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  • 07/26/14--13:19: QA for fMRI, Part 2: User QA

  • Motivation

    The majority of "scanner issues" are created by routine operation, most likely through error or omission. In a busy center with harried scientists who are invariably running late there is a tendency to rush procedures and cut corners. This is where a simple QA routine - something that can be run quickly by anyone - can pay huge dividends, perhaps allowing rapid diagnosis of a problem and permitting a scan to proceed after just a few minutes' extra effort.

    A few examples to get you thinking about the sorts of common problems that might be caught by a simple test of the scanner's configuration - what I call User QA. Did the scanner boot properly, or have you introduced an error by doing something before the boot process completed? You've plugged in a head coil but have you done it properly? And what about the magnetic particles that get tracked into the bore, might they have become lodged in a critical location, such as at the back of the head coil or inside one of the coil sockets? Most, if not all, of these issues should be caught with a quick test that any trained operator should be able to interpret.

    User QA is, therefore, one component of a checklist that can be employed to eliminate (or permit rapid diagnosis of) some of the mistakes caused by rushing, inexperience or carelessness. At my center the User QA should be run when the scanner is first started up, prior to shut down, and whenever there is a reason to suspect the scanner might not perform as intended. It may also be used proactively by a user who wishes to demonstrate to the next user (or the facility manager!) that the scanner was left in a usable state.


    Decide on the test configuration

    Although different users may want to test the scanner in different configurations - a major variable would be the head RF coil, if your scanner has more than one - there is benefit in maintaining a standard test configuration for all User QA. It makes differentiating real scanner problems more tractable. Besides, there's no reason why you can't add further bespoke tests for an individual user. The intent of the common procedure, then, is to test the scanner in a way that is as close to routine operation and default configuration as possible. We should select the RF coil and a phantom accordingly.

    If you only have one head RF coil then you have no choice to make in that regard. But if you have more than one coil then logic suggests you should select the most commonly used coil for User QA. Next, decide on a phantom. A dedicated phantom used only for User QA is ideal, but it shouldn't matter provided you have available something stable. You could use your Facility QA phantom or an FBIRN phantom, for example, but think carefully about the other uses of the phantom before committing. (See Note 1.)

    Set up a standard operating procedure (SOP) for the phantom and RF coil so that every user attempts to perform identical operations every time the User QA is conducted. The current User QA protocol for my scanner is here.


    Decide what to test

    In my User QA I really want the user to determine two key points quickly: in its present configuration, will the scanner (1) acquire images and (2) acquire reasonable EPI for fMRI? The first question can be answered by a simple localizer scan. On Siemens scanners this is most often a three-plane gradient echo scan requiring less than fifteen seconds. For the second question - EPI for fMRI - I use an attenuated version of one of the EPI protocols used in the comprehensive Facility QA routines that will be the subject of future posts. The User QA version is only 3.5 minutes so that the total time required to set up the phantom, insert it into the bore, acquire the localizer and EPI and evaluate the data is a total of five minutes. (I timed it.) If that is too long for you, a shorter EPI run would probably suffice.


    Evaluate the performance

    If the user is unable to get the User QA routine to complete successfully then we are already in trouble-shooting mode. So, successful completion is our first goal; it establishes that the scanner is going through the correct operations and seems to be working normally. After that, a modicum of experience (or dedicated training, if you prefer) should permit any qualified operator from conducting a reasonable assessment of the data. I am in the habit of contrasting the EPIs so that I can see the background noise, then initiating a cine loop during which I watch for anything to change. I might then repeat the cine loop with the contrast set for the phantom signal itself, in case there is a subtle problem with the RF transmission, say. I don't do anything more fancy than this. Furthermore, I don't actually require my users to evaluate their User QA data quality but it's clearly prudent (and simple enough) to learn how to do it.


    What to do if all is well

    From the user's perspective, if everything runs smoothly and the data appear as expected there's nothing further to do for User QA. Carry on with the experiment.

    At my facility I request that all User QA data be transferred to our offline data storage host, where the data will reside for 30 days, just in case I want to review it for any reason. I will probably review the most recent User QA data as a first step if I'm called to look at a problem with the scanner.

    I don't archive the data but if you had the time and the resources you could do so. I tend to review the last 30 days of User QA results if a real scanner problem is detected, e.g. gradient spiking, in case the User QA history can give me a better indication of when the issue first began. So far it hasn't helped me, but I live in hope!


    What to do if something isn't right

    Since one of the main aims of User QA is to determine pilot error, the first action on the user's part should be to determine whether the procedure was followed appropriately. If so, and if time is of the essence, it may be time to call for assistance. Alternatively, a quick bit of sleuthing can pay dividends. How much and what sort of sleuthing? That will depend heavily on the user's experience level.

    Common problems:

    • Software glitch arising out of an interrupted boot procedure.
    • Failure to insert the sample properly.
    • Failure to connect the RF coil properly.
    • Conductive debris in the coil sockets, or in/on the phantom. 
    • Bent or broken pin(s) on the RF coil plugs.

    Uncommon problems:
    • The last user left the on-resonance frequency outside the 600 Hz range used by the automated adjustment, e.g. because that person was testing a development pulse sequence and forgot to return the scanner frequency to its starting point.
    • Other custom configuration changes - RF amplifier in standby, one or more gradient amplifiers in standby, something unplugged inside a cabinet  - as implemented by physicists and engineers who supposedly know what they're doing, but forget to undo what they did prior to departing.
    • Real scanner issues, such as gradient spiking.

    If the results of User QA suggest that the scanner might have a real problem, it doesn't hurt to re-run the User QA from scratch to verify every step in the chain before moving on to more involved testing. Intermittent problems, as may be caused by conductive debris in a socket, say, can be difficult to diagnose. I will usually want to assess the reproducibility of a problem before I do anything else. And who knows, if you do manage to find and remove a small iron filing from a plug and return the scanner to normal operation, chances are you'll get to scan as you'd intended!


