The T2 contrast mechanism relies on its sensitivity to changes in molecular mobility; most notably, cellular edema produces hyper-intense regions on a T2-FLAIR image.

      The history of FLAIR for imaging TBI is mixed. While it has shown value in separating moderate and severe cases from mild TBI (Chastain et al. 2009), there are many clinical mTBI cases that produce negative FLAIR scans. Furthermore, while broad injury categorization is feasible, FLAIR is unable to produce a good predictor of eventual outcome on an individual patient basis. These limitations have provoked the testing of other sequences in the hope of securing better sensitivity in mTBI cases and better specificity and outcome prediction across the spectrum of TBI severity.

      Susceptibility-weighted imaging (SWI):

      SWI exploits the magnetic susceptibility differences of blood, iron and calcium deposits from normal gray and white matter tissue. Local variations in T2* are produced by hemorrhages, abnormal blood oxygenation, and by blood breakdown products. SWI uses a high-resolution, three-dimensional velocity compensated gradient echo sequence from which a final SW image is derived that uses both the magnitude and phase information. A filtered version of the phase image is multiplied with the magnitude image to augment contrast in the final SW image. In essence, then, SWI is an enhanced T2*-weighted imaging method. (Some reports refer to this method as BOLD imaging due to their similar T2* basis, but this is an inaccurate use of the BOLD label because concentrations of iron generate the dominant features.)

      SWI has primarily been considered to be helpful in the identification of hemorrhagic axonal injury where smaller hemorrhages are invisible on CT or conventional MRI sequences. SWI may be especially helpful in the detection of microhemorrhages in early acute and sub-acute phases of injury, and in detecting areas of hypoxia-ischemia-induced secondary injury. For example, SWI at 1.5 T was able to detect a larger number of lesions and to define smaller areas of damage than CT, T2-weighted imaging, and FLAIR imaging when applied to a diverse TBI patient group an average of 5.6 days post-injury (Chastain et al., 2009). However, SWI at 1.5 T has not yet been shown to be superior to T2-weighted imaging or FLAIR in discriminating between good and poor outcomes.

      SWI is also being used to locate chronic lesions. Spitz et al. (2013) found that SWI at 3 T was able to detect more lesions and be a better indicator than FLAIR for outcome, but these scans were obtained an average of 18 months after injury. SWI was able to identify TBI-related lesions in almost one third of patients for whom FLAIR was unable to detect any lesions; the lesion volume determined by SWI was also related to injury severity. However, earlier work by Geurts et al. (2012) using MRI an average of 7 weeks after injury, suggested that SWI and FLAIR are equally useful for identifying lesions. Clearly, the time since injury is a critical factor in establishing contrast and, therefore, the most appropriate pulse sequence to use.

      A potential limitation of SWI is that the contrast mechanism tends to make the size of a susceptibility-induced feature appear larger than the actual lesion size. Nevertheless, lesion size and location may be poor markers for deficits or eventual outcome to the point that the limitation is irrelevant. Other challenges in using SWI for TBI include its susceptibility to artifacts at air–tissue and skull-tissue interfaces, including the orbital frontal areas, a region of distinct vulnerability in TBI. Finally, interpretation of SW images requires adequate training and familiarity with the technique in order to differentiate (normal) veins from small hemorrhages.

      Diffusion tensor imaging (DTI):

      DTI contrast is based on restrictions and hindrances to the free diffusion of water in tissue. One use of DTI is to compute scalar values that represent the bulk and anisotropic diffusion rates, such as the apparent diffusion coefficient (ADC) and fractional anisotropy (FA), respectively. ADC is a directionless estimate of the average bulk water movement in a voxel whereas FA estimates the preference for a directionality of water diffusion, e.g. due to cellular membranes and other barriers to free diffusion. ADC tends to increase with vasogenic edema, when water flows out of capillaries and into the interstitial space, but it decreases with cytotoxic edema, when a greater fraction of water molecules is retained in ischemic, swollen cells. FA, being a function of anisotropic restrictions to diffusion such as occurs along myelinated fibers, is especially sensitive to white matter (WM) tract damage.

      According to a review by Benson et al. (2012), there are several mechanisms that will result in FA decrease in WM after injury, including (vasogenic) edema, ischemia, neurodegeneration and metabolic disruption, while few if any mechanisms result in FA increases. Understanding these characteristics permits ADC, FA and other scalar parameters derived from the DTI to be used together to characterize pathology. For example, decreased FA associated with increased ADC suggests vasogenic edema, whereas increased FA associated with decreased ADC suggests cytotoxic edema. Decreased FA associated with decreased longitudinal water diffusivity indicates impaired axonal transport. Decreased FA, together with a slight or no increase in ADC together with increased radial diffusivity (RD) is typical of sub-acute or more chronic axonal injury from trauma.

      MacDonald et al. (2011) studied a group of soldiers who had clinical evidence of mTBI, including exposure to a blast, without abnormalities on conventional MRI, at a median of 14 days post-injury. A significant number of injured patients showed decreased FA and increased ADC compared to controls. Of particular note, the regions where white matter abnormalities were seen (middle cerebellar peduncles, orbitofrontal cortex) differed from the typical regions (cingulum, uncinate fasciculus, anterior limb of the internal capsule) observed in non-blast mTBI.

      Importantly, changes in DTI-derived measures have shown correlation with injury severity, functional outcome, neurologic functioning, and cognitive ability. Longitudinal studies have also indicated that DTI might serve as a tool for revealing changes in the neural tissue during recovery from TBI. But while DTI derived measures are able to sort mild from more severe TBI, to date there has been no way to determine whether an individual patient has a mild TBI. Reasons for this lack of specificity include the diffuse nature of TBI-related anatomical changes, very small abnormalities caused by the TBI, and confounding features related to aging or other disease processes. As a consequence, although there have been several group studies, routine use of MRI-derived measures to diagnose mTBI have yet to be realized.

      When DTI abnormalities have been detected in acute cases of mTBI the results have been ambiguous, with both increases and decreases in fractional anisotropy (FA) being observed within the first 24 hours after injury (Kou et al., 2013). This lack of specificity restricts the utility of DTI as a marker for outcome and has led to the suggestion that multiple factors, such as neuroimaging and blood protein levels, may provide more useful information.

      Diffuse axonal injury may be detectable by DTI in moderate and severe TBI, but a recent study showed no significant differences for FA in mTBI patients compared to controls (Spitz et al., 2013). Moderate and severe TBI showed significant widespread reductions in FA. An issue raised by this study is how regions of interest are determined for quantitative comparisons. Anatomical ROIs as well as tract-based spatial statistics (TBSS) were tested. Use of TBSS was considered to offer more flexibility in evaluation than a prioriROIs. But TBSS was still unable to identify abnormalities in mTBI. Other methods for localizing pathology are required.


      T1ρ relaxation: an overview

      A starting point for understanding the origin of T1ρ relaxation is given in Wikipedia (http://en.wikipedia.org/wiki/Magnetic_resonance_imaging):

      “(Water) molecules have a kinetic energy that is a function of the temperature and is expressed as translational and rotational motions, and by collisions between molecules. The moving dipoles disturb the magnetic field but are often extremely rapid so that the average effect over a long time-scale may be zero. However, depending on the time-scale, the interactions between the dipoles do not always average away. At the slowest extreme the interaction time is effectively infinite and occurs where there are large, stationary field disturbances (e.g., a metallic implant). In this case the loss of coherence is described as a "static dephasing". T2* is a measure of the loss of coherence in an ensemble of spins that includes all interactions (including static dephasing). T2 is a measure of the loss of coherence that excludes static dephasing, using an RF pulse to reverse the slowest types of dipolar interaction (in a spin echo). There is in fact a continuum of interaction time-scales in a given biological sample, and the properties of the refocusing RF pulse can be tuned to refocus more than just static dephasing. In general, the rate of decay of an ensemble of spins is a function of the interaction times and also the power of the RF pulse. This type of decay, occurring under the influence of RF, is known as T1ρ. It is similar to T2 decay but with some slower dipolar interactions refocused, as well as static interactions, hence T1ρ ≥ T2.”

      Understanding the T1ρ phenomenon can thus be extrapolated from a consideration of spin echoes. We start with a simple spin echo sequence, 90x– TE/2 – 180y– TE/2. This single spin echo refocuses dephasing that arises from any process that is time-invariant across the two halves of the echo time, TE. The refocusing time scale can be shortened, however, by including additional refocusing pulses while measuring the signal at the same overall TE. This is the classic Carr-Purcell-Meiboom-Gill (CPMG) echo train. The apparent T2that is measured will increase as the number of refocusing pulses is increased, for a fixed total TE, because the diffusion sensitization of the signal is reduced in concert. This well-known result has been utilized historically to study the process of molecular diffusion.

      As more and more refocusing pulses are included in a CPMG echo train (of constant length, TE), the inter-pulse evolution delay becomes very small and the measured apparent T2 approaches the true T2. If we now simply discard the small inter-pulse delays we have replaced a train of refocusing pulses and inter-pulse delays with a constant “refocusing” field that persists throughout TE. Because there are no inter-pulse delays, and therefore no phase evolution periods absent an RF field, it is convenient to change nomenclature and use TL (to indicate a spin “lock”) for the duration of the long B1 pulse, rather than TE.

      We now consider how the transverse magnetization decays under an effective constant field attained by the long duration B1 pulse. Viewed in a rotating reference frame at the Larmor frequency w0 = gB0, the effective field reduces to Beff(t) = B1(TL). The direction of B1(TL) is parallel with the magnetization direction established by the original 90x-degree excitation pulse; in this example Beff is along y’ of the rotating reference frame. Thus, the subsequent decay of coherent magnetization occurs along the direction of B1(TL). For this reason the loss of coherence under the effective field B1(TL) is characterized by a time constant T1ρ in order to disambiguate it from T1 and T2relaxation, processes which occur in the absence of an RF field. It is also important to note that the T1ρ decay involves loss of transverse magnetization orthogonal to the B0 axis but parallel to the RF field axis. For this reason the T1ρ relaxation time constant is often referred to as “T1 in the rotating frame.” The longitudinal designation is in reference to relaxation parallel with B1(TL), not parallel with B0as for T1. (For completeness it is worth nothing that there is also a T2ρ decay, which occurs during rotary echoes formed by periodically reversing the direction of the RF field and measuring the decay of magnetization that is transverse to the RF spin lock field. T2ρ is not considered further in this proposal because of the sparse literature using T2ρ in human brain or in animal models of brain trauma.)

      The particular molecular interactions that dominate T1ρ relaxation are determined by the frequency of the RF pulse used to interrogate the spins. The interaction frequency of primary interest equates to 42.58 Hz/mT. Given an exemplar spin lock field magnitude of 20 mT, the frequency expected to dominate relaxation is ~850 Hz. At body temperature, much proton exchange kinetics, especially water-protein interactions, occurs in a low frequency range from a few hundred Hz to a few kHz. The exchange kinetics – and T1ρ - thus depends strongly upon cellular energetics, protein concentration, pH, temperature, viscosity and processes that mediate water concentration and mobility within cells (Makela et al.2001). The spin lock field strength-dependence is one of the major features of T1ρ imaging. It is possible to tune the T1ρ relaxation time contrast to match expected proton exchange frequencies.

      T1ρ imaging:

      The standard T1ρ contrast module consists of a 90-degree excitation pulse followed by a long duration, low amplitude “spin locking” B1field placed on-resonance. Once the spin locking relaxation period has ended the remaining magnetization may be returned briefly to the z-axis with a second 90-degree pulse. The net result of a 90x– B1SL(TL) – 90-x preparation sequence is thus a longitudinal magnetization that is dependent upon T1ρ. Regular imaging sequences may then be applied to sample the T1ρ-dependent signals. The imaging scheme can be any imaging pulse sequence, although the long duration of the spin locking composite suggests that 3D sequences will be preferred. (In this regard, spin locking is similar to T1-weighted imaging using inversion recovery.) Pulse sequence issues pertinent to T1ρ imaging are considered in more detail below.

      The simplest approach to T1ρ imaging is to establish a weighted contrast, as with conventional T1 and T2-weighted imaging. More often, however, T1ρ is quantified for a particular magnitude of B1SLusing several different TL periods; four or five TL periods are commonly used in generating a T1ρ map. Finally, it is also possible to measure a range of T1ρ for different amplitudes of B1SL, a process termed T1ρ dispersion imaging that is analogous to (static) field cycling relaxometry.


      T1ρ imaging of TBI and stroke

      Whereas FLAIR imaging reflects cellular water mobility and water concentration via T2, and diffusion imaging attempts to measure bulk water motion directly, T1ρ imaging is concerned with the interaction between water protons and other molecules and is expected to contain unique information on tissue status. This sensitivity to proton exchange suggests that it may be a useful modality to measure cellular dysfunction, e.g. pH changes. T1ρ contrast has been shown to be sensitive to some molecular processes earlier or more specifically than other MRI contrast methods, especially in pathologies involving disruption of cellular energetics.

      T1ρ contrast MRI should be extremely useful in the acute phase of brain injury, when cascades of biochemical changes are occurring extremely rapidly. NMR spectroscopy, most notably 31-phosphorus NMR, has demonstrated its utility in studying rapid energetics – even during exercise – and so the potential for conducting metabolic imaging by proxy, based on the vastly more sensitive 1-H nucleus, is an intriguing possibility.

      In an experimental model of TBI (fluid-percussion induced-TBI in rat brain), Immonen et al. (2009) found that both T1ρ (using an 80 μT spin lock field) and T2 measured at 4.7 T three days after injury correlated with final hippocampal atrophy determined post-mortem 6 months later. Due to the similar temporal characteristics of the two contrast mechanisms, Immonen et al. concluded that the relaxation time changes reflected the severity of vasogenic edema, a phenomenon seen in prior stroke studies. At 23 days post-injury, T1ρ, T2 and ADC increases predicted the long-term neurodegeneration of the ipsilateral hippocampus. However, in a subsequent report (Immonen et al. 2009b), the authors noted that T1ρ measured at the injury site exhibited significant differences from control animals as few as three hours after the injury, whereas T2 changes were insignificant at that time. These acute phase results are in general agreement with other studies comparing T1ρ to T2 and T1.

      Perhaps the most compelling demonstration of T1ρ utility has been shown in a rat model of stroke using middle cerebral artery occlusion, MCAO (Jokivarsi, et al. 2010). In that work, T1ρ was shown to provide a linear index of post-ischemia delay by comparing T1ρ in infarcted tissue to tissue on the contralateral hemisphere. The T1ρ difference correlated with time since MCAO. The T2 difference between the ipsilateral and contralateral tissue was initially negative, however, becoming positive 1.5-3 hours post-MCAO. A non-linear response is also generally reported for diffusion imaging studies of stroke. Thus, while DWI may be a sensitive way to localize tissue damage, the quantities that are computed from DWI do not have the specificity to permit accurate quantitation of ischemic duration.

      Very little work has been done on TBI using T1ρ in either animal models or clinically. A recent paper (Immonen et al. 2013) used a fluid percussion injury model in rat and demonstrated that T1ρ is highly sensitive for predicting increased seizure susceptibility and epileptogenesis after TBI. At 9 and 23 days post injury, a change in T1ρ of the perilesional cortex showed the greatest predictive value for increased seizure susceptibility at 12 months. But further work using T1ρ for trauma pathology has been hampered by the potential for heating arising from the long duration spin lock pulses.


      Pulse sequences for T1ρ imaging in human brain

      There are two parts to achieving successful T1ρ imaging of human brain: an acceptable contrast module and a rapid image acquisition scheme. With regard to the T1ρ contrast module, it should be robust to B0 and B1heterogeneities expected across the head, and it must be applied within safety limits for tissue heating. Heating from B1 is measured using a specific absorption rate (SAR) in units of W/kg. Even moderate B1amplitudes applied for tens of milliseconds can approach or exceed SAR safety limits and the problem is compounded by the use of higher static magnetic fields because SAR scales quadratically with frequency.

      Until very recently, most attempts to do T1ρ imaging in human brain used a standard spin locking pulse as described previously. (Some publications refer to this approach as “continuous wave” spin locking because the locking B1field is applied at constant amplitude.) The first studies on human brain generally focused their efforts on the imaging readout in an attempt to make the total acquisition time practicable. A rapid imaging method is required to sample the T1ρ-prepared spins, both to permit acquisition of data in a clinically acceptable time but also to permit extension of repetition times in order to offset some of the SAR limitations arising from the contrast module.

      An initial study of T1ρ in human brain was performed at 0.1 T (Ramadan et al., 1998). SAR issues were largely avoided by the use of a low static field, permitting measurement of T1ρ in 50 mT steps up to 250 mT. Image acquisition was performed with a single slice GRE acquisition. In 2004, Borthakur et al. acquired T1ρ of human brain at 1.5 T using a conventional spin lock module followed by a fast spin echo (FSE) readout using centric phase encoding. Two-dimensional readout was used in that report although the authors noted that a 3D gradient echo readout scheme could have been used, as they had applied previously in skeletal imaging. SAR considerations limited the spin lock amplitude to a maximum of 11.7 mT. T1ρ dispersion data were reported for 1.5-11.7 mT.

      In 2004, Wheaton et al. again used a single slice GRE scan at 1.5 T to measure T1ρ in human brain, but in order to reduce SAR the amplitude of the spin lock field was reduced to 4.7 mT for the outer thirds of phase-encoded k-space while the full amplitude of 11.7 mT was used only for the central third of (low frequency) k-space. The T1ρ values produced by this sequence were within 2% of those using 11.7 mT for the entire k-space. The same group demonstrated faster T1ρ imaging in 2006 by the use of EPI readout at 1.5 T (Borthakur et al., 2006), permitting the use of a constant amplitude spin lock of 11.7 mT.

      Very recently, 3D turbo spin echo (TSE) readout with CSF suppression was demonstrated at 3 T in a study of age-related T1ρ changes in human brain (Watts et al., 2013). Whole brain coverage was obtained with 1.8 mm isotropic resolution in a total acquisition time of 14 min. SAR was rendered acceptable – 69% of the safety limit of 3 W/kg average for the head - using a spin lock amplitude of 11.7 mT.

      The initial attempts to modify the standard spin lock contrast module tended to focus on artifact reduction rather than the SAR issue. Witschey et al. (2007) used phase alternation, a variant of rotary echoes, within the spin lock to overcome B0 and B1 imperfections. Michaeli et al. (2006) introduced a fundamentally different approach to the spin lock. Instead of constant amplitude pules, spin locking is effected via a train of adiabatic full passage (AFP) sweeps; the dynamics of the AFP sweeps maintains the magnetization precessing around the effective field in the desired manner. The original AFP work was performed at 4 T and used single slice readout, either segmented 2D Turbo FLASH or segmented 2D spiral. Even so, the peak B1 amplitude in the AFP sweeps was 59 mT, a high value made feasible with the use of a small surface transmit coil approximately 14 cm in diameter.

      The concept of a variable spin lock field during the preparation period has been extended very recently. A gradient-modulated variant of the AFP train was presented by Andronesi et al. (2013) and used to reduce SAR at 3 T for whole brain imaging with 3D Turbo FLASH readout. The isotropic resolution was 1.3 mm and the whole acquisition of T1ρ at 9 mT took 5 min 40 sec, generating a SAR of approximately 1.8 W/kg. They used the standard body transmit coil and a 32-channel head coil for reception.

      It now seems feasible to acquire whole brain T1ρ images in clinically acceptable times, using the combination of new spin lock contrast modules coupled with efficient 3D imaging readout. In the most recent applications of 3D T1ρ imaging (Watts et al., 2013 and Andronesi et al., 2013), extensive use was made of methods that reduce the total data acquisition needed, e.g. parallel imaging (SENSE, GRAPPA) and partial Fourier acquisition, and also methods that reduce SAR, e.g. variable flip angle TSE. It should be noted that a body transmit RF coil was used in both of these studies, limiting the peak spin lock field to between 9 and 12 mT for acceptable SAR. (The Siemens Trio body coil can attain maximum field strength of B1 = 23.5 mT.) Whether this low range of spin lock field amplitudes is useful for imaging acute neuropathology has yet to be demonstrated.


      ULF T1 imaging of human brain

      In their study on a rat model of irreversible stroke, Jokivarsi et al. (2010b) offered this observation:

      “To supersede T2 in the detection of irreversible ischemia, T1ρ MRI must be measured with B1in the range of 60 to 170 mT, which does not cause tissue heating with the data acquisitions used here, even in flow-compromised tissue (Grohn et al, 2000), but yet needs to be proven in humans within the SAR guidelines.”

      In that study they had used a spin lock of 38 mT in order to emulate what they estimated might be feasible clinically, but as a result the dynamics of T1ρ had generated only marginal sensitivity advantage over T2. In their earlier studies they had used higher spin lock fields and had demonstrated very significant advantages of T1ρ contrast. That low spin lock amplitudes should be less beneficial is understandable because as the spin lock amplitude approaches zero, T1ρ ® T2.

      At present, the range of spin lock fields suggested by Jokivarsi et al. is beyond the technical and safety limits at 3 T and probably at 1.5 T as well, suggesting that exploring alternative approaches to obtaining T1ρ-like contrast is warranted for human brain. Fortunately, there is an alternative to high field T1ρ in assessing proton exchange-dominated contrast: ultralow field (ULF) B0 imaging. Spin-lattice relaxation in B0 is a function of the thermal fluctuations occurring at the Larmor frequency. Thus, T1 at B0of tens to hundreds of microtesla will have similar frequency dependence as T1ρ relaxation under the B1SL used to date in spin locking methods on high B0 scanners. (Here, high B0 means any static magnetic field greater than 0.1 T.) (Update on 2nd January 2015: In recent work we have actually determined that ULF T1 and T1ρ have different dependencies, with T1ρ showing strong sensitivity to chemical exchange and perhaps water diffusion through microscopic magnetic susceptibility gradients, mechanisms that seem to be non-existent for ULF T1. This work is being prepared for submission.) Indeed, it is worth mentioning that the first paper on T1ρ imaging (Sepponen et al., 1985) set out to develop a method specifically to permit ultralow field relaxation time contrast but with the benefits of higher SNR attainable with a larger polarizing field. (We can think of high field T1ρ imaging as an attempt to mimic ULF MRI contrast on a high field scanner.) It seems as if the benefits of high field polarization may now have reached the point where T1ρ cannot be applied optimally, however. Fortuitously, with the development of pre-polarization and a SQUID detector at ULF, we are finally in a position to achieve high SNR as well as low field contrast directly.

      Detailed comparisons of the relaxation mechanisms underlying T1ρ at high field and T1 at ULF have yet to be performed. While we might expect some small differences, e.g. due to chemical shift and magnetic susceptibility effects for T1ρ at high B0(Makela et al., 2004), the T1ρ and ULF T1 are expected to be similar in magnitude and have similar dependence on molecular dynamics such as proton exchange. (See above highlighted comment.)

      Qualitatively similar ULF T1 and high field T1ρ values have been reported for human brain. Inglis et al. (2013) found T1 at B0 = 130 μT of 61 ms and 141 ms for WM and GM, respectively. Ramadan et al. (1998) found T1ρ at B1SL = 150 μT (measured at B0 = 0.1 T) of 120 ms and 137 ms for WM and GM, respectively, but also reported anatomical variation of T1ρ for WM that was not observed for GM. Borthakur et al. (2004) reported T1ρ at B1SL = 11.7 μT (measured at B0= 1.5 T) of 85 ms and 99 ms for WM and GM, respectively. Finally, Andronesi et al. (2013) determined T1ρ at B1SL = 9 μT (measured at B0 = 3 T) of 79 ms and 96 ms for WM and GM, respectively. Clearly, variation due to different subjects as well as experimental methods suggests that in-depth comparison of these results is unwise. But the results are reasonably consistent and suggest that ULF T1 imaging is a good alternative to high field T1ρ imaging of brain.

      In pilot experiments on excised, fixed rat brains we have compared directly T1ρ measured with B1SL = 35 μT at 9.4 T to ULF T1at B0 = 130 μT. Three of the rats had MCAO surgery (one hemisphere) 8 or 24 hours before sacrifice; the other two rats had sham surgery. (We are still blind to the assignment.) The fixed rat brains were sectioned to enable the hemispheres to be measured separately on the ULF system. Cerebellum and cervical spinal cord was excluded. We found good reproducibility of T1at ULF (two sets of measurements) albeit with a systematic reduction of ULF T1compared to high field T1ρ:


      In spite of a low number of samples it is evident that there is a linear relationship between the two sets of relaxation times. Systematic differences could arise from methodology, e.g. the very high field used in measuring T1ρ. Chemical shift effects as well as magnetic susceptibility gradients across a small, irregular sample could have profound effects on T1ρ measured at 9.4 T. Another possibility is the presence of small amounts of free fixative. At ULF we were careful to remove as much free fixative as possible after noting that any visible fluid tended to bias the T1 towards artificially high values (closer to that for free water in fixative). It is conceivable that slight differences in sample preparation for the T1ρ measurements at 9.4 T, which were performed in Finland, could be the largest source of discrepancy. This work is ongoing. (Update on 2ndJanuary 2015: We haven’t pursued this track recently. We can’t do animal model work ourselves and the ULFMRI scanner resolution is limited to ca. 2 mm voxels, making images of small rat brains rather poor. So instead we have refocused our efforts on human brain and we have no current plans to scan more fixed rat brains.)


      Potential of ULF T1 and HF T1ρ for brain trauma imaging

      There is now compelling literature to suggest that low field relaxation times are sensitive to the rapid changes occurring in the acute phase of traumatic injury. If we assume that low field relaxation, whether T1ρ or ULF T1, does indeed offer a novel window on traumatic injuries such as stroke and TBI, the question arises as to how to make the technology available to the patient.

      A recent study of mTBI by Yuh et al. (2013) included this perspective:

      “Routine performance of brain MRI on mTBI patients may not currently be cost-effective. However, smaller, less costly head-only MRI scanners are under development. These, among other continuing advances in MRI technology, may ultimately render the expense and logistics of acute MRI scans less prohibitive.”

      It is worth emphasizing that a scanner optimized for imaging of acute brain trauma (including stroke) may need to be different than one optimized for methods (such as SWI and DTI) currently used for chronic studies. Higher B0 leads to greater contrast in SWI, while for DTI higher B0 improves SNR. In the case of T1ρ imaging, however, the SNR benefit that arises as B0is increased is offset by the considerable SAR, leading to severe restrictions on the spin lock field amplitudes that can be utilized. It could be preferable to use T1ρ contrast at a relatively low field of 0.5-1 T.

      There may also be utilitarian and safety reasons to develop a dedicated MRI scanner for acute trauma assessment. Chronically, the benefits of high B0 can be utilized with ample time to consider contraindications to the high magnetic field, any incidental pathology, atrophy and so on. In the acute phase, however, and especially when the injury is suffered on the battlefield, the initial consideration shifts from optimal contrast to optimal safety and ease of access. Whereas it is typical for an emergency room to have immediate access to a dedicated CT scanner, few centers have short-notice access to MRI for patients that may be on life support or otherwise may present contraindications for any procedure presently considered to be non-essential.

      That said, given the widespread availability of clinical MRI scanners there is clear merit in attempting to evaluate T1ρ contrast for studying TBI with existing technology. A parallel approach is to develop ULFMRI for studying acute brain trauma. Compared to T1ρ at high fields, using ULFMRI could offer several possible benefits for acute trauma imaging. It should permit easier use of life support apparatus, and should be better suited to patients at risk of contraindications for high field MRI. ULFMRI could also offer significant performance benefits over high field T1ρ imaging, such as allowing imaging in the presence of metal in the patient’s head, e.g. surgical implants to stabilize facial fractures, shrapnel, etc.Finally, a ULFMRI scanner designed specifically for acute trauma injury would likely be smaller, cheaper and easier to site than a high field scanner using a superconducting magnet.


      Deleted on 2nd January 2015: Sections on Future Work and a Timeline.


      References

      Andronesi et al. (2013).
      Whole brain mapping of water pools and molecular dynamics with rotating frame MR relaxation using gradient modulated low-power adiabatic pulses.
      NeuroImage, Epub (2013).

      Benson et al. 2012
      Detection of hemorrhagic and axonal pathology in mild traumatic brain injury using advanced MRI: Implications for neurorehabilitation.
      NeuroRehabilitation 31, 261–279 (2012).

      Bigler, 2013.
      Neuroimaging biomarkers in mild traumatic brain injury (mTBI).
      Neuropsychol Rev 23, 169-209 (2013).

      Borthakur et al. (2004).
      In vivo measurement of T1ρdispersion in the human brain at 1.5 tesla.
      J Magn Reson Imaging 19, 403-9 (2004).

      Borthakur et al. (2006).
      A pulse sequence for rapid in vivo spin-locked MRI.
      J Mag Res Imaging 23, 591-6 (2006).

      CA Chastain, UE Oyoyo, M Zipperman, E Joo, S Ashwal, LA Shutter, and KA Tong. Predicting Outcomes of Traumatic Brain Injury by Imaging Modality and Injury Distribution.
      J Neurotrauma 26:1183–1196 (2009).

      Geurts et al. 2012
      The reliability of magnetic resonance imaging in traumatic brain injury lesion detection.
      Brain Inj. 26, 1439–1450 (2012).

      Hunter et al. 2012
      Emerging Imaging Tools for Use with Traumatic Brain Injury Research.
      J Neurotrauma 29, 654-71 (2012).

      Immonen et al. (2009)
      Quantitative MRI predicts long-term structural and functional outcome after experimental traumatic brain injury.
      NeuroImage 45, 1-9 (2009).

      Immonen et al. (2009b)
      Distinct MRI pattern in lesional and perilesional area after traumatic brain injury in rat – 11 months follow up.
      Exp Neurol 215, 29-40 (2009).

      Immonen et al. (2013)
      MRI biomarkers for post-traumatic epileptogenesis.
      J Neurotrauma 30(14), 1305-9 (2013).

      Inglis et al. (2013)
      MRI of the human brain at 130 microtesla.
      PNAS 110(48), 19194-201 (2013).

      Jokivarsi et al. (2010)
      Estimation of the onset Time of cerebral ischemia using T1rhoand T2 MRI in rats.
      Stroke 41, 2335-40 (2010).

      Jokivarsi et al. (2010b)
      Correlating tissue outcome with quantitative multiparametric MRI of acute cerebral ischemia in rats.
      J CBF Metab 30, 415-27 (2010).

      Kou et al., 2013
      Combining biochemical and imaging markers to improve diagnosis and characterization of mild traumatic brain injury in the acute setting: results from a pilot study.
      PLOS One 8(11), e80296 (2013).

      MacDonald et al. (2011)
      Detection of blast-related traumatic brain injury in U.S. military personnel.
      N Engl J Med 364(22), 2091-100 (2011).

      Makela et al. (2001)
      Proton exchange as a relaxation mechanism for T1 in the rotating frame in native and immobilized protein solutions.
      Biochem Biophys Res Commun 289(4):813-8 (2001).

      Makela et al. (2004)
      B0 dependence of the on-resonance longitudinal relaxation time in the rotating frame (T1rho) in protein phantoms and rat brain in vivo.
      Magn Reson Med 51, 4-8 (2004).

      Michaeli et al. (2006)
      T1rho MRI contrast in the human brain: Modulation of the longitudinal rotating frame relaxation shutter-speed during an adiabatic RF pulse.
      J Mag Res 181, 135-47 (2006).

      Ramadan et al. (1998).
      On- and off-resonance spin-lock MR imaging of normal human brain at 0.1T: Possibilities to modify image contrast.
      Mag Res Imaging 16(10), 1191-9 (1998).

      Sepponen et al. (1985).
      A method for T1rho imaging.
      J Comput Assist Tomogr 9(6) 1007-11 (1985).

      Spitz et al. 2013
      White matter integrity following traumatic brain injury: the association with severity of injury and cognitive functioning.
      Brain Topogr. 26, 648-660 (2013).

      Watts et al. (2013)
      In vivo whole-brain T1rho mapping across adulthood: Normative values and age dependence.
      J Mag Res Imaging, Epub (2013).

      Weiner et al. 2013
      Military risk factors for Alzheimer’s disease.
      Alzheimer’s and Dementia 9, 445-51 (2013).

      Witschey et al. (2007)
      Artifacts in T1rho-weighted imaging: Compensation for B1 and B0 field imperfections.
      J Mag Res 186, 75-85 (2007).

      Wheaton et al. (2004)
      Method for reduced SAR T1rho-weighted MRI.
      Magn Reson Med 51, 1096-1102 (2004).

      Yuh et al. 2013.
      Magnetic resonance imaging improves 3-month outcome prediction in mild traumatic brain injury.
      Ann Neurol 73, 224-35 (2013).





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      Open access online science publisher The Winnower has made a huge leap in scientific publishing and now offers bloggers a way to assign a digital object identifier (DOI) to any post uploaded to their site. Once there, the post can be reviewed, etc. just like any other online paper. They will also be archiving soon via CLOCKSS. (See Note 1.) With a DOI plus archiving it means that a post (and any reviews) should be traceable in perpetuity. Very good developments!

      I had the privilege of helping Josh, The Winnower's industrious founder and administrator, create a new publication category for Neuroimaging and then used my post on physiologic confounds as the first test case. The post now has its own DOI (DOI: 10.15200/winn.142919.97862 ) should you prefer that to a URL. I submitted via a Word document because I've found that Blogger will occasionally hide irrelevant HTML code that needs to be edited out by hand. This can be a problem if you're in the habit, as I am, of copy-pasting text (e.g. quotes from papers) into a blog post. There is a new facility that will submit directly from a blog but in my first test (using Blogger) there were some major formatting issues. Josh informs me that he will be adding a facility for the Blogger API eventually, but using Word as an intermediate step gives me a chance to clean up links to references and that sort of thing. I have plans to submit many other posts to The Winnower so please let me know either in the comments below, in a review on the The Winnower version of the post or via Twitter whether you encounter problems, have suggestions for improvements, etc. Also, do please consider submitting your own blog posts to The Winnower. Let's build the Neuroimaging category!

      I was around for and involved in the nascent Web of the early '90s (see Note 2) and I distinctly remember NCSA's Mosaic, the Planet Earth Home Page virtual library, UC Irvine's online bookshop, and the Cambridge coffee pot. But the development of open, online publishing supported by social media like blogs and Twitter, as well as post-publication peer review (PubPeer and PubMed Commons), feels like a true revolution for science. I may be wrong but it feels like we will look back at the current period as a major change in the way we interact. It only took us two decades. Now, however, The Winnower is contributing disproportionately to our future. Thank you.

      ____________________


      Notes:

      1.  I already archive my important blog posts at The Internet Archive's Wayback Machine. In addition to providing an invaluable service, the Wayback Machine is also a wonderful way to get nostalgic and kill a few hours :-/

      2.  I had an online poster at NMR Poster95, the first e-poster meeting of folks doing NMR and MRI. The front page of the poster got archived but the clickable poster itself is now defunct, I'm afraid. The wonderful Internet Archive does have other pages from my website at the time, however. They began archiving in 1996, and the earliest copy of my old website dates from January, 1997. Thank you, Internet Archive! There is also a summary of the first two NMR e-poster conferences in this paper from 1997.


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      So I have a memory like a sieve except that it's profoundly less useful in the kitchen. And because I know from painful experience that anything I don't document never happened, I am going to help myself and you by creating in real time a tutorial to upload blog posts from Blogger to The Winnower, should you be so inclined. Why do it? DOI is one reason.

      Those of you who were smart enough to begin your blog's existence on Wordpress can use a fancy plugin for your API. Those of us who now have too much inertia on Blogger to relocate must do a little more work and use some intermediate steps, but it really isn't that hard. What's more, the intermediate steps offer an opportunity for proofreading and fine-tuning that you might like to do anyway. Let's do it!


      Initial steps

      First up, I assume you have created an account on The Winnower.  Then select the SUBMIT option in the top menu. At that point the step-by-step instructions can be found in the link in the top-right corner of the page, Submit Your Paper:



      You'll get a new window pop up; skip on down to the Word instructions:


      Here you will want to download the Word template and also read through all the formatting guidelines before moving on:



      So now let's shift to the template document just downloaded. Aha! This looks familiar! I couldn't for the life of me recall how I'd managed to have on my laptop the neatly formatted docx file of an earlier post I'd uploaded to The Winnower last December. The memories are beginning to flow. What a relief!


      Okay, so now I need to get myself offline for a bit and copy-paste today's upload into the template. I don't recall hitting any major obstacles previously but if I do I shall be sure to report them here in a mo.

