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If a question brought you here, I’d love to hear it.

Ben

An image space consists of dimensions broken up into units of size. Within that space, the image itself is composed by adding pixel intensity at the location of each unit. Therefore, an anatomical brain image might be a volume consisting of 3-dimensions.

We can make different types of tissue have different brightness by being clever with our measurement techniques. For example, the anatomical image is sensitive to differences in the fat and water content of the tissue. Other images may be more sensitive to certain kinds of damage, or blood flow, or oxygen content, or many other things.

One of the trickiest bits of analyzing these images is getting all these spaces to line up. For instance, you might be interested in seeing where some brain activity is occurring. Images that are sensitive to brain function are not very sensitive to brain anatomy, making finding the specific location of the activity difficult.

To know exactly where the activity is occurring in an individual, we need to acquire a good anatomical image along with the functional image, and then try our best to line up the two. Another common challenge is that researchers want to analyze multiple research subjects so that our statistics are valid and generalizable to the population. But people have different- looking brains! We need to find some good way of lining up the images of everyone in our sample. Finally, we want our results to have some correspondence to other results in our field of study, so we need some way of making our images understandable with reference to a standard. This makes it possible to do things like meta-analysis across studies.

The strategies and tools that help us take on these challenges are legion. They don’t always work well, and they aren’t always easy to understand. A Guitar Hero aficionado, going to bed after a sick sesh of jamming, may close their eyes and find, to their amazement, that the colored notes are still scrolling at them in an endless stream. My experience learning how to manipulate and move between image spaces was similar. I would close my eyes at the end of a long day and see images flipping, rotating, expanding, warping, and changing contrast. Image spaces would enter my dreams and permeate my thoughts.

Somewhere in this process, I started thinking of myself — all the weird constellation of traits that I think of as me — as one of these spaces. Let’s call it my “native space,” since this is the term imagers use to refer to an individual’s own brain image. I realized that I transform my own native space in all sorts of ways to fit in different contexts. My brain calculates all the same “image transformations” that I do at work all the time. Except my brain does it intuitively with everything happening under the hood. By thinking about myself in terms of image spaces, I’ve gained some deep insights about myself and my relationships. I believe that these insights have made me a more thoughtful and compassionate person.

One of the first lessons that I took away was thinking about my identity as linear transformations between different types of contexts. A linear transformation is a function that preserves the structure of a vector space. That means that lines that were parallel stay parallel. In neuroscience research, it is very common to use a simple kind of linear transformation called a “rigid body transformation” to align two brain images from a single participant. In 3D images, rigid body transformations allow for 3 “translations”, which are shifts up or down, side to side, or forward and backwards, and 3 “rotations”, which are turns around the three axes.

For metaphor building, let’s also reduce my personality to three dimensions. Since it’s convenient, I’ll use the three dimensions of personality that the largely discredited and dastardly psychologist, Hans Eysenck, came up with using factor analysis. To be perfectly clear, I’m not advocating for the reality of these three personality dimensions; they’re just useful here for demonstration. His personality factors are dimensions of 1) extraversion-introversion, 2) neuroticism-emotional stability, and 3) psychoticism-social normality.

Now imagine that on a given day, my personality falls within some small shape of these three dimensions. The “intensities” might represent the likelihood of me representing a particular set of dimensional weights. Day to day, this shape stays relatively constant, but my average extraversion, neuroticism, and psychoticism move around. I identify myself based on this shape.

One area where I use these rigid body transformations is when I am correcting images from misalignments caused by a research participant’s motion over time. In some types of scans, we collect an image of the whole brain about every 2 seconds for 10 minutes or longer. When we put a participant in a scanner, we have them lay on a moving tray that will go into a tight bore surrounded by the powerful and loud magnets. Even though we do our best to make participants comfortable, even going so far as to tuck them in with a warm heated blanket, the experience can still be disconcerting for first-timers. Ideally, participants would not move at all during the scan. Even though we place pads around the participant’s head in the MRI scanner, it is still likely that they will move a few millimeters here and there over a long scan. That is problematic for doing statistics on activity over time because a few millimeters are sometimes the difference of “inside where the brain was” or “outside where the brain was.” To deal with this problem, before I do any statistics, I try to do a rigid body transformation of every frame to line up all the images to where the brain was at the beginning. Sometimes, the subject sneezes. That kind of motion is too great and likely to terrorize my analysis. In those cases, we simply throw out the bad frames in a procedure called “scrubbing.”