    Next post

    Coming soon, for technicians, facility managers and highly motivated routine users!

    QA for fMRI, Part 3: Facility QA - what to measure, when, and why


    __________________________


    Notes:

    1.  I have a dedicated Facility QA phantom that is used only by staff. The stability of that phantom is critical to my being able to detect subtle scanner problems. I don't want to risk it getting damaged by frequent use! Similarly, although the FBIRN gel phantom is a lovely piece of kit, it's not cheap. If it gets broken being used three times a day then the replacement cost is significant. I thus chose to use the standard doped water phantoms provided by Siemens. They're cheap and easy to replace if/when they leak. And if I can't maintain precisely the same phantom over time for User QA it doesn't matter all that much.


    (Download the User QA procedure used at UC Berkeley, for a Siemens TIM/Trio scanner, here.)






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  • 07/29/14--11:01: Free online fMRI education!

  • UCLA has their excellent summer Neuroimaging Training Program (NITP) going on as I type. Most talks are streamed live, or you can watch the videos at your leisure. Slides may also be available. Check out the schedule here.

    I am grateful to Lauren Atlas for tweeting about the NIH's summer fMRI course. It's put together by Peter Bandettini's FMRI Core Facility (FMRIF). It started in early June and runs to early September, 3-4 lectures a week. The schedule is here. Videos and slides are available a few days after each talk.

    Know of others? Feel free to share by commenting!

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    As I mentioned in the introductory post to this series, Facility QA is likely what most people think of whenever QA is mentioned in an fMRI context. In short, it's the tests that you expect your facility technical staff to be doing to ensure that the scanner is working properly. Other tests may verify performance - I'll cover some examples in future posts on Study QA - but the idea with Facility QA is to catch and then diagnose any problems.

    We can't just focus on stress tests, however. We will often need more than MRI-derived measures if we want to diagnose problems efficiently. We may need information that might be seem tangential to the actual QA testing, but these ancillary measures provide context for interpreting the test data. A simple example? The weather outside your facility. Why should you care? We'll get to that.


    An outline of the process

    Let's outline the steps in a comprehensive Facility QA routine and then we can get into the details:

    • Select an RF coil to use for the measurements. 
    • Select an appropriate phantom.
    • Decide what to measure from the phantom.
    • Determine what other data to record at the time of the QA testing.
    • Establish a baseline.
    • Make periodic QA measurements.
    • Look for deviations from the baseline, and decide what sort of deviations warrant investigation.
    • Establish procedures for whenever deviations from "normal" occur.
    • Review the QA procedure's performance whenever events (failures, environment changes, upgrades) occur, and at least annually.

    In this post I'll deal with the first six items on the list - setting up and measuring - and I'll cover analysis of the test results in subsequent posts.


    Choose an RF coil

    RF coils break often. They are handled multiple times a day, they get dropped, parts can wear with scanner vibration, etc. So it is especially important to think carefully before you commit to a receiver coil to use for your Facility QA. What characteristics are ideal? Well, stability is key, but this is at odds with frequent use if you have but a single head coil at your facility. If you have multiple coils and are able to reserve one for Facility QA then that is ideal. The coils in routine use can then be checked separately, via dedicated tests, once you're sure the rest of the scanner is operating as it should.

    When selecting a coil you also want to think about its sensitivity to typical scanner instabilities. If you have an old, crappy coil that nobody uses for fMRI any longer, don't resort to making that the Facility QA coil just because it's used infrequently! You want a coil that is at least as sensitive to scanner problems as those coils in routine use.

    I use the standard 12-channel RF coil that came with my system. I happen to have two of these beasts, however, so if there is ever any question as to the coil's performance I am in a position to make an immediate swap and do a coil-to-coil comparison. I also have a 32-channel head coil. I test this coil separately and don't use it to acquire scanner QA measurements, but that's just personal choice. I've found that the 32-channel coil breaks more often than the 12-channel coils, simply because it has five plugs versus just two plugs for the 12-channel coil.


    Select a phantom

    Here there is really no excuse not to have a phantom dedicated to Facility QA. This phantom should be used only for QA and only by technical staff. You might want to purchase a phantom for this purpose, or simply designate something you have on-hand and then lock it away.

    What characteristics should the phantom have? In my experience it doesn't matter all that much provided it approximates the signal coming from a human head. It doesn't need to be a sphere, but it could be. I decided to use one of the vendor-supplied doped water bottles when I devised my Facility QA scheme, and it was for a very simple reason: it was what I had! Perhaps I could have ordered, say, a second FBIRN phantom but I simply wasn't that forward-thinking.

    I did, however, take the precaution of having a dedicated holder built for the cylindrical bottle I use. This holder keeps the phantom in exactly the same orientation with respect to the magnet geometry, thereby assuring near-identical shimming for every Facility QA session. (Shim values are some of the ancillary information we'll record below.) Reproducible setup is arguably more important than the particular characteristics - shape, size, contents - of the phantom.

    There may be some other considerations before you commit to your Facility QA phantom. Do you need to compare your scanner's performance with other scanners? Cross-validation may require a specific phantom. Also, do you need to measure the performance of anatomical scans, or can you (like me) focus almost exclusively on fMRI-type stability testing? You may even need two or more phantoms to run all the Facility QA tests you need.


    Decide what MRI data to acquire

    Here's my Facility QA protocol in a nutshell:

    • Localizer scan (15 sec)
    • 200 volumes of "maximum performance" EPI at TR=2000 ms (6 min 44 sec total scan)
    • 200 volumes of "maximum performance" EPI at TR=2000 ms (6 min 44 sec total scan)
    • 200 volumes of "typical fMRI" EPI at TR=2000 ms (6 min 44 sec total scan)
    • Various service mode QA checks (approx. 15 min) 

    Including setting up the phantom and recording various ancillary data, the whole process takes about 45 minutes to perform. This allows a further 15 minutes to analyze the EPI data on the scanner, for a total one hour commitment.

