Education, tips and tricks to help you conduct better fMRI experiments.
Sure, you can try to fix it during data processing, but you're usually better off fixing the acquisition!

Monday, December 13, 2021

A core curriculum for fMRI?

Blimey. Judging by the reaction to my earlier Tweet, there's something to be done here. And it makes sense because fMRI has been around for thirty years yet seems to be as ad hoc today as it was at its genesis. We're half the age of modern electronic computing, twice as old as Facebook and Twitter. FMRI predates the NCSA Mosaic web browser, for goodness sake. Let that sink in for a minute.

This is something I've been pondering for a long time. In 2010 I thought I might write a textbook to capture what I saw as the fundamental knowledge that every fMRIer would need to know to set up and run an experiment. I started writing this blog as a way to draft chapters for the textbook. The textbook idea got, uh, shelved as early as 2011, once I realized that a blog is better-suited to delivering content on a subject that is inherently dynamic. Try embedding a movie in a textbook! But then the blog, in its original guise, sorta ran out of steam a few years ago, too. And the main reason why I ran out of steam is directly relevant to this post. I was getting into areas about which I know little to nothing, in an attempt to be able to write blog posts relevant to fMRI research. Take the post series on "fMRI data modulators," my rather clunky term for "that which causes your fMRI time series to vary in ways you probably don't want." I was having to try to teach myself respiratory and vascular physiology from scratch. The last time I sat in a formal biology class I was 13. Recently, I've encountered machine learning, glucose metabolism, vascular anatomy and a slew of other areas about which I know almost nothing. Where does one start????

On the assumption that I'm not alone, it would seem there's a trade to be made. If I can teach k-space to a psychologist, surely a statistician can teach an anatomist about normal distributions, a biochemist can teach an electrical engineer about the TCA cycle, and so on. With very few exceptions, all of us could really use better foundational knowledge somewhere. We are all impostors!

No doubt your first reaction is "Sounds lovely, but nobody has the time!" I respectfully disagree. You are likely already spending the time. My suggestion is to determine whether there might be a more efficient way for you to spend your time, by joining a pool of like-minded "teachers" who will cover the things you can't or won't cover. So, here is a throwaway list of things to consider before you pass judgment:

  • We are all trying to do/learn/teach the same things! There ought to be a more efficient way to do it.
  • The core concepts needed to understand and run fMRI experiments change relatively slowly. The shelf life of the fundamentals should last a decade or more. Updates can be infrequent.
  • Most of us have limited teaching resources. Few, if any institutions can cover all the core areas well.
  • My students become your postdocs. Wouldn’t you want them to arrive with a solid base?
  • Today’s students are tomorrow’s professors. If we are to improve teaching overall, we have to start at the bottom, not at the top.
  • A lot of topical problems (including poor replication, double-dipping, motion sensitivity, physiologic nuisance fluctuations) could be reduced at source by people setting up and executing better experiments through deeper knowledge. Crap in, crap out.
  • We are all super busy, yet the effort to contribute to a distributed syllabus could be a wash, perhaps even a net reduction, because you won't have to BS your way through stuff you don't really understand yourself.
  • I want to learn, too! It’s hard to determine an efficient path through new areas! I need a guide.


That just leaves the final step: doing it. Until he regrets his offer, Pradeep Raamana has generously offered use of his Quality Conversations forum to commence organizing efforts. I envision a first meeting at which we attempt to define all the main areas that comprise a "core syllabus" for fMRI. This would include, at the very least, NMR physics, MRI physics, various flavors of physiology, some biochemistry, neuroanatomy, basic statistics, machine learning, experimental design and models, scanner design, etc. If we can identify 6-8 umbrella areas then I'd look to create teams for each who would actually determine what they consider to be core, or fundamental, to their domain. Most likely, it's the stuff with a very long shelf life. We're not trying to be topical, the goal is to give everyone practicing fMRI a basic common framework. We want to define the equivalents of the Periodic Table in chemistry, Newtonian mechanics in physics, eukaryotic cell structure in biology, etc.

Doable? Drop your thoughts below.

Tuesday, February 16, 2021

Restraining the 32-channel coil

There has been a move towards custom head restraint in recent years. These devices are tailored to fit the subject in such a way that any movement of the head can be transmitted to the coil. It is therefore imperative to make sure that the RF coil is also well restrained.