I have a very similar self-referential process to track myself from day to day. Even though my personality-shape changes slightly over time, most of the time I remain convinced that I am basically the same person. Sometimes, I don’t recognize myself because I fall outside of this shape. Maybe one day, I’m feeling especially “psychotic”, way outside of my normal shape. These days may be filled with accusations from my partner (“who are you?”) and apologies (“I wasn’t being myself!”).

If someone I know and am close to is acting a lot differently than I expect in a negative way, I think it’s good to provide that feedback, but I’ve also learned to trust that it will pass, just like a sneeze. Huge, sudden, permanent translations in personality are unlikely. Instead of worrying about whether I’ve missed some latent, nasty trait for the past ten years, I just scrub.

These small perturbations in my own home are easy to keep track of and continue to feel “Ben-ly.” But I’ve also discovered that I tend to transform my personality in systematic ways to meet the needs of other contexts. For instance, I try extra hard to be extraverted in professional and social events, and I find myself naturally less psychotic around Christmas. These types of transformations may need more degrees-of-freedom than the small or random shifts in my “native” space.

In brain imaging, a situation arises frequently where you have different types of brain images, but all coming from the same individual. Sometimes the MRI sequence slightly deforms parts of certain images or the sizes of the images differ.

I talked before about translating and rotating to fit two shapes together. There are also two other types of linear transformations: shearing and stretching (also called scaling).

These are useful tools when two brain images are from the same individual. They do represent the same brain, after all, so we don’t expect the differences between the images to be too extreme. It takes some work to find an optimal “affine transformation matrix” that aligns one image to another image space, with its different dimensions and contrasts. There are lots of tricks out there to help with this, which work with varying degrees of success.

It is no different in my own life. Even though I’m still me, it helps to apply small transformations appropriate to the various “spaces” in my own life. It takes some time to learn a personality transformation that is optimal for a certain kind of space. However, one cool thing about finding a transformation between two spaces is that once you find it, you can just use that exact same matrix if you ever need to bring something from one space to the other space again in the future. It’s helpful to solve a transformation for a space once rather than needing to recalculate it every time. Let’s say I’m trying to transform my “native” personality to “party” personality. Parties will vary slightly, and so some tweaks might be ideal, rather than relying on the exact same transformation each time. Even so, you save a lot of work by applying a known transformation. For example, I might have learned that to best transform myself to “party space” I must try to be a lot more extroverted and pro-social (e.g. shearing and scaling myself).

In imaging, we can take a hybrid approach. Find the best transformation once, and then start there before making any more image-specific adjustments. One benefit to finding the transformation matrix is that you can take the inverse of that matrix in order to move backwards from the new space (e.g. “party” space) to the original space (“native” space). This is an important concept because our alignment often works better if we go from the lower resolution image to a higher resolution image at first. Taking the inverse of that transformation that maps a low-resolution to a high-resolution image is often a better strategy than trying to find the transformation from high-res to low-res using fitting algorithms.

This principle felt unintuitive to me when I learned it, and it feels unintuitive when I apply it to my personality. When I think of what I need to do to transform into party-Ben, it makes sense that I should think about what I need to do differently than normal to fit in at the party. But my “native” space is high-resolution. I know it pretty darn well. Party space is a little hazier. What if I try to go from low-res to high-res and ask “how does the party need to change to perfectly fit my native self?” Asking this question feels good because it affirms my sense of identity and that I don’t need to change anything about myself for the party. It asks hypothetically how the party can change for me, which is validating and takes me out of the natural tendency to feel like I am the one who automatically needs to change for the world. It also helps me see the differences a little more clearly. The concept of a general party is kind of vague and undetailed. But a party space just for me?! I can give you lots of details about how I would change any given party. If I want, I can use that information as a clear indicator about how to best immerse myself in the party environment. For example, at a recent party, all of my friends decided that it would be extremely fun to start singing together. I am kind of shy, especially about singing, and I don’t really like doing it, especially in public. But by focusing specifically on my default personality’s mismatch with the current environment, I identified a specific way that I could better immerse myself in the party. I ramped up the extraversion, tapped into my love of music, tried to find some courage, and started singing.

