Could MSM be a useful tracer for determining CSF flux in the human brain?

 A few years ago I was involved in a project to develop a better chemical shift reference for in vivo MR spectroscopy (Kaiser et al. 2020).  As often happens in science, life, logistics and money conspired to change the directions of those involved and this project got put on the shelf to gather dust. We no longer have either the people or the capabilities to pursue it further. Perhaps someone else would be able to take it on and see whether there are more uses than as a chemical shift reference.

One of the angles we were considering in 2020 was the possibility of using MSM as a tracer for measuring CSF flux in the brain. Various approaches have been developed using MRI, but they are all rather difficult. One involves an intrathecal injection of a gadolinium contrast agent and then looking for signal losses depicting where the Gd contrast diffuses to (Iliff et al. 2013). Negative contrast is always a complication for MRI because signal voids often arise from imperfections in the magnetic field. Another method uses an arterial spin label (ASL) and long post-labeling delays to assess the amount of water passing from the vascular compartment to the tissue compartment, i.e. through the blood-brain barrier, as an index of what is assumed to represent the inflowing part of the glymphatic system (Gregori et al. 2013, Ohene et al. 2019). These methods are low sensitivity and highly prone to motion. A third approach uses low diffusion-weighted imaging to try to differentiate CSF from other water compartments (Harrison et al. 2018). But again the method is inherently sensitive to bulk motion and it's not entirely clear to me how well the signals represent the CSF to interstitial fluid flux versus other microscopic compartments. So, would MSM offer simultaneously positive contrast and improved sensitivity? And would its clearance give an indication of the CSF flux through brain tissue?

MSM is methysulfonylmethane, the trade name for what a chemist would call DMSO2. It is labeled by the FDA as GRAS: "generally regarded as safe." As such, there are few regulations for its use and so you can find it in everything from dog food to ointments for a bad knee. You may well be consuming it and not have a clue. But the good news is you can buy pills of MSM for your experiments. There's no special permission needed, you can get these at your local pharmacy. (A word of caution: the amount listed on the package may not match what is actually in the pills! Do your own assay!) Then, once you've got this past your IRB, you can dose subjects with acute or chronic doses and see what happens to the MSM level in the brain.

MSM is a small, polar molecule which probably distributes throughout biological tissues with approximately the same concentration profile as water. The more water content in the tissue, the higher the MSM concentration is likely to be after a few hours. But this is a guess. What we do know is that entry into the brain is rapid. We can see MSM in a brain spectrum within 10 minutes following an oral dose. The MSM signal then remains fairly stable for several hours, which is a property we wanted for our chemical shift reference. 

But what is driving the clearance rate? In our early tests, we observed a half life in normal brain of about 3 days. This was for a single acute dose. In later tests (not included in the 2020 paper) we saw about the same washout time for a single 6 g dose as for a single 2 g dose. We also had a subject (me!) take a 1 g dose every day for 30 days to ensure steady state concentration, then observed the washout. Again, a half life of about 3 days. 

 

For clearance, we assume the MSM partitions and clears down its concentration gradient. Presumably the MSM distributes into the brain via the blood. Once we stop giving new oral MSM the blood concentration falls to near zero, and presumably clearance of the MSM in the body then occurs via the kidneys. If the routes out of brain tissue include the blood and perhaps CSF clearance, then what matters is the concentration gradients between brain tissue and blood and, perhaps, brain tissue and CSF.

This is where the idea of using MSM as a CSF flux (glymphatic system) tracer comes in. If the half life is around 3 days in normal brain, does the rate of clearance change with sleep deprivation, bouts of vigorous exercise or other challenges to an individual? What about differences between individuals? Do older subjects clear MSM more slowly than younger subjects on average? Women faster than men? Is the density of aquaporin channels a prime determinant of the clearance rate from brain, or is MSM able to diffuse across all membranes with approximately the same rate? And is CSF flux through brain tissue an important determinant of the clearance rate, or incidental to it? We were never able to test these ideas. 

As a practical matter, MSM can be observed easily in a 1H MR spectrum. Its chemical shift of pi (3.142 ppm) and sharp line makes it easy to fit separately from brain metabolites. We also never tested the ability of chemical shift imaging (CSI) to observe MSM, but there's every reason to think that a CSI method which can reliably image the NAA, creatine and choline singlet peaks will be able to map MSM perfectly well, too.