On Siemens Trio and Prisma scanners, the 32-channel head coil is a special case. It was designed independent of the standard head coils. Restraint on the bed is thus a bit of an afterthought. Sticky pads on the base of the coil are designed to prevent movement through friction, but there are gaps on all four sides and no specific mechanism - slots, grooves, etc. - to lock the coil into a particular position. On my Trio, I was in the habit of putting the 32-channel coil all the way back to the frame of the bed, assuming that the most likely direction of motion from a subject would be backwards. Problem solved, right? No. By putting the coil all the way back, when using custom head restraint I actually put stress on the front two coil cables and this led to intermittent receive RF artifacts. A more refined fix was necessary.

My engineer built a simple frame (see photos below) that fits snugly into the rear portion of the bed frame and forces the coil onto protrusions that hold the standard (12-channel) coils properly. It also shims out the left and right gaps so there is no chance of side to side motion, either. With this device in place, the coil can only go one way: up. 

There has been some debate in the literature about the utility of custom head restraint for motion mitigation, with one group finding benefits while another found it made things worse. I note that both groups were using 32-channel coils on a Prisma, so proper head coil restraint may be a reason for different outcomes. I am now working on a fix for Prisma scanners and will do a separate post on the solution once it's been tested. (ETA April-May.) Until then, if you use a 32-channel coil on any Siemens scanner, my advice is to use additional restraint and make sure your coil is in a reliable, stable position. 


The coil restraint shim is put into position before the 32-channel coil.

Coil restraint shim in position.

Monday, February 25, 2019

Using multiband-EPI for diffusion imaging on low-dimensional array coils

This is a continuation of the previous post looking at MB-EPI on a receive coil with limited spatial information provided by its geometry, such as the 12-channel TIM coil or the 4-channel neck coil on a Siemens Trio.

Simultaneous multi-slice (SMS), aka multi-band (MB), offers considerable time savings for diffusion-weighted imaging (DWI). Unlike in fMRI, where MB factors of 4 or more are quite common, in DWI few studies use MB factors greater than 3. While it may be feasible in principle to push the acquisition time even lower without generating artifacts using a large array coil like the Siemens 32-channel coil, we run into another consideration: heating. Heating isn't usually a concern for gradient echo MB-EPI used in conventional fMRI experiments. In fMRI, the excitation flip angles are generally 78° or less. But with DWI we have a double whammy. Not only do we want a large excitation flip angle to create plenty of signal, we also require a refocusing pulse that is, by convention, set at twice the flip angle of the excitation pulse. (The standard nomenclature is 90° for excitation and 180° for refocusing, but the actual angles may be lower than this in practice, for a variety of reasons I won't go into here.) Now the real kicker. The heat deposition, which we usually measure through the specific absorption rate (SAR), scales quadratically with flip angle. Thus, a single 180° refocusing pulse deposits as much heat as four 90° pulses! (See Note 1.) But wait! It gets worse! In using simultaneous multi-slice - the clue's in the name - we're not doing the equivalent of one excitation or refocusing at a time, but a factor MB of them. Some quick arithmetic to give you a feel for the issue. A diffusion scan run with 90° and 180° pulses, each using MB=3, will deposit fifteen times as much heat as a conventional EPI scan run at the same TR but with a single 90° pulse. On a 3 T scanner, it means we are quickly flirting with SAR limits when the MB factor goes beyond three. The only remedy is to extend TR, thereby undermining the entire basis for deploying SMS in the first place.

But let's not get ahead of ourselves. With a low-dimensional array such as the Siemens 12-channel TIM coil we would be delighted to get MB to work at all for diffusion imaging. The chances of flirting with the SAR limits are a distant dream.