      I'm back. The first thing I had to do was decide the source of the material I'm going to copy-paste. I keep PDF copies of all my posts so I began by trying that, but it's not ideal because of all the extraneous words in the menus, etc. when you print to PDF from a browser. Makes it hard to get a clean text selection over anything more than a paragraph. Thus, I elected to go back into Blogger API and open the post I'm uploading, as if I am going to edit that post. The Blogger API is pretty simple although it has a habit of hiding irrelevant HTML if you've pasted text from formatted sources. In any event, the Blogger API makes it convenient to separately copy-paste the title and post body. I edited the author and address information by hand.

      The abstract is my first opportunity to revise what's in the blog post for The Winnower. The post I'm uploading had two prior incarnations: a preliminary draft with a call for input, followed by a first completed version. An introduction to the post resides only in the first version but it needs to be updated to reflect the evolution in subsequent posts. Okay, then. Snip, snip, splat, done. A new abstract is created!



      References!

      Here's an important point. Notice what I did and didn't do with the Poldrack reference in my abstract. * The first post I uploaded to The Winnower contained a bazillion references. It was a literature review! I went back and forth with Josh about the citation recommendations in his guidelines. I was beta-testing the upload procedure so I tried to play along, except that the way I'd used simple hyperlinks (mostly to PubMed IDs) in the blog post meant that I would have many days' work to conform to the guidelines. Once the blog was uploaded and all the links worked I decided that it was good enough to proceed with the links alone. Check it out, see if you agree. I am assuming that PubMed and the NIH will be around a while so I'm counting on PMIDs to be resolvable indefinitely. Of course, I see the irony in not using a DOI instead of the PMID here given one of the goals of this upload, but I tend to use PMIDs a lot more than DOIs to this point. If in doubt, do both. Otherwise, I'm going to give my personal opinion that it is better to have got your post uploaded with PMIDs, links or whatever you've got than to procrastinate for wont of conforming to some standard. Sure, DOIs and full citations for every reference would be lovely, but we're busy here! The Winnower papers work perfectly well with hyperlinks so provided your links have some survivability to them I think you're good to go. **

      * I just noticed that links aren't given differential formatting automatically in the abstract of The Winnower articles, making them hard to see. But you can mouse-over them and they reveal themselves. There are four links in the abstract to my first uploaded post. See if you can find them. In today's test I shall add bold, italics and underline to the abstract template and see if I can emphasize the links...

      ** Links in the manuscript body (that is, anything outside of the abstract) format automatically to reveal themselves, so just insert links as usual in either the Blogger API or in Word and you're good to go.


      Now it's time to copy-paste the body of the post between the [manuscript] anchors of the template. Pretty easy stuff, except that again I'm going to take the opportunity to tweak some things. I'm going to move the abbreviations up to the top, and I need to address the issue of highlighting and other formatting that resides in the blog post.

      I'm busily copy-pasting from the Blogger API into the template. I'm grabbing entire chunks of text, figures 'n all. I simply select the "Match Destination Formatting" option that Word gives me whenever a new pile of stuff is dropped wholesale into the template. Some tabs are vanishing so there's a little bit of work comparing the format of the published blog post to the template draft, but so far so good. Though it should be noted that I expect a lot of my reformatted text to appear differently once it's converted to HTML and uploaded to The Winnower, so I'm not spending very much time making my document purdy.

      Figures and tables

      I'm also being pretty cavalier with figures and tables. In this post the only figures are actually tables of variables. The table extends slightly more than one page. Rather than spend goodness knows how much time creating a new table or making everything fit on a single page, I'm again going for expediency. So it's Word > print to PDF > export as PNG > insert into template. Sorry, Josh. We're online here, so all that really matters is that the final document is readable. If I've wasted a third of someone's screen then I apologize. I refer you to my earlier opinion regarding overcoming procrastination.

      The upload

      I'm nearly there! The template document is complete and I've double-checked that the abstract lies between the [abstract] anchors and everything else resides within the [manuscript] anchors. It's upload time.

      The next step is to export an .htm version out of Word. At this point my document is called checklist_to_Winnower.docx rather than word_template.docx. I'm going to assume that The Winnower's servers like to discriminate between uploads. That said, I don't actually think it matters what you call your .docx or the subsequent .htm file. So I'm just selecting "Save as Web Page..." per the instructions: ***



      *** The option to "Save entire file into HTML" creates a number of .xml that aren't needed for the upload. Thus, you can opt for "Save only display information into HTML" when exporting from Word.  

      Note the requirement to select the "Web Options..." and then ensure you have the Unicode (UTF-8) option selected:


      Word immediately opens the .htm file for me but as a skeptic I will quickly review the file in my browser. The first part looks okay except for a deliberate mistake :-/ Anyone spot it?



      Yes, that's right. My post is presently entitled "Windows Template." So just a few moments while I correct that oversight... Done. I have to export to .htm, of course, so I'll spend a few minutes checking the rest of the export first. I rather like the idea of enforced proofreading. This isn't quite enforced but the hassle of re-exporting certainly encourages it here. Hands up who has reviewed a paper where one of your earliest thoughts is along the lines of "WTF? Do they expect me to proofread it then review it? Can I be a co-author?" Spend the time, people. 99 percent done, halfway there.

      Okay, so I've scan-read the .htm file (I don't take my own advice) and I'm ready to proceed. I notice a new folder containing the images and some .xml files. The instructions tell me I'll be needing the image files, at least, during the upload. Here goes.



      The first page of the upload is self-explanatory, but you do need to come up with up to five keywords. Make them count. Somewhat usefully, the interface shows you all the keywords that people have used before although they're not categorized.

      The next page asks me to upload my document, or I could perhaps use a URL to the .htm file residing in my browser. I haven't tried the latter before so I'll use the DOC button, as I think I did last December. Then I just have to find and upload the .htm file and the three image files in the post:




      Having checked the two radio buttons and selected PROCESS UPLOADED FILES you will definitely want to preview your paper. My draft is well spaced. Too well spaced, in fact. A few diagnostics shows that a new line after an anchor, a header, a sub-header, a figure or a paragraph is sufficient to insert a half line space. A blank line results in 1.5 lines in the uploaded version. So I'm going to go back and remove the blank lines between my paragraphs, etc. in the Word document. It makes the Word version look crap but I'm confident the formatting will work properly once The Winnower gets a hold of it. This is what it now looks like as .htm with blank lines removed:


      It looks clunky as .htm (and in Word) but once uploaded the formatting gets sorted out quite nicely.

      I've previewed my paper and it's good enough. I notice the links in the abstract are displaying bold and italicized but not underlined (two out of three ain't bad!) which discriminates them nicely from the plain text:


      While it's not perfect, the theme of this post is to "git r' dun" so I'm going to resist the temptation to get anal at this late stage and instead select the PROCESS UPLOADED FILES button to release the beast.

      Oh, this is cool. On the third of the four upload pages you are given the option to provide "additional assets" to your paper. I was expecting to be asked for my credit card and CVV code. But no! It's a place I can drop a PDF version of the checklist, to compliment the one that already resides on Dropbox. Nice.

      All that remains to do is notify anyone I want to review the paper - no thanks, I'll keep it a secret ;-) - and.... Voila! Success!!!!



      The new paper is now live right here. I've checked that the links and the downloadable PDF work properly but I'll leave any more strenuous proofreading and corrections for a later date. Likewise, I'll wait on assigning a DOI because I don't see the button that was there when I used it a month ago on another post. For now, then, it's a wrap.


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      The latest version of the fMRI acquisition checklist is now available at The Winnower via this link. It can also be located/cited using DOI: 10.15200/winn.143191.17127 .

      Updates/changes from v1.2:
      • The “Pre-scan normalization” parameter has been renamed “Signal intensitycorrection/normalization” to broaden its scope.
      • Reviewed and revised explanatory notes.
      • New parameters: RO partial Fourier scheme, Number of echoes, Saturation bands, Gradient non-linearity correction, Z-shim gradient correction, On-resonance adjustment.

      The current list should work now for most simultaneous multislice (SMS) EPI, multi-echo EPI (or spiral) and 7 T experiments, but additional emphasis will be placed on these advanced methods in the next round. Please let me know of parameters you'd like included. The next planned update will include as far as possible the vendor-specific nomenclature for each parameter. I anticipate a late 2015 release date. There are no plans yet to do a machine-readable version of the checklist, as originally discussed, but that is only because nobody has been asking for it. Please get in touch if this is something you're interested in.



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      One of the delightful aspects of running an imaging facility is the sheer variety of projects coming through the door. Late last year my boss told me he'd been discussing with a group from Emory University about doing fMRI on trained dogs at our center. I'll confess to receiving the suggestion unenthusiastically, if only because I envisioned a mass of bureaucracy followed by a head-on logistical collision between the dog group and the dozens of human users. Activity at our center oscillates between hectic and frenetic, depending on the day. But, as it turned out I needn't have worried. The bureaucracy was handled admirably by the Emory folks while the logistical issues simply failed to materialize because of the professionalism of the dog fMRI team. It's been an enjoyable experience. And there are dogs. Many boisterous, happy, playful yet exceedingly well-trained dogs. Like these:



      Motivation

      Greg Berns (on Twitter here) at Emory decided a few years ago to do something that few thought was possible: to scan dogs with fMRI while they are awake and behaving, using nothing more than the same tactics as we prefer to use for human MRI subjects, i.e. training. No sedatives, no anesthetics. Nothing but some good old-fashioned familiarization.

      But why try to do dog fMRI at all? In Greg's case the initial question was what fMRI might tell him about his own pet dog's brain. A simple example: was his dog excited to see Greg come home from work because it was the prelude to dinner - a conditioned response - or was the dog genuinely pleased to see Greg, the person, and dinner was a nice bonus thank you very much? I'll let Greg take it from here, courtesy of a recent TEDx talk:



      Greg's initial dog fMRI project also featured in a segment broadcast on CBS's 60 Minutes in October, 2014.

      As mentioned in the TEDx video, the project that Greg wanted to run at my center uses service dogs-in-training. All the dogs are from Canine Companions for Independence (CCI), based in Santa Rosa, CA. Ignoring Bay Area traffic our location is sufficiently convenient to conduct scans on these dogs. We also happen to have the same scanner that Greg has at Emory.


      Training

      In human fMRI research the vast majority of the time and effort goes into the data processing and statistics. For dog fMRI, on the other hand, there is a truly massive front end load to train the subjects to get them into the scanner in the first place. It's a huge, intricate, time-consuming undertaking with each dog going through up to three months of MRI-specific acclimatization to the sights, sounds and feel of the scanning environment. Learning the task they're to perform is trivial by comparison. But it's far from a burden. As service dogs-in-training these animals spend many hours of every day gleefully learning to locate correctly various objects left laying around the CCI campus, or how to open a door with a rope, or how to react to a fire alarm versus a door bell, or any number of other vital activities they will eventually perform out in the real world.

      I got a tour of the mock scanner at CCI-Santa Rosa with Kerinne (on the right, below) and Erin, two of the awesome CCI dog trainers. You can also see Fritz, our volunteer for the day, in his kennel behind Erin's right elbow.



      As with any human MRI the first task is safety. For dogs it's extra important to protect their sensitive hearing. Unlike humans, dogs tend to shake earplugs out of their ears unless the devices are literally strapped in, so the dogs are fitted with a sports wrap to hold the plugs in place:



      Over the course of several weeks the dogs are trained to place their heads in a styrofoam muzzle rest positioned inside a wooden ring mimicking an RF receiver coil. They also learn a task, signaled by the trainer, that tells them when they can expect to get a small food reward. Eventually, the dogs learn to climb a set of steps and lie down with their muzzles on the chin rest while they lie still in a mock MRI, which beeps and clicks using recordings from the real thing. Unlike the real thing, however, it is perfectly safe for me to record videos from all angles and up close. Here's Fritz during a training session:





      Scan day

      The dogs are trained in groups, typically 8-10 at a time, to be scanned over a single weekend. On scan day the dogs are chauffeured over to Berkeley with their trainers and assistants. Luckily, not only is Berkeley a dog-friendly campus but it also has many open spaces for the CCI handlers to exercise the dogs while they wait. As you might imagine, a day trip out with their friends can get dogs pretty excited so the CCI staff have a bonus challenge to keep their charges focused. Nobody said hard work couldn't also be fun!

      The setup at the scanner matches very closely that in the mock scanner, except for the addition of mats and covers to protect the magnet and the scanner suite from bits of dog treat and dog hair. We don't get too much of the latter because all the subjects get a bath the day before. Each dog is introduced to the scanner suite for a quick familiarization and as soon as the humans are ready (most delays are for the humans!) the dog goes through its now familiar routine.

      Interestingly, none of the dogs scanned so far seems to be bothered by the presence of the magnetic field. Whether they sense and ignore it or cannot sense it we don't know. In any event their reactions to the magnetic field are not appreciably different than the average human subject. The advantage for the dogs, as for humans who have been trained in a mock scanner, is that they are acclimatized to the dark, confining tube that makes lots of noise. The biggest distraction seems to be the novel environment they've just experienced - the car ride, the Berkeley campus, new humans around the imaging center - but going from play mode to work mode is a quality specifically selected for in service dogs.

      After trying different coil options Greg settled upon the standard Siemens 2-channel human neck coil for the dogs. It's sufficiently large and open that the dogs can lie in the sphinx position to get their heads inside, with front paws either side, and the handler can stand behind the magnet to signal the behavioral task. The neck coil generates decent images. Here's a screenshot of an anatomical scan on the left and a mosaic of EPIs on the right:




      There's plenty of contrast while the spatial resolution and SNR aren't too bad for a brain about the size of a lemon. Dogs have thick skulls as well as large muscles around the head. These dogs are either retrievers or Labradors about a year old and are all fit and healthy so subcutaneous lipid signal tends to be similar to what I'm used to seeing with human heads. Fat suppression is used for EPI just as for human fMRI. Other parameters are described in this recent PeerJ paper:
      Functional scans used a single-shot echo-planar imaging (EPI) sequence to acquire volumes of 24 sequential 3 mm slices with a 10% gap (TE = 28 ms, TR = 1,400 ms, flip angle = 70°, 64 × 64 matrix, 3 mm in-plane voxel size, FOV = 192 mm). Slices were oriented dorsally to the dog’s brain (coronal to the magnet, as, in the sphinx position, the dogs’ heads were positioned 90° from the usual human orientation) with the phase-encoding direction right-to-left. Sequential slices were used to minimize between-plane offsets from participant movement, and the 10% slice gap minimized the crosstalk that can occur with sequential scan sequences.

      If you're interested in the entire scan protocol, including some of the earlier approaches that have been improved upon in the current experiments, there's more information in this PLOS ONE paper from 2013.


      Dealing with motion

      Motion is the arch enemy of fMRI whatever the species. As already mentioned, the best approach is to have compliant subjects. Greg's muzzle/chin rest design serves twin purposes. It allows the dogs to relax in a position where the head is reasonably well restrained left-right and head-tail, and it is a mark for the dogs to return to whenever they move to get a reward. As with human fMRI, watching the inline display as the EPIs are acquired is highly informative. It's not uncommon for a dog to be able to go 60 seconds with motion effects visible only at the level of the N/2 ghosts, as for a motivated human subject. And as for returning to their marks, it has to be seen to be believed. I once spent several minutes being highly amused by the ability of one dog to get a treat, swallow it and then return to the same position such that a triangle of small blood vessels ended up in precisely the same position in the same EPI slice. I had my finger on the screen as a reference. Except for the contrast changes due to blood flow in those vessels there were no other signs of life!

      The EPI is run continuously while the dog does the task and gets treats along the way. The entire run is thus contaminated by numerous intended movements - for rewards - as well as the occasional unintended movement - most often a dog licking/swallowing a few seconds after returning to its mark. An observer notes the times in the scan the dog is being treated, to assist in the elimination of motion in post processing, but as with human fMRI the effects of gross head motion tend to be unambiguous whether they're intended or not. Unlike humans, however, these dogs tend not to fidget with the rest of their bodies, not even wagging their tails when they are on task. And they don't fall asleep, either.

      I'm not involved in the processing so I can't comment on how well or how poorly one can chop out motion-contaminated EPI frames and splice together the remainder for analysis. A major difference with most task-based fMRI is that Greg's team simply discard the majority of frames where motion occurs. The trick, then, is to decide the cutoff for discarding data. This is the procedure outlined in Greg's most recent PeerJ paper:
      Because dogs moved between trials (and when rewarded), aggressive censoring was carried out, relying on a combination of outlier voxels in terms of signal intensity and estimated motion. Censored files were inspected visually to be certain that bad volumes (e.g., when the dog’s head was out of the scanner) were not included. The majority of censored volumes followed the consumption of food. On average, 51% of total EPI volumes were retained for each subject (ranging from 38% to 60%).

      Other than aggressive censoring, the time series are also motion-corrected using a 6-parameter affine registration and the motion traces are used as regressors in the statistical analysis. Standard stuff. Whether this is better or worse than what is commonly done for human fMRI I'm not qualified to say. What I do like is the expectation that a lot of data will be discarded and this is budgeted for in the acquisition. With most human fMRI the motion issue tends to be considered post hoc and rarely do people expect to throw out a lot of data.




      Future developments

      What could be done to improve dog fMRI? I'd like to test our ability to measure head movement with a peripheral device such as a pressure sensor placed on the chin rest. We have the equipment and we could probably incorporate the sensor, which in our case is a circular disk less than 2 cm diameter, into the chin rest in such a way that the dog doesn't even notice it. We record movement (usually chest movement for humans) using BIOPAC equipment which also records TTL signals sent once per TR during the EPI time series, so synchronization is trivial. Perhaps we could use such a measure to define automatically those segments of an EPI time series that should be thrown out, then motion-correct the remainder. We might then start to look at the effects of respiration and consider ways to further clean the time series.

      There are also opportunities to upgrade the image acquisition. For a start we are using a two-channel (human) neck coil but the majority of the brain signal comes from the upper loop. This configuration negates the option to do simultaneous multislice (a.k.a. mutiband) EPI for fMRI. The size of the elements in the coil is suboptimal from a signal-to-noise (SNR) perspective, too. The open, circular geometry of the neck coil permits Greg to scan a wide range of dog sizes with the same equipment so I consider that a fixed specification for now. A coil mounted somehow to the dog's head has been considered and rejected because of the need for cabling. Cables would likely be too restrictive and/or get broken. An inductively-coupled head-mounted coil (which doesn't require a cable) might work, but it's a lot more complicated to design and getting multiple channels would be a problem. The option I've considered most seriously is a flexible "blanket" array coil, say 16 channels, that could be attached to a custom-built rigid former that allows the blanket to be mounted firmly as either a full or semicircle, easily mimicking the geometry of the current neck coil setup. A 16-channel array coil would permit a slice acceleration factor of at least R=2, perhaps even R=3 for SMS-EPI. It's something for me to consider as I see different RF coil designs become available.

      I am also interested to know if we can acquire other types of functional scan from some of the better performing dogs. Both arterial spin labeling (ASL) and resting-state fMRI scans are highly motion-sensitive, but I'm fairly sure that some of the dogs would be able to remain as still as a human for the four or five minutes both of these types of scans require. Training in the mock scanner will tell us where the limits are. It might be appropriate to acquire ASL or rs-fMRI in shorter blocks with aggressive scrubbing of spoiled volumes. Scrubbing for rs-fMRI may change the nature of the data and I'm generally not a fan, unlike for task-based fMRI which can be considered as individual task blocks for statistical testing. But it could work for ASL. We shall have to try it and see.

      ____________________________




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    • 10/03/15--20:04: Functional MRI of dolphins?


      • Those of you who follow me on Twitter may have noticed that I've been scanning some post mortem brains of Cetacea over the past year or so. That's whales, dolphins and porpoises to you and me. The brains come in all shapes and sizes, from a rather tiny Amazon river dolphin, about the size of a fist, to fin and sei whale brains that are so wide they have to be inserted sideways, hemisphere-first, into the 3 T (human) head coil. The conditions and ages of the brains vary tremendously as well. Some have been fixed in formaldehyde for decades yet yield remarkably decent signal, others have been stored in ethanol and are as hard as rubber with T2 to match. A few months ago we obtained a recently deceased fresh brain of a white-sided dolphin which we were able to scan within about twelve hours of its demise. The image quality was magnificent.

      What do we plan to do with all the post mortem data? That is still being formulated. Initial motivation for the project came from some Berkeley anthropologists with an interest in comparative neuroanatomy across higher mammalian species. Coincidentally, Greg Berns' group at Emory has recently produced a nice example of dolphin brain tractography and his recent study is a good example of what might be done in future. There's a commentary on Greg's study here, and an example image from his paper below. We are now determining how we might combine resources, share data and all that good stuff. More on what will be available to whom and when as we progress.

      From: Berns et al. DOI 0.1098/rspb.2015.1203

      In any case, after I posted the white-sided dolphin MRI to Twitter someone asked, likely facetiously (I suppose that should be flippantly), whether functional MRI was next. FMRI of dolphins was the subject of an April Fool's Day joke a few years ago, and it does seem far-fetched at first blush. So, too, does studying trained dogs with fMRI, but Greg Berns' team is already doing that. Since thought experiments are cheap I figured I'd write a blog post to consider what might be feasible today if one were sufficiently motivated (read sufficiently well funded) to want to do fMRI of cetaceans. If nothing else we might learn something as we're forced to consider the manifold factors.


      What's been done before?

      Like any good experiment, thought or otherwise, we should begin with a review of the literature. (A month in the lab will frequently save an hour in the library, as the old aphorism has it.) And it turns out that some clever folks at UCSD have done both PET and SPECT scanning of a dolphin in the last few years. It's not fMRI but it is functional brain imaging and they used structural MRI (at 0.5 T) to locate the activity. Here's a dolphin getting a structural MRI scan in this figure from their 2006 paper:

      (From Ridgway et al., 2006. DOI: 10.1242/​jeb.02348)


      Structural MRI has a more extensive literature than the functional stuff. If you search PubMed for "dolphin brain MRI" you get 19 publications, most of which concern post mortem specimens. Some brains were fresh and in situ, others were fixed. But the papers from UCSD that reported PET and SPECT results seem to be the only published academic studies using MRI of a live dolphin, too. There are reports of dolphins receiving MRI for diagnostic medical purposes, such as this one, but they show CT scanners in their illustrations.


      Sizing the hardware and getting a dolphin-friendly "patient bed"

      I shall assume we want to be able to scan an adult bottlenose dolphin. The main constraints are the mass of the animal and the size and location of the dorsal fin and pectoral flippers. Open style (stand-up) magnets accommodate the dorsal fin easily but the magnet strength is low. Likewise, open MRIs of the type used by UCSD (shown above) accommodate the pectoral fins but again the magnetic field is low. The best I've found is the new 1.2 T Oasis from Hitachi. An impressive field strength for an open MRI, but let's aim for at least 3 T assuming we want lots of fMRI contrast and high resolution anatomical scans.

      A wide bore 3 T MRI might work for us but before we commit to what's on the market we need to consider the dimensions and mass of an adult dolphin in more detail. The following dimensions were estimated from a paper on dolphin growth rate together with a basic view of dolphin anatomy courtesy of the Texas Marine Mammal Stranding Network. Our model adult bottlenose dolphin is 2.7 m long. The distance from the center of its brain to the front of the dorsal fin is about a meter. This distance is compatible with Verio & Skyra magnets from Siemens. The Skyra has 70 cm usable bore diameter with 173 cm total length, meaning that the center of the magnet is 86.5 cm from the front flange of the magnet. The leading edge of the dorsal fin would end up just outside the magnet.

      But what about that 70 cm bore? For a length of 2.7 m the girth anterior to the dorsal fin is about 1.9 m. This gives a diameter at the widest point of 61 cm assuming our dolphin is (a) slim n' trim and (b) well approximated by a cylinder. We probably shouldn't assume either of these to be true. Furthermore, the entire bore isn't usable because we need some sort of table or other support to move the animal in and out of the magnet, and make sure it's not uncomfortable during the scan. While a flat table as for humans might work given a sufficiently large magnet bore, for a dolphin it's not the ideal shape. If our dolphin is 61 cm across at the widest point and the magnet bore is 70 cm diameter then we only have 9 cm to play with. An adult dolphin is considerably heavier than your average human, too, at 200-250 kg. We're going to have to replace the patient table. Would 9 cm be enough space to accommodate some sort of curved bed/sled that could move the animal in and out? Perhaps, with suitably strong materials or with the magnet bore lowered relative to the surrounding floor, as happens for some very large, high field magnets.


      Cross section through a modern superconducting MRI magnet, showing all the principal components except the plastic covers that provide the external facade as well as some acoustic noise attenuation.
      From: Lvovsky, Stautner & Zhang, Supercond. Sci. Technol.26 093001 (2013). doi:10.1088/0953-2048/26/9/093001


      Beyond supporting the mass of the animal there is an additional qualifier to the curved "dolphin bed." As I'll discuss more below, dolphins are highly sensitive to vibration and hear the majority of sounds through bone conduction via their lower jaw. Thus, serious consideration would be needed for anti-vibration measures when selecting materials for our bed/sled. It's difficult to fully decouple a bed from the magnet, even with cantilevered designs, so some sort of anti-vibration liner is probably implied.

      What about gradients, RF transmission and reception? Here we can extrapolate reasonably well from what we use today for humans. As shown in the above illustration, a magnet-sized (so-called body) RF transmit coil and gradients are already accounted for in a commercial 70 cm clear bore system such as a Skyra. If it transpires that a larger clear bore is needed, e.g. to accommodate the custom bed/sled, then one obviously needs larger, custom gradient and body RF transmit coils proportional to the larger magnet bore. These wouldn't be cheap but the engineering is entirely tractable.

      Signal reception could be achieved by something as simple as a blanket coil. The dolphin's brain is located just behind its blowhole, as shown below. We obviously don't want the animal to suffocate in the MRI so we would need to modify the blanket coil for breathing. Alternatively, one might easily pursue a custom coil design that resembles a large bowl and incorporate sufficient space for the blowhole. There are many, many options when it comes to RF signal reception, depending on whether you want to do accelerated image acquisition, how the coil is to be held in place, etc.

      Basic anatomy of the Atlantic Bottlenose Dolphin. From the Texas Marine Mammal Stranding Network website.




      Health and welfare issues

      In addition to the vibration, what about sound pressure levels through the air? These are a big deal for humans, and dolphins are known to hear frequencies almost an order of magnitude higher than humans. Hearing is arguably a dolphin's most sensitive sense. If one were to design and build a custom 3 T magnet with a larger bore - say a usable bore of 1 meter diameter - then there would be sufficient space to install aggressive acoustic and vibration damping. In the absence of a more refined solution, one simple option would be to construct a fiberglass bore liner onto which several different densities of acoustic foam can be attached. (We did this for an old 4 T magnet years ago and the reduction in sound pressure level was better than 20 dB in the bore. Adding extra foam to the backs of the magnet covers - the facade you actually see from the outside - also reduces echoes and reduces acoustic noise both in the bore and outside.) Anyway, the bottom line on acoustic noise and vibration is that much designing and testing would be needed to ensure that the final sound/vibration level was safe.

      At the same time it is important to ensure the shape and surface of the "dolphin bed" is comfortable and doesn't place undue pressure on any part of the animal's body. With humans we've used memory foam on the patient bed of my Siemens 3 T since we realized that people fidget less when they're comfortable. Memory foams allow better distribution of weight to minimize pressure points. For an animal used to having its weight supported by water this issue becomes even more critical, especially if one is expecting to conduct a scan over twenty minutes or so.

      Keeping MRI subjects cool is always important, both for safety and comfort, but it is paramount for an animal that experiences external water cooling naturally. Blowing air down the bore is okay for (some) humans in an MRI but it will tend to dry out the dolphin's skin. The bore could be lined instead with a water misting system to keep the animal moist as well as cool. The magnet would need to be drained appropriately, and there would need to be suitable precautions to ensure water doesn't get where it's not wanted, such as into connectors for an RF coil. Would the mist or water pooling on the bed/sled cause issues for the MR images themselves, e.g. due to signal aliasing? Probably not if the right RF coil is used for signal reception. A full bore "body" transmission coil is already heavily (electrically) loaded with a 200+ kg dolphin inside it; a few liters of water isn't going to matter much. On reception, if we're using a curved surface coil like a bowl or even a blanket coil then we really only need to worry about water pooling on the coil's upper surface, a situation that could be ameliorated with drainage if the shape doesn't fix the problem for us. And of course we can always turn the misters off when we're actually scanning.


      How to approach fMRI of dolphins?


      Physiology and BOLD

      The goal here is functional MRI. Do we expect dolphin brains to exhibit BOLD contrast as we are used to seeing it in humans? On the one hand there is an abundance of evidence that mammalian brains exhibit rather consistent BOLD responses to stimuli. Studies of monkeys, dogs, cats and rodents have all shown robust BOLD contrast, whether awake or anesthetized. (Dead fish? Not so much.) We also have the UCSD studies using PET and SPECT as evidence that cerebral blood flow (CBF) might be estimated using the same methods in dolphins as for humans. So the chances of getting measurable BOLD and/or CBF changes in response to tasks would seem to be pretty good.

      Cetaceans have evolved to dive and may be able to endure hypoxia for long periods compared to land-based mammals like us. Might this ability affect the BOLD contrast in interesting ways? Bottlenose dolphins generally have higher hematocrit and red blood cell levels than us land-lubber humans, unless we are high-altitude endurance athletes. And, just like us, the animal's fitness is a prime determinant of its blood constituents so we would want to record as many physiologic parameters as we can in order to distinguish neurovascular changes from concomitant effects. Variable hematocrit has already been implicated as an important experimental factor when studying exercising marine mammals. We're not going to have a dolphin diving in our MRI of course, but the dolphin's exercise history and general fitness may become crucial if we expect to compare fMRI results across multiple sessions. Definitely lots of factors to bear in mind when designing the fMRI experiment.

      As for effects of the magnetic field, there is recent evidence that bottlenose dolphins can detect magnetism in otherwise identical lumps of metal concealed in opaque plastic barrels suspended in the water. But other than approaching barrels containing magnetic material more quickly, there was no difference in subsequent behavior: "The fact that all other behaviours did not differ between magnetized and control stimulus may reflect that magnetic fields are neither particularly attractive nor repulsive to dolphins."That's good. What we don't know, of course, is whether the strong magnetic field of an MRI would induce the dolphin equivalent of vertigo or other sensations that humans experience routinely.


      Training for and conducting an fMRI experiment

      I'm sure that most of you will have considered the ethics of attempting fMRI of a dolphin well before this point. I have neglected it until now simply because I wanted to consider the practical hurdles first. If we had run into an insurmountable problem then the ethics, while interesting, become moot. At the point of wanting to conduct an actual experiment, however, the ethics of the enterprise loom large and unavoidable.

      Let's set a few limits. I'll assume that we want to scan animals that are available only because of extenuating circumstances. We are not going to attempt to scan wild dolphins unless by some miracle a wild dolphin can be coaxed into a scanner and lay still for the duration without significant assistance. Seems unlikely, even if an MRI scanner were partly submersed off a dock in Santa Cruz and a dolphin could swim right up into it. So we are talking about attempting to use dolphins in captivity only. I don't particularly relish the thought of dolphins in captivity for any reason, but I do understand that there are times when dolphins end up there and it may be inappropriate to release them. For example, some animals may have been rescued as orphaned youngsters or born into captivity and so lack the survival skills of an adult wild dolphin. Others may have been injured to the point where survival in the wild is unlikely. No doubt this would be the most contentious aspect of doing fMRI of a dolphin so I'll leave the ethics for you to debate in the comments below.

      For the purposes of this thought experiment I shall assume that we have an adult dolphin living in captivity through no fault of its own. Furthermore, I shall assume that a committee of suitably qualified folks has determined that the perceived benefits of scanning the animal are sufficient to overcome objections and risks, just as we decide when studying any vulnerable population. As we move on to consider the training required for a dolphin fMRI experiment we will need to think carefully about the types of stimuli and responses we might want to use and determine the limitations imposed.

      Perhaps the simplest fMRI experiment would be the "resting state." Here we might conceive of training a dolphin to lie still in a tube for ten minutes in exchange for a reward, such as Craig Bennett's dead salmon. This might permit discovery of the intrinsic connectivity in the dolphin's brain. Task-based fMRI would involve a far higher degree of training, but with a suitable mock scanner and enough fish rewards there would be a good chance of success if what's already been achieved with dogs is any guide.

      In most human fMRI studies, and in the earliest dog fMRI studies, the stimuli are visual. Is that a good choice for a dolphin? Since I'm not a marine mammal expert I have no idea. What I do know, however, is that a dolphin's eyes are more on the sides of its head than forward-looking, as for humans and dogs. If we are going to use visual stimuli then we need to be thinking about the binocular visual abilities of the dolphin, perhaps resorting to twin goggles for monocular or binocular display, or a single target mounted sufficiently far from the dolphin's nose for it to see with both eyes.

      Other stimuli might be possible. Those clever people at UCSD came up with a device that permits a dolphin to echolocate while out of water. The echolocation clicks "produced by the dolphin are detected with a hydrophone embedded in a suction cup on the melon." For stimuli: "The experimental approach relies on the generation of electronic, or “phantom” echoes, rather than the use of a physical target directly ensonified by the animal." How does the dolphin sense the echoes? "The echo signals are then projected to the dolphin via a transducer placed on the lower jaw." Here's a schematic of the echolocation pathway for a dolphin, taken from the Wikipedia page on animal echolocation:


      Sound generation, propagation and reception in a toothed whale. From the Wikipedia page on animal echolocation.

      That would surely be the hard part done, especially as the melon is forward of the brain and blowhole. When mounted the device ought not be in conflict with a receive RF coil. Making the device MR compatible ought to be straightforward by comparison to inventing it in the first place! Would it be possible to use this device during scanning while ensuring good attenuation of the scanner's vibrations and noises? The scanner peak acoustic frequencies are typically less than 3 kHz whereas dolphins echolocate from 40-150 kHz. Even allowing for a few harmonics there should be minimal energy produced by the scanner in that range.

      The whistles and clicks that humans can hear are obviously at much lower frequency than those used in echolocation. Could we use the audible vocalizations as our dolphin version of a button response box? Perhaps. The interference of scanner sounds is an issue but again, with good acoustic engineering and careful tuning of the scanner peak frequencies I don't see why this couldn't be achieved, provided the vocalizations don't require movement of the head or body. These are issues that those doing human fMRI of audition and speech already deal with.


      So long, and thanks for all the fish

      Functional MRI of dolphins would seem to be a reasonable experiment to pursue if one has sufficient resources and the freedom to perform extensive customization of a scanner. I don't foresee any major technical impediments even if the logistics would be a tad involved. And, as they are the second most intelligent species on this planet, after the mice, I think it would be fascinating to see a dolphin's brain in action. Who knows, perhaps with fMRI we will be able to decode the dolphins' warnings of our imminent destruction by the Vogons. Or we could try to find a Babel fish to shove in an ear. Choices, choices.

      _____________________




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    • 12/02/15--10:59: Making tracks for charity


      • This is going to be among the more unusual blog posts I've written. Here goes nothing.

      The back story. A few years ago I decided to take a stab at writing fiction. The result was Bubble Chamber, an academic satire with a scientific twist. My intention? To see if I could write a novel, to actually finish it and get it into the public domain as an end point. Check! The process wasn't even sufficiently painful to dissuade me from future forays into novel writing. Just not yet, I have too many other irons in the old fire.

      Towards the bottom of the post you can read a synopsis of Bubble Chamber along with a little contrived biography of yours truly. I am also tempted to include here a link to Amazon so that you might procure a copy of my literary scribbling for yourself. But I won't do that just yet. We need to make a quick detour.


      Dementia Adventure


      It transpires that a young Englishman by the name of Chris Graham has been steadily riding his bicycle, solo and unsupported, around the circumference of Canada and the USA since the beginning of May. He is now in Maine, approaching Canada for the second time and about to commence the last leg of his marathon journey. His current location, as well as the route already completed, can be seen in real time courtesy of his YBTracking page. A flight back home to the UK on Christmas Eve beckons as the final reward for all of Chris's hard work. (Motto: If in doubt, pedal!)

      Chris is doing his ride for charity. Specifically, Chris is raising money for Alzheimer's Research UK. You see, Chris is likely to develop dementia in a not too distant future. He carries the gene for early onset Alzheimer's, a disease that has claimed the lives of many of his close relatives, including is father and grandfather, at an age that Chris himself is now rapidly approaching. You can learn more about Chris and his family history on his web page.