    I use two types of EPI acquisitions (see Note 1) in my Facility QA protocol: one which is (close to) "maximum performance" and one that is representative of a typical user's parameters for fMRI. There have been instances when a problem has shown up in the user scan and not in the maximum performance scans, most likely because the user scan is applied in a slightly different axial-oblique orientation that requires driving the imaging gradients differently.

    The idea with the maximum performance scans is to kick the scanner where it hurts and listen for the squeal. The first time series is inspected for problems but isn't analyzed further. It's essentially a warm-up scan. I fully analyze the second scan, however, making several measurements that reflect the temporal stability of signal, ghosts and noise. More on those measurements in later posts.

    Why only one warm-up acquisition of under 7 minutes? More warm-up scans could be warranted if you have the time. My scanner achieves a thermal steady state in about 15 minutes. But I also have very efficient water cooling that means even a short delay between EPI runs, e.g. to re-shim, will cause a major departure from equilibrium. I determined that I could get sufficient stability in imaging signal after a 7-minute warm-up, so that's what I use. If you have reason to worry about the thermal stability of your passive shims in particular, then by all means warm up the scanner for 15-30 mins before running your QA. I've found it's not critical for my scanner and, as with all things QA, it's a tradeoff. More warm-up scans would take me over an hour for the entire Facility QA procedure.

    The parameters for the "maximum performance" EPI acquisitions are in these figures (click to enlarge):


    There are 40 descending slices acquired axially (perpendicular to the long axis of a doped water bottle) with the slice packet positioned at the bottle center. Provided the positioning is reproducible on the phantom, I don't think the particular slice position and orientation is as important as acquiring as many slices as possible in the TR of 2000 ms. We want to drive the scanner hard. The TE, at 20 ms, is comparatively low for fMRI but it permits a few more slices in the TR. I decided to use 2 mm slice thickness to drive the slice selection gradients about as hard as they ever get driven. But I decided to keep the matrix (64x64) and field-of-view (224x224 mm) at typical fMRI settings because, with the the echo spacing set short, I could get a larger number of slices/TR than with higher in-plane resolution. It's just another one of the compromises.

    A word about the echo spacing. My scanner will actually permit a minimum echo spacing of 0.43 ms for a 64x64 matrix over a 224 mm FOV. I test at 0.47 ms echo spacing, however, because I observed that my EPI data were too sensitive to electrical power instabilities at the shortest possible echo spacing (see Note 2). Backing off to 0.47 ms eliminated most of the acute power sensitivity yet maintains an aggressive duty cycle and permits me to disentangle other instabilities that could manifest in the ghosts. (Recall that the N/2 ghosts are exquisitely sensitive expressions of EPI quality, as covered here and here.)

    In the third and final EPI time series of my Facility QA protocol I test an EPI acquisition representative of a typical fMRI scan. As before, I use a standard (product) pulse sequence:


    All pretty basic stuff. Thirty-three slices acquired at an angle reminiscent of AC-PC on a brain, and other parameters as appropriate for whole brain fMRI. As with the first "max performance," or warm-up, time series, I don't actually record anything from this time series. It is inspected for obvious problems only. I've found that analyzing the performance of the second "max performance" time series is generally sufficient to detect chronic problems. Intermittent problems, such as spiking, are addressed in separate, dedicated tests (see below).

    Why don't I just do three acquisitions at maximum performance? I could, I suppose, but I prefer to have at least one look at the scanner performing as it does for a majority of my users' scans. It gives me an opportunity to assess the severity of a (potential) problem detected in the earlier time series, hence to make a decision on whether to take the scanner offline immediately, or whether to re-check at a later time and try to minimize the impact on the users.

    What isn't tested in my protocol?

    It should be clear that exhaustive testing of every parameter is impractical. In the protocol above I am using only two image orientations and two RF flip angles in total, for example. It is quite possible that gradient spiking will show up earliest in one specific image orientation because of the particular way the imaging gradients are being driven. Even testing all the cardinal prescriptions - coronal, axial, sagittal - would increase the total time considerably yet there's no guarantee that spiking would always be caught (see Note 3).

    As for the RF flip angle, if the RF amplifier develops a problem at high power settings and I test only at the relatively low powers used in EPI for fMRI, I may well miss a slowly degrading RF amp. I would hope to catch the degrading performance eventually in the measurements that I do make. Still, if you were especially worried about the stability of your RF amp you could add a high flip angle time series to the tests. You need to determine the priorities for your scanner based on its history and the way it gets used.

    Some other things I'm not testing directly: gradient linearity, magnet homogeneity, eddy current compensation, mechanical resonances. Many of these factor into the EPI data that I do acquire, so I'm not completely blind to any of them. My Facility QA protocol is primarily aimed at temporal stability as it affects fMRI data. Your facility may require additional MRI-based tests, e.g. gradient linearity determined on an ADNI phantom. And, of course, your scanner should be getting routine QA performed by the vendor to ensure that it stays within the vendor's specification.


    Ancillary data for Facility QA

    Now let's shift to considering other data we might record, either because the data could reveal a problem directly or because it might help us diagnose a problem that manifests in the time series EPI data.

    These are the fields presently recorded to my QA log:

    Date & time of test - Self explanatory. Essential for proper interpretation!

    Visual inspection of the penetration panel - Has someone connected an unauthorized device causing an RF noise problem, perhaps?

    Visual inspection of the magnet room - Is anything out of place or otherwise obviously wrong?

    System status prior to QA - Record whether the scanner was already on, or was started up prior to performing QA. Electronics can be funny that way.