In imaging, finding these linear transformations is usually not too difficult, but there are more difficult situations. These usually involve generalization. For example, I often want to aggregate brain imaging data in a study so that I can do group-level statistics. This poses a significant challenge. Everyone has a different brain; How can I say that one person’s brain activity is more or less in the same spot as someone else’s brain activity? One common approach is to bring everyone’s brain image into a “standard space.”

One of the really useful things about a standard space is that a lot of scientists use only a handful of the same standard brains and coordinate systems to describe scientific results. One common standard space is called Talairach space, named after the neurosurgeon, Jean Talairach, who originally developed the coordinate system. His atlas was based on a single post-mortem dissection of a human brain. In our social world, it is also useful to have a “standard space” of the cultural expectations for personality and behavior. Sometimes we look to “influencers,” which, like the Talairach atlas, are based on a single individual that we want to conform to. But using single exemplars for cultural expectations can be dangerous. Often single examples have weird quirks. For the same reason, in imaging, the field has mostly moved to using brain templates made by averaging the brains of many humans. The most common standard space in this category is a set of templates made by the Montreal Neurological Institute.

Just like in brain imaging, there may be different pockets of people that use different standard spaces. When we meet a stranger, we start from our standard space and try our best to get back to their native space by going in the opposite direction. We start from our idea of what is normal and use that to try to find deviations from our expectations in the stranger. Since these kinds of transformations are non-linear, in order to do this, we need an accurate “warp field.” We can think of this warp field as our map of how the individual differs from the standard space.

The first lesson here is that your image of others and their image of you starts in a standard space. I think it is useful to remember that everyone’s model of everyone else is tenuous, unless you know them well. I also have the intuition that some people build accurate representations of strangers much faster than other people. Just like understanding a good algorithm, explicitly defining and practicing these skills could really benefit our social relationships.

In imaging, one analysis step that often helps with improving transformations is to do “bias field correction.” When you acquire an image in a not perfectly homogenous MRI environment, your images can be corrupted by a low-frequency, smooth signal that overlays the actual image. Like an inhomogeneous MRI environment, my own brain is full of distorting effects that can lead to systemic biases in how I view other people. A good first step, when I am trying to judge someone new, is to examine my picture of this person for these sorts of low-frequency artifacts. If you are a very careful imager, you might actually measure the bias field inhomogeneity itself, rather than just trying to identify and remove the low-frequency bias in your image. One interesting way to measure your own biases in judging others is through the implicit association test (IAT) [https://implicit.harvard.edu/implicit/takeatest.html]. This test is useful for identifying unconscious implicit beliefs that you may have and not be aware of that influence how you judge others.

Another effective strategy for finding good non-linear warping is to “smooth” the images that you are trying to align. You can think of smoothing as blurring or smearing the intensities of an image, which makes edges less distinct. I like to keep in mind that when I’m first trying to understand someone, it’s good to try to focus on the broad patterns, rather than single events, behaviors, or single sentences they say. Emotionally charged events, which can be either positive or negative, are like points of high contrast in an image. Your attention is automatically drawn to them to find information, but they are often not the most useful features for non-linear warping and can often lead to mistakes.

Another major insight for me was that, given the same amount of time, I will do a worse job at understanding somebody the further they are away from my standard space. In fact, a major problem with using standard spaces in brain imaging is that images that are further away from the standard brain will inevitably have a worse transformation. They simply don’t match up well.

Instead of forcing every brain into the same standard space, some researchers now advocate for a more nuanced approach. An alternative to transforming between volumes is to parcellate the brain into smaller regions on an individual basis, and then doing statistics on those parcels. This requires more work and is also dependent on having good parcels. Neuroscientists like to argue about which parcellations are the best. As computation becomes faster and cheaper, more scientists are doing analyses using multiple different kinds of parcellations.