So, there you have it. A free idea for someone to explore and perhaps exploit for the purposes of assessing CSF clearance, sleep, dementias and so on.

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Functional connectivity, ha ha ha.

 

If you do resting-state fMRI and you do any sort of functional connectivity analysis, you should probably read this new paper from Blaise Frederick:

https://www.nature.com/articles/s41562-024-01908-6

I've been banging the drum on systemic LFOs for some time. Here's another example of how not properly thinking through the physiology of the entire human can produce misleading changes in so-called FC in the fMRI data. That said, I don't think Blaise has the full story here, either. For one thing, the big dips in his Fig 1b suggest that something is being partially offset with the on-resonance adjustment that is conducted automatically at the start of each EPI time series, so I have a residual concern that there are magnetic susceptibility effects contributing here somewhere. (Perhaps the magnetic susceptibility effects are what's left to drift higher after RIPTiDe correction, as in Fig 6b, for example.) The point is that not having independent measures of things like arousal, or proper models of physiologic noise components like sLFOs, or a full understanding of what's happening in the scanner hardware (including head support) during the experiment can lead to an assumption that things are neural when there are better explanations available. 

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Link added on 6/23/2024: Blaise Frederick discussing systemic LFOs on "Coffee Break!"

 

Core curriculum - Cell biology: synapses and neurotransmitters

 

The action potential from one neuron may or may not trigger further action potentials in neurons it connects to via synapses. A typical neuron with its single axon may make thousands of synapses to the dendrites of these "downstream" neurons. The locations of the synapses matter, in the sense that position relative to the downstream neuron's cell body provides a sort of weighted importance to any one synapse, as does the type of synapse. For fMRI we don't need to get too deep into the details of these connections, but we do need a basic understanding of the differences between excitatory and inhibitory connections. For the most part, whether a connection is excitatory or inhibitory is determined by the type of neurotransmitter released at the synapse.

First, let's get an overview of types of synapse and neurotransmitter, and the difference between excitatory and inhibitory neurotransmission:

Next, a little more detail and some context: 

In case it wasn't already clear, here's a nice explanation linking the pre-synaptic neuron's electrical potential to neurotransmitter release at the synapse:

Categorizing any one neurotransmitter as excitatory or inhibitory is a reflection of its usual effect on the electrochemical potential in post-synaptic neurons. The actual effect on any one post-synaptic neuron - whether that neuron is rendered closer to or farther away from its threshold voltage - can depend on the location of the synapse as well as the neurotransmitter(s) released in the synaptic cleft. Still, we can usefully categorize neurotransmitters according to their broadly different functions around the body:

In case you're interested in the structure of these neurotransmitters - perhaps because you are researching the effects of exogenous compounds ("drugs") on brain activity - here's a little more biochemistry:

Most of the videos above have focused on the neurotransmitter in the synaptic cleft. Naturally, the receptors on the post-synaptic neuron are critical to signaling. So let's take a slightly closer look at receptor types: 

And finally, a little more detail on the importance of synaptic location, not just type, in determining the type of action produced by a neural circuit:

That should suffice as a basic introduction to neurotransmission for the bulk of fMRI experiments, where we are looking at the collective effects of millions of neurons and trillions of synapses in any given voxel. Additional videos suggested by YouTube should provide good branches for those of you wanting more detail.

At this point, I want to shift to looking at the axon structure and its myelin sheath because this is an important distinction at the level of the fMRI voxel. We will tend to categorize any given voxel as containing mostly white matter (myelinated axons) or mostly gray matter (cell bodies). We will look at these in turn.

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Post-publication bonus video! I came across this video on some recent discoveries on dendritic activity while hunting for introductions to myelin structure. It's well worth a watch.

 

 

Core curriculum - Cell biology: the neuron's action potential

 

The last post reviewed the origins and properties of the resting membrane potential. Specifically, we are most interested in the membrane potential of neurons because they have an activated state that leads to signaling between neurons. Signaling from one neuron is achieved via an action potential from the cell body (soma) down its axon to synapses with other neurons. There are several good summary videos available online. Try them all to reinforce your knowledge.

Finally, in this post we get our first real look at synapses and excitatory and inhibitory neurotransmitters as part of a graded potential:

Now that you've seen the electrochemical action potential, in the next couple of posts we can dig more into neuron-neuron signaling, including synapses and the role of chemical neurotransmitters.

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BONUS: a speedy review. All familiar stuff now, right?