Phantom tests for diffusion imaging

The initial tests were on the FBIRN gel phantom. I compared MB=3 and MB=2 for the 32-channel, 12-channel and neck coils using approximately the same slice coverage throughout. The TR was allowed to increase as needed in going from MB=3 to MB=2. Following CMRR's recommendations, I used the SENSE1 coil combine option throughout. I also used the Grad. rev. fat suppr. option to maximize scalp fat suppression, something that we have found is important for reducing ghosts in larger subjects (especially on the 32-channel coil, which has a pronounced receive bias around the periphery). For the diffusion weighting itself, I opted to use the scheme developed for the UK Biobank project, producing two shells at b=1000 s/mm² and b=2000 s/mm², fifty directions apiece. Four b=0 images are also included, one per twenty diffusion images. (For routine use we now actually use ten b=0 images, one every ten DW images, for a total of 111 directions.) The nominal spatial resolution is (2 mm)³. The TE is 94.8 ms, which is the minimum value attainable at the highest b value used.

There are over a hundred images we could inspect, and you would want to check all of them before you committed to a specific protocol in a real experiment because there might be some strange interaction between the eddy currents from the diffusion-weighting gradients and the MB scheme. For brevity, however, I will restrict the comparisons here to examples of the b=0, 1000 and 2000 scans. I decided to make a 2x2 comparison of a single band reference image (SBRef), a b=0 image (the b=0 scan obtained after the first twenty DW scans), and the first b=1000 and b=2000 images in the series. While only a small fraction of the entire data set, these views are sufficient to identify the residual aliasing artifacts that tell us where the acceleration limit sits.

First up, the results from the 32-channel coil, which is our performance benchmark. No artifacts are visible by eye for any of the b=0, b=1000 or b=2000 scans at either MB=2 or MB=3:

32-channel coil, MB=3. TL: Single band reference image. TR: first b=0 image (21st acquisition in the series). BL: first b=1000 image. BR: First b=2000 image

Saturday, February 16, 2019

Using multi-band (aka SMS) EPI on on low-dimensional array coils

The CMRR's release notes for their MB-EPI sequence recommend using the 32-channel head coil for multiband EPI, and they caution against using the 12-channel head coil:

"The 32-channel Head coil is highly recommended for 3T. The 12-channel Head Matrix is not recommended, but it can be used for acceptable image quality at low acceleration factors."

But what does "low acceleration" mean in practice? And what if your only choice is a 12-channel coil? Following a couple of inquiries from colleagues, I decided to find out where the limits might be.

Let's start by looking at the RF coil layout, and review why the 12-channel coil is considered an inferior choice. Is it simply fewer independent channels, or something else? The figure below shows the layout of the 12-ch and 32-ch coils offered by Siemens:

From Kaza, Klose & Lotze (2011).

In most cases, the EPI slice direction will be transverse or transverse oblique (e.g. along AC-PC), meaning that we are slicing along the long axis of the magnet (magnet Z axis) and along the front-to-back dimension of the head coil. Along the long axis of the 12-ch coil there is almost no variation in the X-Y plane. At the very back of the coil the loops start to curve towards a point of convergence, but still there is no distinction in any direction in the X-Y plane. Compare that situation to the 32-ch coil. It has five distinct planes of coils along the Z axis. With the 32-ch coil, then, we can expect the hardware - the layout of the loops - to provide a good basis for separating simultaneously acquired axial slices, whereas there is no such distinct spatial information available from the coil elements in the 12-channel coil. In the 12-channel coil, every loop detects a significant and nearly equal fraction of any given slice along Z.

Sunday, January 13, 2019

Arterial carbon dioxide as an endogenous "contrast agent" for blood flow imaging

I nearly called this post Low Frequency Oscillations - part III since it closely follows the subject material I covered in the last two posts. But this is a slight tangent. Following the maxim "One scientist's noise is another scientist's signal," in this post I want to look at the utility of systemic LFO to map blood flow dynamics, an idea that was suggested in 2013 by Lv et al. based on the earlier work from Tong & Frederick that I reviewed last post. There is also at least one review of this topic, from 2017.

Let me first recap the last post. There is sufficient evidence, supported by multiple direct and indirect lines of inquiry, to suggest a blood-borne contrast mechanism that produces a prominent fluctuation at around 0.1 Hz in resting-state fMRI data. (Here, I assume a standard T₂*-weighted EPI acquisition for the resting-state fMRI data.) Furthermore, the same fluctuation can be found anywhere in the body. That is, the fluctuation is truly systemic. The best explanation to date is that non-stationary arterial CO₂ concentration, brought about by variations in breathing rate and/or depth, produces changes in arterial tone by virtue of the sensitivity of smooth muscle walls to the CO₂ dissolved in arterial blood. I shall assume such a mechanism throughout this post, while noting that the actual mechanism is less critical here than whether there is some utility to be exploited.