The interesting thing about parcellations is that it reduces an individual to a small number of regions-of-interest (maybe 120 points instead of 100,000 points in the original image), and yet if we have a good system for parcellating the brain in native space, this can often be more useful than analyzing brains in standard space. The approaches are inherently different. Transforming images to a standard space boldly asserts that we can identify small details about your own brain in that standard space. The parcellation approach is humbler, giving up on small details in favor of learning a structure. One of the advantages is that you don’t see the standard space image and accidentally start believing that it is real. It can be tempting, when you see a very brain-looking brain, to forget that it is highly modified and wasn’t actually the brain that you imaged.

The parcellation approach is akin to memorizing a list of some number of questions that you think represents a person’s personality and asking the list of questions to everyone you meet. This is more work but is probably helpful for getting some useful information about an individual that can help understand a person within a group context.

These strategies for generalization are very useful for finding broad patterns. However, sometimes our methods are problematic when we have dynamic or longitudinal data. When we have timeseries data, we sometimes want to apply a single transformation to each of the images collected over time for consistency and to save time. This seems principled enough. If you find a good transformation between the first image in the time series and the image that you want to align it to, you should be able to apply that same transformation to all the images in the time series. A similar principle holds for longitudinal or multi-session experiments. For example, I might have two imaging sessions, separated by a week or so, where I collect functional MRI data, but I only collect an anatomical image in one of the sessions. If I find a transform to the first session and keep all the imaging parameters the same, it might be tempting to just use the same transform for the second dataset.

In practice, this doesn’t always work very well. There are small changes over time that can make the transform you found earlier obsolete. This is usually less of an issue from images in the same session and more of an issue when imaging over multiple sessions. One thing that I often notice here is that sometimes using a transform that you previously found for dynamic data will almost always work. Because of that, when it doesn’t work, it can take you by surprise! It can be easy to miss.

In social life, this happens most often when tracking the personality of the people you have relationships with over long periods of time. Some of the most famous personality researchers, Costa and McCrae, did a series of experiments on lifespan changes in dispositional personality traits. They found extremely high reliability in personality scores across even relatively long periods of time and famously concluded that personality becomes “set like plaster” by the age of 30. Just like in imaging, the non-linear transformations you use to understand someone’s personality relative to some cultural standard almost always work. But if you look at Costa and McCrae’s data [https://doi.org/10.1037/10143-002], you’ll notice that while this pattern is true on average, some folks’ personalities change dramatically. In a few cases, there is even a negative correlation with their previous personality traits. I think it’s useful to keep in mind that while the transformations you use to understand the people in your life are mostly consistent, it’s still possible for them to change.

Thinking about the principles of transformations in imaging has changed the way I think about my own personality and how I make judgements of others. Overall, I think that these changes have been positive. I avoid treating individuals as good examples of standard spaces. I try to smooth over the traits I think I see in new acquaintances and try to first focus on the broad strokes. I am more careful about my understanding of someone in cases where someone is a lot different than the norm. I use specific questions as a tool to know someone better. I keep a lookout for when my mental models of other people in my life are no longer working well. I do my best to know my own biases. I don’t get too worried when the people I know well (or I) am acting or feeling a lot different than normal. And I’ve gotten into the habit of thinking explicitly about the ways I want to adapt myself to different kinds of environments without feeling like it’s an obligation by thinking about transforming the environments around me to my own native space.

My first car was my grandpa’s 1986 Chevrolet Celebrity. It had a lot of character. About 25% of the time, if you stopped at a red, the car would stall, and I became adept at putting it in park, restarting the ignition, and getting back into drive before the car behind me started honking. I was not surprised that an older car had these kinds of quirks. As time passes, we expect “wear-and-tear” on machines to cause certain types of system failures. We accept some of them and try to fix the ones that are too dangerous. You would probably not be surprised to hear that it is theoretically possible to keep a car running forever if you were good about maintenance and willing to replace any parts that wore out. Irv Gordon demonstrated the potential longevity of vehicles when he earned a World Record for driving his 1966 Volvo P1800 3,200,000 miles. Most of us would never put so many miles on our cars because it would be so expensive, but not because it is impossible.