Core curriculum - Cell biology: cell membranes and the resting potential

 

A lot of the important functions of neurons (and glia) happen at their cell membranes. In the case of neurons, in addition to the membrane around the cell body (the soma), we also need to understand what happens along the neuronal processes (aka neurites): the dendrites (inputs) and the neuron's axon (the output). 

Let's begin this section by reviewing the structure of the cell membrane.

 

 

Transport across the cell membrane was introduce above. There are different mechanisms of membrane transport, each establishing certain behaviors of a cell.

The sodium-potassium pump is one of the most important membrane transport mechanisms for neural signaling. Let's take a closer look.

 

The cell's resting membrane potential was mentioned in the last two videos. The resting potential is an important starting point for understanding neuronal signaling via action potentials. For the last part of this post, we will look in more detail at the origins of the electrical potentials and electrostatic gradients across a cell membrane at rest.

In the next post we can start to look at cell signaling. Specifically, we are most interested in a neuron's action potential, which is the main way neurons communicate with each other.

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Can we separate real and apparent motion in QC of fMRI data?

 

A few years ago, Jo Etzel and I got into a brief but useful investigation of the effects of apparent head motion in fMRI data collected with SMS-EPI. The shorter TR (and smaller voxels) afforded by SMS-EPI generated a spiky appearance in the six motion parameters (three translations, three rotations) produced by a rigid body realignment algorithm for motion correction, such as MCFLIRT in FSL. The apparent head motion is caused by magnetic susceptibility variations of the subject's chest as he/she breathes, leading to a change in the magnetic field across the head which, in turn, adds a varying phase to the phase-encoded axis of the EPI. This varying phase then manifests as a translation in the phase-encoded axis. It's not a real motion, it's pseudo-motion, but unfortunately it is a real image translation that adds to any real head motion. I should emphasize here that this additive apparent head motion arises in conventional multi-slice EPI, too, but it's generally only when the TR gets short, as is often the case with SMS-EPI, that the apparent head motion can be visualized easily (as a spiky, relatively high frequency fluctuation in the six motion parameter traces). In EPI sampled at a conventional TR of 2-3 sec, there are only a small handful of data points (volumes) per breath for an average breathing rate of 12-16 breaths/minute and this leads to aliasing of most of the apparent head motion frequency. It may still be possible to see the spiky respiration frequency riding on the six motion parameters, but it's not always there as it is for TR much less than 2 seconds.

Once we'd satisfied ourselves we'd understood the problem fully, I confess I let the matter drop. After all, we have tools like MCFLIRT that try to apply a correction to all sources of head motion simultaneously, whether real or apparent. But now I'm wondering if we might be able to evaluate the real and apparent motion contributions separately, with a view to devising improved QC measures that can emphasize real head motion over the apparent head motion when it comes to making decisions on things like data scrubbing. Jo has been dealing with the appropriate framewise displacement (FD) threshold to use when including or excluding individual volumes. (See also this paper.)

Let's review one of the motion traces from my second 2016 blog post on this issue:

These traces come from axial SMS-EPI with SMS factor (aka MB factor ) of 6. The x axes are in seconds, corresponding to TR = 1 sec. (The phase-encoded axis is anterior-posterior, which is the magnet Y direction.) On the left is a subject restrained with only foam, on the right the same subject's head is restrained with a printed head case. During each run the subject was asked to take a deep breath and sigh on exhale every 30 seconds or so. We clearly see the deep breath-then-sigh episodes in both traces, regardless of the type of head restraint used. Yet it is also clear the apparent head motion, which is the high frequency ripple, dominates the Y, Z and roll traces on the left plot. On the right plot, the dominant effect of apparent head motion manifests in the Y trace, with a much reduced effect in the roll axis. Already we are seeing a slight distinction between the translations and rotations for apparent head motion. It looks like apparent head motion contributes more to translations than rotations, which makes sense given the physical origin of the problem. In which case, can we assume that by extension real head motion will dominate the rotations?

For now, let's assume that the deep breath-then-exhale episodes are producing considerable real head motion, in addition to the large apparent head motion spike from exaggerated chest movement. The left plot above shows that pitch, yaw and roll all characterize the six deep breaths readily. They are also visible in Z and X, but with considerably reduced magnitude. There's no clear effect in the Y trace which is dominated by the aforementioned apparent head motion. So far so good! When the head can actually move in the foam restraint, we have clear biases towards rotations for real head motion and translations for apparent head motion. 