      This is no sob story, however. Far from it. In fact, following Chris on his Facebook page has brought smiles and laughs to many thousands of people this year. Watch one of his video updates then tell me you won't be back to see what happens next! Better yet, quit reading this blog and pop on over to Chris's Just Giving web page to send him some money right now, then go peruse the photos and videos on his Facebook page. Thanks, and goodbye!



      If I didn't just lose you to an immediate donation then I have a proposition for you. Chris is a mere three lousy UK grand from hitting a £40K target and I want to do a little something to help him get there. All proceeds arising out of sales of my aforementioned novel were committed 100% to charity from the moment I conceived of writing it. That hasn't and won't change. An assisted living charity in the eastern US has received very occasional, very small checks over the years. But for the rest of this year at least, all proceeds from sales of Bubble Chamber - available via this Amazon page - will go to Dementia Adventure. I am also going to send along an equivalent amount of my own money for each copy sold so that you have extra inducement to go buy the book.

      If you prefer to ignore my book entirely and simply donate to Chris that is just peachy. Or donate to Chris and then go buy the book, I won't stop you. Either way, join Chris virtually as he goes into the last three weeks of a truly epic bike ride. Along the way, take the time to think about just what it is you want to achieve with what remains of your life. We could probably all use a little bit more of Chris Graham's irrepressible spirit.


      _________________


       

      Bubble Chamber - a novel


      Simon Mills had never actually planned to become a scientist. But he hadn't managed to avoid the eventuality either. As with most things in his life, Simon's drift into academia just sort of happened, the result of a series of coincidences and twists of fate over which he seemed to exert minimal control, regardless of whatever alternative path he might have preferred. 

      So, instead of spending his early twenties enjoying a slow, predictable existence in the English countryside of his youth, Simon finds himself first as a reluctant post-graduate student in London, working for a quintessentially eccentric professor, and then - and much to his chagrin - propelled across the Atlantic to the USA, to pursue some harebrained experiment or other. All in the name of science. 

      Simon lurches from one unsolicited career opportunity to another, finally getting himself caught up in an academic political battle going on half a world, and a whole scientific dimension, away from his own. Not that distance or relevant experience appear to be factors when it comes to Simon Mills' vocation. It seems fortune just won't leave him alone.


      From the Publisher


      In the tradition of British academic satire, from Kingsley Amis and Tom Sharpe to Malcolm Bradbury and David Lodge, one theme had been consistent: the protagonists hailed from the literary and liberal arts. Now, in Bubble Chamber, it's the turn of physical scientists to come in for some ridicule. 

      Bubble Chamber is the story of what can happen when the virtuous pursuit of knowledge collides dramatically with human nature. It tracks the journey of a newcomer to this world of conflicting interests. A graduate student just entering the final year of a PhD in chemical physics, Simon Mills is catapulted into the pressure cooker of international research science. Naïve, honest and utterly convinced of his own mediocrity, Simon finds himself landing one unsolicited opportunity after another, on a path that propels him from the drizzle in London to tropical downpours in Florida, on a collision course with an academic political crisis happening a continent away and in a field supposedly quite distinct from his own. It would seem that fate has plans for Simon Mills. And success, wanted or not, just keeps on happening.


      About the Author


      SCH Thurston is a real scientist at a real university somewhere in America. When he isn't fighting academic political battles or trying to balance declining budgets, he can be found dabbling in a bit of actual research.

      Thurston is in the process of writing a second novel. Another academic satire, "Lem" is set in a regional university in small-town Australia. Without wanting to give too much away, the story blends space exploration with ecology and the 'green' zeitgeist, throws in a critique of public relations and media manipulation for good measure, and ends up with an utterly ridiculous take on how science is portrayed to the public at large. It is a satirical look at the interplay between journalists in the mainstream media and scientists in academic research. In Thurston's ill-considered opinion, both sides could probably do better.

      (Note: "Lem" is on indefinite hold after 50,000 words. Other projects and international events conspired to render a major plot rethink essential. One day.)

      (A free copy of Bubble Chamber to anyone who can guess how my nom de plume came about.)



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      Most labs have plastic goggles for correcting a subject's vision in the MRI. Here's our box of tricks:



      These goggles work pretty well for a standard sized head coil, such as the 12-channel TIM coil on my Siemens Trio. But for a tighter fitting coil, such as the 32-channel head coil, there is simply no way to cram a subject wearing goggles into the space available. For a start the goggles' frame prevents the subject's nose from penetrating the appropriate gap in the front of the coil.

      A simple solution is to relocate the corrective lenses on the outer surface of the head coil. All that's required is a different way to hold the lenses in place. My ace engineer, Rick, made this pair of holders for our 32-channel coil:



      The plastic strip with the label on it is actually optional, it's a way to keep the otherwise independent lens holders together in the store cupboard. Here's what one of the holders looks like with lens inserted:



      The plastic mounts into the gap in the RF coil with moderate friction to hold it in place. In the above photo you can also see the black velcro strips that can be used to hold the pair of holders together, but as mentioned above the holders actually mount independently.

      Below are a few more quick pix of a holder showing its basic construction: a block cut to the right size to fit snugly into the socket on the coil, and a smooth groove to hold the lens in place. I have no idea if there is a company making these things but they are basic enough that you could fashion some yourself in an afternoon.






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      Several people have approached me for advice on using simultaneous multi-slice (SMS) EPI for fMRI experiments. This is the sequence also known as multiband (MB) EPI. I'll come back to nomenclature in a moment. First, though, a brief introduction to what may become a lengthy series of posts. I'm going to focus on BOLD-based fMRI exclusively for the time being - sorry diffusion and ASL folks - and because I presently only have a Siemens Trio at my disposal, everything I write will have strong bias in that direction. That said, I do anticipate writing later posts dealing with SMS-EPI (for fMRI) on a Siemens Prisma at least, and I can already envisage a need for posts dealing with receive field normalization, in-plane parallel imaging, distortion correction options, reconstruction options and multi-echo SMS, to name just a few advanced topics. But first things first - to get going!


      Options for SMS-EPI on a Siemens 3 T scanner

      I am aware of three SMS-EPI pulse sequences for a Siemens Trio. One comes from the University of Minnesota's Center for Magnetic Resonance Research (hereafter CMRR), one comes from the Martinos Center for Biomedical Imaging at Massachusetts General Hospital (hereafter MGH), and one comes from Siemens as a work-in-progress (WIP) aftermarket sequence. For this post I'm going to be using the sequence provided by CMRR. Since CMRR refer to their sequence as multiband (MB) EPI I shall stick to this nomenclature here, and reserve the term SMS-EPI to apply to the broader family of pulse sequences. I may do posts on the MGH and WIP sequences in the future, but the CMRR sequence has been used the most broadly to date (e.g. the Human Connectome Project, which I'll discuss at length below) and so it offers the most immediate, road-tested place to start.

      Other pertinent background. In addition to being on a Siemens Trio, I am going to assume you have a 32-channel receive-only head coil. The MB-EPI and other SMS-EPI sequences can be made to work with a 12-channel coil but only in a much reduced fashion because the 12-channel coil is a Total Imaging Matrix (TIM) coil that generates a lower number of effective channels than the headline number suggests. Specifically, the 12-channel TIM head coil acts like a 4-channel coil (in Triple mode) and that means it has limited RF heterogeneity with which to encode simultaneous slice information. Furthermore, the geometry of the 12-channel coil isn't ideal because it comprises nearly parallel, only slightly curved struts that have very little spatial variation along the coil axis, which happens to be the axis most people want to slice along (for axial images). There's a comparison of my 12-channel to 32-channel coils in an earlier post on receive field heterogeneity as an artifact rather than a feature.

      Your first task is to obtain the CMRR pulse sequence, for which you'll need a master research agreement (MRA) with Siemens and then a C2P agreement with CMRR. Not 100% sure what C2P stands for but I believe it's Core Competence Partnership. In any event, once you have the sequence in-hand you should of course read the release notes and review their gallery of images before you do anything more. I would also suggest that you or your facility physicist spend a couple of hours perusing the history of the current and closed issues in their GitHub. You may well find some of your technical questions already answered. And given that this is a relatively high risk experiment compared to single shot EPI, you or someone around you is going to want to have a decent amount of expertise testing on phantoms and volunteers before you begin using SMS-EPI for an entire study.


      Why SMS-EPI at all?

      For most fMRI researchers, SMS-EPI is likely to be of interest when you want voxels with dimensions smaller than about 2.5 mm on a side, and/or you want a repetition time (TR) substantially below 2 seconds while maintaining at or near whole brain coverage. There are several published studies showing advantages of SMS-EPI for fMRI. A PubMed search for "multiband EPI" and then "SMS EPI" will locate them. Add "BOLD" as an additional search term if you want. Advantages of SMS-EPI versus regular multislice EPI may include better characterization of physiologic noise, leading to better discrimination of resting state networks, higher spatial resolution without losing whole brain coverage, and possibly higher sensitivity to BOLD changes. I'll leave the motivation to you, but you will definitely want to review all the published studies relevant to your neuroscience. If in doubt, keep it simple!


      What the Human Connectome Project recommends

      The HCP team wrote two papers containing essential information on their fMRI protocol. I'm going to review the salient parts of these papers before I get into their specific recommendations because what they did for HCP is actually different than what they recommend. Why? Because they had a customized 3 T scanner - the "Connectome Skyra," with more capable gradients than any stock 3 T.

      In the paper describing the resting-state fMRI portion of HCP (doi:10.1016/j.neuroimage.2013.05.039) they explain how they settled upon 2 mm isotropic voxels, noting:
      Pilot studies using a range of resolutions and EPI accelerations indicated that reducing voxel size to less than 2 mm was not beneficial at 3 T, in terms of the spatial detail discernible in the rfMRI resting-state correlation structure. This was not only due to the reduction in SNR with increased spatial resolution, but also the relatively large point spread function of the BOLD effect at 3 T (Parkes et al., 2005), which was measured to be ~ 3.5 mm at full-width-at-half-maximum (FWHM) caused by the dominant draining vein contribution at this field strength (Uludağ et al., 2009).
      In the finalised protocol this acceleration is used to acquire 2 × 2 × 2 mm data with a temporal resolution of 0.72 s. While a larger voxel size would result in even faster imaging and better SNR, this choice provides a good overall balance in which both spatial and temporal resolution are a significant improvement compared with conventional rfMRI datasets.

      Other key parameters followed from the voxel size and consideration of signal losses relative to BOLD sensitivity:
      Echo-time (TE) was, after much discussion and evaluation, set to 33 ms. Again, this choice is a trade-off; long TE increases BOLD contrast, but decreases overall signal level and increases signal dropout in areas of B0 inhomogeneity. The TE for optimal functional CNR is equal to T2* when thermal noise dominates; however, T2* varies spatially, meaning that no single TE can be optimal throughout the entire brain. Acquiring multiple echoes in a single EPI readout train, or in separate acquisitions with multiple TEs, was not acceptable due to significantly prolonged readout duration and/or TR. Thus, the shortest TE that could be achieved without the use of partial Fourier or in-plane accelerations was selected, to minimise signal dropout. At 2 mm resolution, with the Connectome scanner gradients, this TE was 33 ms, given the excitation pulse width (~ 7 ms) required to achieve the (Ernst) flip angle (52°) for multiband x8. The use of partial Fourier to reduce TE resulted in larger signal dropouts than acquiring a full Fourier coverage of k-space with longer TEs (likely caused by local phase ramps in regions of B0 inhomogeneity shifting signal outside the acquired k-space region); hence partial Fourier was not utilised. The EPI echo train length is 52.2 ms for the final HCP fMRI protocol.

      These are useful points to keep in mind as you proceed. If you want to review all the MB-EPI parameters as used in the HCP, check out pages 41-3 of the PDF accompanying their release of data from 900 subjects. The HCP team also wrote a paper on their minimal processing pipeline (doi:10.1016/j.neuroimage.2013.04.127) which you will want to read before setting up your own experiment, but we don't need to dig into that just yet because, as I mentioned above, what the HCP recommends we do is different to their setup.

      So, just what do the HCP team suggest we do on a standard 3 T scanner such as my Trio? As noted above, we don't have a high-performance custom gradient set as on the "Connectome Skyra" so we have a few more limitations to deal with. (Perhaps those of you with a Prisma will be able to push things harder. I'll deal with Prisma-specific protocols in a later post.)

      For the HCP recommendations we need to review pages 3-7 of that PDF accompanying the 900 subjects data release:
      For functional imaging, key choice points relative to the HCP fMRI acquisitions involve the multiband (MB) factor, spatial resolution, TE, and phase encoding direction (the latter three of which all interact). While gradient strength is not as critical for fMRI (relative to dMRI), the Connetome Skyra gradients do allow it to operate at a lower echo spacing than a conventional 3T scanner (e.g., 0.58 ms vs. 0.69 ms at 2 mm, all other things being approximately equal). The limitations of maximal readout gradient [Siemens Trio (TQ) ~ 28mT/m, Verio (VQ) and Skyra (XQ) ~ 24 mT/m] and forbidden echo spacing (due to acoustic resonances) make 2 mm more of a “stretch” resolution on these 3T magnets.

      More of a stretch? Yes, sadly that's not just a figure of speech. The echo train length will necessarily increase on a scanner with weaker gradients. We'll deal with this issue for the in-plane optimization, below. Until then, here's what the HCP summary recommendations look like:
      Note that considerable benefit as regards the accuracy of the mapping of activation to the cortical surface is already achieved by going to a 2.5 mm isotropic resolution, albeit with further incremental gains in accuracy in going down to 2.0 mm (Glasser et al. 2013). Overall then, we recommend that users of Trio, Verio, and Skyra systems test resolutions of 2.0 to 2.5 mm for fMRI and make a selection based on their requirements for temporal SNR, statistical power, and acceptable degree of susceptibility distortion, and signal dropout.
      The good temporal stability of the Connectome Skyra and the low electronic noise of the Siemens Tim 4G© platform allow the HCP to robustly generate good quality BOLD data at an MB factor of 8 without in-plane acceleration. Users of other systems will want to look carefully at whether they are happy with the levels of residual aliasing and temporal SNR at MB=8. In general, we recommend a MB factor of MB=6 for robust image quality while retaining high temporal resolution for these systems.

      There's one final piece of advice concerning the phase encode direction. HCP opted to use a rectangular field-of-view, with phase encoding alternating L-R and R-L. But they don't suggest we do this:
      ....we recommend using either anterior-to-posterior (AP) or posterior-to-anterior (PA) phase encoding (rather than the RL and LR phase-encoded pairs used in the HCP acquisitions), so that there is not a right/left susceptibility asymmetry (bias) in the aggregate data. In pilot testing, we could not discern an overall preference for either AP or PA phase encoding, since each resulted in a different amount of signal dropout and local distortions in different brain areas with susceptibility artifact, and this dropout differs greatly depending on slice orientation (e.g., T>C vs C>T). Thus, we recommend that users make the choice between AP and PA phase encoding based on their own particular research aims and goals. Note that AP or PA phase encoding will require use of a full FOV in the phase direction (“FOV phase = 100%”), which will lengthen the total echo train, leading to some increase in T2* blurring, susceptibility distortion, and signal dropout (via increased minimum TE) compared to the HCP acquisitions. In practice, this effect will be at least partially mitigated given the shorter minimum echo spacing achievable in the AP/PA phase encoding direction (due to lower peripheral nerve stimulation limitations with AP/PA than RL/LR). “Compensating” for these effects via use of partial Fourier and/or in-plane GRAPPA involve their own tradeoffs (e.g., for in-plane GRAPPA, reduced image SNR and a lower acceptable maximum multiband acceleration factor and thus longer minimum TR). The HCP investigated these tradeoffs to some degree during pilot testing, and ultimately settled on RL/LR phase encoding with no partial Fourier or in-plane GRAPPA as yielding the best overall quality on the Connectome Skyra. For users of other Siemen’s 3T systems desiring 2.3 – 2.5 mm isotropic spatial resolution with only a single phase encoding direction, we suggest trying AP or PA phase encoding without in-plane GRAPPA or partial Fourier (allows a minimum TE of ~ 33 ms), and a multiband factor of 6. For users desiring 2.0 mm resolution, 7/8 partial Fourier may be desirable (allows a minimum TE of ~ 36 ms).

      These suggestions give us sufficient information to proceed. To make this a tractable project I shall first describe in general terms the main factors to consider, then put all the decisions together into three explicit starting point protocols.


      Slice dimension optimization

      To a first approximation, the amount of slice acceleration - what is generally called the MB or SMS factor - depends upon the RF coil selection. As noted, we are assuming a 32-channel head coil. Not only does this coil have more independent channels than the standard 12-channel coil, but the elements also have very different geometry. In the 12-channel coil the elements are arranged as near parallel struts; there is little receive field variation along the coil's length. The 32-channel coil's elements are arranged more like the hexagonal patches on a soccer ball. There is a high degree of receive field anisotropy parallel as well as perpendicular to the coil axis. Thus, in principle we might choose to slice along any axis when using the 32-channel coil.

      So, what direction should we place our slices? Efficiency dictates that we cover the shortest brain dimension with the slowest sampling. Since slices acquire more slowly than either of the in-plane dimensions, even with SMS enabled, it means that we should expect to use axial or axial-oblique slices. The 32-channel coil has plenty of receive heterogeneity along the axial direction (magnet Z axis) to perform good MB-EPI. You could use AC-PC or some other oblique axis equally well.

      How fast should we go? The HCP suggests that we set MB as high as 6. This results in good images on my Trio and so this has become my practical limit, too. Would I consider going to, say, 8? I might, but it's worth bearing in mind that an MB of 6 is already six times faster than regular multislice EPI. That's a phenomenal gain! An MB of 8 is only 33% better than an MB of 6. And it's higher risk. Conversely, if an MB of 6 nets all the brain coverage you want with a minimum TR that is much shorter than you require, e.g. to use a particular task script that triggers off the TTL pulses happening once/TR, then you should consider dropping the MB to 4 or even 3 to reduce the possibility of reconstruction artifacts, and to decrease the image reconstruction time. (Vide infra.) In sum, then, accelerate as much as you need to, but not more!


      In-plane optimization

      You might think that in-plane optimization should be trivial because it's the slice dimension being accelerated in SMS-EPI. In practice, however, the separation of the simultaneously acquired slices needs some help from a slight modification to the in-plane gradients, via a method called blipped CAIPI. Reconstruction artifacts - say we try to push the acceleration factor higher than can be supported by the RF coil - may appear in the image plane, not just as modulations only visible in the slice dimension. You may see these artifacts described as "residual aliasing" or, more commonly for the SMS literature, as "leakage artifacts." I'll look at these in detail in a subsequent post.

      There is also a prosaic reason why we shall want to change the parameters in-plane. When the slice thickness goes down there is a tendency to want to improve the in-plane resolution simultaneously. Many (most?) people seek voxels having nominally isotropic dimensions - cubes - or very close to it. What this means is that because we have enabled higher resolution in the slice dimension we're now motivated to drive the resolution in-plane. There's no obligation to do so, it is a choice but it's one that many of you will succumb to.

      Following the lead of the HCP recommendations, there are a few guiding principles that we shall use for our starting point protocols. Unfortunately, high resolution in-plane takes gradient time and so the minimum TE will end up being longer for high in-plane resolution than for the 3 to 3.5 mm voxels you're used to. Our guide will be to try to set TE as close to 30 ms as possible, but not longer than 40 ms, in order to match the typical T2* of most of the brain. We should expect regions prone to signal dropout - the frontal and temporal lobes - to suffer more the longer TE gets.

      We generally want to drive the echo spacing as short as it will go, while noting that some scanners have mechanical resonances that can make ghosting bad. Newer Siemens operating software - certainly the SyngoMR VD line as used on Skyra, for example - generally prohibits the use of an echo spacing known to cause mechanical resonances. But on my Trio I can set the gradients to any physically plausible value and thus I need to be aware of the consequences.

      Finally, use of GRAPPA in-plane is not yet encouraged by me (or HCP) unless you take special measures to ensure that head/body motion is considerably lower than used in most routine fMRI studies. All the subject has to do is swallow during the autocalibration scans (ACS) and a whole time series can be wrecked. Also, in agreement with the conclusions of the HCP team, I've noticed that the raw image SNR is far lower when GRAPPA is enabled. So no GRAPPA for now. There are a few recent options for ACS that may make GRAPPA sufficiently robust we might want to consider it in future, so I'll do a dedicated post on using GRAPPA with SMS-EPI soon. Until then, if you insist on enabling GRAPPA with your SMS-EPI then you should probably take your cue from this paper. (Note, however, that GRAPPA was always enabled in their comparison, no non-GRAPPA option to compare against.)


      Dealing with dropout

      For voxels smaller than 2 mm on a side we shall necessarily have a TE in excess of 30 ms, causing more dropout in frontal and temporal regions than you would like. While the HCP was able to avoid using partial Fourier courtesy of their zippy custom gradient set, for us it is going to be a useful trick. It's not without consequences, but we have a degree of control over their severity by setting our partial Fourier fraction in conjunction with the direction of the phase encoding; either A-P or P-A. If you aren't familiar with partial Fourier, especially the interaction of partial Fourier with the phase encoding direction, then I strongly suggest you read the three posts linked in this paragraph. For MB-EPI we shall be using the Siemens default "early" echo omission, in order to reduce the minimum TE. (There is a "late" option but it's not useful for us when we want to shorten TE.)


      Dealing with distortion

      There are generally two reasons to want to use in-plane acceleration such as GRAPPA. One reason is to try to shorten the TE, the other reason is to reduce the amount of distortion in the phase encoding direction. We are already expecting to employ partial Fourier to reduce TE below 40 ms for MB-EPI, so that leaves the latter reason: distortion reduction. Now, we are going to have severely distorted MB-EPI images because we are expecting to employ high resolution, and high resolution in EPI means long echo trains. Long echo trains produce severely distorted EPIs. Sorry. This is one of those situations where we have to choose between two unpalatable options: motion sensitivity or distortion. For now I choose distortion because there is some hope to mitigate the worst effects with a post-processing step employing a field map, or functional equivalent. Motion, on the other hand, tends to trash a time series irrevocably.

      So let's take a quick look at what the HCP did for distortion in their protocol:
      To more rapidly measure the B0 field for correction EPI distortions, we acquire two spin echo EPI images with reversed phase encoding directions (60 s total for 3 pairs of images). (Note that we refer to these images as spin echo field maps, though they measure the field by reversing the phase encoding direction, which is a very different mechanism from standard field maps that use a phase difference calculated from two different TEs.) These spin echo EPI images have the same geometrical, echo spacing (0.58 ms in our scans), and phase encoding direction parameters as the gradient echo fMRI scans. These images enable accurate correction for spatial distortions in the fMRI images so that they can be precisely aligned with the structural images. Two of these spin echo EPI field mapping pairs are acquired in each functional session, for added robustness with respect to acquisition errors and subject movement, along with one set of B1− receive field-mapping images (with identical parameters to those described in the structural session).
      For the functional pipelines, a field map is required, because any neuroimaging analysis that aims for precise cross-modal registration between functional and structural (or other data modalities) will require EPI distortion correction. In general, either standard gradient echo field maps or spin echo EPI field maps can be acquired, though spin echo EPI field maps can be acquired more quickly with less chance of motion corruption. The geometrical parameters (FOV, matrix, phase encoding direction, resolution, number of slices) and echo spacing must be matched between the gradient echo EPI fMRI timeseries and the spin echo EPI field maps.
      EPI fMRI images contain significant distortion in the phase encoding direction. This distortion can be corrected with either a regular (gradient-echo) field map or a pair of spin echo EPI scans with opposite phase encoding directions. Reversing the phase encoding direction reverses the direction of the B0 field inhomogeneity effect on the images.

      There are other options, such as the true (gradient-echo) field map mentioned by HCP. Siemens offers a sequence called gre_field_mapping and released as part of their BOLD software package. (Check the SIEMENS tree in the Exam Explorer.) But on my scanner at least, the minimum slice thickness is 2 mm and you may wish to match exactly the field map with the MB-EPI slices when the latter are thinner than 2 mm.

      The issues of acquisition time, motion sensitivity, correction efficacy and so on make this a major enterprise and I don't have any quick suggestions for you, so I shall leave this extensive topic for a dedicated post once I have more details to offer. Until then I suggest you take a look at what Oregon's LCNI recommends, and perhaps consider using FMRIB's FUGUE software, in addition to considering HCP's suggestions.


      Other issues

      Shimming:  The CMRR-supplied protocols come with Advanced shim enabled and that should be considered the default. It takes about 90 seconds but the benefits in frontal and temporal lobes should be worth the extra time. If you're looking to nick a few minutes in a lengthy scan session then you might consider starting with the Advanced shim for the first fMRI run, then use Standard shimming for all subsequent runs.

      Flip angle:  The HCP used an approximate Ernst angle for their TR; 52 degrees for TR = 720 ms. I tend to use a lower FA than the Ernst angle for two reasons. First, as shown in this paper from Gonzalez-Castillo et al. (doi:10.1016/j.neuroimage.2012.10.076), reducing the FA can reduce physiologic noise without reducing BOLD sensitivity. (Years ago on a 4 T we always used a very low FA, such as 20 degrees for a TR of 1-2 seconds, in order to reduce the inflow effects. It worked very well.) The second reason might be less of an option. The amount of subject heating (assessed by the specific absorption rate, SAR) scales as the square of the FA. So even a small reduction in FA can reduce SAR considerably. If you find yourself running into the SAR limit then you can reduce the FA until you're good to go. The down side to a lower than Ernst angle FA? Image contrast may be affected slightly, but since we prefer temporal stability over anatomical content then altered contrast shouldn't be a huge burden. (For Gonzalez-Castillo et al. it was an advantage because they were using water-excite rather than fat presaturation on a GE scanner and so they had low anatomical contrast at the Ernst angle.)

      RF pulse width:  The HCP used an RF pulse width of 7120 us whereas the default on my protocols is set to the maximum of 10500 us. I haven't tried altering the RF duration at all yet, and I will note that I can get down to 1.5 mm slices using the default which is why I've not played with it. The "Connectome Skyra," with its higher peak gradient capabilities, could probably get thin slices with the shorter duration RF. On our stock scanners we should expect to need longer pulses. So until someone shows me why 10500 us is a bad choice I plan on continuing with it.

      Receive coil heterogeneity:  According to the PDF accompanying data release, the HCP acquired receive field "bias" maps but actually used a different approach:
      The BIAS_BC and BIAS_32CH scans are collected as analogs of Siemens'“Prescan Normalize” procedure, but these also are not being used. Rather, HCP is using the T1w and T2w scans for estimating the receive-coil bias field (see Glasser et al. 2013).
      Having looked at both the PDF and Glasser et al. it's still not entirely clear to me whether they were using the T1W and T2W scans or the two named BIAS scans. Perhaps someone can enlighten me. In any case, I advocate enabling the "Prescan Normalize" option on MB-EPI provided you also select the option to save both the normalized and raw time series (enable "Unfiltered images"). Reconstruction time is dominated by the SMS unaliasing algorithm, not the normalization, and so the only significant cost to having two data sets is twice the amount of data to store. More on bias field correction in a later post, after I've had a chance to assess the pros and cons of the Siemens routine versus acquiring one's own reference data set.

      Coil combine mode:  I would only use the default Sum of Squares for simplicity, as the HCP did. I don't know enough about the alternative methods yet, but my intuition is that fancier coil combination methods may not play nicely with the SMS unaliasing routine. 

      Single band images (SBref):  The HCP used the single band references as their template for realignment (a.k.a. motion correction) and to permit more accurate registration of EPI to T1W anatomical scan. I can't say whether the SBref makes a better or worse template for motion correction than the first MB-EPI, the middle MB-EPI, or some average. This is a post-processing question that we can ignore at acquisition time, provided we ensure that we have the SBref saved along with the MB-EPI time series.

      Reconstruction speed:  So this will likely be your loudest complaint when first using SMS-EPI. I am reliably informed that the GPU-based reconstruction engines on the newer scanners can process SMS-EPI in real time. Not so the Step IV MRIR board on my Trio, and this is the fastest reconstruction board you can get for the Trio. That's why it's useful not to set the MB factor to some gratuitously high number for the sake of having more data. More may or may not be better, but it will most definitely be slower. With MB=6 you can expect the reconstruction to run longer than the acquisition by between 1.5 and 3 times. Plan your session accordingly. Also note that a maximum of twelve reconstructions can be in the Siemens queue at one time. I've never hit that limit, or even come close. What tends to be a bigger deal is the need to keep the next user from terminating a reconstruction at the end of the current session.

      Slice order:   Simultaneously excited slices already have a sizable gap between them such that (a) we don't need to specify additional gap, and (b) we can reconsider the desire to use contiguous slices for reducing motion sensitivity, as is common for regular single shot EPI. For an MB of 6 there will be a gap of five slice thicknesses between the simultaneous slices. For ordering, there is a difference between specifying interleaved slice order versus contiguous (ascending/descending) order, but it isn't a stark difference as for regular EPI. So for convenience I have stuck with interleaved slices. I doubt there is much practical difference if one selects a contiguous order, but that would necessitate me checking for artifacts all over again. I don't expect there to be an appreciable difference in motion sensitivity, for example.

      Motion sensitivity:  I can only offer an anecdotal report on the motion sensitivity of MB-EPI. In my experience, moderate subject movement (a swallow, say) during acquisition of the single band reference images has a much smaller effect on image artifact level than the same motion would during ACS for in-plane GRAPPA. This is good! The HCP did some piloting and assessed the ability of ICA to clean the time series, but I suggest that you treat MB-EPI (and all SMS-EPI sequences) as intrinsically more motion-sensitive than regular EPI, and adapt your subject preparations and head restraint systems accordingly.


      Putting it all together

      I've created three introductory protocols for MB-EPI based on three different voxel sizes: 1.5 mm, 2.0 mm and 2.5 mm dimensions, all isotropic:

      Main parameters for three starting MB-EPI protocols (click to enlarge)

      The full parameters are available in a PDF, a printout of the full protocol from the Exam Explorer. The protocol was created using CMRR's MB-EPI sequence version R012c (R013 is out as of 14th December, 2015) on my Trio running SyngoMR VB17A. There are two sets of scan parameters for each voxel size in the protocol, one with phase encoding set A-P and the other set to P-A. Phase encoding reversal is achieved via the parameter "Invert RO/PE polarity" on the Sequence tab. Enabling this option is functionally equivalent to setting the "Phase enc. dir" dialog box to 180 (degrees), which is why you still see A-P listed under that parameter. All six scans have prescan normalization enabled, SBref saved and excite (RF) pulse duration set at 10500 us.

      There are some caveats that require explanation. The 1.5 mm isotropic voxel protocol doesn't cover the whole brain in the TR of 1300 ms. It might just cover whole cortex with judicious slice tilting to snag temporal poles as well as the full frontal and parietal cortices. You'd need to extend the TR out to about 2000 ms to include cerebellum. You might consider setting MB up to 8 to improve the coverage, but I can't vouch for the leakage artifacts that might result.

      For the 2.5 mm isotropic voxel protocol the whole brain is covered easily with 64 slices in a TR of 1200 ms. If you wanted an even shorter TR, down to 800 ms, you might consider setting the MB factor to 6 rather than 4.

      The 2 mm isotropic voxel protocol uses an echo spacing of 0.69 ms, right in the middle of the range 0.6-0.8 ms where mechanical resonances for axial slices are expected to be worst on my Trio. Unfortunately, there is insufficient gradient strength to get below 0.6 ms, and setting the echo spacing at 0.8 ms would necessitate a TE above 40 ms, thereby requiring 6/8ths partial Fourier to get back below 40 ms. In my throwaway tests the ghosting from the echo spacing of 0.69 ms weren't terrible (see the example data below) whereas the dropout from using a longer TE and/or higher fraction for partial Fourier produced noticeable dropout. You would want to verify on your scanner that such an echo spacing doesn't produce show-stopping ghosts; test on a phantom. The mechanical resonances differ from installation to installation.


      Example data

      Below are 3D-MPR displays of a single volume of MB-EPI data acquired from the same brain using the three protocols just outlined. Here the "Prescan Normalize" filter has been applied. I also have 50-volume time series acquisitions with and without "Prescan Normalize" that you can download and tinker with yourself (295 MB zipped file). Included in the file are the "split" mosaic images I used to create the 3D-MPR views.

      2.5 mm isotropic voxels (click to enlarge)

      2 mm isotropic voxels (click to enlarge)

      1.5 mm isotropic voxels (click to enlarge)



      References and further information:

      Human Connectome Project:
      Appendix I – Protocol Guidance and HCP  Session Protocols
      (900 Subjects Release, 8th Dec 2015)

      The minimal preprocessing pipelines for the Human Connectome Project
      MF Glasser et al., NeuroImage80, 105-24 (2013)
      doi:10.1016/j.neuroimage.2013.04.127

      Resting-state fMRI in the Human Connectome Project
      SM Smith et al., NeuroImage80, 144-68 (2013)
      doi:10.1016/j.neuroimage.2013.05.039

      You may also want to look through the contents of the special issue of NeuroImage80, 1-544, Mapping the Connectome, in which the above two papers were published. While I didn't use them for this blog post, the following two articles may be useful for context:
      The WU-Minn Human Connectome Project: An overview
      DC Van Essen et al., NeuroImage80, 62-79 (2013)
      doi:10.1016/j.neuroimage.2013.05.041

      Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project
      K Ugurbil et al., NeuroImage 80, 80-104 (2013)
      doi:10.1016/j.neuroimage.2013.05.012

      The following papers provide some useful background on the use of blipped CAIPI for reducing artifacts in SMS-EPI:
      Rapid brain MRI acquisition techniques at ultra-high fields.
      Setsompop K, Feinberg DA, Polimeni JR, NMR Biomed. 2016 Feb 2.
      doi: 10.1002/nbm.3478

      Evaluation of slice accelerations using multiband echo planar imaging at 3 T.
      Xu J, et al., NeuroImage83:991-1001 (2013).
      doi: 10.1016/j.neuroimage.2013.07.055

      A comparison of various MB factors with GRAPPA R=2 always enabled:
      Evaluation of 2D multiband EPI imaging for high-resolution, whole-brain, task-based fMRI studies at 3T: Sensitivity and slice leakage artifacts.
      Todd N, et al., NeuroImage. 2016 Jan 1;124(Pt A):32-42.
      doi: 10.1016/j.neuroimage.2015.08.056


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    • 06/28/16--09:17: Exploiting Tanzania


      • So a massive helium reserve may have been found in Tanzania's Rift Valley. Wonderful. All the western headlines this morning have put a typically western spin on it. Hurrah! We are saved! We get to go plunder a foreign place again for what we need to save our own lives! Before we get too carried away with ourselves, let's take a few seconds to think about a few things. Like, say, how many MRI scanners are there in Tanzania right now? How many Tanzanian lives will be saved? Anyone care to estimate? This scanner in Dar es Salaam makes headlines when it breaks down.



      What about the wildlife in Tanzania? Will lives be saved there, too? Note the concentration of national parks and game reserves in and around the Rukwa region of Tanzania. Now, I'm not intimately familiar with how helium gas is extracted, concentrated or liquefied but I'm going to guess that some of it has to be done where the gas is found. Even if the gas doesn't just float conveniently into collection chambers instead of needing some sort of gas forcing process (We love fracking, right?) and miles and miles of pipelines, it's a fair assumption that there will be massive energy needs to liquefy it. Then the cryogenic liquid helium must be transported. So we'll need roads, maybe an airport for the suits to get in and out quickly, and perhaps a railway to move the product to a sea port. Or we could just push the gas down a long pipe to the coast where it could be liquefied, then transported abroad. This is all going to be great news for African nature, I'm sure of it!


      I would prefer that we take our cue from the researchers quoted in the BBC article.

      Prof Chris Ballentine, of the Department of Earth Sciences at the University of Oxford, said: "This is a game-changer for the future security of society's helium needs and similar finds in the future may not be far away."

      And colleague Dr Pete Barry added: "We can apply this same strategy to other parts of the world with a similar geological history to find new helium resources."

      Good, because taking the usual westerners'"easy out" and exploiting the far away place where nimbys don't exist (and can be ignored even if they do) is the coward's solution. Let's go find helium in the Cascades or Hawaii or somewhere closer to those who actually get to benefit from MRI, then see how we react to the extraction options.

      In the mean time, here's what Rukwa, Tanzania looks like today. This is the Katavi National Park, right in the middle of the Rukwa region.



      Here's what the National Helium Reserve in Amarillo, Texas looks like.