    Suite temperature and humidity - I have a desktop monitor that lives in the operator's room. Ideally I'd record in the magnet room with remote sensors, but measuring in the operator's room is a reasonable check on the suite's condition. A consistent temperature in the magnet room is important for general magnet stability. Humidity is critical for proper functioning of gradients in particular. Low humidity may cause spiking, but it can also increase the rate of component failures from static discharges. Furthermore, if you have an electrical equipment room that isn't at a near constant temperature, e.g. because people go into it frequently, then you will want to measure the temperature of that room separately. RF amplifiers are often air-cooled so changes in the surrounding air temperature tends to translate into RF amplifier instabilities.

    Prevailing weather - I use the weather report from a nearby airfield. It gives barometric pressure, relative humidity, air temp and dew point, and the prevailing conditions (e.g. sunny, cloudy, rain, etc.). If you have a mini weather station of your own that's even better! Lest you think this information is overkill, more than one site has found that their magnet went out of specification when the sun was shining on the MRI suite. Extreme temperature may have direct effects, e.g. via passive shielding or building steelwork, or indirect effects, e.g. high electrical load for your building's air conditioning. Large, rapid changes in barometric pressure may affect magnetic field drift in some magnet designs, too.

    (The following data may only be available via a service mode interface. Check with your local service engineer.)

    Gradient coil ambient temp - Temperature of the gradient coil (or return cooling water) before commencing QA. The equilibrium temperature is a function of the cooling water temp to the gradient coil and should be consistent.

    Gradient coil temps before/after each time series EPI acquisition - Useful to determine if you are generating excess heat, e.g. because of an increased resistance in a gradient circuit, or if the gradient water cooling has a problem, e.g. low pressure or flow rate.

    Magnet temperatures - You may be able to record the temperatures of some of the various barriers between the liquid helium bath (at 4 K) and the MRI suite (290-3 K, or 17-20 C). Your scanner vendor is likely monitoring these numbers remotely, but it doesn't hurt to keep a check on things yourself, especially if your site is prone to periods of extreme vibration - earthquakes, passing freight trains - or you have just had someone accidentally stick a large ferrous object to the magnet. Internal magnet temps can be an early indication of a possible quench due to a softening vacuum shield, amongst other things.

    Helium level - Another good indication of something going wrong inside the magnet, although with the refrigeration units (cold heads) on modern MRIs the helium level over time may actually be a better indication of the health of your helium recycling than of the magnet per se.

    Linewidth - This is the post-shim water linewidth for your QA phantom. If the position of the phantom is reproducible in the magnet bore then the linewidth should be similarly reproducible.

    Magnet center frequency (in MHz) - Together with the magnet temp(s) and helium level, relative stability of on-resonance frequency is a good indication of overall magnet health. Changes may occur with weather conditions or suite temperature, however, so be sure to consider all parameters together when performing diagnostics.

    Room temp shim values - A phantom placed reliably in the magnet should yield reproducible shim values when an automated shimming routine is used. (Auto-shimming is the default on all modern scanners.) There are eight RT shims on my scanner: three linear shims (i.e. the gradients themselves), X, Y and Z, and five second-order shims, Z2, ZX, ZY, X2-Y2 and XY. Record them all. Changes in the RT shims may indicate that you have a problem with your phantom (a leak?) or the phantom holder, or they could be an indication that the passive shim trays  - thin strips of steel positioned between the magnet and the gradient set - are working loose due to vibration.

    Service mode tests - I include the vendor's RF noise check and spike check routines because these are two relatively common problems and I prefer to diagnose them directly, not via EPI data, if at all possible. You may not have permission to run these tests, however. If not, you could either rely on analysis of the time series EPI data discussed above, or add further acquisitions designed to be maximally sensitive to spikes and RF noise (see Notes 3 and 4).

    Additional RF coil tests - My 32-channel coil can be tested with a dedicated routine available under the service mode. I don't acquire any EPI data with this coil.

    Service/maintenance work log - It is imperative to keep a record of any work performed on the scanner, and to refer to this log whenever you are interpreting your QA records.

    Anything else? -  That's rather up to you. Electrical supply data can be very useful if you can get it. I can get minute-to-minute voltages for my (nominal) 480 V supply. I don't bother getting these reports for every Facility QA session we run, but I ask for them if I see anything strange in my test data.


    Establishing your baseline

    Having determined the data you'll record it's time to define "normal" for your scanner and its environment. In my experience, six months of data allows me to characterize most of the variations. I want to know what the variance is but I am also keen to know why it is how it is.

    There are no shortcuts to obtaining a baseline, you have to acquire your Facility QA as often as you can. If you have a new facility or a new scanner then you probably have a lot of scanner access; your routine users haven't started getting in your way yet. It should be feasible to run once a day, five days a week for at least the first several weeks, then you can begin to reduce the frequency until you are running once a week or thereabouts.

    Recently fixed/upgraded scanners should be tested more frequently, in part to check that there are no residual problems but also to redefine the baseline in case it has shifted. More on interpreting the data in the next post.


    When to run Facility QA

    You have your baseline and you know what normal looks like for your scanner. To science! Except you now have to decide how often to check on your scanner's status to ensure that all remains well. Or, if you're a realist, to determine when something starts to go wrong.

    Many people will prefer to have a fixed time in which to run QA. It may be necessary to fix the time slot because of scanner and/or personnel schedules. Is this a good thing? Not necessarily. Some degree of scatter may catch problems that vary with scanner usage, or with time of day. Say you decide to do your Facility QA on a Saturday morning because it's when you have plenty of time. That's fine, but if your building is barely occupied and the electrical load is significantly lower than during working hours Mon-Fri then you may miss an instability that affects midweek scans. So if you opt for a fixed slot for QA, first establish in your baseline measurements that the time of day and the day of the week are insignificant drivers of the variance in the measurements you're making.