In the title I put "contrast agent" in quotes. That's because the CO₂ isn't the actual contrast agent, but a modulator of contrast changes. When the smooth muscle walls of an artery sense a changing CO₂ concentration, they either expand or contract locally, modulating the blood flow through that vessel. In the brain, a change in a blood vessel's diameter causes a concomitant change cerebral blood volume (CBV), hence cerebral blood flow (CBF). There may be a local change in magnetic susceptibility corresponding to the altered CBV in the arteries and capillaries. But the altered CBF will definitely produce the well-known change in magnetic susceptibility in and around the venous blood that can be detected downstream of the tissue, i.e. the standard BOLD effect. The actual contrast we detect is by virtue of changes in T₂* (for gradient echo EPI), plus the possibility of some flow weighting of the arterial blood depending on the combination of flip angle (FA) and repetition time (TR) being used. As a shorthand, however, I shall refer to arterial CO₂ as the endogenous contrast agent because whenever an artery senses a change in CO₂ concentration, there will be a concomitant change in vessel tone, and we will see a cascade of signal changes arising from it. (See Note 1 for some fun with acronyms!)

Time shift analysis

Most published studies attempting to exploit systemic LFO have used fixed time shifts, or lags, in their analysis. You just need a few minutes' worth of BOLD fMRI data, usually resting state (task-free). The analysis is then conceptually straightforward:
  1. Define a reference, or "seed," time course;
  2. Perform cross correlations between the "seed" and the time course of each voxel, using a set of time shifts that typically spans a range of 15-20 seconds (based on the expected brain hemodynamics);
  3. Determine for each voxel which time shift gives the largest cross correlation value, and plot that value (the delay, in seconds) to produce a lag map.

There are experimental variables, naturally. The duration of the BOLD time series varies, but most studies to date have used the 5-8 min acquisition that's common for resting-state connectivity. Some studies filter the data before starting the analysis. Different studies also tend to choose different seeds. There are pros and cons for each seed category that I assess in the next section. Time shifts are usually increments of TR, e.g. the lag range might be over +/- 5 TRs for a common TR of 2 sec. And, in producing the final lag maps, some studies apply acceptance criteria to reject low correlations.

Let's look at an example time shift analysis, from Siegel et al. (2016). The raw data were filtered with a pass-band of 0.009 - 0.09 Hz. For cross correlations, they used as their seed time course the global gray matter (GM) signal. Cross correlations were computed voxel-by-voxel for nine delays of TR = 2 sec increments, covering +/- 8 sec, followed by interpolation over the lag range. The time shift corresponding to the maximum cross correlation was assigned that voxel's lag value in the final map, as shown here:

Fig. 1 from Siegel et al. (2016).

Thursday, June 14, 2018

FMRI data modulators 3: Low frequency oscillations - part II

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

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

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

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

The effects of respiration on BOLD data were recognized in the mid-nineties as an important consideration for fMRI experiments. By the late nineties, several groups began to investigate the effects of intentionally held breaths on BOLD signal dynamics, using as their basis the phenomenon of arterial CO as a vasodilator. Other groups (e.g. Mitra et al., 1997) observed low frequency fluctuations in BOLD data that suggested a vasomotor origin, or found fluctuations in cerebral blood flow (CBF) measured by non-MR means (e.g. Obrig et al., 2000). It wasn’t until 2004, however, that Wise et al. showed definitively how slow variations of arterial CO concentration were related to, and likely driving, low frequency variations in BOLD time series data:
PETCO-related BOLD signal fluctuations showed regional differences across the grey matter, suggesting variability of the responsiveness to carbon dioxide at rest.”
“Significant PETCO-correlated fluctuations in [middle cerebral artery] MCA blood velocity were observed with a lag of 6.3 +/- 1.2 s (mean +/- standard error) with respect to PETCO changes.”

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

Saturday, March 24, 2018

FMRI data modulators 3: Low frequency oscillations - part I

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

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

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

What exactly do we mean by low frequency oscillations anyway?

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

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

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