      Practically twins! And finally, here's what the Rukwa mine project looks like. This is the coal mine where the coal for producing electricity in the local power plant comes from. Because we'll be needing electricity. Lots of it. Global warming shmobal warming.



       Hurrah! We are saved! MRI for westerners forever!

      --------------------

      Update on 30 June, 2016:

      I found the location of the helium reserve on the Helium One website. I estimated the location of the Rukwa Project, as it's called, on a Google map below. Many media outlets reported the helium find as being a "game changer." Freudian slip? Perhaps a game reserve changer at a minimum.



      And for the record, as I hope I made clear in response to the comments from sfz, I'm not yet either for or against developing this gas field. I am against incomplete journalism, however. There are many issues that need to be addressed and questions asked from the developed countries who stand to benefit the most from this discovery.

      Update on 1st July, 2016:

      Looks like a new airport won't be needed. A new airport in nearby Mbeya opened in 2012 with an 11,000 ft runway. Now if we can only learn about the ways drilling might proceed with minimal impact on the game reserve. There's more science in a brief article online by Jon Gluyas, a member of of the team that developed the search methods and found the Rukwa reserve.





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      In an earlier post I presented three starting protocols for the CMRR version of SMS-EPI, referred to as the MB-EPI sequence here. I'll use italics to indicate a specific pulse sequence whereas SMS-EPI, no italics, refers to the family of simultaneous multi-slice methods. In this post I'll develop a similar set of three starting protocols for the Massachusetts General Hospital (MGH) version of SMS-EPI, called Blipped-CAIPI. I'm going to build upon the explanations of the last post so please cross reference for parameter explanations and background.

      As for the previous post there are several things to bear in mind. This series is Siemens-centric, specifically Trio-centric. While many of the concepts and parameter options may apply to other platforms there will be minor differences in parameter naming conventions and, perhaps, major differences in implementation that you will need to consider before you proceed. For Siemens users, I am running aging software, syngoMR version B17A. The age of the software and the old reconstruction board on the scanner means that you can expect to see much faster reconstruction on a newer system. I hope, but cannot guarantee, that the actual image quality and artifact level won't differ massively from a Trio running VB17A to a new Prisma running VE11C. I'll keep you updated as I learn more.


      Preliminaries

      As before, for this post I am going to be using a 32-channel receive-only head coil. The SMS-EPI sequences can be made to work with a 12-channel coil but only in a reduced fashion because the 12-channel coil has minimal receive field heterogeneity along the magnet z axis - the struts run parallel with the magnet axis except at the coil's rear, where they converge - and generally we want to do axial slices (along z) for fMRI. I don't yet know whether SMS-EPI would work well on the 20-channel head/neck coil on a Prisma, it's something I hope to investigate in the near future. But a 64-channel head/neck coil on a Prisma will definitely work for SMS-EPI. Better or worse than a 32-channel coil on a Prisma? I have no idea yet.

      The Blipped-CAIPI sequence version 2.2 was obtained through a C2P (Core Competence Partnership) with MGH. Installation was a breeze: a single executable to port to the scanner and one click, done. The development team offers an informative but brief 7-page manual which will be useful to anyone who has read the SMS-EPI literature and has a basic understanding of how SMS works. It's not a starting point for everyday neuroscience, however. The manual mentions a .edx (protocol) file as a starting point for 2, 2.5 and 3 mm resolution scans, but in the file I downloaded for VB17A the contents didn't include it. Perhaps contact MGH if you are on another software version and you'd like a .edx file rather than building your own protocol, e.g. by recreating what you see here.


      General usage issues

      My immediate sense on initial testing was that overall performance of Blipped-CAIPI is quite similar to that of MB-EPI. Timing parameters and spatial resolution limits come out to be very close because they are primarily functions of the scanner hardware rather than the pulse sequence implementation. The small differences arise out of the implementation, the most significant of which is the default RF pulse type and duration. With MB-EPI one sets the duration explicitly, subject to the constraints of the RF amplifier peak output, slice thickness, cross-talk and SAR. With Blipped-CAIPI the RF duration is controlled indirectly, via a VERSE factor. More on that parameter below.

      As for MB-EPI, I recommend enabling the Prescan Normalize option and instruct the scanner to save both the normalized and the raw images. The reconstruction overhead is minuscule, while a factor of two more data is a small price to pay for flexibility. However, you may prefer to acquire and use your own receive field correction map in post processing, as the Human Connectome Project did. I don't have specific recommendations today on how best to acquire your own receive field maps, but it's on the list of topics to be covered in this post series on SMS methods.

      Both the Blipped-CAIPI and MB-EPI sequences use what is called in the literature the "slice-GRAPPA" reconstruction method (Setsompop et al. 2012). The "split-slice-GRAPPA" recon (Cauley et al. 2014) can be used instead by enabling the LeakBlock parameter on the Special tab of the MB-EPI sequence. In Blipped-CAIPI, however, I think the split-slice-GRAPPA is enabled automatically if one turns on conventional GRAPPA for in-plane acceleration. It's also my understanding that split-slice-GRAPPA (a.k.a. LeakBlock) should be used only when using in-plane GRAPPA with SMS. Still with me? I apologize on behalf of MRI physicists everywhere for a dog's breakfast of nomenclature. Anyway, I'll try to get more information for a recon-specific post at a later date. I'm still feeling this stuff out for myself.


      Blipped-CAIPI parameter options

      Single band reference images:  (Called SBref for MB-EPI.) The single band reference images option is apparently enabled on the Special card, but it is inaccessible and the single band reference images aren't actually saved on my VB17A version. I assume this is just a bug.

      Dummy scans: A feature in Blipped-CAIPI is the ability to set dummy scans for the single band reference data independent of separate dummy scans inserted between the reference data and the accelerated time series (the latter is what you think of as "your data"). This assures proper T1 steady states for both the reference and time series data (in the absence of motion) even though the reference data are acquired as quickly as possible (minimum TR). With MB-EPI one can simply insert a handful of volumes of no interest and ignore these in the final time series, so there's minimal practical difference.

      VERSE factor:  I have no experience with VERSE so I'm going to simply quote from the MGH manual:

      Apply the Variable-rate selective excitation (VERSE) method12 to reduce peak voltage and SAR of the MultiBand pulse. When set to level 1, the VERSE method is not used and a standard [Shinnar-LeRoux] SLR pulse is played. The higher the VERSE value, the more reduction in peak voltage and SAR. This comes at a cost of slice profile distortion at off-resonance. The sequence only allows VERSE factors at which profile distortions are generally in an acceptable range. However, one should still set the VERSE level to the lowest level that will not cause RF clipping or exceed SAR limits. 
      12 Conolly S, Nishimura D, Macovski A, Glover, G. (1988). Variable­ rate selective excitation. Journal of Magnetic Resonance 78: 440–458.

       And for more information, here's a paragraph from a recent review of SMS methods by Barth et al.: 
      Variable Rate Excitation (VERSE) 

      The true RF power is determined by the power integral of the RF over time. By varying the slice selection gradient with time, the k-space representation of the RF pulse is no longer mapped linearly to the time domain, and power can be reduced without modifying the slice profile. This is the principle of VERSE (30). Power reduction is achieved by slowing down k-space traversal at coordinates where most energy needs to be deposited (i.e., the peak of the [multiband] RF waveform) by temporarily reducing the amplitude of the slice selection gradient. Time lost by doing this can be recovered by speeding up at times of low RF amplitude. VERSE has great potential to reduce the power of a pulse but is very sensitive to off-resonance effects (30). Whereas a standard RF pulse off-resonance only experiences a slice shift, the sensitivity of a VERSE pulse to off-resonance varies with time due to the varying ratio between the gradient strength and the magnitude of the local field inhomogeneity, which can lead to a corrupted slice profile as demonstrated in the original study (30). In practice, the low extent to which VERSE needs to be applied to 180refocusing pulses with moderate multiband factors (MB < 4) at 3 Tesla (T), still allows an acceptable slice profile and high effective bandwidth-time product (31).

      So, we should expect to see enhanced signal dropout in the usual problem regions - frontal and temporal lobes, deep brain regions - if we enable the VERSE method. In all the tests I show below I left the VERSE factor at unity, i.e. disabled. I never ran into an RF clipping or SAR-limited situation for flip angles of 45 degrees. Perhaps I'll look at the use of VERSE as a sub-component of a future post on slice-to-slice cross-talk. Until then I'll leave VERSE turned off.

      SMS shift:  This is the blipped-CAIPI field-of-view (FOV) shift factor from which the pulse sequence derives its name (Setsompop et al. 2012). Here I set the SMS shift to 3 (i.e. FOV/3) based on hints from MGH and the literature. I haven't explored the consequences of different shifts. The MB-EPI sequence sets the FOV shift automatically, but the default without in-plane acceleration appears to be FOV/3.

      Number of slices:  The Blipped-CAIPI sequence forces you to use an odd number of slice packets, where the number of slice packets is the total number of slices divided by the SMS factor. For example, it won't allow 60 slices when the SMS factor is 6, but 54 or 66 slices are allowed. The restriction against even multiples of the SMS factor is to reduce the potential slice-to-slice cross-talk, an issue I'll go into in depth in the next post because we also need to consider motion when considering cross-talk. (In the mean time, see Note 1.) All I can say right now is that I've not seen much cross-talk (on a stationary phantom) when using even multiples of the SMS factor with MB-EPI.

      Compression factor:  This is a way to reduce the total number of effective channels in the RF coil through software combination. One gives up unique spatial information in exchange for less computation and faster reconstruction. Here's the description from the MGH manual:

      Compression Factor: The amount of compression applied in a Geometric Coil Compression (GCC) scheme to reduce the number of effective coil channels and hence speed up the slice-GRAPPA reconstruction. The Compression Factor can be set to within the range of 1-4. At level 1, no compression is applied. At level 2, coil channels are compressed down by 50% (e.g. a 32-channel coil is compressed down to 16 virtual channels). At level 4, coils are compressed down to 25% (e.g. a 32-channel coil is compressed down to 8 virtual channels). Compression should mainly be used for high channel count arrays such as a 32 or 64 channel coil, where the slice-GRAPPA computation is more intensive and reconstruction time is longer. At level 2 compression (for these high channel count cases), no degradation in reconstruction performance is observed while achieving a fast image reconstruction (typically real-time in most cases). At compression level 4, some minor degradation in reconstruction performance can be observed for high SMS factors acquisitions.

      Kernel size:  This is another parameter that is set automatically in the MB-EPI sequence, but with Blipped-CAIPI we have three options: 3x3, 5x3 and 5x5. This from the MGH manual:
      Kernel Size: Size of the sliceGRAPPA kernel (ky × kx) used to reconstruct the images. Larger kernel sizes typically provide better reconstruction with the cost of longer reconstruction time. For low SMS factors (2-4), a 3×3 kernel size is sufficient. At higher SMS factors, a larger kernel size should be used.
      I had no idea how the kernel size would affect image quality so this became the subject of preliminary testing, along with the Compression Factor as a way to influence reconstruction speed.


      Blipped-CAIPI reconstruction speed

      The reconstruction speed can be a major factor when deciding whether to use SMS or not. While the actual reconstruction performance depends on your software and recon hardware and mine are both rather old (VB17A with a Step IV MRIR), I thought it might be useful to give you a sense of the relative performance. I ran three throwaway tests with the following parameters fixed: 100-volume time series, 2 mm isotropic voxels, SMS factor of 6, FOV/3 CAIPI shift, TR=1000 ms, 66 total slices. I was particularly interested to see how much the kernel size and the compression factor (CF) affected reconstruction speed.

      3x3 kernel, CF = 1
      The first image appeared after 40 sec, and each subsequent volume took 2.2 sec to process so that the final image reconstructed 4 min 20 sec after acquisition start.

      5x5 kernel, CF = 1
      The first image appeared at 3 min 15 sec, long after the acquisition of all 100 images had finished. The volumes then reconstructed at a rate of ~2.6 sec/volume so that the final image reconstructed 7 min 30 after acquisition start.

      5x5 kernel, CF = 2
      The first image appeared after 50 sec, and each subsequent volume took ~2.2 sec to process so that the the final image appeared 4 min 25 sec after acquisition start.

      The reconstruction is slow whichever way you look at it. But increasing the kernel size without also using CF > 1 makes the recon very slow indeed. Given the radical effects of CF and kernel size on reconstruction time, what can we say about image quality? Does speed come at a price we can afford to pay? Let's take a look at some specific acquisition parameter sets and then look at the effects of reconstruction parameters on image quality.


      Blipped-CAIPI starting point protocols

      Here are three starting protocols designed to match as closely as possible the parameters given in the previous post, for the MB-EPI sequence:

      Main parameters for three starting Blipped-CAIPI protocols. (Click to enlarge.)

      The full parameters are available via Dropbox in this PDF. As for the previous MB-EPI starting protocols, the number of slices for 1.5 mm isotropic resolution is insufficient to cover the whole brain. Expect to increase the TR and number of slices by about 50% for whole brain. Alternatively, you might consider increasing the SMS factor up to 8 to improve the coverage, but I haven't tested it and I would be concerned at the potential leakage artifacts that might result. (See Note 2.)

      The coverage for the 2.5 mm isotropic voxel protocol is more than sufficient for whole brain when using 68 slices in a TR of 1200 ms. But if you specifically want shorter TR, down to 800 ms or so, you might consider setting the MB factor to 6 rather than 4.

      The in-plane parameters are very nearly identical between Blipped-CAIPI and MB-EPI, which is to be expected given that the SMS scheme applies in the slice dimension. Small differences arise out of the duration of the SMS excitation pulse for each pulse sequence, and these lead to slight differences in minimum TE in particular. Any differences in read gradient bandwidth and echo spacing are negligible.

      As for MB-EPI, the 2 mm isotropic voxel protocol for Blipped-CAIPI uses an echo spacing (of 0.70 ms) that is in the middle of the range 0.6-0.8 ms where mechanical resonances for axial slices are expected to be worst on a Trio. There is insufficient gradient strength to get below 0.6 ms, while setting the echo spacing at 0.8 ms necessitates a TE greater than 40 ms. This could be offset by setting 6/8ths partial Fourier to get back below 40 ms, but that is likely trading one potential artifact (slightly higher ghosting) for another (slightly higher dropout). In my phantom tests the ghosting from the echo spacing of 0.70 ms weren't terrible (see the example data below). But you should verify on your scanner that such an echo spacing doesn't produce show-stopping ghosts because the mechanical resonances differ from installation to installation.


      Blipped-CAIPI performance on a phantom

      My first task was to determine a suitable non-SMS control acquisition. I could have set up the product ep2d_bold sequence, say, with all the parameters matched to the SMS protocols except the TR, but I wanted to do a quick comparison between MB-EPI and Blipped-CAIPI and so I'm using the single band reference (SBref) images available from the MB-EPI acquisition as the standard to match. Some things to note. Firstly, the contrast in the SBref images may well be different to SMS because the TR is different. In a reasonably homogeneous gel phantom with a few air bubbles this isn't likely to be a big deal, but it does mean we can expect some intensity differences. Secondly, I want to emphasize that this is a throwaway comparison. Let me state for the record that I don't have a preference, all I have is more experience with MB-EPI than with Blipped-CAIPI. You may interpret any differences you see below any way you like, but I would caution against over interpreting anything you see. For starters I didn't even try to reproduce what I've done and you know that's a bad habit!

      The following tests were conducted on an FBIRN gel phantom and with the 32-channel head coil. Each figure comprises four panels to show:
      • Top left = SBref "reference" images
      • Top right = SMS images acquired with MB-EPI
      • Bottom left = SMS images acquired with Blipped-CAIPI, processed with a 5x5 recon kernel and CF = 1
      • Bottom right = SMS images acquired with Blipped-CAIPI, processed with a 5x5 recon kernel and CF = 2
      In the following section I show that the 5x5 kernel option provides the best image quality; other options are 3x5 or 3x3. But since reconstruction time is an issue, I compare here two compression factors (CF) on the basis that CF = 2 might be necessary in practice.

      Let's begin by looking in the image plane, that is, in the conventional Siemens "mosaic" display. I'll use two intensity settings, the first to highlight any artifacts visible on the phantom and the second with the background scaled to reveal the N/2 ghosts and residual aliasing/leakage from the SMS reconstructions. Please note that the different number of slices in the different sequences means that there isn't necessarily a direct correspondence between the panels showing MB-EPI and Blipped-CAIPI data. The bubbles and cracks in the gel are your "anatomical references" to determine which slice is which.

      There are no obvious differences visible on the phantom for 2.5 mm isotropic resolution with SMS factor 4:

      Signal contrasted 2.5 mm isotropic resolution scans, SMS=4: TL: SBref. TR: MB-EPI. BL: Blipped-CAIPI, 5x5 kernel, CF=1. BR: Blipped-CAIPI, 5x5 kernel, CF=2. (Click to enlarge.)

      Residual aliasing is less prominent than the N/2 ghosts, although the ghosts are sharper for Blipped-CAIPI and there is a suspicion of residual aliasing in the MB-EPI artifacts:

      Ghost contrasted 2.5 mm isotropic resolution scans, SMS=4: TL: SBref. TR: MB-EPI. BL: Blipped-CAIPI, 5x5 kernel, CF=1. BR: Blipped-CAIPI, 5x5 kernel, CF=2. (Click to enlarge.)

      I don't see any obvious signal level differences for 2 mm isotropic resolution and SMS factor 6:

      Signal contrasted 2 mm isotropic resolution scans, SMS=6: TL: SBref. TR: MB-EPI. BL: Blipped-CAIPI, 5x5 kernel, CF=1. BR: Blipped-CAIPI, 5x5 kernel, CF=2. (Click to enlarge.)

      But there are now clearly differences in the ghosts. We can use the SBref images' ghosts as our standard to recognize that the Blipped-CAIPI ghosts are crisper as well as more intense, while the MB-EPI ghosts are diffuse and exhibit multiple structures consistent with residual aliasing:

      Ghost contrasted 2 mm isotropic resolution scans, SMS=6: TL: SBref. TR: MB-EPI. BL: Blipped-CAIPI, 5x5 kernel, CF=1. BR: Blipped-CAIPI, 5x5 kernel, CF=2. (Click to enlarge.)

      We seem to have a choice between more intense ghosts or more intense residual aliasing. So what happens when we push to 1.5 mm isotropic resolution and SMS factor of 6? We now see that there are artifacts visible on the phantom. I've picked a few out using orange arrows for the bright artifacts blue arrows for the dark artifacts:

      Signal contrasted 1.5 mm isotropic resolution scans, SMS=6: TL: SBref. TR: MB-EPI. BL: Blipped-CAIPI, 5x5 kernel, CF=1. BR: Blipped-CAIPI, 5x5 kernel, CF=2. (Click to enlarge.)

      At the ghost level we again see better delineated N/2 ghosts for Blipped-CAIPI and diffuse ghosts for MB-EPI, but the artifacts clearly extend across the entire (signal-free) background in both. Scaling the background up to see the artifacts causes the background for the SBref images to saturate, indicating that the artifacts in the accelerated images are now a lot higher than for SBref, and a lot higher than for the prior 2.5 mm or 2 mm protocols:

      Ghost contrasted 1.5 mm isotropic resolution scans, SMS=6: TL: SBref. TR: MB-EPI. BL: Blipped-CAIPI, 5x5 kernel, CF=1. BR: Blipped-CAIPI, 5x5 kernel, CF=2. (Click to enlarge.)


      Now let's look in the slice dimension (reconstructed from the matrix of slices). This confirms no obvious artifacts for 2.5 mm isotropic resolution, SMS factor 4:

      Through plane reconstruction for 2.5 mm isotropic resolution scans, SMS=4: TL: SBref. TR: MB-EPI. BL: Blipped-CAIPI, 5x5 kernel, CF=1. BR: Blipped-CAIPI, 5x5 kernel, CF=2. (Click to enlarge.)


      Nothing obvious for the 2 mm isotropic resolution, SMS factor 6:

      Through plane reconstruction for 2 mm isotropic resolution scans, SMS=6: TL: SBref. TR: MB-EPI. BL: Blipped-CAIPI, 5x5 kernel, CF=1. BR: Blipped-CAIPI, 5x5 kernel, CF=2. (Click to enlarge.)

      But the artifacts again become apparent for 1.5 mm isotropic resolution, SMS factor 6. The orange arrow indicates a dark band on the MB-EPI image, green arrows indicate bright horizontal bands in the Blipped-CAIPI images and yellow arrows indicate phantom-shaped bright regions aliased onto the real image:

      Through plane reconstruction for 1.5 mm isotropic resolution scans, SMS=6: TL: SBref. TR: MB-EPI. BL: Blipped-CAIPI, 5x5 kernel, CF=1. BR: Blipped-CAIPI, 5x5 kernel, CF=2. (Click to enlarge.)


      Let's summarize things so far. The images from 2.5 mm and 2 mm protocols with SMS factors of 4 and 6, respectively, look good in qualitative terms. However, when the resolution is pushed to 1.5 mm, still with an SMS factor of 6, there are weak but visible reconstruction artifacts. For the Blipped-CAIPI sequence these artifacts are apparent whether compression factor 1 or 2 is used, suggesting that we can use CF=2 for reconstruction speed.


      Effects of kernel size on image quality

      I now want to go back a step and look at the kernel size. Above I selected the 5x5 kernel option throughout, while noting that the 3x3 and 3x5 options weren't as good. Here is a comparison of the 3x3 kernel to the 5x5 kernel using the 2 mm isotropic resolution protocol at SMS factor of 6. Top left is SBref, top right is MB-EPI, bottom left is Blipped-CAIPI with the 3x3 kernel and bottom right is Blipped-CAIPI with the 5x5 kernel:

      Ghost contrasted 2 mm isotropic resolution scans, SMS=6: TL: SBref. TR: MB-EPI. BL: Blipped-CAIPI, 3x3 kernel, CF=1. BR: Blipped-CAIPI, 5x5 kernel, CF=1. (Click to enlarge.)

      The artifacts are quite similar between the MB-EPI sequence and Blipped-CAIPI reconstructed with the 3x3 kernel. Does this mean the CMRR recon uses a 3x3 kernel? I don't know but shall endeavor to find out. The 5x5 kernel gives the crisper N/2 ghosts that better resemble the SBref "ideal" ghosts.

      I also ran some 50-volume time series acquisitions that showed reduced temporal signal-to-noise ratio (tSNR) for the 3x3 kernel option:

      TSNR images for 50-volume time series measurements: 2 mm isotropic voxels, SMS factor 6. Left: MB-EPI. Middle: Blipped-CAIPI, 3x3 kernel, CF=1. Right: Blipped-CAIPI, 5x5 kernel, CF=1.


      The reduced tSNR comes from the spatial correlation of the artifacts across the entire image plane. Note the smoothness of the artifacts in the center panel and how they propagate across all the background regions:

      Standard deviation images for 50-volume time series measurements: 2 mm isotropic voxels, SMS factor 6. Left: MB-EPI. Middle: Blipped-CAIPI, 3x3 kernel, CF=1. Right: Blipped-CAIPI, 5x5 kernel, CF=1.


      So we can conclude that the cost of the smaller kernel is higher artifacts, reduced temporal stability and lower power, which doesn't look like a good trade to me. I would prefer to use the 5x5 kernel and set the compression factor to two for speed, although at this point I haven't yet done a full temporal analysis to know that setting CF=2 wouldn't similarly degrade tSNR, of course.


      Blipped-CAIPI performance on a brain

      And so, finally, to the brain. I want to emphasize that whatever you see here should be taken with an even larger pinch of salt than the phantom tests! It's very difficult to discriminate artifacts with brain anatomy getting in the way. But this is our first opportunity to check for new problems, such as an artifact from exquisite motion sensitivity. I'm not actively testing motion sensitivity here, however. That has to wait for a later post.

      For now I offer only 3D multi-planar views for the three starting point protocols at 2.5, 2.0 and 1.5 mm isotropic resolution using compression factors of 1 and 2 (but 5x5 kernel throughout), zoomed and contrasted independently to provide the most comprehensive view of the brain:


      2.5 mm isotropic resolution, Blipped-CAIPI sequence, SMS factor 4, 5x5 recon kernel, CF=1.

      2.5 mm isotropic resolution, Blipped-CAIPI sequence, SMS factor 4, 5x5 recon kernel, CF=2.

      2 mm isotropic resolution, Blipped-CAIPI sequence, SMS factor 6, 5x5 recon kernel, CF=1.

      2 mm isotropic resolution, Blipped-CAIPI sequence, SMS factor 6, 5x5 recon kernel, CF=2.

      1.5 mm isotropic resolution, Blipped-CAIPI sequence, SMS factor 6, 5x5 recon kernel, CF=1.

      1.5 mm isotropic resolution, Blipped-CAIPI sequence, SMS factor 6, 5x5 recon kernel, CF=2.


      What can I conclude from these views? Only that I don't see any obvious new problems requiring further investigation. Together with the phantom tests I would set my personal limits at 1.5 mm isotropic resolution and SMS factor 6. I would only push to 1.5 mm resolution if the experiment absolutely necessitated it, however. I wouldn't want to use an SMS factor greater than 6 for any protocol without a lot more testing, and right now testing a factor of 8 is low on my list of priorities.

      With these starting points and a basic evaluation of two SMS sequences in the bag I'll be moving on to more detailed investigations of the consequences of slice-to-slice cross-talk and motion sensitivity.

      ____________________


      Notes:

      1.  From Setsompop et al. (2012):
      "The simultaneous multi-slice method does put some constraints on the number of slices. For example, the acquisition is simplified if the total number of slices is a multiple of Rsl. A more subtle effect occurs when an interleaved slice order is used. The purpose of interleaving is, of course, to avoid exciting spatially adjacent slices in rapid succession. In a standard interleaved acquisition, adjacent slices are taken TR/2 apart in time. The interleaved Rsl = 3 acquisition has an additional constraint if one wishes to avoid having some spatially adjacent slices acquired in rapid succession. In the simultaneous multi-slice acquisition with a total of Nsl, a total of Rsl subgroups each with Nsl/Rsl slices are created. The successive excitation problem occurs between the top slice of one subgroup and the bottom slice of the subgroup above it. The problem can be avoided if the number of slices in each excitation subgroup is odd. Thus Nsl/Rsl should be an odd integer to avoid signal loss slices with imperfect slice profiles at the edge of each sub-group. If an even integer is chosen, the first slice of each subgroup will be excited right after the excitation of an adjacent slice that corresponds to the last excited slice in an adjacent slice group (from the previous TR). This leads to a signal loss from the slice crosstalk for these edge slices."

      An illustration of the cross-talk is shown in Fig. 3 of Barth et al. (2016) review of SMS methods:



      2.  I'll do a post at a later date that looks at the issue of SMS support in relation to the RF coil being used. A colleague observed that there are only about five discrete rings of coil elements in the Z direction for the Siemens 32-channel coil. The implication seems to be that the blipped CAIPI scheme is doing a lot (most?) of the work of separating the simultaneous slices once the SMS factor gets above 5.



      0 0


      tl;dr   When using SMS there is a tendency to acquire smaller voxels as well as use shorter TR. There are three mechanisms contributing to the visibility of respiratory motion with SMS-EPI compared to conventional EPI. Firstly, smaller voxels exhibit higher apparent motion sensitivity than larger voxels. What was intra-voxel motion becomes inter-voxel motion, and you see/detect it. Secondly, higher in-plane resolution means greater distortion via the extended EPI readout echo train, and therefore greater sensitivity to changes in B0. Finally, shorter TR tends to enhance the fine structure in motion parameters, often revealing oscillations that were smoothed at longer TR. Hence, it's not the SMS method itself but the voxel dimensions, in-plane EPI parameters and TR that are driving the apparent sensitivity to respiration. Similar respiration sensitivity is obtained with conventional single-shot EPI as for SMS-EPI when spatial and temporal parameters are matched.

      __________________

      The effects of chest motion on the main magnetic field, B0, are well-known. Even so, I was somewhat surprised when I began receiving reports of likely respiratory oscillations in simultaneous multi-slice (SMS) EPI data acquired across a number of projects, centers and scanner manufacturers. (See Note 1.) Was it simply a case of a new method getting extra attention, revealing an issue that had been present but largely overlooked in regular EPI scans? Or was the SMS scheme exhibiting a new, or exacerbated, problem?

      Upper section of Fig. 4 from Power, http://dx.doi.org/10.1016/j.neuroimage.2016.08.009, showing the relationship between apparent head motion (red trace) reported from a realignment algorithm and chest motion (blue trace) recorded by a respiratory belt. See the paper for an explanation of the bottom B&W panel.

      Background

      To follow the full chronology please begin by reviewing the series of blog posts by Jo Etzel at WashU. Her first post was on 12th August, 2016. Jo was quickly able to demonstrate the close relationship between chest motion measured by a belt and the head movement reported by realignment. (More detailed investigations here.) These findings matched the reports by Power in http://dx.doi.org/10.1016/j.neuroimage.2016.08.009. So far so good. We explored the possibility that the transmitter frequency feedback on her Skyra scanner - a wide body scanner with gradients prone to thermal drift - might be causing the problem, but that theory fell by the wayside when I was able to reproduce the same effects on my Trio, which doesn't have transmitter frequency feedback. What else changes when one uses SMS-EPI instead of single shot EPI? The most obvious concomitant change is TR. Indeed, many people use SMS specifically to obtain a TR well below 2000 ms. Might there be a different spin history effect, perhaps one with a flip angle dependence? Again, quick tests on my Trio suggested that flip angle, hence a short TR, wasn't the root cause. That said, I'll return to the short TR because it does affect the appearance of respiratory perturbations on motion parameters.

      Thinking about concomitant parameter changes led us to the next candidate explanation. As with the tendency to use a lower TR for SMS-EPI than for regular EPI, there is also a tendency to drive the in-plane gradients harder. Here's the logical progression. We start with a change in the slice dimension whereby SMS permits, say, 2 mm slice thickness and full brain coverage in a TR below 2000 ms. Next, we are tempted to improve the in-plane resolution in order to get cubic voxels. The only way to get finer resolution in-plane is to make the frequency and phase encoding gradients work harder. The SMS scheme doesn't help (or hinder very much - see Note 2) the in-plane dimension, however, and there's only so much one can drive the gradients before the total gradient area must be increased through longer application times. (If you need a refresher on gradient areas and k-space in EPI, take a look at this post and its predecessors.) Thus, a further consequence of pushing up the in-plane resolution is to increase the echo spacing in the EPI readout echo train. This means we can expect higher distortion in the phase encoding axis. Again, let me emphasize that this isn't the fault of SMS but the way in which we seek to use it: for very high spatial resolution in all three dimensions, when only the slice dimension benefits directly from the SMS scheme.

      With all these issues in mind, this week we ran some tests to try to isolate the B0 modulation from other possible mechanisms, especially direct mechanical motion.


      Recent tests

      We used two forms of head restraint in an attempt to separate real (mechanical) head movements from modulation of B0 via magnetic susceptibility of the chest. In the first set of measurements we used a custom 3D-printed head restraint which I will describe in detail in a later post. We then repeated the measurements using standard foam padding as the head restraint. The custom head holder doesn't totally eliminate head motion, but it's considerably better at restraining the head than foam pads! We used the 32-channel head coil on a Siemens Trio running VB17A. For SMS-EPI we used the MB-EPI sequence (R014) from CMRR.

      The subject conducted the same self-paced breathing task for each run. He waited until about 30 seconds into the run (so that all single band reference images had been acquired and a T1 steady state was established) then inhaled deeply and exhaled immediately, as if sighing. The deep breath-and-sigh was repeated three more times, with gaps of approximately ten seconds in between. The idea was to maximize the chest expansion but without causing too much in the way of physiologic response (via hypercapnia), as one gets with a held breath.

      For each type of head restraint we ran SMS-EPI with MB=6 acceleration for axial, sagittal and coronal slices, and then ran product EPI (ep2d_bold sequence) for the same three orientations. Other parameters: voxel dimensions 2mm x 2mm x 2mm, TE = 35-36 ms (coronal slices had slightly different gradient timing to get under the stimulus limit), TR = 1000 ms, flip angle = 30 deg, 66 interleaved slices with no gap for MB=6, 11 contiguous slices with 10% gap for ep2d_bold.

      For convenience you may want to download the QuickTime videos (126 MB zip file) embedded below before reading further. (For full raw data, see Note 3.) It can be quite difficult to see subtle effects in YouTube videos, whereas with the QuickTime videos you can zoom and change the looping speed (initially set to 4 fps) easily. Here I show volumes 80-120 of 200-volume data sets, zoomed to give the best view of the pertinent features. In axial slices, for instance, motion is most easily visualized in superior slices, where small movements in the slice dimension produce large changes in the amount of brain tissue present. In a future post I hope to present the full motion traces for the tests, but for now I'm afraid you'll have to make do with these.

      With only foam padding we can easily detect a lot of through-plane motion as well as some in-plane motion in the axial images acquired using MB=6:



      This is the sort of motion that might be seen in some of the real data sets that have been reported. Now, noting again how difficult it is to see small effects in YouTube videos, contrast the above with what happens when the subject's head is held securely by a custom restraint:


      There is now very little translation visible in-plane, while the through-plane motion has also been reduced considerably. There is some residual through-plane motion, however, suggesting that either the head case is unable to reduce head-to-foot (magnet Z axis) movements as well as it does X or Y axis movements, and/or the chest movement is perturbing the magnetic field along Z and producing apparent movement effects via B0 modulation. I'll come back to this distinction below.

      Next, we would like know if what is observed for MB=6 is reproducible with conventional EPI. If so, it's unlikely that the problems reported for real data are due to the SMS scheme itself. Here is the product ep2d sequence with foam padding head restraint:


      The in-plane and through-plane motions are very similar to those seen in the previous MB=6 data for foam head restraint. Similarly, using the custom restraint does a good job of prohibiting in-plane (X, Y axis) head movements but does leave small through-plane (Z axis) motion, just as was seen for the MB=6 data with custom head restraint:



      At this point, then, there is good evidence that the SMS scheme is not responsible for a majority of respiratory motion sensitivity. The respiratory oscillations being reported are more likely due to some other feature(s) of the acquisition.

      To get a better understanding of the motion sensitivity it's useful to separate the main field direction and the slice dimension. A sagittal acquisition has the twin benefits of slicing in what is usually the least motion-contaminated direction - the subject's left-to-right (magnet X) axis - as well as preserving the phase encoding direction anterior-posterior (A-P), as for the previous axial slices. We may thus assume that sensitivity to motion, or off-resonance effects, will be similar in the A-P direction (magnet Y axis) for axial and sagittal slices, but the slice dimensions will have different motion sensitivities.

      Let's go in the same order as before, starting with foam padding and MB=6 acquisition:



      The through-plane motion that dominated axial slices has now largely vanished. There are two types of motion to distinguish here: translations in-plane, and changes in the amount of distortion. Motion effects on distortion are apparent as occasional stretches in the A-P (phase encoding) direction, as well as shearing in the cerebellum and spinal cord. When a custom head restraint is used to secure the head we see a big reduction in the translations in-plane, but the stretches and shearing in the A-P direction remain:



      This pattern of apparent movement is consistent with modulation of the on-resonance frequency, i.e. by the expected magnetic susceptibility effects of chest motion. Changing the resonance frequency is equivalent to a phase shift in the phase encoding direction, and phase shifts produce translations in the phase encoding direction. Degradation of the shim also increases the amount of distortion, producing stretches and the appearance of shearing that is most easily discerned where the magnetic field homogeneity is already lowest, i.e. the inferior portions of the brain and the upper spinal cord.

      As before, the next task is to verify that the effects seen for MB=6 are reproduced in conventional EPI, and they are. Here are the foam restraint images for sagittal slices acquired with the conventional ep2d sequence:



      Using the custom head restraint again largely eliminates the head-to-foot translations while the stretches and shearing in the A-P direction remain:



      Le's take a moment to reconsider the translations in the head-to-foot axis, which is the magnet Z axis. As I mentioned above, when using axial slices one cannot distinguish real movement in Z from modulation of the magnetic field along Z. The sagittal acquisitions - whether MB=6 or ep2d - reveal that the custom head restraint does a pretty good job of ameliorating real motion along Z. But there was still a rather pronounced "apparent motion" in the axial slices when using the same head restraint. Thus, it seems more likely that the residual through-plane motion effects in the axial data were due to magnetic susceptibility modulation of B0 than direct, mechanical movement. We can't be entirely sure - these data don't permit a categorical distinction of the two effects - but this explanation fits the data so far.