However, when we think about the human body, we are more pessimistic about the potential of dramatically extending the lifespan, even though the average lifespan of a human is longer than the average lifespan of our cars. The potential lifespan of a human has gradually increased due to medical technology, but not many people I talk to seem to be super optimistic that we’ll be able to push our lives to be much longer than 120 years.

I study a lot of physiology, which is basically the engineering principles of the body’s machinery. I think that where many people still see death as a quintessential fact that defines what it means to be human, I see death as a failure of a machine that we don’t quite know how to fix yet. When we know how to fix it, it will be like the car situation — expensive, but it will be possible just to keep living. It is likely to be one of the more complicated social issues in our history as a species when this occurs. I think we should be considering it seriously.

First, consider that the biology of aging as a field of study is very young. It didn’t get going until the 1980s when researchers identified a biochemical, genetic pathway that modulated lifespan in several different animals for the first time. We are rapidly learning more about the causes of aging and potential therapeutic strategies to prevent it. This acceleration is hard to keep up with.

The field of aging research is starting to converge on a general framework that helps us understand why we get old. Natural selection occurs through genes, so our biology is optimized to reproduce within some time window. Multiple different biological mechanisms involved in aging are thought to interact with this principle. One that is very easy to understand is that harmful mutations to our genes that only manifest after the optimal window for reproduction easily accumulate. In contrast, mutations that cause negative phenotypes before or during reproduction are selected against.

A second interacting factor is that we evolved in a context of limited resources, so there was an evolutionary trade-off between growth, reproduction, and DNA repair and maintenance. Genes care about reproduction. Growth and DNA repair are only useful insofar as they benefit reproduction. Evolution optimized the balance between these three factors according to the available resources of our past instead of our present.

The last and most complex issue is that sometimes adaptations that benefit reproduction early in life might exhibit deleterious effects later. Evolution isn’t selecting nearly as well for traits after the peak of reproduction. For example, there is a neat little mechanism that helps prevent us from getting cancer. Cancer can occur when specific genes mutate due to damage or mistakes in replication. Since how many times a cell has replicated is correlated with its likelihood of developing cancer, biology invented cellular senescence. A cell tracks how many times it replicates, and then if it’s gone too many times, it stops replicating and broadcasts to everyone around it that it’s old and potentially dangerous. Immune cells usually then come in and clean them up. Unfortunately, more and more senescent cells accumulate with age, including immune cells, and eventually, the body can’t keep up. Then all this senescent signaling becomes problematic because it has adverse effects on the cells around them. The small warning signals accumulate into panicked mobs of cells, changing their function in response to the perceived damage and threat. This proinflammatory cascade can even make cancer more likely, the exact process it was designed to prevent. But it doesn’t matter; the system did an excellent job avoiding cancers before the peak of reproduction.

I am so optimistic about making real progress in aging therapies because we already have so many “anti-aging” mechanisms built into our biology. Nature has handed us a lot of great strategies to tackle aging. The hardest part of the work is already done.

Comparative biology is a valuable way to identify outliers in nature that we can learn from. Unfortunately for us, humans are already one of these outliers. We already have much longer lives than we ought to, and so we have to look to even more extreme outliers than ourselves to gain new insights. One of these even more extreme outliers in aging is the naked mole-rat. Naked mole-rats live on average ten times longer than their close evolutionary relatives. Even at that astounding age, it’s unclear that aging really plays any significant role in their death. They die from infections, starvation, predators, or assassination, not from the age-related diseases that kill many of us. The probability of death is stable across the entire life of the naked mole-rat. Figuring out what is different about them has been a recent pursuit of aging researchers.

One very important discovery made in 2018 is that naked mole-rats still have cellular senescence. That discovery was heartening in some sense. The fundamentals are the same; we may be able to dramatically increase our healthspan through minor tweaks to our physiology. So what things do seem different? Much of the story seems to be in the amount of resources naked mole-rats evolved to devote to DNA repair. Their DNA damage response works better.