    If you find that time of day or day of week is a significant factor in your scanner's performance then you may wish to try to rectify the source(s) of the differences first, if this is possible. If not then fixing the day and time of your Facility QA may be required in order to work around the instabilities that are beyond your control. Remember, the point of Facility QA is not to show that the scanner's performance is constant 24/7/365, rather it is to catch changes (usually deterioration) in its performance under fixed test conditions.


    Next post in this series: processing and interpreting your Facility QA data.


    ___________________________


    Notes:

    1.  If your facility, like mine, uses a customized pulse sequence for routine fMRI acquisitions, resist the temptation to use that sequence for Facility QA. Instead, use one of the product sequences (I use Siemens' ep2d_bold) and then set the parameters as close as you can to what you usually use in your everyday sequence. Why? Because the service engineer is going to want to know what sequence you used when you found the problem you're reporting. They will want to see the problem demonstrated on a standard sequence in case it's a coding issue. (Yes, it really does happen that physicists screw up their custom pulse sequences! ;-) So save yourself the extra time and effort and remain as close to "product" in your Facility QA as you can.

    2.  At very short echo spacings the fraction of ramp-sampled data points approaches or exceeds the fraction of sampling that happens along the flat top of the readout gradients. Even tiny shifts in the gradient waveform to the ADC periods will yield intense, unstable N/2 ghosts. (See the section entitled "Excessive ramp sampling" in this post.) A common cause of mismatch is due to the electrical supply at the instant the gradients are commanded to do something. Now, my facility has pretty good electrical power stability these days, but it's not perfect. (I don't have a separate, dedicated power conditioner for the scanner.) So if the voltage on the nominal 480 V, 3-phase supply changes with load elsewhere in the building, these changes pass through to the gradient amplifiers and may be detectable as periodically "swirling" N/2 ghosts. It is actually quite difficult to tie these swirling ghosts to the electrical power stability because other instabilities may dominate, depending on your facility. For example, in my old facility my scanner had its own external chiller comprising two refrigeration pumps that cycled depending on the heat load in the gradient set. When running EPI flat out the pumps would cycle every 200-300 seconds, and this instability was visible as a small instability with the same period in the EPI signal. But now that I have a building chilled water supply rather than a separate chiller the water cooling is essentially constant (and highly efficient!), revealing the next highest level of instability underneath, which in my new facility is the voltage on the 480 V supply.

    3.  Siemens offers a separate gradient "Spike check" routine that the service engineer can use. If you know the service mode password you can use it, too. I've found that the dedicated routine is hit and miss compared to EPI for detecting spikes, but the difference may simply be due to the amount of time spent testing. If an EPI time series is 6 minutes long there are many opportunities to catch spikes. The service mode spike check runs for only a few seconds (although it does sweep through all three gradients at many different amplitudes). Sometimes it takes many repetitions of the spike check to confirm spikes that I think I've detected in EPI.

    4.   In addition to the spike check mentioned in note 3, the vendor will have an RF noise check that acquires periods of nothing, i.e. the receivers are simply opened to sample the environment in the absence of gradient and RF pulses. Different carrier frequencies and bandwidths are tested to span the full range used in MRI acquisitions. If you are unable to use dedicated routines for either spike or RF noise checking then don't despair, test EPI data can be used to check for significant problems. The process becomes heavily dependent on analysis, however, so I'll cover it in future posts, on processing your QA data. In my opinion, for catching problems the dedicated routines are preferable for both their specificity, sensitivity and speed. The EPI test data can then be analyzed to confirm that all is well, rather than as the primary way to detect problems. I see this as an overlap between Facility QA and Study QA, so I'll revisit it in later posts.





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      So we finally have some grant awards on which to judge the BRAIN Initiative. What was previously a rather vague outline of some distant, utopian future can now be scrutinized for novelty, practicality, capability, etc. Let's begin!

      The compete list of awards across six different sections is here. The Next Generation Human Imaging section has selected nine diverse projects to lead us into the future. Here are my thoughts (see Note 1) based mostly on the abstracts of these successful proposals.


      Dissecting human brain circuits in vivo using ultrasonic neuromodulation

      See 1-R24-MH106107-01 for the abstract.

      Ultrasonic neuromodulation is a recent addition to the family of "minimally invasive" stimulation methods (TMS, tDCS) being used to prompt neurons (and other brain cells?) into doing something. In this case, ultrasound waves serve as an energy source to provoke some sort of neural response. The mechanism could be via localized heating, say, and one of the main goals of this project is to determine just how ultrasound interacts with brain tissue.

      Strictly speaking, transcranial ultrasound isn't an imaging method per se, rather it's a manipulation designed to allow imaging methods to see something different after the manipulation. Combining methods - here, ultrasonic neuromodulation with fMRI or EEG - should enable some unique experiments, e.g. to test network modularity. In this regard it's akin to TMS-fMRI. Knockout models have always been critically important in neuroscience and neurology, I see this project as a logical extension of those approaches.


      Path towards MRI with direct sensitivity to neuro-electro-magnetic oscillations

      See 1-R24-MH106048-01 for the abstract.

      This proposal extends prior attempts to use high field MRI to measure directly the electromagnetic activity associated with concerted neural firing. I generally refer to this family of methods as neuronal current imaging (NCI). To date, the only compelling demonstrations of NCI via MRI have involved bloodless preparations, because BOLD signal changes (as well as changes in cerebral blood volume, CBV) tend to overwhelm the tiny signal changes driven by electromagnetic fields associated with neurons working. This is still my biggest concern with this new proposal. I'm not saying it can't be done, only that BOLD is a ubiquitous weed that contaminates every fast imaging pulse sequence yet invented and applied at operating fields above a tesla or so. (My rule of thumb: in primary cortex, expect 1% change in BOLD per tesla of operating field.) CBV changes are also a huge concern when using amplitude changes in signals.