      Shifting to a coronal prescription may provide more evidence for true mechanical motion effects in the head-to-foot direction when using a custom head restraint, if such movement exists. In the default setting provided by Siemens, the H-F axis will be the frequency encode dimension while L-R will be the phase encoding dimension. (See the post on stimulus limits for an explanation of the default gradient directions used by Siemens.) Modulation of B0 will produce only very tiny shifts in the frequency-encoded direction; the phase encoding dimension is the one most sensitive to resonance frequency shifts. So we can also predict that chest motion will produce stretches and shearing in the L-R direction in coronal EPI.

      With simple foam padding there is a large amount of translation visible, here mostly in the H-F axis, that suggests direct mechanical motion is dominating the instability of MB=6 coronal images:



      By using the custom head restraint we can eliminate the H-F translation to reveal more clearly the shearing effects that are most easily identified in the cerebellum, where the magnetic field homogeneity is low:



      As for the sagittal data, then, holding the head securely leaves residual "apparent motion." Magnetic susceptibility effects dominate to produce distortions and shearing from modulation of B0 by chest movement.

      All that remains is to verify the same behavior for conventional EPI as for SMS-accelerated EPI. Here are the product ep2d coronal images with only foam padding:



      Plenty of translation as well as shearing on offer! But the custom head restraint eliminates the former to leave the latter:



      We again have consistent behavior between SMS-EPI and conventional EPI. The head restraint system is the primary determinant of the motion effects seen in the time series. Residual or "apparent motion" effects left over in images acquired with good head restraint can be explained by the well-known properties of EPI. That is, by the sensitivity of the phase encoding axis (mainly) to off-resonance effects.


      Summary

      The first conclusion is trivial: good head restraint matters! We have always known this, but the ability of movement to dominate an EPI time series becomes more obvious the higher the resolution we try to use. Again, a moment's thought tells us this is also a trivial point. An image with voxels 1 cm on a side is already so lacking in detail that a few mm movement in any direction is unlikely to be easily detected by eye. Or, we might invert this thought and state that sub-voxel motion is hard to detect by inspection. This is an important point for those of you concerned about insidious motion contamination in resting-state fMRI in particular. Just because your motion parameters from realignment are "good" does not imply that your data are uncontaminated by motion!

      But I'm getting ahead of myself. Here we are specifically concerned with SMS and any potential for greater motion sensitivity than for regular EPI. And my conclusion is that to a first approximation the motion sensitivity of SMS-EPI is not radically different to regular EPI, for matched spatial and temporal parameters.

      Why, then, did people suddenly become concerned about motion in SMS-EPI? I think it's to do with the tendency to match the in-plane resolution to the slice thickness. In other words, it's the way SMS-EPI is being used rather than a problem with the SMS scheme per se. It is rare for someone to do 2 mm isotropic voxels with conventional EPI, but high spatial resolution is common once one gets a hold of an SMS sequence. Smaller voxels exhibit higher apparent motion sensitivity than larger voxels. What was intra-voxel motion becomes inter-voxel motion, and you see/detect it. Furthermore, higher in-plane resolution means greater distortion - shorter echo spacing in the EPI readout echo train - and concomitant greater sensitivity to changes in B0.

      Higher spatial resolution is usually coupled with a tendency to use faster sampling (TR < 2000 ms) with SMS-EPI, and this also increases the visibility of oscillations at respiratory frequencies. Most respiration is sampled above the Nyquist frequency for TR=2000 ms, but this doesn't mean that respiratory oscillations can be readily identified in the motion parameters generated by realignment. Put another way, respiratory modulation is certainly present in your conventional EPI sampled at TR=2000 ms, whether or not you can identify it by inspection! Furthermore, the tests here with a custom head restraint indicate that you can't eliminate chest motion effects no matter what you do. They are "baked in" to your data. This is a big subject for another day.

      What about additional motion sensitivity in the SMS scheme? The tests here suggest that a large fraction of the motion sensitivity in SMS is very similar to that for conventional EPI, which is not to say that SMS doesn't have additional sensitivities we should try to understand. For example, there is a possible mismatch between the single band reference data acquired at the start of the run and the accelerated time series data. For small motions - a few mm - the mismatch may not matter too much, since the spatial heterogeneity in the RF coil's receive field tends to vary quite slowly over short distances. This is something that needs to be investigated on its own. Likewise, the particular SMS reconstruction method, which may vary with the (vendor-specific) implementation and perhaps with options therein (see, for example, the Leak Block option in the CMRR sequence and literature on split-slice GRAPPA) may produce subtle motion effects in the data, some of which may be projected well beyond a slice and its neighbors.

      There may be additional motion sensitivities that SMS might introduce as a further consequence of the way its used, rather than as an intrinsic property of SMS methodology. I'm thinking specifically of a potential T2 dependence, in addition to the well-known T1-dependent spin history mechanism, that may arise in species with long T2 (CSF is the prime concern) whenever the TR approaches the T2 of some signal component. This "steady state free precession" (SSFP) effect was demonstrated for serial single-shot EPI whenever coherent magnetization managed to survive the readout echo train and persists into the subsequent slice acquisition. (It is a consequence of insufficient crusher gradients at the end of a slice acquisition.) Some degree of robustness to SSFP effects may be provided by using a flip angle well below the Ernst angle. But optimal flip angle, and the possibility of SSFP effects, are both subjects for a later date. I will try to run some tests and make more detailed recommendations on flip angle selection for SMS-EPI (that is, for short TR fMRI) in the near future.

      _____________________


      Notes:

      1.  The initial report came from Jo Etzel at WashU. She emailed me the same day that I happened to be reading Jonathan Power's latest preprint: http://dx.doi.org/10.1016/j.neuroimage.2016.08.009. In that paper, Power mentions that he observed relationships between apparent head motion, as reported by realignment parameters, and chest movement detected with a belt in several sets of SMS-EPI data he inspected. Annika Linke at SDSU then reported seeing similar oscillations in SMS data acquired on a GE Discovery MR750 scanner, indicating a problem independent of the scanner manufacturer. I subsequently received reports from sites with different Siemens platforms, including Prisma, Verio and Skyra. Thanks to all who offered data and experiences!

      2.  As explained in my intro to SMS post, there is generally a need to use a longer excitation RF pulse width for SMS than for regular EPI, mostly because one is trying to define thinner slices. So the minimum TE tends to be a tad longer for SMS-EPI than regular EPI. This difference essentially disappears if one tries to define the same thin slices for regular EPI, except that this is rarely done in practice because the total brain coverage is so small.

      3.  Each run comprised 200 volumes; the videos show only volumes 80-120. Data with the printed head restraint were acquired first, then the foam restraint was used. 1.4 GB zip file: https://dl.dropboxusercontent.com/u/26987499/patient.zip


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      This is a follow-up post to Respiratory oscillations in EPI and SMS-EPI. Thanks to Jo Etzel at WashU, you may view here the apparent head motion reported by the realignment algorithm in SPM12 for the experiments described in the previous post. Each time series is 200 volumes long, TR=1000 ms per volume. The realignment algorithm uses the first volume in each series as the template. The motion is plotted in the laboratory frame, where Z is the magnet bore axis (head-to-foot for a supine subject), X is left-right and Y is anterior-posterior for a supine subject.

      In the last post I said that there were five total episodes of a deep breath followed by sigh-like exhale, but actually the subject produced a breath-exhale on average every 30 seconds throughout the runs. (This was a self-paced exercise.) Thus, what you see below (and in the prior post) has a rather large degree of behavioral variability. Still, the main points I made previously are confirmed in the motion traces. I'll begin with the axial scan comparison. Here are the motion parameters for the MB=6 axial acquisition with standard foam head restraint (left) versus the custom printed restraint (right):

      MB=6, axial slices. Left: foam restraint. Right: custom 3D printed headcase restraint

      The effect of the custom restraint is quite clear. The deep breath-then-sigh episodes are especially apparent when using only foam restraint. Note the rather similar appearance of the high frequency oscillations, particularly apparent in the blue (Y axis) traces between the two restraint systems, suggesting that the origin of these fluctuations is B0 modulation from chest motion rather than direct mechanical motion of the head. We cannot yet be sure of this explanation, however, and I am keeping an open mind just in case there are small movements that the custom head restraint doesn't fix.

      The product EPI acquisition of axial slices shows a similar benefit of the custom restraint:

      Product EPI, axial slices. Left: foam restraint. Right: custom 3D printed headcase restraint

      Furthermore, comparing the above two figures we can confirm that the overall motion sensitivity of the SMS acquisition (MB=6) and product EPI is quite similar for both foam and custom restraints, when spatial and temporal parameters are matched (except for total number of slices per TR). In spite of the task variability the peak-to-peak excursions are consistent across the restraint system being used.

      I find it interesting that the realignment algorithm reports similar motion for product EPI, with only 11 total slices, as for SMS-EPI with 66 slices. The total anatomical content in the 11 axial slices located at the top of the head is markedly lower than the near full brain coverage of the 66 slice acquisition. Yet there doesn't appear to be a large cost for the poorer coverage. Perhaps there is slightly greater low frequency drift being reported for product EPI than SMS-EPI when using foam padding. The rather similar drifts apparent when using custom restraint would suggest that gradient heating isn't the cause. It's something I will look into in more detail separately, since Jo has kindly re-run the motion correction using a subset of the full SMS-EPI data.


      Next I want to consider the coronal comparisons, because we retain the issue of low brain coverage and low anatomical content for the product EPI images. The 11 slices of the product EPI acquisition were positioned over the occipital cortex. Here is the MB=6 coronal acquisition with standard foam head restraint (left) versus the custom printed restraint (right):

      MB=6, coronal slices. Left: foam restraint. Right: custom 3D printed headcase restraint

      The excursions in pitch (red traces) and yaw (brown traces) in particular, appear to be larger for the coronal than the previous axial prescriptions, whether using foam or custom head restraint. Recall that the coronal acquisitions used L-R phase encoding and so perturbations of B0 change the distortion in that dimension to produce shearing. This clearly violates the rigid body assumption of the realignment algorithm and I can only assume that we are seeing some consequence of this reflected in the motion parameters. A good issue for someone with a lot of image processing expertise to dig into, or comment on. For the time being I am going with the hypothesis that the larger fluctuations for coronal than axial slices when using the custom restraint are due primarily to a greater sensitivity to B0 perturbation by chest movements when using L-R phase encoding in coronal slices. But I am leaving open the possibility of uncorrected direct motion.

      The coronal data acquired with product EPI don't show the same systematic effects in pitch and yaw as the SMS data. Instead the traces are rather messy:

      Product EPI, coronal slices. Left: foam restraint. Right: custom 3D printed headcase restraint

      Is the absence of any dominant directionality in the traces a consequence of the reduced coverage in the product EPI scans? For now I'm assuming this is so. More on this in the next post.


      Finally we can consider the sagittal scans. As a reminder, the slice direction is orthogonal between sagittal and axial slices, but they share a common phase encoding direction (anterior-posterior, or magnet Y axis). Differences between axial and sagittal data should thus reflect primarily differences in the slice dimension sensitivity to head motion and B0 modulation. Here are the motion parameters for the MB=6 sagittal scans with standard foam head restraint (left) versus the custom printed restraint (right):

      MB=6, sagittal slices. Left: foam restraint. Right: custom 3D printed headcase restraint

      When using custom head restraint the high frequency oscillations that were seen in the MB=6 axial scans are very similar to the oscillations in the MB=6 sagittal data, again consistent with B0 modulation from chest movements and implying a primary sensitivity in the phase encoding direction. What about other features that may differ between axial and sagittal data? There is a suggestion of reduced low frequency, drift-like motion for the sagittal scans. Until I've repeated the experiment I'd have to concede that gradient heating is the most likely explanation. Still, I wouldn't exclude the possibility just yet that perhaps the sagittal prescription has a lower sensitivity to both motion and B0 modulation from respiration, given that for axial scans we are slicing along the main magnetic field (Z) axis. It's something I plan on investigating.

      The eleven sagittal slices acquired with product EPI have greater anatomical content than either the axial or coronal counterparts because I positioned them right down the midline. While there may still be issues with limited coverage compared to the full brain coverage permitted with SMS-EPI, we might expect a greater similarity with the MB=6 acquisition. Here are the sagittal EPI data:

      Product EPI, sagittal slices. Left: foam restraint. Right: custom 3D printed headcase restraint

      To my eye there is more similarity between the motion traces produced for sagittal product EPI and MB=6 than there was for either axial or coronal. Again, with N=1 we can't be sure, but this is an interesting starting point for future investigations where total brain coverage might be an important variable in the protocol. And we can't forget that the shearing produced in inferior portions of the brain will violate the rigid body assumption, as for the coronal data.


      Final thoughts

      In spite of the single set of tests presented here, the most important findings as reported in the last post are confirmed in the motion traces. The custom head restraint makes a truly massive difference to overall motion sensitivity. But the use of SMS-EPI does not ipso facto enhance the motion sensitivity. For matched spatial and temporal resolution we see just as much motion sensitivity for conventional EPI. Having reduced the head motion there is the residual issue of fluctuations at respiratory frequencies, caused by main field modulation. The sensitivity to both direct motion and respiratory effects varies considerably with assignment of the logical axes. No surprises there.

      These results offered a few interesting new thoughts to guide future tests. How much does the total brain coverage affect realignment algorithm efficacy? When distortion effects clearly violate the rigid body assumption, what are the consequences when the head moves or when respiratory effects produce shearing? These are questions for another day. For now, though, I contend that we have enough evidence to suggest that most if not all fMRI studies - especially those using high spatial resolution - need to be doing even better with their head restraint, and that if one is contemplating a high-resolution fMRI experiment then a deeper consideration of respiratory effects is also warranted.

      Thanks again to Jo Etzel for stimulating the current investigation with her own observations, and for all her stellar work since.



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      Accurate separation of the simultaneously acquired slices is one of the bigger limitations of the SMS-EPI method, compared to the processing used for conventional multislice EPI. The default SMS reconstruction, as used in my two introductory posts on the SMS sequences from CMRR (MB-EPI) and MGH (Blipped CAIPI), is a slice dimension adaptation of the GeneRalized Autocalibrating Partial PArallel (GRAPPA) method that was originally applied in-plane to acceleration of the phase encoding direction. It's not essential to understand the GRAPPA method applied in-plane for the purposes of understanding this post or for SMS reconstruction more generally. But if you're curious I wrote a brief introduction to in-plane GRAPPA in 2011. That post was specifically concerned with motion sensitivity of (in-plane) GRAPPA. I'll be looking in more detail at the motion sensitivity of SMS in a future post. In this post I want to compare the standard SMS reconstruction - what is generally termed Slice GRAPPA - with an alternative known as Split Slice GRAPPA. The latter option is termed "Leak Block" in the CMRR pulse sequence, MB-EPI.


      What's the concern?


      CMRR's parameter nomenclature offers a strong clue to the problem. In conventional EPI reconstruction we use a 2D Fourier transform (FT) which produces some amount of ringing. We also use slices that have some degree of cross-talk to neighboring slices, arising out of the limitations of frequency selectivity. So, while we think of voxels as perfect little rectangles or cubes, in reality they are blurry beasts that spread their signal into adjoining voxels because of a non-rectangular point-spread function (PSF). The dimensions we assign a voxel are entirely nominal.

      With SMS we have a broader spatial problem than just non-cubic PSF. Separation of the simultaneous slices can leave signal in an incorrect position that is quite some distance from where it is supposed to be. It's a longer length scale error than the simple PSF of a voxel. Let's suppose we acquire four 2 mm slices simultaneously, 84 total slices. In one SMS acquisition we will have four slices separated by one quarter of the total slice dimension extent of 168 mm, or about 42 mm (assuming no additional inter-slice gap). Do a quick thought experiment. Imagine that in the first slice there is a very strong activation and nothing in the other three. If there is a large residual spatial error arising from poor SMS separation then we might start seeing this activation projected 4.2, 8.4 or even 12.6 cm from where it should be! And how would we know that the distant activation sites were erroneous?

      This slice leakage, as it's usually called in the literature, may be strongest for simultaneously acquired neighbors but may extend throughout the slice dimension, between simultaneously acquired slices that might be quite far apart in anatomical space. And, as the thought experiment illustrates, one might assume that distant leakage would be harder to spot than the conventional cross-talk between successively acquired slices in conventional multislice EPI, or errors arising from the PSF more generally. The PSF can usually be interpreted as a local phenomenon, with errors decreasing monotonically from a voxel. Not so with SMS slice separation, meaning there is more risk of interpreting a false positive remote from the true activation site.

      At this point we can recognize that reducing leakage is a noble, perhaps essential, goal. As usual with MRI, however, there's a catch. Reducing leakage using the Split Slice GRAPPA reconstruction may come at the cost of increasing in-plane artifacts. The overall (total) artifact level might be higher, too. I'll go into these issues in some detail below. The goal of this post is to perform a rudimentary assessment of the artifacts and determine the circumstances when Split Slice GRAPPA might be preferred over the conventional Slice GRAPPA reconstruction. For the CMRR sequence this amounts to whether or not to enable the Leak Block option.


      What does the literature tell us?


      "A couple of months in the laboratory can frequently save a couple of hours in the library." This aphorism is attributed to the chemist Frank Westheimer, in 1988. I first came across it courtesy of the Unix command fortune, which we used run automatically after logging in to a Sun workstation back in the 17th century (or thereabouts). It has been updated for the 21st century by Runyon's corollary: "A couple of hours on the Internet can frequently save a couple of minutes in the library."Even so, reading the literature before heading down to the scanner can still be a useful exercise, provided one takes the precaution to disable e-mail and Twitter first.

      The paper that introduced the Split Slice GRAPPA reconstruction for SMS-EPI is by Cauley et al. (2014). I've quoted below some of the most important points from the paper, beginning with a brief review of the history of SMS then highlighting the motivation for Split Slice GRAPPA over Slice GRAPPA. My clarifications are in [square brackets] and I truncated some sentences... to remove formulas that we don't need for this post:
      SMS is a promising parallel imaging modality that has been shown to work well when the simultaneously acquired slices have substantial distance between them. However, for brain imaging the smaller FOV along the slice direction limits the distance factor and the simultaneously acquired slices are more difficult to separate.
      Controlled aliasing (CAIPI) techniques have been introduced in (9) to perform shifts across the slices to more easily unalias the accelerated data.

      A recent work (14) examined using blipped-CAIPI to achieve spatial shifts in the PE [phase encode] direction, between simultaneously excited slices to avoid voxel tilting artifacts. This has enabled SMS acquisitions with high acceleration factors with a low g-factor penalty; allowing for significant gains in the temporal efficiency of diffusion imaging and fMRI acquisitions.

      The blipped-CAIPI scheme is now used by default in both the CMRR and MGH sequences, so the issue of tilted voxels is no longer a concern. Let's go on.
      Similar to [in-plane] GRAPPA (3), the SG [Slice GRAPPA] method uses training data to fit a linear model that is used to unalias the simultaneously acquired slices. With SG, distinct kernels are used to unalias each of the simultaneously acquired imaging slices. It was illustrated in (14) that the fitted SG kernels showed a strong dependence on the static coil sensitivity profiles and not on the training data image contrast. This is a desirable property that allows the SG kernels to be used to accurately unalias SMS data that can have different contrast from the training data, e.g. in the case of diffusion encoding. However, in this work we will show that when using high slice acceleration together with in-plane accelerations the contrast independent property of the SG kernels will suffer. This results in an increased dependency of the kernels on the training data image contrast and causes increased signal leakage between the unaliased simultaneously acquired slices.

      While the standard SG kernel fitting produces kernels that minimize the image artifact, the SP-SG [Split Slice GRAPPA] method takes a more balanced approach. The SP-SG method simultaneously minimizes errors coming from both image artifacts and leakage. This is accomplished by forming a new kernel fitting objective function to consider the importance of both sources of error.

      In particular, the robustness of the fitting kernel across b-values is demonstrated through reductions in artifacts and improved SNR. Based on this work, the SP-SG method has the potential to enable a more robust and less artifact prone SMS acquisitions at high acceleration factors. This should facilitate further improvements in temporal efficiency of fMRI and diffusion imaging acquisitions.

      ...reducing the total artifact without placing restrictions on the intra- and inter-slice artifacts can lead to a dependency on artifact cancellations. With the SG method the intra- and inter-slice artifacts might be arbitrarily large but combine to help reduce the total artifact...

      Note that the SP-SG formulation will result in higher total artifact error (5). That is, for SG reconstruction each convolution matrix will directly contribute toward reducing the RMSE [root-mean-square error]while no condition is placed on the inter-slice artifact... For SP-SG reconstruction we attempt to limit the influence of inter-slice artifacts... This additional condition for SP-SG can increase the kernel fitting RMSE with respect to the SG objective. However, with the SG method, using all of the slice convolution matrices to improve the kernel fitting RMSE can result in artifacts during the application of those kernels to images with different contrasts. This is caused by changes in the inter-slice leakage that no longer contribute toward reducing RMSE. By limiting the dependency on inter-slice leakage artifacts during the training process the SP-SG method is less vulnerable to produce artifacts based upon image contrast change.

      So the Slice GRAPPA reconstruction might be sub-optimal for diffusion-weighted imaging (DWI), where a non-DW scan, often referred to as a b=0 image, is typically used to generate the reconstruction kernel before the kernel is applied to all the DW images in the data set.

      There's an additional factor, another potential difference between SMS used for DWI and SMS used for fMRI. For DWI we almost always use in-plane acceleration to render the echo time (TE) acceptably short. If we also want to use SMS then we would be under-sampling two of the three spatial dimensions. How might in-plane acceleration interfere with SMS? Back to Cauley et al.
      In-plane acceleration reduces the effective amount of PE shift that can be applied in a SMS acquisition. In this work, a FOV/3 shift was used within the in-plane accelerated “reduced” FOV [field-of-view]. This corresponds to a FOV/6 shift in the full FOV [for acceleration factor of R=2 in-plane], which results in relatively small distances between the aliased voxels. Therefore, with our combined acceleration approach, we expect the contrast dependency property of the SG kernel to be similar to MB=6 acquisitions with no in-plane acceleration (and significantly larger than the MB=3 only case).

      In fMRI acquisitions the image contrast does not change significantly for a time-series. Therefore, the total artifact error of the standard SG method should be lower than that from the SP-SG method. However, the SG method will still result in more signal leakage because of the kernel fitting dependency.

      For example, in fMRI applications it might be desirable to sacrifice intra-slice artifact performance to reduce the inter-slice leakage artifact. This can be viewed as a specificity and sensitivity trade-off. The inter-slice leakage artifact can cause a reduction in specificity by creating displaced false positives due to signal leakage. On the other hand, the intra-slice artifact will cause a spatially varying signal attenuation/amplification for a given slice. This will affect the sensitivity to activation detection and with large attenuation false negatives can occur. However, small modulation on signal level is not particularly harmful while a small leakage can result in a large displacement of detected activation. This is particularly evident when the acquisition is physiological noise dominated. In this regime, the relatively small attenuation/amplification due to the intra-slice artifact will affect both the signal and noise equally and there should be no net effect on the sensitivity to activation.


      Let's turn our attention to another paper: Evaluation of 2D multiband EPI imaging for high-resolution, whole-brain, task-based fMRI studies at 3T: Sensitivity and slice leakage artifacts, by Todd et al. (2016). They assessed leakage for these parameter combinations:
      MB factors of 1, 2, 4, and 6, combined with in-plane GRAPPA acceleration of 2 × (GRAPPA 2), and the two reconstruction approaches of Slice-GRAPPA and Split Slice-GRAPPA. 

      Unfortunately, the one condition not tested was SMS-only, i.e. SMS without in-plane GRAPPA, most likely for the convenience of predicting false positive locations in a consistent manner, as will be explained below. Never mind.

      The aim of the Todd study was to determine leakage via projection of false positives from the actual locations of activity, for a simultaneous visual and motor task: a 10 Hz flashing checkerboard and finger-to-thumb tapping. It's the real version of the thought experiment I did above. First, they established the true activation sites in the visual and motor cortices, and in the cerebellum. The locations of false positives were then determined using seed voxels in the true activation locations, as follows:
      To determine if a false-positive activation occurred at an aliased location, the voxel with the largest t-score value for all activation clusters defined by SPM were considered as “seed” voxels. For each “seed” voxel chosen, the t-score values in the single voxel at the possible alias locations were evaluated. The detection of a false positive had to satisfy both criteria that (1) the single voxel at the exact alias location had a t-score value larger than the significance threshold corresponding to p < 0.001 (uncorrected), and (2) a 3 × 3 × 3-voxel volume at the alias location in the three other multiband scans did not have any voxels with t-score values that exceeded the significance threshold. The second criterion was designed to guard against the possibility that the aliased location could fall within a region of true positive activation.
      How did they determine all possible aliased locations? This is where the consistent use of in-plane GRAPPA enters the picture:
      The expected alias locations of a particular voxel were inferred from the multiband factor, in-plane GRAPPA factor, and in-plane CAIPI-shift. Since all multiband factors used in-plane GRAPPA 2 and in-plane CAIPI-shift FOV/3, there are two alias locations per simultaneously acquired slice, one shifted by (FOV/3)*m and one shifted by (FOV/3)*m + FOV/2, where m is the number of simultaneously excited slices away from the original slice.
      (For anyone attempting to repeat this study, please see Note 1 for an important caveat regarding the FOV shift amount.)

      Todd et al. observed 106 false positives for Slice GRAPPA versus only 11 for Split Slice GRAPPA:
      When using Slice-GRAPPA reconstruction, evidence of false-positive activation due to signal leakage between simultaneously excited slices was seen in one instance, 35 instances, and 70 instances over the ten volunteers for the respective accelerations of MB 2 × GRAPPA 2, MB 4 × GRAPPA 2, and MB 6 × GRAPPA 2. The use of Split Slice-GRAPPA reconstruction suppressed the prevalence of false positives significantly, to 1 instance, 5 instances, and 5 instances for the same respective acceleration factors.

      After accounting for multiple comparisons they estimate that up to seven false positives might arise by chance. So, the Split Slice GRAPPA seemed to work rather well, whereas leakage was a problem for Slice GRAPPA once the SMS factor was 4 or higher and when also using in-plane GRAPPA. The aliased location of some of the false positives with Slice GRAPPA is important to note, too:
      False-positive activation was seen not only in the simultaneously excited slice immediately adjacent to the true positive origination slice, but also sometimes in a location two slices away. Of the 106 instances of false-positive detection, there were 22 cases in which false positives were seen in more than one alias location. There were no instances of false-positive activation being detected in a location three slices or more away from the true activation origin.

      Let's sum up. The main recommendations arising from Todd's study are as follows:
      ...false-positive activation arising when BOLD signal changes due to true positive activation in one slice leak into other simultaneously excited slices can occur when using multiband factors of 4 or higher combined with in-plane accelerations,

      A very conservative approach for high-resolution whole-brain fMRI studies would be to use multiband acceleration factor 2, in-plane GRAPPA acceleration factor 2, and Split Slice-GRAPPA reconstruction.

      This is all good to know. But we don't yet know what might be appropriate if we disable in-plane GRAPPA and use just SMS acceleration. We also have a vast parameter space to explore, as Todd et al. caution:
      It is important to point out that most 3 T studies, which use more conventional resolutions of 2–3 mm, do not typically employ in-plane accelerations, nor is the need for it significant given the lower resolutions of those studies.
      ...the effectiveness and optimization of the CAIPI shift factor was not evaluated for the experimental conditions used in this work and the use of different shift factors may have altered the findings presented here. 

      I think we have a starting point. I'll keep the spatial resolution reasonably high, at 2 mm isotropic - Todd et al. used 1.5 mm cubic voxels - and stick with the default CAIPI shifts (as described in Note 1).


      Throwaway tests on a Trio


      I currently have version R014 of MB-EPI installed on my TIM/Trio running VB17A software. Todd et al. used version R011a of the same sequence. (See Note 1 for an important change in the default CAIPI shift in R012 and later.) I used a 32-channel receive-only head coil and an FBIRN gel phantom. The fixed acquisition parameters were: voxel size = 2mm isotropic, TE = 36.4 ms, echo spacing = 0.7 ms, read bandwidth = 1814 Hz/pixel, RF duration = 8200 us, axial slice prescription with A-P phase encoding, 7/8ths partial Fourier in the phase encoding dimension. The number of slices, TR and SMS factor were then varied as described below.

      For the remainder of this post I'm going to switch to using the CMRR nomenclature. That is, in the MB-EPI sequence the Split Slice GRAPPA reconstruction is activated by enabling the Leak Block option. Otherwise, when Leak Block is off we are using the default reconstruction, i.e. Slice GRAPPA. I'm also going to use the Siemens nomenclature for in-plane acceleration. The in-plane acceleration method will always be GRAPPA (the other Siemens option being mSENSE) while the degree of in-plane acceleration is given by the iPAT factor. This creates one more parameter option to consider: the way the auto-calibration scans (ACS) are acquired for in-plane GRAPPA at iPAT = 2. The Siemens default is a single shot ACS, whereas distortion effects are matched properly only when using 2-shot (interleaved) ACS. See Note 2 for more information on the ACS options.

      Artifacts visible in signal regions:

      We were warned by Cauley to expect more in-plane artifacts when using Leak Block, so I opted to begin with an evaluation of in-plane artifacts before moving on to an assessment of leakage artifacts between slices. Todd concluded that an SMS factor of 4 or more, with iPAT = 2, was "accelerating too far." For the initial tests I wanted to explore the extremes. I was also interested to know if the potential for distortion-related artifacts from 1-shot ACS might be an issue overlooked by Todd et al., since they used the Siemens default for in-plane GRAPPA. I therefore tested single shot ACS versus a 2-shot segmented ACS that can be selected with a sequence flag, for SMS factors of 3 and 6:

      SMS = 3, TR = 2000 ms, 66 interleaved slices. Top-left: no iPAT, Leak Block off. Top-right: iPAT = 2 with 1-shot ACS, Leak Block off. Bottom-left: iPAT = 2, segmented 2-shot ACS, Leak Block off. Bottom-right: iPAT = 2, segmented 2-shot ACS, Leak Block enabled. (Click on the image to enlarge.)

      SMS = 6, TR = 1000 ms, 66 interleaved slices. Top-left: no iPAT, Leak Block off. Top-right: iPAT = 2 with 1-shot ACS, Leak Block off. Bottom-left: iPAT = 2, segmented 2-shot ACS, Leak Block off. Bottom-right: iPAT = 2, segmented 2-shot ACS, Leak Block enabled. (Click on the image to enlarge.)

      There is clearly an interaction, leading to artifacts, for the case of SMS = 6, iPAT = 2 and use of Leak Block reconstruction. Disabling Leak Block (bottom-left and top-right panels in the above figure) eliminated the artifacts for both 1-shot and 2-shot ACS. Furthermore, to save space I displayed only the results for 2-shot segmented ACS when using iPAT = 2 and Leak Block enabled (bottom-right), but the results were very nearly identical for single shot ACS, for both SMS of 3 and 6. The consistent results suggest that for a spherical phantom with minimal structure - just a few air bubbles - there is no major interaction of the ACS scheme with the Leak Block reconstruction. Rather, it is the interaction of the Leak Block reconstruction with the total acceleration - iPAT = 2 and SMS factor 6 - that produces the artifacts. These result might not hold in a heterogeneous brain but for phantom testing purposes, given the similar performance of 1-shot and 2-shot ACS above, from this point on I opted to use just the 1-shot ACS. (See Note 3 for one additional test for iPAT = 3.)

      In the next set of tests I sought to establish the point at which artifacts are introduced as the SMS and iPAT factors are increased. Here are the tests for SMS factors of 4, 5, and 6, with iPAT = 1 and 2:

      SMS = 4, TR = 2000 ms, 68 interleaved slices. Top-left: no iPAT, Leak Block off. Top-right: no iPAT, Leak Block on. Bottom-left: iPAT = 2, Leak Block off. Bottom-right: iPAT = 2, Leak Block enabled. (Click on the image to enlarge.)

      SMS = 5, TR = 2000 ms, 60 interleaved slices. Top-left: no iPAT, Leak Block off. Top-right: no iPAT, Leak Block on. Bottom-left: iPAT = 2, Leak Block off. Bottom-right: iPAT = 2, Leak Block enabled. (Click on the image to enlarge.)

      SMS = 6, TR = 1000 ms, 66 interleaved slices. Top-left: no iPAT, Leak Block off. Top-right: no iPAT, Leak Block on. Bottom-left: iPAT = 2, Leak Block off. Bottom-right: iPAT = 2, Leak Block enabled. (Click on the image to enlarge.)


      If you have a very good eye then you might just pick up subtle intensity variations introduced by iPAT = 2 and SMS = 5 when using Leak Block. The combination iPAT = 2, SMS = 6 and Leak Block produces clear in-plane artifacts. The corresponding single band reference (SBref) images - not shown - are artifact-free, however, confirming that we are indeed seeing an interaction of the Leak Block SMS reconstruction with the in-plane acceleration. (See Note 4 for a comparison of the MGH sequence, Blipped CAIPI to CMRR's MB-EPI. They perform similarly.)

      Slice leakage:

      What about the effect of Leak Block on slice leakage? What benefit might we get for the cost of the in-plane artifacts seen above? I'm going to use the simplest analysis I can think of: inspection. Leakage artifacts are easily seen in regions that should be noise if one positions the slices to one side of the phantom. It turns out that the leakage patterns are quite periodic, reflecting the SMS factor being used, and they extend in regular fashion off into the noise.

      To help you identify the different artifact sources, consider this matrix of expected artifacts that corresponds to the panels in the three figures below: 

      Artifact types expected in the next three figures. N/2 ghosts are intrinsic to EPI acquisition and always present. Slice leakage is expected for Slice GRAPPA reconstruction of SMS-EPI but minimally for Split Slice GRAPPA (i.e. Leak Block) reconstruction. Residual aliasing is a feature of in-plane GRAPPA (i.e. iPAT).


      Here are views of the leakage artifacts corresponding to the three tests for SMS factors of 4, 5, and 6, with iPAT = 1 and 2. The slice positions are slightly different than in the three figures above, and the image contrast has been optimized for the leakage artifacts, but otherwise these three composite figures match the three composite figures above:

      SMS = 4, TR = 2000 ms, 68 interleaved slices. Top-left: no iPAT, Leak Block off. Top-right: no iPAT, Leak Block on. Bottom-left: iPAT = 2, Leak Block off. Bottom-right: iPAT = 2, Leak Block enabled. (Click on the image to enlarge.)

      SMS = 5, TR = 2000 ms, 60 interleaved slices. Top-left: no iPAT, Leak Block off. Top-right: no iPAT, Leak Block on. Bottom-left: iPAT = 2, Leak Block off. Bottom-right: iPAT = 2, Leak Block enabled. (Click on the image to enlarge.)

      SMS = 6, TR = 1000 ms, 66 interleaved slices. Top-left: no iPAT, Leak Block off. Top-right: no iPAT, Leak Block on. Bottom-left: iPAT = 2, Leak Block off. Bottom-right: iPAT = 2, Leak Block enabled. (Click on the image to enlarge.)

      The benefit of Leak Block (right-hand panels) is obvious. All the left-hand panels contain artifacts consistent with inter-slice leakage. The artifacts look like the wet rings left by beer glasses on a bar! Maybe someone should contrive the acronym COASTER for the next version of Split Slice GRAPPA. (See Note 5.)


      Conclusions


      In this post I considered only axial slices on a stationary phantom. The performance of any particular SMS, iPAT and Leak Block parameter combination will likely differ as soon as there is significant sample/subject motion. And, since head motion is anisotropic, performance will also vary with the assignment of the slice and phase encode axes relative to the brain. (See the previous post for some examples.) Indeed, the assignment of the slice and phase encode axes is important even in the absence of motion because the layout of the detector loops in the 32-channel head coil is asymmetric, and we should therefore expect that performance of both SMS and iPAT will change if coronal or sagittal slices are used.

      Given the caveats, what can we say with any certainty? As far as they go, my observations on a phantom are consistent with the findings of Cauley et al. and Todd et al. in brains. The combination of SMS = 6 and iPAT = 2 is at or past the limit of what can be done with axial slices using a 32-channel coil at 3 T. Image artifacts become quite prominent when using Leak Block (aka Split Slice GRAPPA) with SMS = 6 and iPAT = 2. If SMS = 6 and iPAT = 2 are deemed essential then I would suggest not using Leak Block. Stick with the default Slice GRAPPA reconstruction. Alternatively, if you want to use SMS = 6 and you don't need iPAT then by all means use Leak Block. Or, if you decide that iPAT = 2 and Leak Block are essential then I would reduce the SMS factor below 6. Todd suggests using an SMS factor below 4 if iPAT = 2.