So what sorts of tricky hacks evolved? You might not be impressed. Nature is a lazy engineer, preferring to copy and paste from already completed projects rather than inventing a completely new solution. We share a protein with naked mole rats whose job is to activate antioxidant and DNA damage repair genes. That seems like a beneficial gene. Naked mole-rats copied it, so now they have three copies, which is thought to increase the baseline expression of the gene. Awesome. What else? Both humans and naked mole-rats have proteins that label damaged parts of the DNA to be fixed. Naked mole-rats express more of this protein, which makes DNA damage repair faster. There are a few other hacks like this, including better-behaved senescent cells and preventing the accumulation of epigenetic errors, but I hope you’re starting to see my point. The solutions seem within reach. We don’t have to rewrite our genomes. We just have to figure out ways to coax our own biology to use the solutions that it already has.

Another telling discovery that made headlines some years ago was that infusions of young blood in old animals reversed some markers of aging. I think it made headlines because of its entertaining vampiric and dystopian undertones. It was just too easy to imagine billionaires pumping in the baby-blood every day after a joyful jaunt to outer space. But the discovery was fascinating to scientists for a slightly more subtle implication. There seemed to be some substance circulating in youngsters that was actively working against aging. Aging, it seemed, was more than just acquiring irreparable damage. The body knows, at least a little bit, how to repair itself. We just need to figure out what it’s doing.

Researchers are rapidly developing these “coaxing” strategies that utilize the built-in signaling mechanisms intrinsic to our cellular biology. For example, we have identified many molecules now that act as “senolytics,” which modulate the function of our biology in some way to selectively induce the death and clearance of senescent cells. Since so much of the dysfunction in aging seems to start with an over-abundance of senescent cells, there is hope that these therapies will help with aging generally. Most of this work has been done in the last five years, so it’s still early in the research, but the strategy looks promising.

Another strategy involves cellular rejuvenation. An incredible Nobel-prize-winning innovation was made in 2006 by Shinya Yamanaka, who discovered that exposing adult cells to certain proteins (now known as Yamanaka factors) would turn the differentiated adult cells back into stem cells. This technology has a lot of promise for many uses but has recently been identified as a potential aging therapy. The logic of it is really cute. When you expose cells to these Yamanaka factors, they seem to reverse their development and eventually look like embryonic cells…so what if you just stop that process a little bit early so that the cells look like they’re from a spry undergraduate instead of an embryo. Incredibly, this seems to work in cell culture. It’s still too early to tell if this will pan out in living humans and how dangerous it is.

These are just two strategies of a growing list of ideas, including activating Sirtuins, increasing NAD+ levels through supplementation, and metabolic enhancement through drugs like metformin. I won’t go into all the mechanistic details of these ideas, but I’m very excited and optimistic about this rapid development of therapeutic strategies that can be integrated and stacked on top of each other. These are not mutually exclusive ideas.

However, I think a major area where much more development is needed is aging specifically in the brain. Unlike much of the rest of the body, we only replace neurons in a few particular regions. “Reversing” the development of our brain cells, ala Yamanaka factors, might not be the best idea because we might expect that all those age-related changes to our brain cells also contain a lifetime of memories and learning. Keeping the rest of our bodies in good working order should do some good towards keeping our brains healthy, but eventually, I think we will need specific strategies for the cells that we can’t easily replace. We know many processes that kill our neurons begin from dysfunctional external environments like rampant inflammation from aging endothelial cells or over-active microglia. I’m not even sure that anyone has observed much age-related dysfunction in neurons in isolation. It’s even possible that our neurons could survive much longer than the rest of our tissues. Since neurons cannot be replaced, there would be intense evolutionary pressure to repair any damage very quickly. If that were the case, we would have another “outlier” to study, even more closely matched to the rest of our human biology, that might help shed light on how to increase helpful processes like DNA repair. We can simply ask, “how do neurons do it?”

We are semantic apes. We love categorizing things. Even in Genesis, God seems to be amused by Man’s propensity to name things, bringing “all the wild animals and all the birds in the sky” to “see what the man would name them.” We love to classify and differentiate. But it’s easy to see why this exercise might amuse God. Nature is happy to challenge us with annoying exceptions to all of our beautiful categories. It seems that Nature is unwilling to behave even in our core physics, foiling notions like the separability of waves and particles or space and time. I am a neuroscientist, and in biology, we have our own nuisances. One of these annoying problems is answering, “when is death?”