      Then there's sensitivity. For Lorentz force-based contrast, as used previously by this group, the desired effect increases with the magnetic field used to induce it. The problem, however, is that BOLD also scales not less than linearly with operating magnetic field. In sum, then, I see this proposal as interesting but technically challenging in a way that is unlikely to find it displacing other methods any time soon. For such a project, it seems to me that the farther they can get from high field magnets and conventional MRI sequences for spatial encoding, the better off they might be. The group at Los Alamos tried NCI using their ultralow field MRI scanner. They may have circumvented the BOLD contamination issue but that just leaves the not inconsiderable sensitivity issue to address. (See Note 2.)


      Imaging in vivo neurotransmitter modulation of brain network activity in real time

      See 1-R24-MH106083-01 for the abstract.

      This is a curious proposal. It's also the one I have the least background knowledge about. The abstract is scant on details, but it sounds like they are proposing to build transmitting agents that can be inserted into the brain - circulating in the blood, perhaps - and thence to report on the neurotransmitter status nearby. It does sound rather "Innerspace" to me, I have to say.

      Photoacoustics are mentioned as part of one aim. This involves firing laser light at tissue such that an ultrasound pressure wave is generated from the rapid heating. The principles are well established. Whether they will be amenable to use in a "minimally invasive/non-invasive" manner, however, remains to be seen. Perhaps they can adapt magnetoacoustics to the task instead, to eliminate the laser light and its associated heating. I'll be watching this team with interest. I could see them making successful bench-top demonstrations and proofs of principle, but getting agents into brains and reporting signals out of brains will be a massive sensitivity and safety challenge, it seems to me.


      Magnetic particle imaging (MPI) for functional brain imaging in humans

      See 1-R24-MH106053-01 for the abstract.

      Swapping one method reliant on vascular changes (present-day fMRI) for another doesn't, at first blush, seem very ambitious. Critics of fMRI are always lambasting the indirect view of "neural activity" provided by blood flow and volume changes. But the rationale for this project rests on the large potential gain in SNR compared to BOLD-based fMRI. The claim is that MPI would offer more than two orders of magnitude in sensitivity. This is likely true. However, there are some limitations to consider. First and foremost, MPI requires that the signaling agent - magnetic particles of iron oxide, or similar - be injected into the blood stream. This is immediately going to dissuade many people from volunteering for studies, and there is always the potential toxicity to consider. (The nice thing about hydrogen nuclear spin is that it's already everywhere in the brain and the blood, in the form of water. And it's non-toxic!) Perhaps a reduced subject pool is acceptable if it means that we can get better signals from those who do volunteer. Time will tell.

      The other issue is imaging speed. As acquired at present, MPI needs a certain amount of rastering - usually the sample is moved relative to a field-free point or field-free line - which makes the acquisition of a full image considerably slower than for fMRI. Based on my experience with neuroscientists and fMRI to date, unless and until one can get whole brain MPI in two seconds or less, it will be a hard sell. So that is where I would want to see the biggest developments from present technology if I was to view this as a true potential replacement for fMRI.

      Still, I find the whole premise that MPI could replace fMRI unlikely, given that fMRI scanners also make rather good anatomical MRI scanners, hence to permit reasonably good localization of those functional blobs in situ. MPI needs supporting anatomical information, such as that obtained by a separate MRI, in order to make sense of its signals. At best I would think that MPI and MRI might be made complimentary. I see the choice of one versus the other as a false dichotomy.

      There is one possibility that I am very keen to see tested, however. In fMRI we have a huge number of different motion sensitivities, from T1 effects to receive field biases to magnetic susceptibility gradients. It's a complicated mess. If MPI could be made somehow less motion-sensitive than fMRI - perhaps motion would just blur an image and cause false negatives, without the chance of lots of false positives - then it might find a deserving role in mapping brain function, even if it is "just another" vascular method as presently envisaged.


      Vascular interfaces for brain imaging and stimulation

      See 1-R24-MH106075-01 for the abstract.

      If proposal 1-R24-MH106083-01 eluded to the movie, "Innerspace," this one virtually hijacks the plot! The whole idea is to devise new imaging reporter systems that can be introduced via the vasculature "to deliver recording devices to the vicinity of neurons buried inside the brain parenchyma." It's invasive by definition, but that may not be the biggest obstacle by a long way. Getting the agents to go where they are required, to anchor for a while, and then to have sufficient power to transmit their signals to the surface of the head, are all truly massive difficulties.

      Still, with any luck proposals like this one will cross-fertilize with those using optogenetics, photoacoustics and other sensor systems and who knows, perhaps some sort of mini-machine might be devised that can be used in the vasculature without killing either Martin Short or Dennis Quaid.


      MRI corticography (MRCoG): micro-scale human cortical imaging

      See 1-R24-MH106096-01 for the abstract.

      Given that I have the most background knowledge on this proposal it isn't perhaps surprising that I might find it to be the most tractable of the nine. I would even go so far as to say that it is low risk. The premise is straightforward: given that large arrays of small coil loops have difficulty gaining depth penetration for the entire brain, don't aim for the entire brain. Aim to image just the cortex instead. Seen this way, the weak signals from deeper tissue are a contaminant to be eliminated - likely feasible - thereby facilitating smaller image fields-of-view and higher spatial resolution using essentially the same sort of spatial encoding as we use now. Granted there might be benefits to coupling these cortical coil arrays with faster and/or stronger gradients to push the resolution still further, but head gradient sets and even surface gradient sets are already out there.