      As it happens, I'm not a big fan of iPAT for fMRI because of its motion sensitivity. (See posts here and here.) There are recent developments that aim to improve the robustness of the ACS to motion in GRAPPA, such as FLEET, but these methods aren't yet widely available for conventional EPI or SMS-EPI. Methods like FLEET attempt to reduce the motion sensitivity of the ACS, but as yet I've not seen any methods that address the potential for mismatch between the ACS and the under-sampled time series. So my preference for fMRI would be to eliminate iPAT and use an SMS factor up to 6, with Leak Block enabled.

      For diffusion-weighted imaging, on the other hand, the use of iPAT is all but required in order to keep TE reasonable. While I have yet to run any specific tests for DW-SMS-EPI, the results above suggest that a moderate SMS factor of 2-4, with  iPAT = 2 and Leak Block enabled should be acceptable. I'll present the results of DW-SMS-EPI tests in a future post. In the next post I want to assess the impact of motion on SMS-EPI for fMRI applications.

      Until then, Happy New Year!

      _______________________


      Notes


      1.  From Todd et al.:
      MB factors 2, 4, and 6 all used an in-plane CAIPI shift of FOV/3 that was automatically set by the sequence.
      This is because they used CMRR's sequence version R011a for which a CAIPI factor of FOV/3 was the default. But this was changed from R012 on, when for GRAPPA with R=2 acceleration the CAIPI factor was increased to FOV/4. It is still FOV/3 when in-plane GRAPPA isn't used, however. See the R014 release notes for more details. In MGH's Blipped CAIPI the default is FOV/3 but the factor can be changed by the user.


      2.  The single shot ACS uses a k-space increment in the phase encode direction that corresponds to the full FOV; no aliasing. Being single shot, it's fast and is somewhat robust to motion. But it means there is a difference in the distortion of the ACS and the under-sampled EPI data that comprise the fMRI time series because the latter use a k-space increment that is twice as big, resulting in a total echo train length half as long (and a FOV half as big, creating aliasing). Such a mismatch in distortion properties creates artifacts in regions of strong magnetic susceptibility gradients.


      3.  I did a comparison of MB=3 to MB=6, using both iPAT=2 and iPAT=3, just in case the interaction of high MB factor and iPAT=2 is a special case. It's not. Top-left: SBRef images from MB=3, no iPAT, as a gold standard. Top-right: MB=3, iPAT=2, segmented ACS, Leak Block on. Bottom-left: MB=6, iPAT=2, segmented ACS, Leak Block on. Bottom-right: MB=6, iPAT=3, segmented ACS, Leak Block on. Only MB=3, iPAT=2 is artifact-free. Accelerating to higher rates of MB x iPAT isn't advisable.




      4.  I was able to produce similar artifacts using the MGH sequence, Blipped CAIPI. It uses Split Slice GRAPPA reconstruction by default when iPAT acceleration is enabled.

      As for MB-EPI, iPAT alone was artifact-free, and only the interaction of iPAT and Split Slice GRAPPA produces artifacts. Here are the results for SMS = 6:


      Top-left is MB-EPI with iPAT = 2, Leak Block off. Top-right is MB-EPI with iPAT = 2 and with Leak Block enabled. Bottom-left is Blipped CAIPI, no iPAT. Note the absence of artifacts. Bottom-right is Blipped CAIPI with iPAT = 2, and now Split Slice GRAPPA recon is being used we see artifacts that are somewhat similar (but clearly not identical) to those with MB-EPI, iPAT = 2 and Leak Block enabled. I wouldn't expect identical performance because there are a number of other implementation differences between MB-EPI and Blipped CAIPI. The important point to recognize is that the use of Split Slice GRAPPA (or Leak Block, if you prefer) instead of Slice GRAPPA reconstruction has fundamental consequences regardless of the particular implementation. The artifacts produced by the interaction of SMS, in-plane GRAPPA and Split Slice GRAPPA reconstruction are a feature, not a bug, in accord with the comments in Cauley et al. (2014).

      5.  Okay, fine. I'll start. Control Of Awful Separation To Eradicate Replicas. COASTER.


      References


      Cauley SF, Polimeni JR, Bhat H, Wald LL, Setsompop K.
      Interslice leakage artifact reduction technique for simultaneous multislice acquisitions.
      Magn Reson Med.72(1):93-102 (2014).
      doi: 10.1002/mrm.24898

      Todd N, Moeller S, Auerbach EJ, Yacoub E, Flandin G, Weiskopf N.
      Evaluation of 2D multiband EPI imaging for high-resolution, whole-brain, task-based fMRI studies at 3T: Sensitivity and slice leakage artifacts.
      Neuroimage. 124(Pt A):32-42 (2016)
      doi: 10.1016/j.neuroimage.2015.08.056


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      This will be brief, a simple demonstration of the sort of features visible in a "Power plot" of an EPI time series. The goal is to emphasize that chest motion produces apparent head motion effects in typical analyses. Here the subject's head was held very firmly in the 32ch coil of my Siemens Trio using a custom printed head case. See the posts from October last year for more details. In this test the subject inhaled to near maximum and exhaled immediately, repeating the procedure every 30 seconds or so in a self-paced manner. The subject breathed normally otherwise. Critically, note that no breaths were held.


      What we see are two striking features. First, there is banding with a period of approx 30 seconds, and the bright bands correspond with apparent head movement reported as framewise displacement (FD) in the top red trace. (TR is 1700 ms.) Some of this may be real head movement, but a lot arises from chest displacements modulating the magnetic field. This is the feature I want to emphasize. We need to be aware that not all sources of frame-to-frame variation reported by a volume registration (aka motion correction) algorithm are necessarily actual head motion. Last October I showed in a series of simple demonstrations how chest motion produces shearing and translations of EPI signals in a manner consistent with perturbation of magnetic field, rather than head motion per se. It's important for you to distinguish these two phenomena because the volume registration algorithm cannot differentiate them. It does its best to match volumes no matter the source of differences.

      The second feature in the plots above I'm not going to get deep into here. It's for another day. But it's pretty hard to miss the dark bands that follow tens of seconds after each bright band. Notice that the dark bands don't tend to coincide with increased FD. That is, the origin of the dark bands isn't actual or apparent head motion but something else. They come from changes in BOLD signal as the arterial CO2 changes. This is the part of the "physiologic noise" that people try to model with things like RETROICOR and RVT, or from end-tidal CO2 measurements. Here, the perturbation in BOLD signal is driven by the strange breathing task, but it's not motion or motion-like. It's real physiology in the brain.

      That's all for now! More posts on this stuff in the coming weeks.




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      As I mentioned yesterday, there is a tendency when reviewing the output of a volume registration ("motion correction") algorithm to attribute all variations to real head motion. But, as was demonstrated last October, the magnetic susceptibility of the chest during breathing produces shifts in the magnetic field that vary spatially across the head, producing translations and shearing in EPI data that the volume registration algorithm can't distinguish from real head motion. Here I want to quickly review other major mechanisms by which we can get apparent head motion.

      Let's start with contributions to real head motion. These include slow compression of foam designed to restrain the head, relaxation or tension of neck muscles, swallowing, fidgeting and the like. Printed head cases, bite bars and other restraint systems are of use here. Then there are body motions, including the extremities, that produce movement of the head via the neck. This is why you should instruct your subjects not to move at all during the scan. Telling a subject he shouldn't move his head is tantamount to saying that moving his feet is okay, and it's not. Subjects should move, e.g. to scratch or stretch, only when the scanner is silent.

      Also included in the mechanical motion category is respiratory chest motion that couples unavoidably to the head because of that pesky neck thing. Pulsations of the brain with the cardiac cycle are another source of unavoidable direct motion in the organ of interest. The latter is real brain motion, of course.

      Next, body motions (including from respiration) can produce head movement in the magnetic field via instability of the patient bed. Back in the early 2000s we had a Varian 4 T scanner. We had to construct rollers to catch and support the bed sled in the magnet bore because we had a cantilevered bed that deflected like a springboard otherwise. Every tiny movement of the subject caused the bed sled to bounce. For stability we want a strongly coupled system - subject to bed, bed to gradients/magnet - and we need to avoid any relative movement between them. I was reminded of this mechanism again recently. It's something to keep in mind as we work on respiratory instabilities because I note that my Trio has a bed cantilevered on the magnet face whereas Prisma scanners have a bed supported on the floor in front of the magnet. The latter should be a lot more stable, provided the bed has a solid foundation underneath it.

      So far all the mechanisms I've considered have had a direct mechanical connection between the source of the motion and the brain. Chest motion can also affect the magnetic field via changing magnetic susceptibility from the air-filled lungs, as previously demonstrated. This is a through-space mechanism. In principle, movement of the extremities or any other part of the body (or other equipment in the bore) might also produce perturbation of the magnetic field across the head via magnetic susceptibility, but my intuition is that this would be a minor contributor to overall instability compared to the effects from the chest.

      A well-known motion-like effect arises from thermal drift in the magnet. The gradients get warm with use and over time this causes drift in the magnetic field, e.g. via passive shimming iron that doesn't have the water cooling of the gradient set. Re-shimming can offset some of the effects of this mechanism between runs, but not within a run. When viewed from the perspective of your agnostic volume realignment algorithm, thermal drifts appear a lot like slow (real) head movements, e.g. as foam compresses or neck muscles relax. Re-shimming between runs helps with both, but I'm afraid it doesn't do anything within a run. De-trending is usually used to good effect here.

      There are doubtless other sources of instability that can manifest as apparent head motion - anything that causes shifts in the on-resonance frequency during an EPI time series will do it - but here I've covered the main mechanisms of concern. Given robust head restraint to mitigate most of the direct head motion mechanisms (except brain pulsations), it seems that the next largest instabilities to tackle are the respiratory motion mechanisms. We have three to work on: residual direct motion through the neck, magnetic susceptibility of the chest, and the possible deflection of the patient bed.



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      In my last post I summarized the main routes by which different forms of actual or apparent motion can influence fMRI data. In the next few posts, I want to dig a little deeper into non-neural causes of variation in fMRI data. I am particularly interested in capturing information on the state of the subject at the time of the fMRI experiment. What else can be measured, and why might we consider measuring it? Brains don't float in free space. They have these clever life support systems called bodies. While most neuroimagers reluctantly accept that these body things are useful for providing glucose and oxygen to the brain via the blood, bodies can also produce misleading signatures in fMRI data. My objective in this series of posts is to investigate the main mechanisms giving rise to fluctuations and biases in fMRI data, then consider ways other independent measurements might inform the fMRI results.

      Many causes, much complexity

      There are three broad categories of fluctuations or biases imprinted in the fMRI data. I've tried to depict them in Figure 1. At top-right, in a cartoon red blood vessel, is the cascade of physiological events leading to BOLD contrast. Next, on the left, there are perturbations arising from the subject's body. Some of these are direct effects, like head motion, and some are propagated via modulation of the same physiological parameters that give rise to BOLD. Breathing is a good example of the latter. A change in breathing depth or frequency can change the arterial concentration of CO2, leading to non-neural BOLD changes. Furthermore, the breathing rate is intricately tied to the heart rate, via the vagus nerve, and so we can also expect altered brain pulsation. In the final category, depicted in my figure as scanner-based mechanisms at the bottom, we have experimental imperfections. In the last group are things that could be reduced or eliminated in principle, such as thermal drift in the gradients, wobbly patient beds, and resonance frequency shifts across the head arising from changing magnetic susceptibility of the chest during breathing. The thin blue lines connecting the different parts of the figure are supposed to show the main influences, with arrowheads to illustrate the directionality.

      (Click image to enlarge.)

      Figure 1. Major routes of modulation in time series data in an fMRI experiment. The flow chart in the depiction of a blood vessel, in red, is based on a figure from Krainik et al. 2013 and shows the main events leading to BOLD via neurovascular coupling. Main body-based mechanisms originate on the left, and scanner-based experimental imperfections are depicted on the bottom. All mechanisms ultimately feed into the fMRI data, depicted at center. Yellow boxes contain some of the main modulators of mechanisms that can produce either fluctuations or systematic biases in fMRI data.

      Abbreviations: ANS - autonomic nervous system, HR - heart rate, CBVa - arterial cerebral blood volume, CBVv - venous cerebral blood volume, CMRO2 - cerebral metabolic rate of oxygen utilization, CBF - cerebral blood flow, OEF - oxygen extraction fraction, deoxyHb - deoxyhemoglobin, AR - autoregulation, pO2 - partial pressure of oxygen (O2 tension), pCO2 - partial pressure of carbon dioxide (CO2 tension).


      As if that wasn't already a lot of complexity, I'm afraid there's more. In the yellow boxes of Figure 1 are some of the main modulators of the underlying mechanisms responsible for perturbing fMRI data. These modulators are usually considered to be confounds to the main experimental objective. I posted a list of them a few years ago. Caffeine is probably the best known. It modulates both the arterial cerebral blood volume (CBVa) as well as the heart rate (HR). We already saw that HR and breathing are coupled, so this produces a third possible mechanism for caffeine to affect fMRI data. There's also an obvious missing mechanism: its neural effects. Some direct neural modulators are summarized in Figure 2, placed in their own figure simply to make this a tractable project. I'll be going back to reconsider any direct neural effects at the end of the series, to make sure I've not skipped anything useful, but my main emphasis is the contents of Figure 1.

      Figure 2. Potential modulators of neural activity during an fMRI experiment.



      Measuring the modulators

      There are about a dozen mechanisms leading to fluctuations in fMRI data. Note that some paths depicted in Figure 1 may contain multiple discrete mechanisms. The figure would be far too cluttered if every mechanism was depicted. Take head motion. It could be foam compressing through no fault of the subject, or it could be the subject fidgeting, or apparent head motion arising from the sensitivity of the EPI acquisition to off-resonance effects (for which there are at least two main contributions: thermal drift in the scanner and chest motion in the subject). I tried to estimate how many combinations are represented in Figure 1 but quickly gave up. It's several dozen. I'm not sure that knowing the number helps us. Clearly, it's an omelette.

      So, what can we do about it? Well, there are only so many things one can measure before, during or after an MRI scan, so we should probably start there. In the first set of posts in this series I'll look at non-MRI measures that can be performed during fMRI data acquisition, to track moment to moment changes in some of the parameters of Figure 1. These will include:
      • Heart rate
      • Blood pressure
      • Vascular low frequency oscillations in the periphery
      • Respiration rate
      • Expired CO2
      • Electrodermal activity
      • Eye tracking
      • Head motion

      Then, in the next set of posts I'll shift to assessing ancillary MRI measurements that can inform an fMRI experiment, such as:
      • Anatomical scans
      • Baseline CBF
      • Blood oxygenation
      • Cerebrovascular reactivity
      • Calibrated fMRI (which is actually a slightly different way of doing the fMRI experiment, but requires some ancillary steps)

      Finally, I'll consider informative, non-MRI data you could capture from questionnaires or relatively simple non-invasive testing. With better understanding, I am hoping that more researchers begin to consider physiology as earnestly as they do the domains involving psychology and statistics.

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      It's 2027 and you are preparing to run a new fMRI experiment. Since 2023 you've been working on a custom 7 T scanner that was developed to mitigate several issues which plagued the early decades of fMRI. Long gone are the thermal shim and gradient drifts of yesteryear, courtesy of an intelligent water cooling system that maintains all hardware at near constant temperature even when the scanner is run flat out. Your scanner also has a custom gradient set with active shielding over the subject's chest. It means the rise time of the gradients is limited only by peripheral nerve stimulation in the subject's face and scalp, not by the possibility of causing fibrillation in the heart. You can use a slew rate four times faster than on the scanner you had back in 2017, meaning distortions of your 1 mm cubic voxels, acquired over the entire brain (including cerebellum!) are minuscule. What's more, your images no longer suffer from translations and shearing because of the subject's chest motion. Your scanner tracks the magnetic field across the subject's head and actively compensates for the effects of breathing. When used with the comfortable head restraint system that mates directly to the receiver electronics - which itself monitors changes in coil loading to ensure the 128-channel array coil doesn't impart its own bias field onto your images - you have finally got to the point in your career where you no longer worry about head motion.

      Almost. There's no doubt the hardware of the future could be remarkable compared to today's scanners. Our current scanners are clinical products being used for science rather than scientific instruments per se. However, even if we were to supersede BOLD with a non-vascular "neural current" contrast mechanism, the basic physics of MRI suggests that we will have to consider real brain motion in the future, just as we do today. Perhaps we can differentiate this brain motion from the contrast of interest using multiple echoes or some other trick, but I don't envisage being able to ignore the brain's vasculature entirely, whereas I am optimistic that improved scanner engineering might one day ameliorate the mechanical and thermal instabilities. Real brain motion and regional variation in pulsatility are likely to be biological limits that must be accommodated rather than eliminated.


      What are the mechanisms of concern?

      We can restrain the subject's skull quite well using a bite bar or a printed case. Inside the skull, however, is a gelatinous blob of brain, highly vascularized, under a small positive pressure (the intracranial pressure, ICP). The brain will tend to throb with the heart rate (HR) as blood is pumped into the brain through the arteries. The arterial network is spatially heterogeneous and so we see heterogeneous motion across the brain. The arteries enter at the base of the brain, causing the entire midbrain and brainstem to move relative to the cortex. Locally, tissue close to large vessels can demonstrate greater displacements than tissue just a a few millimeters away. These regional perturbations will arise with a range of delays relative to the cardiac output, as the blood pressure wave migrates from the heart. The greater the distance from the heart, the longer the lag. We'll see in a later post how this phenomenon can be used to estimate blood pressure.

      There are also cardiac driven pulsations in the cerebrospinal fluid (CSF). These can be visualized as small displacements of tissue adjacent to the ventricular system as well as in sulci of the cortex. Pulsation in CSF and the changing velocity of blood in large vessels also tend to produce image contrast changes. This isn't real brain motion, of course, but it is a consideration if one is attempting to use local signal properties or overall image contrast to ameliorate regional pulsatility. A new paper by Viessmann et al. provides a timely investigation of the issues, concluding that fluctuations in partial volumes of blood and CSF/interstitial fluid give rise to local T2* changes over the cardiac cycle. So the final complexity is again temporal. The cardiac cycle is itself non-stationary, leading to dynamic changes in the locations of blood, CSF and brain tissue.



      How is heart rate measured inside the scanner?

      Outside the MRI environment it is possible to obtain detailed information on cardiac function through the electrocardiogram (ECG). Inside the MRI, the typical ECG response shape is significantly altered by magnetohydrodynamic and cardioballistic (or Hall) effects. (These effects also cause a significant source of artifacts in EEG recorded inside an MRI.) ECG also requires electrodes be placed on the chest, adding extra setup complications and privacy issues. Breasts and body fat can make it very difficult to get a good ECG signal from many subjects.

      A convenient in-scanner measure of HR can be obtained from a photoplethysmograph, more commonly referred to as a pulse oximeter, albeit with less precision than when using the ECG outside the MRI. The signal processing used in pulse oximetry varies with the manufacture. Typically, the signal is filtered to optimize the instantaneous HR plus the (arterial) oxygenated hemoglobin fraction of total hemoglobin, termed SpO2. A detailed comparison found that pulse oximetry correlated strongly with heart rate variability (HRV) measured using ECG and we may thus consider oximetry an acceptable option for HR monitoring during fMRI.

      While simple to use, pulse oximetry does have limitations. Motion of the sensor can be a problem, leading to erratic traces and the possibility that the signal is lost entirely. The sensor should be secured properly and subjects instructed on what not to move in order to maintain good data. Most often a finger is used for oximetry but if a study requires bimanual responses then a toe might be considered. Note, however, that certain subjects with low peripheral circulation can be hard to record from. The scanner suite temperature can be a major factor; consider subject comfort and warmth.


      When might heart rate vary?

      Numerous factors can alter HR. Recent exercise is an obvious one. Even at rest, however, the instantaneous HR is a consequence of complex interactions between the sympathetic and parasympathetic nervous systems, together referred to as the autonomic nervous system. For example, a degree of arrhythmia (usually referred to as the respiratory sinus arrhythmia, RSA) is a natural fluctuation of HR produced during the normal breathing cycle. Instantaneous HR increases slightly during inspiration and decreases slightly during expiration. The RSA produces modulation of around 0.15-0.4 Hz on top of typical resting heart rates in the range 0.5-1.5 Hz. A slower modulation, typically 0.04-0.15 Hz, may also occur. The precise mechanisms generating the slow modulation are less well understood but are generally considered to involve changes in arterial tone, and/or cerebral autoregulation to maintain a constant cerebral blood flow in spite of changes in overall blood pressure. I will be doing a separate post on these vascular low frequency oscillations; they can be detected in the periphery with suitable modifications to the pulse oximetry.

      Cardiac output is also affected by arousal. It is is common to find components of HRV used as a measure of autonomic activity, e.g. anxiety, in psychology experiments, but given the broad spectrum of potential influences on HRV it is obviously important to apply rigorous control to extraneous factors, including exercise, pharmaceutical and other compounds including caffeine and alcohol, and some disease states, particularly diseases affecting the vasculature.


      Using heart rate data

      The HR is generally rapid compared to a typical fMRI sampling rate of 0.5 Hz for TR = 2000 ms. In this case the effects of HR will be aliased, making it easy to mistake cardiac for low frequency neurovascular fluctuations, or a task effect. For task-based fMRI, a convenient tactic is to ensure experimental power is placed well away from aliased cardiac (and respiratory) frequencies. This is usually assumed to be sufficient, especially if physiological noise regressors are used in the analysis. The situation is more complicated in resting-state fMRI because there is no external driving function against which to evaluate brain activity.

      The issue of HRV as a potential confound in resting-state fMRI data was first addressed by Shmueli et al. in 2007 using cross-correlation of the cardiac waveform with the fMRI data, allowing for different delays between the two data sets. The generality of HRV analysis was extended by Chang, Cunningham & Glover (2009) using the concept of a canonical cardiac response function (CRF) convolved with the HR time series data, by analogy with the hemodynamic response function (HRF) used in event-related fMRI analyses. Using this analysis method, Chang and colleagues were able to delineate transient changes of autonomic nervous system states manifest in brain network connectivity during rest, suggesting that the specificity of their method is sufficient to discriminate confounding signal changes when arousal may not be under experimental control. More recent work by Falahpour et al. explored the utility of subject-specific CRF, while very recently de la Cruz et al. suggested that it might be better to separate subjects into groups based on HR, and use a separate mean group CRF for low (48-68 bpm) and high (68-96 bpm) heart rates. The differing explanatory power seems to be related to slight differences in the dynamics of HR variation, raising the possibility that subjects with higher HR may be more highly aroused, perhaps because of greater scanner anxiety. Alternatively, the HR grouping might be a consequence of underlying cardiovascular health, or differing starting conditions such as recent exercise or caffeine use. These findings are a timely warning that even when attempting to remove HR effects from resting fMRI data, group-wise differences in HR could lead to significant residual effects dependent on mean HR.

      There are now several comprehensive reviews that consider cardiac "nuisance signals" and "data cleaning" or "de-noising," especially for resting-state fMRI where physiological confounds are of great concern. If you are interested in applying such methods then I strongly urge you to read all the reviews and then the primary references before doing anything. I suggest you start with the excellent review by Caballero-Gaudes & Reynolds. If you're not already convinced of the complications, this paper should get you over that particular hump. As yet, there is no consensus on a best approach to dealing with nuisance signals. One reason for this is prosaic: different studies investigating physiological signals have different independent data to utilize. Some consider HR alone, some consider HR plus one or more further independent traces, such as chest motion and expired CO2. What you do depends on what you've got available to you. Then there is the vast parameter space to consider, with TR, voxel size, echo train length and many other parameters likely to contribute to the relative efficacy of one "nuisance signal" reduction method over another for a particular application. And finally there are statistical implications, as Caballero-Gaudes & Reynolds highlight:
      "...similar to other approaches based on nuisance regression, adding more physiological noise regressors does not necessarily lead to improvement in BOLD sensitivity and higher statistical significance due to the loss in degrees of freedoms and possible correlations of the physiological regressors with the BOLD fluctuations generated by the experimental paradigm in task-based fMRI or the intrinsic neuronal fluctuations in the resting state. Hence, the optimal set of regressors will depend on the sequence and parameters of acquisition, as well as the regions of interest."


      Final thoughts

      Until someone proves otherwise, I advocate acquiring a separate pulse oximetry signal for all resting state fMRI scans, and it is likely prudent for all task fMRI experiments. Even if you can address your own questions without resorting to physiological data, having independent physiological measures available may make subsequent use of data by others more powerful.


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      If you conduct fMRI experiments then you'll have at least a basic understanding of the cascade of events that we term neurovascular coupling. When the neuronal firing rate increases in a patch of brain tissue, there is a transient, local increase of the cerebral blood flow (CBF). The oxygen utilization remains about the same, however. This produces a mismatch in the rate of oxygen delivered compared to the rate of oxygen consumption. The CBF goes up a lot while the oxygenation usage increases only slightly. Hence, there is a decrease in the concentration of deoxygenated hemoglobin in the veins draining the neural tissue region, in turn reducing the degree of paramagnetism of these veins that yields a signal increase in a T2*-weighted image. The essential point is that it's blood delivery - changes in CBF - that provides the main impetus for BOLD contrast.


      How is blood pressure related to CBF?

      The average CBF in a normal adult brain is typically maintained at around 50 ml of blood per 100 g of brain tissue per minute (50 ml/100g/min). The average number, while useful, represents considerable spatial and temporal heterogeneity across the brain. The typical CBF in gray matter is approximately double that in white matter, and there is significant variation across each tissue type arising from tight metabolic coupling. (See Note 1.)

      At the local level, blood delivery to tissue is controlled by smooth muscles on the walls of arterioles and capillaries. The degree of vessel dilation, relative to that vessel's maximum possible dilation, is called its tone - the vascular tone. There are mechanisms to expand or constrict the smooth muscles, changing the local blood flow in order to maintain the tight local coupling of CBF to metabolic demand while protecting the vasculature and the tissue against damage that might arise with systemic changes in the blood supply from non-neural mechanisms. The totality of these processes is referred to as cerebral autoregulation. More on the non-neural factors later.

      This is all very well, but there is something important missing from this picture. We have neglected to consider so far that the force of blood pumped out of the heart creates a pressure gradient across the arteries and the veins, with the tissue providing a resistance in between. It's this pressure gradient that causes the blood to flow. In fact, simple electrical circuits are a convenient model here. For those of you more familiar with electron flow than blood flow, we can think of the CBF as an analog of electrical current, the pressure difference as a voltage and, naturally enough, the tissue's resistance to flow mimics an electrical resistance. Thus we get:

      CBF = CPP / CVR

      where CPP is the cerebral perfusion pressure, the net pressure gradient - the driving force - that generates perfusion of brain tissue, and CVR is the cerebrovascular resistance. The CVR is the sum total of all mechanisms exerting control over the vascular tone at a particular location. It isn't easily estimated without detailed knowledge of the processes that might be active. The neurovascular coupling pathways contribute to CVR, for example.

      The CPP is more easily estimated. It is defined as the difference between the mean arterial blood pressure (MABP) and either the venous or intracranial pressure (ICP), whichever is lower. A typical ICP is 7-12 mmHg above the ambient (local atmospheric) pressure that is assumed to be 0 mmHg. In other words, the brain is under a small positive pressure that pushes against the skull. We also see that because MABP is quite a lot higher than ICP, there is a very close relationship between CBF, which is our parameter of relevance to BOLD contrast, and the MABP. Under normotensive conditions we can use MABP as a rough estimate of CPP, to give:

      CBF ~ MABP/CVR

      We have essentially averaged over the cardiac cycle here, and reduced the control of the CBF to one global (systemic) parameter - the MABP - that is regulated locally via CVR. So now we need a convenient way to assess the MABP.

      Before we do that, though, let's first get familiar with the blood pressure nomenclature you've almost certainly encountered when you go to see your doctor. "One fifteen over seventy five" is not, in fact, a cricket score. Here are some quotes from the Wikipedia entry on blood pressure:
      "Blood pressure (BP) is the pressure of circulating blood on the walls of blood vessels. When used without further specification, "blood pressure" usually refers to the pressure in large arteries of the systemic circulation. Blood pressure is usually expressed in terms of the systolic pressure (maximum during one heart beat) over diastolic pressure (minimum in between two heart beats) and is measured in millimeters of mercury (mmHg), above the surrounding atmospheric pressure (considered to be zero for convenience).
      For each heartbeat, blood pressure varies between systolic and diastolic pressures. Systolic pressure is peak pressure in the arteries, which occurs near the end of the cardiac cycle when the (cardiac) ventricles are contracting. Diastolic pressure is minimum pressure in the arteries, which occurs near the beginning of the cardiac cycle when the ventricles are filled (filling would be better) with blood. An example of normal measured values for a resting, healthy adult human is 120 mmHg systolic and 80 mmHg diastolic (written as 120/80 mmHg, and spoken as "one-twenty over eighty")."

      With an understanding of the maximum (systolic) and minimum (diastolic) BP values in our kitbag we can go back to the idea of a mean arterial BP. The MABP is the temporal average blood pressure over the cardiac cycle. For normal resting heart rate, a convenient estimate of MABP can be derived from the systolic (SP) and diastolic pressures (DP) as:

      MABP  ~  DP + (SP - DP)/3 .

      The MABP is considered to be the perfusion pressure experienced by organs throughout the body, including the brain. The difference, SP - DP, is termed the pulse pressure and is one of the parameters susceptible to change in certain disease conditions and aging. From Wikipedia again:
      "Pulse pressure is determined by the interaction of the stroke volume of the heart, the compliance (ability to expand) of the arterial system—largely attributable to the aorta and large elastic arteries—and the resistance to flow in the arterial tree. By expanding under pressure, the aorta absorbs some of the force of the blood surge from the heart during a heartbeat. In this way, the pulse pressure is reduced from what it would be if the aorta were not compliant. The loss of arterial compliance that occurs with aging explains the elevated pulse pressures found in elderly patients."


      What's the concern for fMRI?

      Reconsider the relation CBF ~ MABP/CVR. Brain tissues have regional control over CVR to ensure the CBF satisfies local metabolic demands on the one hand, while on the other hand autoregulatory processes assure no local over pressure that might lead to hemorrhage. Small moment to moment changes in MABP can be accommodated by the autoregulatory processes to ensure the CBF is maintained at the rate required by local energy considerations. But how much variation in MABP can the autoregulatory compensating mechanisms handle? And what happens if MABP is abnormally high or low for a prolonged period of time, as might be the case with some disease states? Any systematic differences in MABP between groups, between conditions or over time might drive alterations in CBF, and consequently BOLD, that are interpreted as having a neural basis when they are actually caused by systemic blood pressure effects. Thus, we can recast our question of concern as: "When does MABP vary, and by how much?" This should give us a good starting point for assessing the potential contribution of BP variation to an fMRI study.

      Let's take a look at "normal autoregulation."From Wikipedia again:
      "Under normal circumstances a MAP between 60 to 160 mmHg and ICP about 10 mmHg (CPP of 50-150 mmHg) sufficient blood flow can be maintained with autoregulation.[1][2] Although the classic 'autoregulation curve' suggests that CBF is fully stable between these blood pressure values (known also as the limits of autoregulation), CBF may vary as much as 10% below and above its average within this range.[3]

      Outside of the limits of autoregulation, raising MAP raises CPP and raising ICP lowers it (this is one reason that increasing ICP in traumatic brain injury is potentially deadly). In trauma some recommend CPP not go below 70 mmHg.[4][5] Recommendations in children is at least 60 mmHg.[4]

      Within the autoregulatory range, as CPP falls there is, within seconds, vasodilatation of the cerebral resistance vessels, a fall in cerebrovascular resistance and a rise in cerebral blood volume (CBV), and therefore CBF will return to baseline value within seconds (see as ref. Aaslid, Lindegaard, Sorteberg, and Nornes 1989: http://stroke.ahajournals.org/cgi/reprint/20/1/45.pdf). These adaptations to rapid changes in blood pressure (in contrast with changes that occur over periods of hours or days) are known as dynamic cerebral autoregulation.[3]"

      Variations of as much as 10% from non-neural factors are going to compete handily with BOLD signal changes produced by neurovascular coupling. In normal, healthy volunteers we are primarily concerned about the dynamics of autoregulation in response to acute changes in MABP, and responses "within seconds" sound like the time scale for fMRI experiments.

      The potential for BP variation to confound BOLD signal changes to a stimulus was nicely demonstrated by Wang et al. They induced transient hypertension and hypotension in rats with pharmaceuticals and investigated the relationship between forepaw stimulation and BP, finding:
      "During transient hypertension, irrespective of forepaw stimulation, BP increases (i.e., >10 mm Hg) produced a transient increase in the blood oxygen level-dependent (BOLD) intensity resulting in a significant numbers of voxels correlating to the BP time courses (P < 0.05), and the number of these voxels increased as BP increased, becoming substantial at BP > 30 mm Hg. The activation patterns with BP increases and stimulation overlapped spatially resulting in an enhanced cerebral activation to simultaneous forepaw stimulation (P < 0.05). BP decreases (>10 mm Hg) produced corresponding decreases in BOLD intensity, causing significant numbers of voxels correlating to the BP decreases (P < 0.005), and these numbers increased as BP decreased (P < 0.001)."

      A study by Lui et al. in cocaine-dependent human subjects found that dobutamine infusion raised MABP but produced only localized BOLD signal changes in anterior cingulate that correlated with the BP rise. However, their study didn't employ a task, leaving open the possibility of interactions between tasks and BP changes.

      What about humans not on drugs? Lots of perfectly normal, everyday things affect our BP. There are circadian changes, with greater BP in the morning and evening and lowest BP during sleep. Blood pressure and CBF change during and immediately after exercise, as demonstrated by Macintosh et al. and Smith et al. 

      Abnormal BP is also of concern for patients with likely impairment of cerebral autoregulation, including traumatic brain injury (TBI), hypertension, hypotension (including major blood loss, perhaps including very recent blood donation) and neurodegenerative conditions. For example, Alzheimer’s patients studied with transcranial Doppler ultrasound exhibited a low frequency variability in BP suggestive of impaired homeostasis.

      The broad range of situations in which BP may change, and the strong relationship between MABP and CBF, suggests that caution is warranted. It is conceivable that variations in BP could be as consequential as respiration rate (more specifically, arterial CO2 concentration) or caffeine consumption in causing BOLD signal instability across groups of otherwise similar people, or across time for individuals. And, of course, BP fluctuations could be especially important in tasks where changes in BP might be strongly correlated with certain classes of stimuli.


      Using BP data in fMRI experiments

      Taking a blood pressure measurement before or after a scan may be informative but it's also insufficient. Baseline BP (before MRI) was found by Lu, Yezhuvath & Xiao to offer only a small normalizing effect on visual-evoked BOLD signals when tested across two conditions, whereas other physiological parameters had considerably more explanatory power.

      Gianaros et al. observed a correlation between MABP and BOLD activity in several brain regions of participants conducting a stressful Stroop task, with BP measured once for each block of sixteen 90-second task blocks. In a later study with BP measured in the scanner once a minute, the same task produced a correlation between stressor-evoked MABP reactivity and amygdala activation. 

      If one has a time series of BP data then one can consider "de-noising" methods, such as regressing BP from the signal. Murphy et al. used partially inflated pressure cuffs (more on this below) to record BP once per heart beat. The BP explained 3-14% of the variance in global BOLD signal, which is about the same as is generally explained by more common physiological recordings. For example, Golestani et al. recently showed that cardiac and respiratory variability measures plus expired CO2 accounted for 5-24% of BOLD signal variability, depending on the subject. What's most notable in Murphy's BP study is the optimal lag, which was less than the repetition rate (TR) of 3 seconds. They suggest that the influence of BP on BOLD signal should be near instantaneous if it reflects CBF fluctuations subject to cerebral autoregulation.


      How is BP measured non-invasively?

      The familiar blood pressure measurement is performed on the brachial artery of your upper arm and uses a device called a sphygmomanometer. A what? My thoughts exactly. This is the only time I'll ever use the term since I can't pronounce it. (And if you insist on using unpronounceable medical terms to sound intelligent, in return I shall insist that you refer to MRI as zeugmatography, so there.) In Note 2, below, you'll find an explanation of how BP is measured in your doctor's office using the device with the unnecessarily fancy name. We don't need to get into its details because the standard method gives a single BP value whereas for fMRI - sorry, functional zeugmatographic - experiments we want a method that can give continuous, simultaneous BP sampling of the subject inside the scanner. At a minimum we want one BP value per heart beat.