It sounds like it should be an easy question. Obviously, there are dead people and alive people. Dying seems inevitable because so far, everyone’s done it. But defining the point of death has historically been a moving target. Until the mid-20th century, death was conveniently defined by when any measurable vital function, like the heartbeat, ceased. But it turned out that mechanically shoving air in and out of the lungs and restarting the heart were relatively easy hurdles to overcome. Think of how crazy this is! If you could go back in time to a century ago, you could say, without lying, “oh yeah, in the future, people die and rise from the dead all the time. It’s pretty normal.”

We sort of have a working definition for “brain death” now, although it’s defined differently in different places. Usually, there is some objective element to this, like that the patient cannot sustain vital functions without assistance, which generally requires them to have a functional brainstem. There is also usually some subjective part, like that a medical professional believes that the brain can’t recover. Is brain death real death in a way that the heart-stopping is not? I don’t think so. Besides finding the topic interesting, I’m also interested in inspiring people to consider that some physiological limitations are not set in stone. Once Roger Bannister broke the four-minute mile, everyone started doing it. If we start changing our attitude to think of brain death as a challenge rather than an inevitability, then maybe it will more quickly join heart death and lung death as health events that one can recover from.

Let’s think about the core ideas around what death is. For one thing, the absence of consciousness seems to be necessary. No one seems to be too bothered by the notion of fingernails growing on a corpse, even though that implies that some metabolic process is still occurring. (This is a myth, by the way.) The idea of it is sort of creepy, but not many people would feel the need to keep the corpse around because the fingernails seemed to be alive.

On the other hand, if the corpse occasionally complimented the outfits of its family members at its funeral, everyone would be alarmed. That leads to the second core idea, which is that not only is there no consciousness — we can pull off that feat in our sleep — but that this lack of consciousness is impossible to come back from. But that caveat is tricky because what you can and can’t come back from changes as medical technology improves.

It used to be that if the heart stopped, then you wouldn’t become conscious again. But after learning techniques to restart the heart, it did become possible. So what makes us so sure that “brain death” is final? Our assurance is two-parted. The first part is that we don’t get any new neurons after very early development in most of the brain. We do get some in the hippocampus and maybe some other small areas, but for the most part, if a neuron dies, we just have to do without it. We’re surprisingly good at this. There is remarkable plasticity in the brain, and the organ seems to be good at reallocating what cells are left to make up for lost ones. But sometimes, small populations of cells that control essential functions are killed, and we can’t replace them. Sometimes we lose vast swaths of tissue at once. So one issue is that just keeping someone’s blood pumping won’t rebuild a brain, in the same way that our skin can grow back if damaged.

On top of that, the main issue is that there is extensive damage in the brain that occurs when blood flow ceases. Through my education, this fact perfectly exemplified the Dunning-Kruger effect in psychology. According to this cognitive bias, we over-estimate our expertise when we know a little bit about something. When we learn more, we start underestimating and questioning our knowledge. Early in my biology studies, it made perfect intuitive sense that the brain would be damaged quickly by a lack of blood flow. But as I learned more, it started to make less sense. For instance, muscle tissue can go hours without blood and then is just dandy when you bring blood back. Some other animals, like some turtles, can hibernate for weeks in the winter in environments devoid of oxygen. What gives? Why do brain cells in humans become irrevocably damaged after just a few minutes without oxygen?

Our turtle friend has given us a clue by hibernating. There seems to be some relationship between the damage and metabolism, which refers to the chemical processes that help living beings do everyday life things. When the turtle goes into this state of dramatically reduced metabolism, the lack of oxygen doesn’t cause permanent damage. But why? Let’s get into the weeds!

But first, I just want to mention something here because stuff like this always gets me psyched about science. One of the most fantastic things to me about this topic is that nearly all of our collective human understanding about this question, “what in the world does metabolic rate have to do with brain damage when there is no blood flow,” was discovered within my lifetime! The science isn’t some old classic result from the 1800s. Someone else was confused, just like me, and was trying to figure it out around the same time that I was pretending to be a Power Ranger.