      Limited ambition on the fMRI contrast front is perhaps my main criticism. We know from a lot of animal work, e.g. from the lab of Seong-Gi Kim, that once one attains laminar specificity, CBF or CBV-based contrast attain much the same spatial localization. So, getting away from BOLD would help but the intrinsic biological limits have already been established, I think. That said, it would be a truly massive step from what we can do today, and I don't see any reason why it can't be done for fMRI purposes.

      Magnetic susceptibility contrast mapping of axon fibers is included as a way to improve white matter tractography. This method benefits from higher field, so this entire project would be tailor-made for 7 T, although more limited performance of both the fMRI and tractography could be obtained at 3 T.


      Advancing MRI & MRS technologies for studying human brain function and energetics

      See 1-R24-MH106049-01 for the abstract.

      I'm way behind on the latest physics of high field MRI to assess this proposal in any detail, but as far as I can gather the aim is to use some new (dielectric) materials to squeeze every last drop of SNR out of existing whole body scanners operating at 7 T and higher. With the SNR enhanced over what's possible with today's transmission and reception systems, the hope would be to facilitate even higher resolution using conventional spatial encoding methods. (If novel methods are being considered they're not mentioned in the abstract.) Overall, then, it looks to be a logical extension of the path that's taken us to 7+ T today. Is that a bad thing? Probably not. Attaining maximum performance out of our existing polarizing fields is a laudable aim on its own. We might as well exploit the polarizing field as far as we possibly can, these are expensive beasts!

      The real novelty would come after attaining the SNR gains. The hope would be to boost the performance of rare nuclei for imaging and spectroscopy. (Endogenous) 31-P and (exogenous) 17-O are the two nuclei mentioned in the abstract, but other nuclei would benefit and could become viable candidates for functional imaging in their own right. (Endogenous) 23-Na and (exogenous) 19-F come immediately to mind.


      Imaging brain function in real world environments & populations with portable MRI

      See 1-R24-MH105998-01 for the abstract.

      In this proposal the drive is towards smaller, lower field polarizing magnets such that the smaller, lighter systems would then be transportable and could be deployed in environments quite different than today's MRI suite. It's an interesting proposal in that in some ways we've been here before. Prepolarized MRI systems using pulsed electromagnets at room temperature (with water cooling) have been around for a couple of decades and have already produced images that are rather good. Historically, the motivation claimed was to get cheaper MRI, but it has turned out that better trumps cheaper and there simply hasn't been the demand for producing a commercial product (sadly, imho).

      Could this project reinvigorate the prepolarized MRI efforts as a side effect, then? I certainly hope so, because many of the problems faced by this proposal are common to prepolarized MRI systems aiming to do functional brain imaging, specifically the need to optimize functional contrast methods at magnetic fields that are generally lower than 1 tesla. BOLD could be used but it's a rather weak effect at low fields. CBF imaging is possible in principle, but arterial spin labeling of blood benefits from high field because the blood T1 increases with B0. So it would seem that CBV imaging (i.e. VASO and its ilk) would be the functional method of choice, if endogenous contrast is the goal. This could be done on prepolarized MRI systems with modest effort, no new magnet technology required.

      To me, then, this proposal looks simultaneously ambitious and elementary. If another call goes out looking specifically for mobile MRI scanners, expect to see many more proposals with a lot more mature technology as their base.


      Imaging the brain in motion: the ambulatory micro-dose, wearable PET brain imager

      See 1-R24-MH106057-01 for the abstract.

      This sounds like a laudable goal but whenever I've been involved in discussions about doing PET the first question asked is "How far away is the cyclotron?" Some radionuclides are amenable to transport, so perhaps an ambulatory cyclotron-PET combination isn't implied, but what does seem clear is that only certain species would be suitable for taking out into the big wide world.

      Assuming that suitably long-lived radionuclides can be employed, and assuming that adequate imaging sensitivity can be achieved with the lower concentrations of radionuclides being considered, that just leaves the engineering challenge of building a portable, even wearable, PET scanner. I've no idea what they plan to do in this regard - the abstract focuses almost exclusively on the radionuclide issues - but one might think that lightweight disposable, "one-time use" technologies might be indispensable here. Way back in the last century we had this quaint photographic method that relied upon one-time use film to record pictures when a shutter was opened in the camera and the film was exposed to light. Perhaps something along these lines might replace the ring of scintillation crystals used in conventional PET scanners. Even so, to me it sounds like it would be a hefty piece of kit.

      UPDATE, 3rd Oct 2014: photo of concept via Julie Brefczynski-Lewis,https://twitter.com/practiCalfMRI/status/518102980399083520

      _________________________



      Notes:

      1.  I am a colleague of some of the successful principal investigators, and I know personally several more from other groups. I have no interests that might conflict, however. I may bend a few people out of shape, but that's a risk I'm prepared to take. These are just my current scientific opinions on what has been proposed. Nothing more, nothing less.

      2.  I got into ultralow field (ULF) MRI back in 2004 after reading a now largely discredited paper claiming to detect neuronal currents with MRI. My rationale was that if BOLD scales as a percent per tesla of operating field then reducing the operating field to the point where BOLD all but vanishes is a good start. But once we considered all the other contaminants we realized that CBV changes would persist and likely still be several orders of magnitude larger than anything we might hope to do with NCI. So we switched gears to trying to use the CBV change as a functional contrast mechanism at ULF. Even this less ambitious goal proved to be near-impossible with our setup; we had too much sensitivity to motion and, quite possibly, concomitant changes in cerebrospinal fluid (CSF) that offset our desired signal changes. So then we changed directions again and went after clinical goals instead. That's where we stand today. We haven't worked on functional imaging methods at ULF for several years now, and we have no plans to restart unless someone gives us a huge pot of money to rebuild the entire ULFMRI system to minimize subject motion. Sitting upright isn't gonna do it.



      0 0

      **Please let me know of errors or omissions!**

      This post is a work-in-progress. It will be updated based on feedback. I will remove (draft) from the title when I consider this version to be complete.