      There are commercial devices that will measure BP inside an MRI but they don't satisfy our criterion of a sample per heart beat. These devices use similar principles as the fancy word method, except that electronic circuits replace the human listening for the blood sounds in your arm. They are referred to as oscillometric BP measures. The requirement to inflate a cuff and monitor its release means that these methods take tens of seconds to get a single measurement. It is also uncomfortable and likely distracting for fMRI applications.

      It turns out to be quite difficult to perform continuous non-invasive blood pressure monitoring (NIBP) at all, let alone on a subject in an MRI scanner. Discomfort, motion sensitivity and highly accurate placement of sensors relative to arteries all contribute to a general lack of robustness for many applications. There have been several attempts, however, and we can loosely divide the approaches into three groups: volume clamps (also known as vascular unloading methods), pulse wave velocity recordings, and pulse decomposition analysis. I'll review all three in a bit of detail because it could be instructive for labs attempting custom solutions, and provide guidance on what to look for if ever you go shopping for a commercial device.

      Volume clamp:  A volume clamp, such as the commercially availablePortapres device from Finapres Medical Systems, comprises an optical source and a sensor attached to a finger, like a standard pulse oximeter except that the device also includes a small cuff which changes the pressure applied to the finger. With conventional pulse oximetry we measure the blood volume in the tissue from heartbeat to heartbeat. In the volume clamp method a small pressure is applied to the finger via the cuff, essentially clamping the arterial blood at a constant volume. Now, as the pressure wave arrives from the heart, the finger's arteries regulate their local pressure to maintain constant blood flow and avoid rupturing. Yup, local autoregulation again! A sensor recording the pressure in the cuff now reports changes in the beat to beat blood pressure of the finger, as shown in the figure below. The volume clamp is a relative measure and requires calibration to get absolute BP, but the bigger issue, apparently, is the extreme motion sensitivity.

      Fig. 4 from Peters et al. 2014: https://doi.org/10.1016/j.irbm.2014.07.002. The principle of the volume clamp method is based on a combination of standard pulse oximetry (photoplethysmography) with a pressure cuff on a finger. With the cuff uninflated the pulse oximeter reports the beat to beat blood volume (left side). With the cuff inflated the blood volume is clamped, eliminating the signal in the pulse oximeter (right side, lower trace). However, a manometer recording pressure in the cuff now reports changing blood pressure in the finger (right side, upper trace).


      Pulse wave velocity:  The second class of NIBP monitors involve measuring the time taken for the systolic pressure wave to travel from the left ventricle of the heart to other locations in the body. Every time the heart contracts, ejecting blood into the aorta, it produces a pressure wave that propagates throughout the entire arterial system. (Note that the pressure wave travels faster than the blood flow. You might think of it like a sound pressure wave being carried in the wind. Sound travels faster than the air is moving.) The time taken for the pressure wave to travel to a point is called the pulse transit time (PTT). The pulse wave velocity (PWV) may then be determined using two BP sensors placed a known (arterial) distance d apart, as PWV = d/PTT. The PTT, hence PWV, depends upon systemic blood pressure via characteristics of the vascular system: the elasticity and thickness of the arterial walls, the end-diastolic arterial diameter, and blood density. As we have already seen, as systemic BP increases there are autoregulatory processes to ensure increases in arterial diameter and compliance (the reciprocal of elasticity) to maintain constant blood flow to organs and avoid hyperperfusion. Thus, an increased systemic BP produces decreased PTT and increased PWV.

      All that sounds rather complicated. And if you look up articles on PWV you will find that it is. But we don't need to get that deeply involved in elasticity and whatnot, because a relative BP measure will suffice. We want a plot of changes in BP over time rather than absolute quantification, as would be important in a medical scenario. For our purposes, then, all we need is two sensors a different arterial distance from the heart, and to determine the difference in the arrival times of the pressure waves. For fixed sensor placement, any changes in BP will modulate the difference in arrival times for the pressure pulse and give us our time course. Consider the setup illustrated in this figure from Murphy, Birn & Bandettini:


      In their setup, one pressure cuff is placed on a bicep at the same level as the heart, while a second cuff is placed on a thigh a distance D away. Optical sensors could be used instead of pressure cuffs, but these still produce one BP estimate per heart beat. In a custom device built specifically for MRI compatibility, one optical sensor was placed directly over the aortic valve on the sternum and the second over a carotid artery. In my lab we're tinkering with PTT approaches right now, trying to determine if we can get robust signals out of the same pressure pads we use for monitoring chest motion. Early tests are encouraging, but we're not yet ready to spend money on all the parts we'd need let alone put it into routine use! As soon as there's something definitive to say, count on a blog post on it.

      Pulse decomposition analysis: The PWV methods use two sensors to measure differential arrival times of the same pressure wave. With pulse decomposition analysis (PDA) it's the reverse. The goal is to use a single sensor and measure differential timing information from two (or more) reflected pressure waves. Let's jump right in and look at the anatomical origins of these reflected waves, then we can look at the analysis and methodological limitations. Here's a schematic of the main pressure wave, labeled P1, and two reflected waves, P2 and P3, that are produced at the levels of the renal and iliac arteries, respectively:

      Figure 1 from Baruch et al. 2011. The main arterial tree is depicted on the right. It shows the initial pressure wave, #1, created by blood ejected from the heart, descending from the aortic arch. This wave also travels down the brachial artery, where it is depicted arriving at the radial artery as signal P1. The main pressure wave reflects at the juncture of the thoracic and abdominal aorta, at the level of the renal arteries, and also at the juncture of the abdominal aorta and the common iliac arteries, producing reflected signals P2 and P3 that travel back up and are eventually detected in the radial artery at times T12 and T13, respectively.

      The sensor is on a finger, at the distal end of the radial artery. The signal sensed at the finger is depicted on the left of the figure above. The sensor detects the main pressure wave, P1, after a relatively short, direct journey down the arteries of the arm. Reflected waves P2 and P3 have traveled farther: down to the level of the renal and iliac arteries, respectively, before traveling back up through the arterial tree, over into the radial artery and down to the finger, where they are detected. There are other reflected waves - reflections of reflections - but these are much weaker and we don't need to worry about them. Reflected pulse P2 arrives at time T12, typically 70-140 ms later than P1, while reflected pulse P3 arrives at time T13, 180-400 ms later.

      The amplitude and timing of the primary and reflected pulses are then fed into the PDA model. How does the model estimate BP? According to a validation study performed by the method's inventors:
      "The first reflection site is the juncture between thoracic and abdominal aorta, which is marked by a significant decrease in diameter and a significant change in elasticity. The reflection coefficient of this juncture is highly sensitive to blood pressure changes because of the pressure-dependent expansion of the diameter of the thoracic artery relative to that of the abdominal artery. The second (reflection) site arises from the juncture between abdominal aorta and the common iliac arteries. The renal site reflects the pressure pulse because the juncture of the aortic arteries there features significant changes in arterial diameter and wall elasticity."
      The specific algorithm used in PDA is proprietary, but they do tell us the key parameters in a product manual. The amplitude ratio P2/P1 is used to track beat-to-beat systolic pressure:
      "The physiological model here is that the reflection coefficient of the P2 reflection site is highly pressure dependent. The reason is due to the difference between the Young’s modulus of the thoracic aorta (the “softest” artery of the body) and the abdominal aorta. With increasing systolic pressure the thoracic aorta dilates more than abdominal aorta, resulting in an increasing diameter mismatch between the two aortic sections. Decreasing pressure has the opposite effect, as is easily demonstrated by performing the valsalva maneuver."
      Then, the differential delay T13 between the arrival of P1 and reflected signal P3 is used to track changes in pulse pressure:
      "The physiological model is that, since both pulses travel at different pressure amplitudes, they also travel at different pulse propagation velocities. As the differential pressure between them changes, so will their relative arrival time because their individual pulse propagation velocities change, causing them to accelerate or decelerate relative to each other."
      Recall that pulse pressure is defined as (SP - DP), so now we can easily compute diastolic pressure from the pulse pressure and systolic pressure.


      Continuous NIBP in the MRI scanner

      So, three broad approaches. Which one do we use for routine fMRI? This is where reality bites, I'm afraid. Gray et al. modified their Portapres volume clamp to work in their 3 T scanner. So far, however, I've not found any details on the modifications they made. Murphy et al.and Myllylä et al. both used pulse wave velocity, with pressure cuffs and optical sensors, respectively, but both are also custom setups. Finally, Whittaker et al. (ISMRM abstract #0309, 2016) recently tested the pulse decomposition analysis method used in the commercial CareTaker device, obtained through BIOPAC, Inc.

      I don't know about you, but as an MRI person I have a particular affinity for anything that echoes. The PDA approach is just so damned elegant. Even the nomenclature - T12, T13, P1, P2, etc. - sounds reassuringly familiar.  Except that we have a problem. The previous re-seller, BIOPAC, no longer offers the product. I did a bit of online sleuthing and it looks like CareTaker have gone on to bigger and better things. That is, they got FDA clearance, have given their product's packaging a cuddly facelift and are all set to sell thousands of devices for medical use. The small fMRI research market is probably not on their radar any longer. (I don't blame them. And I wish them continued good luck!) Perhaps we can still get CareTaker devices for research purposes. Other than the packaging it looks to be the same essential device as BIOPAC was re-selling. It may cost a lot more now, given FDA approval, and you may have to go through a medical equipment supply company to get one, but these are issues I've not broached yet.

      The commercial Portapres device isn't compatible with MRI. You'd need to customize it. Pulse wave velocity might be easier to do in principle, but there's no recommended routine cuff-based method yet, and getting two cuffs on a subject, one on a thigh, may not be easy to do. For PWV with optical sensors we have two obstacles. Firstly, they were a custom development in a Finnish lab. Secondly, one optical sensor needs to be placed directly over the aorta, introducing huge privacy issues even if it works really well.

      Given that commercial solutions are not yet guaranteed to work for us, I'm exploring custom approaches to PWV (strictly, PTT) as I mentioned above. We're testing pulse oximeter positions and we're testing pressure sensors. We already tried comparing pulse oximetry on a finger to a single pressure sensor on a femoral artery. The signals for both looked pretty good, except that there is no lag in the optical signal whereas the pressure signal has sufficiently long lag to render the time difference minuscule. And reversing the sensor placement isn't an option. We're now trying to devise robust configurations of two of the same types of sensor, to keep the lags consistent. In the mean time, if anyone purchases a new CareTaker device direct from the company, please let me know how much you paid for it and whether it's working well in your scanner. The Portapres device is still an option, of course, but I am concerned about motion sensitivity as well as overall sensitivity. Tasks that require the use of hands for response essentially rule out pulse oximetry, while my 17 C scanner suite can make it difficult to get good pulse oximetry from many subjects.


      Final thoughts

      There is tantalizing evidence and good theoretical reasons to think that a non-invasive blood pressure measurement would be informative and complimentary to the information available in the heart and respiration rates, and expired CO2. However, at the moment the equipment to do NIBP inside the MRI scanner needs more development and testing. I encourage those labs pursuing BP measurements to get more information out in public as soon as reasonably possible. If we can reach consensus on a methodology then we can figure out how to buy/build the solution and start on the path of routine NIBP measurement.

      Next up in this series: Low frequency oscillations. What are they, and how do they relate to BP?

      ____________________


      Acknowledgements

      Many thanks to Molly Bright and Dan Handwerker for sending me several references and helping me understand the limitations of current NIBP methods.


      Notes

      1. It is generally assumed that the CMRO2 - the metabolic rate of oxygen utilization - is very tightly coupled to local metabolic demand. For the purposes of this post I am going to assume that CBF is also tightly coupled to metabolism. Perhaps the coupling isn't quite as tight between CBF and metabolic demand as CMRO2 and metabolic demand; this is a detail we don't need to worry about here. It is quite clear from PET and other studies that we can use blood delivery as a good proxy for cellular activity, loosely defined, on a timescale of seconds to tens of minutes.

      2.  Measuring blood pressure with a cuff:

      (Extracted from the Wikipedia page on the sphygmomanometer.)
      A sphygmomanometer, also known as a blood pressure meter, blood pressure monitor, or blood pressure gauge, is a device used to measure blood pressure, composed of an inflatable cuff to collapse and then release the artery under the cuff in a controlled manner, and a mercury or mechanical manometer to measure the pressure. It is always used in conjunction with a means to determine at what pressure blood flow is just starting, and at what pressure it is unimpeded. Manual sphygmomanometers are used in conjunction with a stethoscope.

      The cuff is normally placed smoothly and snugly around an upper arm, at roughly the same vertical height as the heart while the subject is seated with the arm supported. It is important that the cuff size is correct: undersized cuffs record too high a pressure, oversized cuffs may yield too low a pressure. Usually three or four cuff sizes should be available to allow measurements in arms of different size.

      A stethoscope is generally required. Manual meters are used by trained practitioners. Listening with the stethoscope to the brachial artery at the antecubital area of the elbow, the examiner slowly releases the pressure in the cuff. As the pressure in the cuffs falls, a "whooshing" or pounding sound is heard (see Korotkoff sounds) when blood flow first starts again in the artery. The pressure at which this sound began is noted and recorded as the systolic blood pressure. The cuff pressure is further released until the sound can no longer be heard. This is recorded as the diastolic blood pressure.

      Digital meters employ oscillometric measurements and electronic calculations. They may use manual or automatic inflation, but both types are electronic, easy to operate without training, and can be used in noisy environments. They measure systolic and diastolic pressures by oscillometric detection, employing either deformable membranes that are measured using differential capacitance, or differential piezoresistance, and they include a microprocessor.

      Digital instruments use a cuff which may be placed, according to the instrument, around the upper arm, wrist, or a finger, in all cases elevated to the same height as the heart. They inflate the cuff and gradually reduce the pressure in the same way as a manual meter, and measure blood pressures by the oscillometric method. They accurately measure mean blood pressure and pulse rate, while systolic and diastolic pressures are obtained less accurately than with manual meters, and calibration is also a concern.



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    • 12/13/17--23:03: COBIDAcq?


      • WARNING: this post contains sarcasm and some swearing.
      (But only where absolutely necessary.)


      COBIDAcq, pronounced "Koby-dack," is the Committee on Best Practice in Data Acquisition. It is based on the similarly dodgy acronym, COBIDAS: Committee on Best Practice in Data Analysis and Sharing. I suppose COBPIDAAS sounds like a medical procedure and CBPDAS is unpronounceable, so COBIDAS here we are.

      Unlike COBIDAS, however, the COBIDAcq doesn't yet exist. Do we need it? The purpose of this post is to wheel out the idea and invite debate on the way we do business.


      Why a new committee?

      You know the old aphorism: "Act in haste, repent at leisure?" It's not just for US tax reform. We have a lot of errors made in haste in fMRI. You may have noticed. Some of the errors may be directly under an experimenter's local control, but many are distributed by influential people or through limitations in commercial products. Whatever the root causes, unless you think fMRI has already attained methods nirvana, there is ample reason to believe we could do a lot better than the status quo.

      The COBIDAS approach is intended to "raise the standards of practice and reporting in neuroimaging using MRI," according to its abstract. I am still seeing weak evidence there has been wholesale adoption of the COBIDAS suggestions for reporting. (Feel free to pick up your latest favorite paper and score it against the COBIDAS report.) Thus, I'm not wholly convinced practice in neuroimaging will benefit as much as intended, except to help people disentangle what was done by others and avoid their mistakes at some much later stage, perhaps. What I'm after is intervention a lot earlier in the process.


      Risks and systematic errors in an era of Big Data

      A long time ago I wrote a post about taking risks in your experiment only if you could demonstrate to yourself they were essential to your primary goals. New is rarely improved without some harmful - often unknown - consequences. Rather, what new usually gets you is new failure modes, new bugs, a need to change approach etc. So if you have a really good idea for a neuroscience experiment and you can achieve it with established methods, why insist on using new methods when they may not help but may cause massive damage? That is tantamount to placing your secondary goals - impressing a reviewer with yer fancy kit - ahead of your primary goals. Crazy!

      There is a lot of energy presently going into data sharing and statistical power. This is great n' all, but what if the vast data sets being cobbled together have systematic flaws; potentially many different systematic flaws? How would you know? There are some QA metrics that attempt to capture some of the obvious problems - head motion or gradient spiking - but do you trust that these same metrics are going to catch more subtle problems, like an inappropriate parameter setting or a buggy reconstruction pipeline?

      I'd like to propose that we redirect some of this enthusiasm towards improving our methods before massively increasing our sample size to the population of a small market town. Otherwise we are in danger of measuring faster-than-light neutrinos with a busted clock. No amount of repeat measures will tell you your clock is busted. Rather, you need a sanity check and a second clock.

      Here are some examples of common problems I see in neuroimaging:
      -  Taking a tool that worked at one field strength and for one sort of acquisition and assuming it will work just as well at a different field strength or with a different acquisition, but with little or no explicit testing under the new circumstances.

      -  New and improved hardware, sequences or code that are still in their honeymoon phase, foisted into general use before rigorous testing. Only years later do people find a coding bug, or realize that widely used parameters cause problems that can be avoided with relative ease.

      -   Following others blindly. Following others is a great idea, as I shall suggest below, but you shouldn't assume they were paying full attention or were sufficiently expert to avoid a problem unless there is documented evidence to refer to. Maybe they got lucky and skirted an issue that you will run into.

      And here's my final motivation. It's difficult enough for experienced people to determine when, and how, to use certain methods. Imagine if you were new to neuroimaging this year. Where on earth would you start? You might be easily beguiled by the shiny objects dangled in front of you. More teslas, more channels, shorter repetition times, higher spatial resolution.... If only we could use such simple measures to assess the likelihood of experimental catastrophe.


      Ways to improve acquisition performance

      I think there are three areas to focus on. Together they should identify, and permit resolution of, all but the most recalcitrant flaws in an acquisition.

      1. Is it documented?

      Without good documentation, most scientific software and devices are nigh on unusable. Good documentation educates experts as well as the inexperienced. But there's another role to consider: documenting for public consumption is one of the best ways yet devised for a developer to catch his errors. If you don't believe this to be true, you've never given a lecture or written a research paper! So, documentation should help developers catch problems very early, before they would have seen the light of day.

      While we're talking documentation, black boxes are a bad idea in science. If it's a commercial product and the vendor doesn't tell us how it works, we need to open it up and figure it out. Otherwise we're conducting leaps of faith, not science.

      2. How was it tested at the development stage?

      Understandably, when scientists release a new method they want to present a good face to the world. It's their baby after all. When passing your creation to others to use, however, you need to inject a note of realism into your judgment and recognize that there is no perfectly beautiful creation. Test it a bit, see how it falls down. Assess how badly it hurts itself or things around it when it crashes through the metaphorical coffee table. Having done these tests, add a few explanatory notes and some test data to the documentation so that others can see where there might be holes still gaping, and so they can double-check the initial tests.

      3. Has it been validated independently and thoroughly?

      Today, the standard new methods pipeline can be represented in this highly detailed flow chart:

      Developer  →  End user

      Not so much a pipeline as quantum entanglement. This is a reeeeeally bad idea. It makes the end user the beta tester, independent tester and customer all in one. Like, when you're trying to complete a form online and you get one of those shibboleth messages, and you're like "What. The. Fuck! Did nobody bother to test this fucking form with Firefox on a Mac? Whaddayamean this site works best with Internet Explorer? I don't even own a PC, morons! How about you pay a high school student to test your fucking site before releasing it on the world?"

      Okay, so when this happens I might not be quite that angry... Errr. Let's move on. After better documentation, independent validation is the single biggest area I think we need to see improvement in. And no, publishing a study with the new method does not count as validation. Generally, in science you are trying your hardest to get things to work properly, whereas during validation you are looking to see where and how things fail. There is a difference.


      What to do now?

      This is where you come in. Unless a significant fraction of end users take this stuff seriously, nothing will change. Maybe you're okay with that. If you're not okay with it, and you'd like more refined tools with which to explore the brain, let your suggestions flow. Do we need a committee? If so, should it be run through the OHBM as the COBIDAS has been? Or, can we form a coalition of the willing, a virtual committee that agrees on a basic structure and divides the work over the Internet? We have a lot of online tools at our disposal today.

      I envisage some sort of merit badges for methods that have been properly documented, tested and then validated independently. There will necessarily be some subjectivity in determining when to assign a merit badge, but we're after better methods not perfect methods.

      How might COBIDAcq work in practice? I think we would have to have some sort of formal procedure to initiate a COBIDAcq review. Also, it's harder to review a method without at least partial input from the developer, given that we might expect some of the documentation to come from them. In an ideal world, new method developers would eagerly seek COBIDAcq review, forwarding mountains of documentation and test data to expedite the next phase. Yeah, okay. Unrealistic. In the mean time, maybe we do things with as much democracy as we can muster: select for review the methods that are getting the most play in the literature.

      One criticism I can envision runs along the line of "this will stifle innovation or prevent me from taking on a new method while I wait for you bozos to test it!" Not so. I'll draw a parallel with what the folks did for registered reports. Not all analyses have to be preregistered. If you don't know a priori what you'll do, you are still free to explore your data for interesting effects. So, if you choose to adopt a method outside of the scope of COBIDAcq, good luck with it! (Please still report your methods according to the COBIDAS report.) Maybe you will inadvertently provide some of the validation that we seek, in addition to discovering something fab about brains!

      Nothing about this framework is designed to stop anyone, anywhere from doing precisely what they do now. The point of COBIDAcq is to create peer review of methods as early in their lifetime as possible, and to provide clear signaling that a method has been looked at in earnest by experts. Neuroscientists would then have another way to make decisions when selecting between methods.

      Okay, that will do. I think the gist is clear. What say you, fMRI world?



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      While the gradient set is water-cooled, the gradient cables and gradient filters still rely upon air cooling in many scanner suites, such as mine. In the case of the gradient filters, the filter box on my Siemens Trio came with an opaque cover, which we replaced with clear plastic to allow easy inspection and temperature monitoring with an infrared (IR) thermometer:

      The gradient filter box in the wall behind my Siemens Trio magnet. It's up at ceiling height, in the lowest possible stray magnetic field. The clear plastic cover is custom. The standard box is opaque white.


      Siemens now has a smoke detector inside the gradient filter box, after at least one instance of the gradient filters disintegrating with excess heat. Still, a clear inspection panel is a handy thing to have.

      The gradient cables between the filter box and the back of the magnet can also decay with use. If this happens, the load experienced by the gradient amplifier changes and this can affect gradient control fidelity. (More on this below.) The cables can be damaged by excess heat, and this damage leads to higher resistance which itself produces more heating. A classic feedback loop!


      The Fluke 561 IR thermometer and a K type thermocouple, purchased separately.


      Monitoring both the gradient filters and cables is as easy as purchasing an IR thermometer and some thermocouples. We have a Fluke model 561 IR thermometer (above), costing about $200, which has three nice features. Firstly, it can be used fairly safely inside the magnet room. The unit uses two AA batteries. There is almost no noticeable force on the unit until you take it right into the magnet bore. In well-trained hands it is perfectly safe to use around a 1.5 T or 3 T magnet. It will also perform flawlessly in the fringe field of a 3 T magnet.

      The second feature is it's main one: the IR sensor. A laser sight allows easy targeting. This permits quick surface thermometry of the cables, as shown in the photo below, and also of the gradient filters if you have a clear cover like I do. There is a surprising amount of temperature variation along the cables, I find, so having the ability to sweep along a cable can be useful.



      The third feature is something you might well skip for simplicity, but I like it. The Fluke is compatible with K type thermocouples. They plug right into the top:


      We have installed six thermocouples inside the gradient filter box, one per cable terminal. We used Kapton heat resistant tape to mount them. You can see the tape in the uppermost photo, as nearly horizontal brown bands on the white gradient cable sleeves. Monitoring is as simple as plugging in the desired plug and pulling the trigger on the meter. The Fluke then displays the temperature of the thermocouple rather than of the IR sensor.

      K-type thermocouples connected to the six cable terminals inside the filter box. The active ends, taped to the cables, can be seen in the uppermost photograph. We leave the thermocouples installed and simply tuck them out the way when they are not in use.


      Using gradient cable and filter thermometry

      At the moment I only measure gradient cable temperatures when I'm running long diffusion scans and I want to be sure that I'm not breaking anything. But it would be very easy to incorporate these measurements into routine Facility QA. I would record the starting and ending temperatures of the thermocouples I have taped into place, for consistency. And of course I'd use a constant acquisition protocol; perhaps a diffusion imaging scan to increase the gradient duty cycle and really drive the system. (Right now my Facility QA consists of three ~6 min EPI runs, so only the readout gradient axis has a reasonably high duty cycle, while the other two channels aren't used enough to provoke much departure from room temperature.)

      We had to have our gradient cables replaced once because of runaway resistances. For certain diffusion scans we could see cable temperatures over 60°C. But at the time we didn't have the thermocouples installed. We were only alerted to the possibility of a cable resistance/heating issue when our gradient control became unpredictable. We would occasionally see changing levels of distortion in EPI scans; sudden stretches or compressions unrelated to subject movement. (These aren't my data but there is a near identical example posted here.) Had we been monitoring the gradient cable temperatures weekly, we might well have seen a trend towards increasing cable temperatures before the intermittent distortion reported by users, and been in a position to alert the service engineer.

      With any luck you will notice the gradient control issues as your first symptom that something is wrong. In the extreme, however, you may find that the gradient connectors decay under the extreme heat. (See the second and third photos in this earlier post on fires in MRI facilities.) By the time your filter connectors are turning to dust you will likely be experiencing occasional spiking events in EPI. It might be externally generated noise that is being conducted into the magnet room by virtue of insufficient RF filtering, or spikes might be generated at the hot connectors. Either way, it's a very good idea to catch the problem long before it gets to this stage!

      In future, perhaps the scanner manufacturers will apply water cooling to the gradient cables, as they do already for the gradient and RF amplifiers, plus the gradient set itself, of course. The gradient filters may be at particular risk if, depending on their housing, there is limited airflow. On our unit the filters are designed to cool conductively via the colder equipment room air conditioning. The gradient cables in the magnet room rely upon the magnet room air, which in my facility is at 17°C. Even then, it is quite easy to get >40°C on the surface of the gradient cables with a 20 minute diffusion scan. It's worth looking into.


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      Low frequency oscillations (LFOs) may be one of the the most important sources of signal variance for resting-state fMRI. Consider this quote from a recent paper by Tong & Frederick:
      "we found that the effects of pLFOs [physiological LFOs] dominated many prominent ICA components, which suggests that, contrary to the popular belief that aliased cardiac and respiration signals are the main physiological noise source in BOLD fMRI, pLFOs may be the most influential physiological signals. Understanding and measuring these pLFOs are important for denoising and accurately modeling BOLD signals."

      If true, it's strange that LFOs aren't higher on many lists of problems in fMRI. They seem to be an afterthought, if thought about at all. I suspect that nomenclature may be partly responsible for much of the oversight. A lot of different processes end up in the bucket labeled "LFO." The term is used differently in different contexts, with the context most often defined by the methodology under consideration. Folks using laser Doppler flow cytometry may be referring to something quite different than fMRI folks. Or not. Which rather makes my point. In this post I shall try to sort the contents of the LFO bucket, and in at least one later post, I shall dig more deeply into "systemic LFOs." These are the LFOs having truly physiological origin; where the adjective is used according to its physiological definition:


      The description I pulled up from the Google dictionary tells us the essential nature of systemic LFOs: at least some of them are likely to involve the blood gases. And I'll give you a clue to keep you interested. It's the CO₂ component that may end up being most relevant to us.


      What exactly do we mean by low frequency oscillations anyway?

      "Low frequency" generally refers to fluctuations in fMRI signal that arise, apparently spontaneously, with a frequency of around 0.1 Hz. The precise range of frequencies isn't of critical importance for this post, but it's common to find a bandwidth of 0.05 - 0.15 Hz under discussion in the LFO literature. I'll just say ~ 0.1 Hz and move on. I added "apparently spontaneously" as a caveat because some of mechanisms aren't all that spontaneous, it turns out.

      For the purposes of this post we're talking about variations in BOLD signal intensity in a time series with a variation of ~ 0.1 Hz. There may be other brain processes that oscillate at low frequencies, such as electrical activity, but here I am specifically concerned with processes that can leave an imprint on a BOLD-contrasted time series. Thus, neurovascular coupling resulting in LFO is relevant, whereas low frequency brain electrical activity per se is not, because the associated magnetic fields (in the nanotesla range, implied from MEG) are far too small to matter.

      Is LFO the lowest modulation of interest? No. There are physiological perturbations that arise at even lower frequencies. These are often termed very low frequency oscillations (VLFOs) because, well, we scientists are an imaginative bunch. These VLFOs generally happen below about 0.05 Hz. The biological processes that fluctuate once or twice a minute may well be related to the LFOs that are the focus here, but I am going to leave them for another day.


      Categorizing LFOs:  How do they originate?

      There is a lot of terminology in use, much of it confusing. After reading a few dozen papers on various aspects of LFOs, I decided I needed to sort things out in my own way. Different fields may use similar terms but may mean slightly different things by them. Generally, the nomenclature changes with the methodology under consideration. An LFO identified with transcranial Doppler ultrasound in a rat brain may not be the same as an LFO observed with optical imaging on a patient's exposed cortical surface during surgery. Reconciling these differences with LFOs observed in fMRI may be quite misleading as a result.

      I finally decided on the four categories of LFO you find below. They are defined in an fMRI-centric way. My goal was to identify the irreducible parts, then try to figure out how different papers use varying nomenclature to discuss the specific mechanisms involved. Hopefully, this allowed me to separate processes in a useful manner from the perspective of an fMRI experiment, since much of the literature on physiological LFOs uses non-MRI methods. To help me relate the processes to fMRI specifically, I resorted to thought experiments. I will include a few in the footnotes so you can check my categorizations. Hopefully, if I have incorrectly characterized or omitted a process, it will be more apparent this way.

      1. Instrumental limitations

      These do not count as true biological LFOs according to my scheme. The most common way to produce variance around ~0.1 Hz in fMRI is through aliasing. We know that if we are acquiring a volume of EPI data every 2 seconds then we are below the Nyquist sampling frequency for normal human heart rates. Some fraction of the respiratory movements might also end up aliased into our frequency band of interest. By assuming an ideal acquisition method that acquires a volume of data not less than twice per heart beat, we begin to eliminate this source of LFO from our fMRI data. (SMS-EPI may permit sufficiently rapid sampling, depending on voxel size.) Which is why I think it is important to separate fundamentally biological processes from things that are fundamentally scanner-based. I contend that sampling rate is a scanner property, and it it is only the interaction of the biology with an imperfect scanner that produces the LFO. Improvements in scanner design and/or pulse sequences will ameliorate these effects.

      An unstable patient table that deflects with a subject's breathing is clearly an instrumental limitation. A rock-solid patient bed eliminates mechanical deflections. Perturbation of B₀ from a subject's breathing is another instrumental limitation. There are a few potential solutions in principle. For example, we could use a field tracker that prevents modulation of the magnetic field over the head from chest motion. Or, if we had a pulse sequence other than EPI, with its low bandwidth in the phase encoding dimension, we could render respiration-induced modulations vanishingly small. (See Note 1.) The important point is that as scanner hardware and sequences are improved, we can expect to make significant advances in the amelioration of these pseudo-biological LFOs.

      2. Cardiorespiratory mechanics

      I apologize for the clunky term. Cardiopulmonary mechanics was another option. Not much better, huh? In this category are processes that derive from body mechanics; that is, the mechanical processes of physiology that originate outside of the brain. The two main sources are a pumping heart and a set of lungs oxygenating the blood. We seek the biological consequences in the brain that are produced by these oscillating organs. (I can't think of other body organs driving any pulsations but I await being enlightened in the comments section.) We have blood pressure waves and respiration-induced CSF pressure changes via the foramen magnum. These processes are independent of whether we are studying a person by fMRI or using any other method. See Note 2 for some thought experiments I used to derive this category.

      The most important cardiorespiratory LFO I've seen in the literature is called the Mayer wave. The commonly accepted definition of a Mayer wave is an arterial blood pressure that isn't constant from heart beat to heart beat, but fluctuates about a resting mean. The fluctuations about the mean arterial BP occur with a frequency of ~ 0.1 Hz. Why the variation? It seems to be related to sympathetic nervous system activity. In lay terms, your "fight or flight" response isn't flat, but very slightly modulated.

      The Mayer waves act at the speed of the arterial blood pressure wave. The effect on the BOLD signal happens as fast as the pressure wave passes through the vascular tree, which we know from a previous post can be estimated with the pulse transit time. At most it takes a few hundred milliseconds for the pressure wave to reach the toes from the aorta. We can expect differences of tens of milliseconds in arrival time across the brain, faster than the typical sampling rate of an fMRI experiment.

      Can we measure it? The Mayer wave is a change in blood pressure, necessitating a good estimate of BP if we are to get a handle on it. We saw in an earlier post that measuring BP non-invasively in the scanner is non-trivial, however, so we shall have to leave isolation of Mayer waves to some future date. In the mean time, I am not unduly worried about Mayer waves as a major source of LFO because, as I shall claim below, there is likely a far more significant process afoot. 

      I don't know enough about respiration-induced pulsation of CSF to estimate the importance of this mechanism at frequencies of ~ 0.1 Hz, except to say that any effects that do exist will be greatest around the brainstem, and will likely decrease the farther one gets from the foramen magnum. As with Mayer waves, I think it's safe to assume that we should worry about other mechanisms first, unless you are doing fMRI of brainstem structures, in which case you should hit the literature and keep this process top-of-mind.

      3. Myogenic processes 

      The third candidate LFO mechanism is vasomotion. Perturbations in the vascular tone - the tension in the smooth muscle of blood vessel walls - may vary over time. Some of the non-neural signaling mechanisms contributing to vasomotion are reviewed here. The effect is myogenic, meaning "in the muscle."

      We assume that vasomotion occurs independent of the contents of the blood in the vessel. Many references also suggest that vasomotion occurs independent of nervous control. In other words, there would be some sort of local oscillatory signaling within the vessel wall that produces an idling rhythm. Additionally, however, vasomotion may be influenced by nervous system responses because the smooth muscles of the arterial walls are innervated. Indeed, this is how we get neurovascular coupling. Thus, some vasomotions might actually be responsible for the target signals in our resting state scans, as suggested very recently by Mateo et al. (See also this 1996 article from Mayhew et al.) So, for the purposes of this post, I shall consider vasomotion as a desirable property, at least for resting state fMRI, and leave the issue of any non-neural components of vasomotion for another day. As things stand, it would be nigh on impossible to separate, using current methods, the target vasomotion - that driven by neurovascular coupling - from any non-neural vasomotion that one might label as a contaminant.

      4. Blood-borne agents

      A fourth category of LFOs was suggested relatively recently. Mayer waves and vasomotion were observed long before fMRI came about. But it was the advent of resting state fMRI that seems to have precipitated the interest in this category. Blood constituents are not stationary. Instead, the concentration of blood gases - oxygen and carbon dioxide in particular - vary based on your rate and depth of breathing, your stress level, and so on. Anything traveling in the blood that either directly or indirectly mediates BOLD signal changes is therefore of concern, and is included in this category.

      The spatial-temporal propagation of LFOs through the brain, arising from blood-borne agents, is naturally at the speed of the bulk blood flow. Whereas Meyer waves propagate through the brain with the velocity of the blood pressure wave, agents carried in the blood tend to move much more slowly. We usually use a mass displacement unit for cerebral blood flow (CBF), typically milliliters of blood per fixed mass of tissue per minute.  But that's not very intuitive for this discussion. Instead, consider the average time taken for blood to transit the brain, from an internal carotid artery to a jugular vein. In normal people this journey takes 7-10 seconds. This is the timescale of relevance to LFOs produced by blood-borne agents.

      The most important vasodilative agent carried in the blood is carbon dioxide. It is so important that I am dedicating the entire next post - part II - to it. I hadn't expected to be digging into CO₂ effects until later in this series, since I had anticipated that all the main LFO effects would be vascular, with no direct overlap to respiratory effects. Live and learn. It's a timely reminder of just how complex and interwoven are these physiologic confounds.


      Summing up the categories

      Okay, to summarize, we have instrumental limitations, which could be eliminated in principle, then three categories of LFO arising out of a subject's physiology. The latter three categories can be expected to occur regardless of the particular MRI scanner you use. These physiological mechanisms arise spontaneously; there is no need to evoke them. Thus, it means they are likely ubiquitous in both resting and task fMRI experiments. 