What about the right plots? Real head motion is far harder to achieve because of the printed head case restraint. But we assume the apparent head motion is basically the same magnitude because it's chest motion, not head motion. So we might think of this condition as being a low (or lowest) real motion condition. As with the foam restraint on the left, we again see Y translations dominated by apparent head motion. The roll axis also displays considerable apparent head motion. And as for the foam restraint, the roll and pitch axes display something that may be real or apparent head motion for each of the deep breath-then-exhale periods. We can't be sure if the head (or the entire head case, or even the entire RF coil!) was really moving during each breath, but let's assume it was. If so, then for good mechanical head restraint we have the same rough biases as for foam restraint in our motion traces: real motion dominates rotations, apparent motion manifests mostly as translations.

Jo sees a similar distinction between real and apparent head motion in the motion parameter plots of her 2023 blog post. In her top plot, which she suggests is a low real motion condition, the apparent motion dominates Y and Z translations and the roll traces, exactly as my example above. Her second plot exhibits considerable real head motion. The apparent head motion is still visible as ripples on the Y and Z translation traces, but now it's clear the biggest changes arise in the three rotations and these changes are probably real head motion. Again, we have real motion dominating rotations while apparent motion manifests more in the translations.

Finally, let's consider Frew et al., who looked at head motion in pediatrics. Here's Figure 3 from their paper:

Using framewise displacement (FD), they show a transition from FD dominated by translations to FD dominated by rotations when considering low, medium and high (real) head motion subjects. Rotations and translations are both affected significantly in the medium movement group. Still, the trend here suggests that we might consider rotations alone as an index of real head motion if, as suggested above, apparent head motion contributes mostly to translations.

So, what might we do to separately evaluate real and apparent head motion? This is where you come in. I only have one starting idea, and that's to shift to considering FD using only rotations, rather than rotations and translations, when setting thresholds for the purposes of QC and scrubbing. Based on what I've presented here, we might be able to set a threshold for FD(rotations only) that will capture most of the real head motion and have a much reduced dependency on apparent head motion. This measure could help avoid mischaracterizing large apparent head motions as events to reject when they are inherently fixable with MCFLIRT and similar. (Real head motion produces a big spin history effect and likely introduces non-linear distortions in the images.) Whether the reverse is true - that is, whether FD(translations only) captures most of the apparent head motion and a reduced contribution from real head motion - I leave as an exercise for another day, but my suspicion is that it is not. Put another way, I think the focus should be on using the rotations to capture and evaluate real head motion. Pooling translations and rotations in measures like FD may be complicating the picture for us.

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Core curriculum - Cell biology: taxonomy

 

Most of the biology we need to learn can be treated orthogonal to the mathematics, whereas the mathematics underlies all the physics and engineering to come. As a change of pace, then, I'm going to start covering some of the biology so I can jump back and forth between two separate tracks. One track will involve Mathematics, then Physics, then Engineering, the other will be Cell Biology, Anatomy, Physiology and then Biochemistry.

 

Let's begin with a simple overview of cell structure:

 https://www.youtube.com/watch?v=0xe1s65IH0w

The owner prohibits embedding this video in other media so you'll have to click through the link to watch.

Next, a little more detail on what's in a typical mammalian cell:

All well and good, but we are primarily interested in the types of cells found in neural tissue, whether central nervous system (CNS) or peripheral nervous system (PNS):

A little more taxonomy before we get into the details of neurons and astrocytes. In this video, we start to encounter the chemical and electrical signaling properties in cells, something we will get into in more detail in a later post. Still, it's timely to introduce the concepts.

As we move towards the neural underpinnings of fMRI signals, we need to know a lot more about neurons and astrocytes. Let's do neurons first.

While this next video repeats a lot of what you've already seen, there is enough unique information to make it worth watching.

Finally, a little more taxonomy that relates types of neurons to parts of the body, something that could be very important for fMRI when we are considering an entire organism.

To conclude this introduction to cell biology and types of neural cells, let's look at glial cells in more detail.

 Another simple introduction, to reinforce the main points:

And a nice review to wrap up.

We will look far more closely at astrocytes in a later video, once we've learned more about blood flow and control. For now, just remember that those astrocyte end feet are going to be extremely important for the neurovascular origin of fMRI signals.

 

That will do for this primer. The next post in this series will concern the resting and action potentials, signaling and neurotransmission.

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