      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. As it says at the top, I'll consider this a draft post pending feedback. Subsequent posts will be designated with a version number.

      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, Docherty & Wandell.
      RIPTiDe by Frederick, Nickerson & Tong (using near-infrared spectroscopy, NIRS)
      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.

      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 website listing many of the common dietary sources of caffeine. Many sources are well-known and obvious. 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 sedatives (except alcohol), Viagra (or its competitors), or antihistamines on BOLD physiology.

      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.

      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.)

      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. I really can't say whether this means we should be monitoring, even controlling, what our subjects eat before getting scanned.


      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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    • 11/14/14--11:10: A failed quench circuit?

    • No doubt you've seen this news doing the rounds:

      Two stuck to MRI machine for 4 hours

      There was, of course, a huge procedural failure that allowed a large, magnetic oxygen cylinder into the MRI facility in the first place. No doubt the investigation will find ample blame to spread around. But the solution to the problem is rather simple: education/training coupled with standard operating procedures to nix the threat. As procedures go it's not especially difficult. (By comparison, over 34,000 people manage to get themselves killed on US roads every single year. Clearly, we can't drive for shit. Our procedures are severely wanting in this department.) And if you're ever in doubt as to whether an item can be brought safely into the MRI suite there is always - always! - someone you can go to for an expert opinion. In my facility no equipment is allowed through the door without that expert opinion being cast.

      So let's shift to the part of this fiasco that really got my attention: the claim that the magnet quench circuit malfunctioned. From the second article, above:
      "At a press conference on Wednesday, a day after this newspaper broke the story, senior officials of Tata Memorial-run Advance Centre or Treatment Research and Education in Cancer (ACTREC) in Khargar said that because a switch to disable the machine's magnetic field malfunctioned, it took engineers four hours to disengage the two employees - a ward boy and a technician -- stuck to the machine, when it should not have taken more than 30 seconds."


      Then, later in the same article:
      "Dr Gupta said the MRI machine was bought just four years back and its last periodic maintenance was carried out by GE engineers just six months back. "We were shocked. Despite several attempts to switch off the machine, we just could not disable it. Ideally, we should have removed both of our employees in 30 seconds, but they were stuck for four hours," he said.

      Dr Gupta said that while ACTREC engineers had reached the spot within minutes of the mishap, they just could not switch off the machine. The manufacturer was informed immediately, but the GE engineer who arrived after an hour too could not turn the machine off. Nearly two-and-a-half hours after the incident, three more GE engineers arrived and replaced the existing electronic circuit with a new one and then switched off the machine."


      So I asked my local service engineer - from Siemens, but I would wager large sums of money that I'd have got a similar response from any other vendor - what guarantees I have that my magnet will quench in the event I need to push the Big Red Button. Here is an edited version of his unequivocal response:

      "Your rundown unit will work.  It is tested thoroughly every year, parts are tested daily, and parts are tested on the fly (always).

      A magnet load simulator tool is connected (in place of the magnet) every year, and the quench circuits are tested into these loads to verify function.  Yes, we push the quench buttons every year. 

      In case of power failure, there is a battery backup for the rundown unit.  The battery is load tested automatically every day at 2 AM to insure it is sustaining a proper charge and capacity (if it fails this test, the scanner will NOT SCAN!).

      There is a small amount of current that is always applied through the quench and switch heaters in the magnet.  Seems like a scary concept, but it is shown not to produce enough heat to activate the heaters.  With this current applied, the voltage drop across the heaters is proportional to their resistance; if this value exceeds the allowed specification (too low or too high), you get an alarm.  You heard this alarm not too long ago when the switch/quench heater connecter was corroded/wet from condensation, and this resistance value was incorrect as a consequence.  So you got first-hand experience of what happens if the heaters aren’t connected with the correct resistance.  Further, this is also tested every year . . . we simulate high + low resistances with a tool for all three heaters in the magnet to check the monitoring circuits for proper function.

      We even disconnect one of the ‘quench buttons’ to be sure the system gives an alarm reporting ‘too few buttons’.  Yes, even the presence of the switch being electrically connected is monitored.

      These tests are monitored with a date/time stamp (file C:\MedCom\log\MsupHistory.log).  This data is transferred via remote connection to our server, and monitored by Erlangen (automated running script, I believe).  When the Siemens Magnet experts (not Oxford!) visit for magnet service, they also download this data from the magnet monitoring system.  If the logs show this test hasn’t been run within an approx 12 month interval, a service call is opened and an engineer is dispatched to check this immediately.

      Again, your rundown unit will work.  The heaters are monitored and known to be connected with the correct resistance, the switches are monitored as being physically connected, the rundown unit ‘quenches’ a simulated magnet, and the battery backup is tested, and the monitoring circuits are all tested.  And we have a quality process in place to be sure these tests/functions are carried out."


      I'm happy enough with this answer, but as someone who always likes backups for everything I still asked whether there was another way to quench the magnet, just in case. I was informed that there are other ways (and I already knew of one of them) but they are rather scary and not recommended for a variety of valid safety reasons. (Personnel safety, not magnet safety.)  They might be used in drastic cases, like perhaps if a fire destroys all the quench circuitry. But this is a very different scenario than trying to rescue someone stuck to the magnet. For everyday safety I am reassured that my quench circuit will activate if the button is depressed.

      Whether or not GE was really at fault in Mumbai we shall learn eventually, I hope. (I have heard rumors that some sites like to bypass their quench circuit in order to avoid having the cost of recharging the magnet should the quench button get activated. Insert your own exclamations of disbelief here because I'm incredulous.) In the mean time, this sorry saga is an opportunity for all of us to review our own procedures and take the extra moments to ensure that we've done everything humanely possible to eliminate risks. There really is no excuse.



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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.
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        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.

        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.