      The pulsatile effects of cardiorespiratory mechanics don't seem to be amenable to independent measurement at the present time. We can possibly infer them from the fMRI data, but then we are forced to deal with the consequences of aliasing and any other instrumental limitations that produce signal variance derived from cardiac or lung motion, manifest via different pathways.

      We also don't seem to have a way to separate in principle any non-neural vasomotion from that which may be driven by neurovascular coupling. Multi-modal, invasive measurements in animals, such as performed by Mateo et al., may be the only way to discriminate these processes.

      That leaves blood-borne agents. Changes in oxygen tension may be important since, for a fixed metabolic rate of oxygen consumption, any process that alters the supply of oxygen in arterial blood necessarily produces a concomitant change in deoxyhemoglobin in venous blood. I am still investigating the potential importance of oxygen tension, but based on several converging lines of evidence, it appears that fluctuations in arterial CO₂ are the far bigger concern.

      Coming up in Part II: Systemic LFOs arising from changes in arterial CO₂ (we think).


      __________________________


      Notes:

      1. If you don't like my field tracker ideal, try this out instead. Imagine we have an fMRI scanner that operates at a main magnetic field of 100 microtesla (μT). A 3 ppm field shift at 3 T equates to nearly 400 Hz; a staggeringly vast frequency shift that would cause horrendous distortions and translations in EPI. But a 3 ppm shift at B₀ = 100 μT corresponds to a frequency of just over 0.01 Hz, against a typical linedwidth of ~20 Hz. The magnetic susceptibility due to chest movement vanishes at this low field. Thus, an ultralow field MRI scanner is robust against the modulation of B₀ from chest movements. The corollary? High field, whole body scanners exhibit enhanced sensitivity to chest movement. (3 ppm at 7 T is a frequency of almost 1 kHz. Ouch.)

      2. Imagine we stopped the subject's heart and chest motions and instead replaced the biological functions of heart and lungs with a machine that scrubbed CO₂ and oxygenated the blood before recirculating it through the arteries. It does this in smooth, continuous fashion, without pulsations of any kind. If the machine delivers oxygenated blood to the brain at the same effective rate as the brain needs, all should be okay and the brain should continue to behave normally. But what would happen to the cardiorespiratory mechanical effects? If the machine is ideal, if it doesn't pulse at all, and there are no moving parts to produce any sort of pressure wave through the body, we would have successfully eliminated two sources of LFO.

      An alternative way to think about LFOs arising from cardio-respiratory mechanics is to note that the pulsations are independent of the substances being manipulated. Pretending for a moment that the biology wouldn't mind, the mechanical effects across the brain would be the same if the heart was pumping olive oil instead of blood and the lungs were inspiring and expiring pure helium instead of 20% oxygen. The respiratory and cardiac mechanical processes would continue unabated, as would any LFOs produced in our fMRI data, because they arise from the pulsations inherent in the plumbing.


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      In the previous post, I laid out four broad categories of low frequency oscillation (LFO) that arise in fMRI data. The first three categories are mentioned quite often in fMRI literature, with aliasing of respiratory and cardiac pulsations being the best known of all “physiological noise” components. In this post, I am going to dig into the fourth category: blood-borne agents. Specifically, I want to review the evidence and investigate the possibility that non-stationary arterial CO₂ might be producing an LFO that is at least as important as aliased mechanical effects. At first blush, this is unsurprising. We all claim to know CO₂ is a potent vasodilator, so we can think of CO₂ in blood as a sort of changing contrast agent that perturbs the arterial diameter – producing changes in cerebral blood volume - whenever the arterial CO₂ concentration departs from steady state.

      Why would arterial CO₂ fluctuate? Why isn't it constant? Simply put, we don't breathe perfectly uniformly. If you monitor your own breathing you’ll notice all sorts of pauses and changes of pace. Much of it depends on what you’re doing or thinking about, which of course gets right to the heart of the potential for fluctuations in CO₂ to be a confound for fMRI.

      I had hoped to begin this post with a review of CO₂ transport in the blood, and from there to relay what I’ve found on the biochemical mechanism(s) underlying vasodilation caused by CO₂. But after several weeks of searching and background reading, I still don’t have sufficient understanding of the biochemistry to give you a concise overview. The CO₂ transport mechanisms are quite well understood, it seems. But how a change in one or more components of CO₂ in arterial blood produces changes in the arterial smooth muscle wall, that is a more complicated story. For the purposes of this post, then, we shall have to content ourselves with the idea that CO₂ is, indeed, a potent vasodilator. The detailed biochemistry will have to wait for a later post. For those of you who simply can’t wait, I suggest you read the review articles given in Note 1. They aren’t aimed at an fMRI audience, so unless you are a biochemist or physiologist, you may not get the sort of intuitive understanding that I have been searching for.


      First indications that arterial CO₂ might be an important source of LFO in fMRI data

      The effects of respiration on BOLD data were recognized in the mid-nineties as an important consideration for fMRI experiments. By the late nineties, several groups began to investigate the effects of intentionally held breaths on BOLD signal dynamics, using as their basis the phenomenon of arterial CO₂ as a vasodilator. Other groups (e.g. Mitra et al., 1997) observed low frequency fluctuations in BOLD data that suggested a vasomotor origin, or found fluctuations in cerebral blood flow (CBF) measured by non-MR means (e.g. Obrig et al., 2000). It wasn’t until 2004, however, that Wise et al. showed definitively how slow variations of arterial CO₂ concentration were related to, and likely driving, low frequency variations in BOLD time series data:

      “PETCO₂-related BOLD signal fluctuations showed regional differences across the grey matter, suggesting variability of the responsiveness to carbon dioxide at rest.”
      “Significant PETCO₂-correlated fluctuations in [middle cerebral artery] MCA blood velocity were observed with a lag of 6.3 +/- 1.2 s (mean +/- standard error) with respect to PETCO₂ changes.”

      The spatial-temporal dynamics observed by Wise et al. certainly fit a blood-borne agent. That is, we should expect lag variations dependent on the total arterial distance between the heart and the tissue of interest; in their case, the MCA.

      A note about nomenclature, and an important assumption. Wise et al., and many others since, used the peak partial pressure of CO₂, a measure of concentration, that is known as the “end tidal” CO₂ - PETCO₂ - in the expired breath as an estimate of the partial pressure of CO₂ in the arterial blood, the PaCO₂. This assumption is based on the lung gases and arterial blood gases being in equilibrium. Clearly, there can be regional differences in blood gases all around the body, but to a first approximation we assume that PETCO₂ reflects PaCO₂.


      How do systemic LFOs relate to BOLD signal changes in brain?

      In 2000, Obrig et al. used functional near-infrared spectroscopy (fNIRS), comprising a single light source and detector pair placed on the occipital lobe, over visual cortex, to show that an intrinsic LFO of oxyhemoglobin could be detected with or without visual stimuli. (See Note 2 for a brief introduction to NIRS principles.) The LFO was attenuated by hypercapnia when subjects breathed 5% CO₂ in air, a result that matched earlier findings by Biswal et al. in 1997. Since the largest fraction of oxyhemoglobin is arterial, the reduction of LFO intensity when inhaling CO₂ suggests a connection between LFOs and arterial CO₂ concentration. Vasodilation is expected to increase CBV towards its ceiling and reduce the capacity for fluctuations. Intriguingly, Obrig et al. also reported that LFO could be detected in signals originating from deoxyhemoglobin at a magnitude about one tenth those in oxyhemoglobin. These fluctuations apparently preceded the LFO in oxyhemoglobin by 2 seconds, although I would now interpret the deoxy- fluctuation as lagging the oxyhemoglobin by 9-10 sec instead. (Justification for reinterpretation of the Obrig result will become clear later.) The important point is that their data showed LFOs in signals from species found predominantly in arterial as well as venous compartments.

      In 2010, Tong & Frederick published the first in a series of studies investigating the spatial and temporal characteristics of LFOs in fMRI data. Functional NIRS was recorded simultaneously with resting state fMRI. The time course of NIRS obtained from the right prefrontal cortex was used as a reference signal to explore the spatial-temporal relationship between NIRS and the entire whole brain fMRI data on a voxel-wise basis. Two forms of NIRS data were used in separate analyses. Signal from oxyhemoglobin is expected to be positively correlated with fMRI signal, being dominated by changes in CBV and CBF. Signal from deoxyhemoglobin arises mostly in venous blood, and its concentration is expected to be inversely correlated with the fMRI data, assuming the standard BOLD model of activation. A NIRS time series was resampled then compared to the fMRI data using shifts of the NIRS data over a range -7.2 to +7.2 seconds, with shift increments of half the TR for the fMRI, i.e. 0.72 sec. Correlations with a positive time shift indicate that an event in the fMRI precedes detection in NIRS data, while negative shifts indicate a lag in the fMRI. Here is an example from one subject, using the oxyhemoglobin signal from NIRS, with a small red circle depicting the approximate location of the NIRS probe being used to measure the reference signal:

      Figure 4 from Tong & Frederick, 2010. (Click to enlarge.)

      Two features are immediately apparent: there are widespread spatial correlations between NIRS obtained from a single location (at the red circle) to the fMRI detected over the entire brain, and these spatial correlations change with the time lag. It would have been eminently reasonable to expect correlations only at the spatial location sampled by NIRS; perhaps 1-2 cm of cortex. Yet we see correlations throughout the brain and a changing dependence on lag. Take, for example, the bright yellow signal in the superior sagittal sinus (SSS) seen in the left panel at time 0.0 s (green box). Staying with the sagittal view of the left panels, look at what happens to the SSS signal at successively later times. The bright yellow region seems to “flow” downward, from parietal to occipital, until at time 4.32 s there is just a small yellow dot remaining at the occiput. If you have the patience, you can divine similar flow patterns between other time windows for other parts of the brain, as described in the paper:

      “From the sagittal view of the z-maps, the BOLD signal wave starts to appear at locations near the callosomarginal, frontopolar and parietooccipital arteries [-5.04 s]. As time progresses, the wave becomes widespread in the gray matter [e.g. -2.16 s], as it passes through capillary beds and then retreats towards the venous systems through several paths, including: 1) the superior cerebral vein to the superior sagittal sinus (also visible from the coronal view) [e.g. 1.44 s]; 2) the inferior sagittal sinus combining internal cerebral vein to the straight sinus; 3) through the transverse sinus (visible in the coronal view); 4) through the anterior and posterior spinal veins. The path the wave follows through the brain strongly resembles that of the cerebral vasculature.”

      That last sentence is crucial. The period -5.04 s to +4.32 s, approximately 9 seconds, compares well with the time taken for full passage of blood through the brain. A blood-borne origin is implied. You can even see deep brain correlations occurring again from +5.04 s to +7.2 s in the figure above, while the spatial distribution at +7.2 s resembles that at -4.32 s. Beyond +5.04 s we may be observing correlations of the current LFO period as sampled by NIRS, with the subsequent LFO sampled by the fMRI, since there are usually patterns in how one breathes.

      With the NIRS setup over frontal lobe, Tong, Bergethon & Frederick (2011) found that breath holds causing brief hypercapnia produced the same sorts of spatially varying optimal lags with a NIRS signal as had resting state fMRI data, supporting the assignment of a blood-borne agent as the cause. So far so good.

      The McLean group then did something inspired: they changed the position of the NIRS sensor to the periphery. This is sound logic if the LFO is systemic – literally, throughout the body – as they suspected it was. So, in their next experiment they added further NIRS sensors to their setup so that they could record from fingers and/or toes at the same time (Tong et al. 2012). This is how NIRS from a finger and toe compare:

      Figure 1 from Tong et al., 2012. (Click to enlarge.)

      There is a striking similarity in the time courses, except that the signal at the toe lags that detected at the finger. The differing hemodynamic delays in the periphery are nicely exemplified by a comparison of the lags between a finger and a toe versus between the two big toes:
      “The LFO signal reaches the [left big] toe 2.16–4 s later than the finger (time delays: Tdelay = 3.07 ± 0.81 s). For three participants, NIRS data was also collected at the right big toe; the LFOs from the two toes had maximal correlations (rmax = 0.85 ± 0.09) with small time shifts between sides (Tdelay = -0.02 ± 0.57 s).”

      The greater distance from the heart to toes than from the heart to fingers explains these results nicely. Naturally, the two big toes should exhibit comparable vascular transit times. This is exceedingly strong evidence of a systemic, blood borne perturbation of arterial blood volume.

      From comparisons between sites in the periphery using NIRS alone, Tong et al. moved to comparing NIRS recorded from a fingertip to fMRI recorded simultaneously from the brain. These results were consistent with their earlier correlations produced with NIRS on the forehead:
      “First, the voxels, which are highly correlated with NIRS data, are widely and symmetrically distributed throughout the brain, with the highest correlation appearing in the draining veins, although there is also significant correlation throughout the gray matter. This global signal confirms that a significant portion of the LFO signal in the brain is related to systemic blood circulation variations. Second, the dynamic pattern reflects the variable arrival times of the LFOs at different parts of the brain, just as it arrives at the finger and the toe with different time delays. This latter observation supports the contention that the LFO signal directly reflects bulk blood flow and confirms our previous, brain-only measurements.”

      We know that aliased cardiac and respiratory frequencies are a major problem for fMRI with slow sampling, i.e. long TR. Here, however, the reference time course is from NIRS sampled well above the Nyquist frequencies of both processes, allowing Tong et al. to make an important inference:
      “Another observation from the present results is that because the LFO used in [regressor interpolation at progressive time delays] RIPTiDe is derived by applying a bandpass filter (0.01 to 0.15 Hz) to the NIRS Δ[tHb], which has been sampled at a relatively high frequency (12.5 Hz), the heartbeat (~1 Hz) and respiratory (~0.2 Hz) signals have been fully sampled; therefore there is no aliasing of these signals into the LFO signal. Consequently, the LFOs we identified in the periphery, and those we identified in the brain with BOLD fMRI, are independent of the fluctuations from the cardiac pulsation (measured by pulse oximeter) and respiration (measured by respiration belt), which provides strong counterevidence to the contention that the non-neuronal LFO in BOLD is mainly the aliased signal from cardiac pulsation and respiration.”

      It is striking to me that some amount of LFO is systemic. Tong et al. didn’t (dare?) venture a candidate blood-borne agent in their 2012 study, although they must have had strong suspicions. But, as we shall see momentarily, by 2014 they were suggesting arterial CO₂ as a good explanation. Let’s assume it is arterial CO₂, although the implication is the same whatever the agent: there is a mechanism for producing vasodilation in the walls of peripheral arteries, just as there is in cerebral arteries. Is that surprising? It isn’t something I would have assumed to be necessarily the case, but I’m not a physiologist. The brain, muscles and dermis could all have evolved quite different sensitivity to arterial CO₂, if there were unique implications for local metabolism. That seems not to be the case. Instead, there is a generalized sensitivity to arterial CO₂ that produces vasodilation. And one consequence of this generalized response is a systemic LFO that can be detected anywhere in the body, including in the brain.


      Doing away with the extra hardware

      Recording NIRS requires custom hardware. (See Note 3.) For their next trick, Tong & Frederick managed to do away with the need for the NIRS hardware altogether. In 2014, they presented a data-driven version of their RIPTiDe method for mapping lags:
      “In this study, we applied a new data-driven method to resting state BOLD fMRI data to dynamically map blood circulation in the brain. The regressors used at each time point to track blood flow were derived from the BOLD signals themselves using a recursive procedure. Because this analytical method is based on fMRI data alone (either task or resting state), it can be performed independently from the functional analyses and therefore does not interfere with the fMRI results. Furthermore, it offers additional information about cerebral blood flow simultaneously recorded with the functional study.”

      A bandwidth 0.05 – 0.2 Hz was investigated in resting state data obtained at a TR of 400 ms (using MB-EPI) to ensure sampling of mechanical respiratory and cardiac fluctuations above the Nyquist frequency. Large blood vessels clear of brain tissue were identified in the raw data – for example, the carotid arteries or jugular veins passing through an inferior axial slice, or the superior sagittal sinus in a sagittal slice – and these vessels were used to define a seed region. The time course from a single voxel in a large vessel is designated the reference regressor: the regressor with zero lag. After voxelwise cross correlations with the reference regressor, a new time series regressor is determined. The time series selected has the highest cross correlation with the original (zero lag) regressor at a temporal offset of one TR. This “moves” the regressor through time by one TR, tracking the propagation of the fluctuations inherent in the original time series. The spatial origins of the new regressor don’t matter. The new regressor comprises the time series of all voxels that obey an appropriate threshold criterion. A second cross correlation is then performed, searching for voxels that give the highest correlation with the second regressor time series, but at a further offset of one TR (which is now two TRs away from the reference regressor). The process repeats until the number of voxels selected as the strongest cross correlation, offset by one TR, is less than some predefined number.

      The iterative procedure can be applied in reverse; that is, the temporal offset between the reference regressor and the next time series is set to be –TR. A negative lag simply means that the cross correlation will be maximized for fluctuations in the search time series that precede fluctuations in the reference time series. Thus, one may iterate forwards (positive TR lags) or backwards (negative TR lags) in time, relative to the start point. Refinement of the seed selection can also be made based on the results of a first pass through the data. One can even use the time series corresponding to the highest number of voxels obtained in a first pass as the optimal seed regressor for a second analysis; a form of signal averaging. In part b of the figure below, a blue circle indicates that the number of voxels sharing fluctuations with a single voxel seed is quite small; only 200-300 voxels. A black circle indicates the set of voxels to be used in a second, optimized analysis. There is a set of 5000 voxels that have common fluctuations in the band 0.05 – 0.2 Hz.

      Whether a single voxel seed or some optimized, averaged seed is used, once a full set of regressor waveforms has been produced recursively, the entire set is used in a GLM to produce z maps of the voxel locations for each lag. An example is shown in part c of this figure:

      Figure 2 from Tong & Frederick, 2014. (Click to enlarge.)


      Tong & Frederick tested their method in a variety of ways. The results were reassuringly robust to seed selection. This makes sense for a biological process – blood flow – that is evolving smoothly in time.

      The dynamic maps produced by the data-driven method resemble those produced in earlier work using a NIRS reference signal:
      “The LFOs are “piped” into the brain though big arteries (e.g., internal carotid artery) with no phase shift. They then follow different paths (arterioles, capillaries, etc.) as branches of the cerebral vasculature diverge. It is expected that each signal would evolve independently as it travels along its own path. The observation that some of them have evolved in a similar way, and at a similar pace, is probably due to the uniformity in the fundamental structures of the cerebral blood system, likely reflecting the self-invariant properties of fractal structures found throughout biological systems.”
      A delay map - figure below - resembles cerebral circulation, as in earlier work using a NIRS reference. (There are also two compelling videos in the Supplemental Information to the paper.)

      Figure 6 from Tong & Frederick, 2014. (Click to enlarge.)



      Converging lines of evidence for arterial CO₂ as a cause of systemic LFO

      Lag-based analyses of fMRI data provide good evidence that a blood-borne agent is inducing systemic fluctuations at a frequency of ~0.1 Hz. Rhythmic dilation and constriction of pial arterioles at 0.1 Hz has been observed propagating on the exposed cortical surface of a patient undergoing surgery (Rayshubskiy et al., 2014). This is further circumstantial evidence in support of a blood-borne agent of some kind. But mechanisms to explain the source of these LFOs remain speculative. What other evidence is there that variation in PaCO₂, specifically, produces a strong systemic LFO in fMRI data?

      Adding to the circumstantial case is the recent work by Power et al. (2017). They were motivated to investigate the empirical properties of the mean global signal in resting state fMRI data, finding variance attributable separately to head motion and hardware artifacts, as well as to the physiological consequences of respiratory patterns. In the absence of large head motion and hardware artifacts, they conclude that most of the remaining variance in the mean global signal is due to respiratory fluctuations, that is, to variations in PaCO₂.

      Having observed that common measures of head motion such as framewise displacement (FD) can reflect physiological (i.e. apparent head motion) as well as real head motion effects, Byrge & Kennedy (2017) investigated the spatial-temporal nature of artifacts following changes revealed in the FD trace. They term this the lagged BOLD structure:
      “Our general approach is to ask whether there is any common structure in the BOLD epochs immediately following all similar instances of the nuisance signal – specifically, following all framewise displacements within a particular range of values – using a construction similar to a peri-event time histogram. If there is any systematic covariance shared by BOLD epochs that follow similar displacements (within and/or across subjects), such a pattern reflects residual displacement-linked noise that should not be present in a perfect cleanup – regardless of the underlying sources of that noise.”

       “Using this method, we find a characteristic pattern of structured BOLD artifact following even extremely small framewise displacements, including those that fall well within typical standards for data inclusion. These systematic FD-linked patterns of noise persist for temporally extended epochs – on the order of 20–30s – following an initial displacement, with the magnitude of signal changes varying systematically according to the initial magnitude of displacement.”

      When the FD is large – perhaps real head motion or an apparent head motion from a deep breath– the BOLD signal attains a negative maximum amplitude some 10-14 sec after the event. But when the FD is small – shallow breaths, perhaps – the BOLD signal produces a positive maximum amplitude at a similar latency. Moreover, the biphasic nature of the BOLD responses in each case also suggests differing mechanisms for differing features. In the case of large FD, there is an initial positive maximum in the BOLD response at a latency of 2-3 sec. But for small FD, the initial response is negative. Figure 1 from their paper is reproduced below. For expediency, you can focus on part (a). The rest of the figure shows that the lagged BOLD structure is observed consistently from two different sites (the rows), and remains in the data after standard preprocessing steps aimed at removing physiological artifacts (the columns). Note the opposite phases for the largest FD (bright yellow) and smallest FD ranges (dark blue):


      Figure 1 from Byrge & Kennedy, 2017. (Click to enlarge.)

      “The lagged BOLD patterns associated with respiration are not the same as the lagged patterns associated with displacements [i.e. head motion], but their similar temporal and parametric properties are suggestive of the possibility that respiratory mechanisms may underlie some of the displacement-linked lagged structure in the BOLD signal.”

      There is another subtle result here. Compare, for example, the darkest blue trace – FD between zero and 0.05 mm – to the bright green trace – FD between 0.35 – 0.4 mm in part (a), above. Counter-intuitively, the smaller FD produce larger subsequent fluctuations in BOLD than some framewise displacements having considerably greater magnitude! So much for eliminating the head motion! If you do that, you reveal another perturbation underneath.

      There are several other intriguing results in the Byrge & Kennedy paper. For example, they assess the spatial distribution of the changes depicted in their Figure 1, finding that the structure is largely global. They also find relationships between the lagged BOLD structure and standard models of respiratory effects, especially respiratory volume per unit time (RVT), but minimal association with cardiac measures. Their conclusion is that there are large fluctuations in resting state BOLD data that can be attributed to respiratory effects, and changes in arterial CO₂ is the most plausible explanation. If you have the time, I suggest reading the paper in its entirety. It is extremely thorough and well-written.

      Right, it’s time for me to close my case for the prosecution: a contention that variation in PaCO₂ is the proximal cause of systemic LFOs. I want to move on to the consequences of systemic LFOs, however they come about.


      How do systemic LFOs affect resting functional connectivity?

      A systemic LFO at around 0.1 Hz is a serious potential confound for resting state fMRI, given the common practice of low-pass filtering fMRI data for subsequent analysis. It is widely believed that BOLD fluctuations below about 0.15 Hz represent ongoing brain activity. How much overlap might exist between sLFO and intrinsic brain activity as represented in BOLD data?

      In their first investigation into functional connectivity, Tong et al. (2013) used NIRS recorded in the periphery – fingers and toes – to assess the contribution of systemic LFOs in the band 0.01-0.15 Hz to brain networks derived from independent component analysis (ICA). They found that spatial maps of sLFO-correlated BOLD signals tended to overlap the maps of several typical resting-state networks that are often reported using ICA. A subsequent study (Tong & Frederick, 2014) using fMRI data with TR = 400 ms, to avoid aliasing of mechanical respiratory perturbations, found much the same thing. The mechanical respiratory effects could be separated from the systemic LFOs, and the sLFO dominated several prominent independent components.

      The earlier studies showed that spatial patterns of sLFO were coincident with resting-state networks commonly reported in the literature. Just how coincidental were those findings? In 2015, Tong et al. inverted the process and set out to determine whether they could establish apparent connectivity in the brain using synthetic time series data having the spatial and temporal properties of systemic LFOs. First, they produced from each subject’s resting state fMRI data a lag map of correlations between a voxel’s time course and NIRS recorded in a finger. This 3D map represents the lag with the strongest correlation between the NIRS signal and each voxel’s BOLD signal. This map was applied to a synthetic BOLD “signal,” comprising sinusoids and white noise. At each voxel, the synthetic time series was scaled by the local signal intensity, and shifted in time using the real lag value produced for the 3D lag map. The spatial-temporal properties of the final synthetic time series thus follow the basic intensity and delay structures of real fMRI data, but are otherwise entirely arbitrary. An example lag map produced using the seed-based regression method is shown below, with the NIRS-based lag map in the inset. (The seed-based and NIRS-based methods generated similar results.) In parts B and C are exemplar synthetic time courses for three voxels with different lags:


      Figure 3 from Tong et al., 2015. (Click to enlarge.)

      Note that the synthetic data comprises sinusoids band-limited to 0.01-0.2 Hz, the same frequency range as the real data. The lag used at each voxel is derived from a biological measurement, that is, from correlations between real BOLD data and NIRS in the subject’s finger (or, alternatively, from a seed-based regression). In this respect, the lags are biological information, and the lags are encoded into (applied to) the synthetic data. But the only way any neuronal relationships can end up in the synthetic data is if the lags happen to contain neurogenic information in the first place. In the case of a NIRS signal measured in the finger to determine the lag maps, we might concede an autonomic nervous system (ANS) response, perhaps. This is unlikely, however, because the temporal characteristics of the systemic LFOs imply a blood-borne agent, whereas nervous system control over vascular tone ought to be more efficient than waiting for blood to arrive. (See Note 4.) Still, let’s allow the remote possibility of a neurogenic basis for the lags and define any implications. If we eventually learn that the systemic LFOs derive from the ANS and not arterial CO₂ (or some other blood-borne agent), we will then have to consider a highly prominent, concerted ANS response obscuring whatever subtle, regional neural activity we might want to see hiding in the resting-state fMRI data.

      Returning to the 2015 paper, the next step was to run group ICA or seed-based correlation analysis, two common approaches to obtain functional connectivity estimates, and assess any false “networks” produced from the synthetic data. These results were compared directly to the same ICA method applied to real fMRI data. In the next figure are eight groups of independent components obtained from spatial correlation with a literature template for resting-state networks. ICs from real data are in the left column, the middle and right columns are ICs derived from the synthetic data created using the seed-based recursive and NIRS reference signal, respectively:


      Figure 5 from Tong et al., 2015. (Click to enlarge.)

      The good news is that the spatial correlation coefficients (see the numbers above the axial view of each IC) are lower in both sets of synthetic data than for real data. The bad news is that one can clearly recognize “networks” arising out of the synthetic data. (The red boxes highlight two instances of networks that couldn’t be isolated.)

      There are also clear similarities in the default mode network returned from a seed-based analysis of real data (left) to that from synthetic data (right):


      Figure 6 from Tong et al., 2015. (Click to enlarge.)

      Not perfect correspondence, but remarkable consistency. Whether you chose to focus on the similarities or the dissimilarities, we can’t escape an obvious conclusion: systemic LFOs can produce patterns that look like resting-state networks.


      How to deal with systemic LFOs in fMRI

      The initial approach to de-noising with RIPTiDe, by Frederick, Nickerson & Tong in 2012, used a reference NIRS waveform recorded from the forehead. In a subsequent study, RIPTiDe de-noising was applied based on the NIRS signal from subjects’ fingers (Hocke et al. 2016). This reduces the chance of accidentally capturing neural activity in the NIRS waveform, and simplifies the setup. The NIRS-based method explained twice as much variance in resting-state fMRI data than de-noising methods requiring models of respiration or cardiac response functions. Furthermore, only a small but insignificant correlation was found between NIRS and a respiratory variation model. Most signal power was not shared between NIRS and respiratory or cardiac variation models. These results suggest a different origin for sLFO signals than are measured with conventional respiratory belt or pulse oximetry traces, even though some respiratory models are designed to account for arterial CO₂ fluctuations. Whether multiple de-noising methods should be nested, and in what order, is a subject for a later date.

      The use of a reference NIRS signal is still a major limitation, especially for data that are already sitting in repositories for which there may be no peripheral physiological data. The data-driven approach, using seeds developed from the fMRI data themselves, overcomes this. (One can still use a NIRS signal as the initial seed waveform, but it isn’t required.) There is more work to do, but even if the original seed is defined in such a way as to capture neurogenic signals accidentally (or intentionally, if you opt for a gray matter seed), the smooth evolution of the regression procedure over several seconds, followed if desired by the definition of an optimal seed (which will likely represent large draining veins) and a second recursive procedure, should ensure that the final set of regressors doesn’t contain neural activity. So, if you want my advice, I would urge to you read up on the latest developments on Blaise Frederick’s github, and start tinkering.


      Conclusions

      Circumstantial evidence from several groups suggests that non-stationary arterial CO₂ is responsible for a systemic LFO in fMRI data. The overlap of this systemic LFO with neurogenic fluctuations of interest in resting-state fMRI suggests that a major physiologic “noise” component is being retained in most functional connectivity studies. Some studies may be partly removing sLFO through global signal regression (GSR), but given the spatial-temporal properties of the sLFO, GSR alone is unlikely to clean the data as well as a lag-based method. And there are statistical arguments against GSR anyway. As a compromise, you might consider dynamic GSR, which uses the lag-based properties to model propagation of LFO through the brain with a voxel-specific time delay prior to regression.

      RapidTiDe, the accelerated version of the original RIPTiDe method, looks like a useful option for de-noising. The use of the fMRI data to derive seed-based lags and regressors for de-noising should be familiar to anyone who has used the popular CompCor method. No additional measurements are required for RapidTiDe. Most fMRI data should contain sufficient vascular information to permit good seed selection, which should enhance its appeal significantly. Even better, code is available now for you to run tests with!

      There are alternative methods that may permit removal of systemic LFOs. I focused on lag-based methods in this post because they provide compelling spatial-temporal demonstrations of systemic LFOs. Collectively, these provide the strongest evidence I’ve found for working under the assumption that the sLFO is due to arterial CO₂. The next step, it seems to me, is to develop routine approaches aimed at accounting for sLFO in resting state fMRI data.

      Finally, a quick note on using expired CO₂ traces to get at fluctuations of PaCO₂. Measurement of expired CO₂ is supposed to be the focus of the blog post after next, according to my original list of fluctuations and biases in fMRI data. Until very recently, I had been assuming we would need expired CO₂ measurements to account for changes in PaCO₂. That may still be true, but as my center has been setting up devices to measure expired CO₂, and as I’ve learned more about lag-based methods such as RIPTiDe, my enthusiasm has shifted towards the latter. There are two main reasons for my enthusiasm for RIPTiDe: 1. the data-driven results are striking, and 2. there are practical hurdles to good expired CO₂ data, and some of these hurdles may be insurmountable. The practical hurdles? For a start, you need a dedicated setup that involves using a mask or nasal canula on your subject. Some people aren’t going to like it. Next, getting robust, accurate expired CO₂ data is non-trivial, even when the mask or canula fits perfectly. There are dead volumes in the hoses to consider, amplifier calibration and sensitivity issues, and other experimental factors. Not all breaths can be detected reliably, either. It can be quite difficult to discern very shallow respiration. (I thank Molly Bright and Daniel Bulte for the warnings. You were right!)

      Even when all these practical hurdles have been addressed, there’s one final factor that can’t be circumvented: recording expired CO₂ only provides you with data some of the time. You have no knowledge about what’s happening during inspiration, or when the subject holds his breath for a few seconds. Everything the subject does has to be inferred retroactively from the expired breath. All of which suggests to me that other methods should be attempted first. I like the Tong and Frederick approach, especially a seed-based method that uses just the fMRI data. This tactic has worked well for regions of no interest in CSF and white matter, as with the CompCor method. So why not a lag-based method using a seed in the vasculature? Cleaning up systemic LFOs, especially if they are ever proven to arise from CO₂ in the blood, could massively improve the specificity of functional connectivity.

      _______________________



      Notes:

      1.  Comprehensive reviews on CO₂ transport in blood, and on cerebrovascular response to CO₂:

      Carbon dioxide transport
      GJ Arthurs & M Sudhakar
      Continuing Education in Anaesthesia Critical Care & Pain, Vol 5, Issue 6, Dec 2005, Pages 207–210
      https://academic.oup.com/bjaed/article/5/6/207/331369
      https://doi.org/10.1093/bjaceaccp/mki050

      The cerebrovascular response to carbon dioxide in humans
      A Battisti-Charbonney, J Fisher & J Duffin
      J Physiol. 2011; 589 (Pt 12): 3039–3048. 
      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3139085/
      https://doi.org/10.1113/jphysiol.2011.206052

      Some highlights: 
      The mechanism by which CO₂ affects cerebrovascular resistance vessels is not fully understood. Increased CO₂ leads to increased [H+], which activates voltage gated K+ channels. The resulting hyperpolarization of endothelial cells reduces intracellular calcium, which leads to vascular relaxation and hence vasodilation.

      The mechanism of regulation of CBF is via pial arteriolar tone, since these provide the main resistance vessels.

      The mechanism underlying this regulation appears independent of the decreased and increased arterial pH levels accompanying the elevated and lowered pCO      ₂, respectively, since CBF remains unchanged following metabolic acidosis and alkalosis. Rather, findings suggest that CBF is regulated by changes in pH of the cerebral spinal fluid (CSF) as the result of the rapid equilibration between CO₂ in the arterial blood and CSF. The lowered/elevated pH in the CSF then acts directly on the vasculature to cause relaxation and contraction, respectively. Thus, the action of pCO₂ on the vasculature is restricted to that of altering CSF pH, i.e., is void of other indirect effects as well as direct effects. 
      But vasodilation is also observed in the periphery where there is no CSF. So, even if this explanation is correct for the brain, I am still left wondering how arterial CO₂ causes vasodilation elsewhere in the body. Let me know if you find a good review, please!

      pCO₂ and pH regulation of cerebral blood flow. 
      S Yoon, M Zuccarello & RM Rapoport.
      Front Physiol. 2012, 3:365.
      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3442265/
      https://doi.org/10.3389/fphys.2012.00365


      2.  Dual wavelength near-infrared spectroscopy (NIRS) can be used to estimate the oxyhemoglobin (HbO), deoxyhemoglobin (Hb) concentrations simultaneously. The method works via differential absorption of light at two wavelengths. The wavelengths are selected to provide optimal absorption of the target chromophores - that is, different forms of hemoglobin - and to minimize absorption by water and other tissue components. The total hemoglobin (HbT) concentration can then be deduced using the Modified Beer-Lambert law. It is generally assumed that HbT provides an estimate of CBV, including arterioles, capillaries and venules. The Hb signal arises mostly from veins when the arterial blood is close to 100% saturated, as in normal subjects. The HbO signal arises from both arterial and venous compartments. There are several references and reviews on all this, but nothing I've found so far is a good introduction for a lay audience (like me). I'll keep an eye out.


      3.  While NIRS and pulse oximetry are based on the same phenomenon - the absorption of light by blood components - NIRS devices usually differ from pulse oximeters in the wavelength(s) used, as well as in the number and placement of sensors, signal processing (such as high pass filtering for pulse oximetry), and other application-specific considerations.


      4.  Here I am ignoring the well-known feedback response to changes in arterial CO₂, since this is the chemoreflex responding to changes in PaCO₂ rather than ANS providing a feed-forward control over local vascular tone. The chemoreflex regulatory mechanism alters the respiration rate and volume of subsequent breaths, to push CO₂ concentration towards an equilibrium value. The total feedback loop can take multiple breathing cycles; tens of seconds. We will see the results of these feedback loops in the fMRI data. Indeed, these are exactly the systemic LFOs that are the focus of this post! So, the ANS is part of the reason for there being a non-stationary arterial CO₂. But it is indirect in the same way that the ANS is also involved in governing the heart rate. When the heart rate changes we don’t claim a direct, neurogenic source of fluctuations in the fMRI data, even though we recognize the crucial role of the ANS in regulating the process. Some of you may be using the heart rate variability as an emotional measure. Perhaps something similar can be done with changes in respiration. In any event, most fMRI experiments are aiming to see something cortical, something beyond the ANS, and so changes in PaCO₂ or in heart rate are at best uninteresting, at worst a nuisance.