The story begins with calcium. Calcium is well known in biology for being important for fortifying bones. Lesser well known is that it is also important for everything. In the 1970s, some researchers had observed that calcium concentration in the liver increased during bouts of restricted blood flow. Weird. Other groups noticed that the same thing happened in the kidney. Super weird. Still other groups saw that it happened in the heart. There seemed to be a pattern. Increases in calcium appeared to be generally correlated to organ damage during restricted blood flow. An obvious question was whether damage to the tissue was causing the increases in calcium or if the accumulating calcium contributed to the damage. This type of question is what experiments were made for!

By the early ’80s, some people found a way to inhibit the accumulation of calcium in tissue. If they inhibited calcium accumulation in the liver during restricted blood flow, there was less damage than when they didn’t. Same thing with the heart. Another pattern. It looked like the calcium accumulation accompanying the lack of blood flow was not good. It wasn’t a massive leap of creativity to say, “Hey, maybe calcium is also involved in all that damage that we see in the brain.” That also turned out to be true. So at this point, an answer was starting to form. When there is no blood, there is too much calcium, which seems to be bad. But I often compare scientists to toddlers who keep asking “why” no matter what answer you just gave. Why does calcium cause damage? Why is it worse in some places than others?

I’m focusing here on results instead of methods, and I always feel a bit hesitant to do this as a scientist because I’m choosing a narrative, and you might interpret an experiment very differently. I think most humans love stories, and we feel very comfortable operating within narratives, but you learn as a scientist that these stories change all the time. All the knowledge you learn in the textbooks is the most popular story at the time. The experimental results don’t vary, but we constantly revise how we interpret them and the narratives we attach to them.

So zooming out even more, I will share the current narrative for why a lack of blood flow damages the brain so badly.

Step 1) Blood flow stops, which prevents our brain from acquiring oxygen and glucose.

Step 2) Oxygen and glucose are essential for creating energy for the metabolic processes in our cells. They are used to make a molecule that acts as the legal tender currency of all cellular operations, called ATP. Since there is no more oxygen and glucose, all of the ATP gets used up.

Step 3) Running out of ATP is terrible everywhere, but it’s particularly bad in the cells in the brain. Brain cells function by maintaining electric potentials on their cell membranes. That way, cells can communicate with each other through chains of depolarization. But those electric potentials don’t just come for free. They are very expensive! There are ion pumps all over the cells that pump in and out charged elements to control the cell’s electric potential. But those pumps require ATP to work.

Step 4) This is the exciting part. You might expect the cells just to relax and shut down operations if these ion pumps stop working. But actually, the membrane losing its polarization (a process called depolarization) is the same important mechanism that brain cells use to “communicate” with other cells via action potentials. The neuron is running out of gas, but the result is paradoxically to frantically tell all the downstream cells to activate! The worst thing that happens when the ion pumps stop working has to do with calcium. Calcium is usually pumped out of brain cells, and then it’s let back into brain cells when the neuron is “firing,” but then quickly pumped out again. When properly regulated, calcium plays a significant role in multiple, unrelated signaling pathways. These functions depend on precise control and timing of when it appears where. With the pumps not working, calcium accumulates inside the cell and starts wreaking major havoc, including initiating signaling cascades that tell the cell to kill itself. It also makes the cell “fire” again more easily, which then tells the downstream cell to let in more calcium, and so this creates a kind of vicious cycle that results in increasing damage in a process called excitotoxicity.

It’s a challenging problem but seems approachable. One strategy is to find a way to safely control these channels that are letting in all of this calcium or other dangerous cations to prolong the time it takes for damage to occur. There are also cell populations in the brain that seem naturally resilient to excitotoxicity that people are researching to figure out why. Finally, if you could turn off metabolism during bouts of lack of blood flow, that could also go a long way to giving healthcare professionals time to fix the problem. Currently, the issue is that if you freeze cells to arrest metabolism, it tends to damage tissue in other ways. Ice crystals mess up the connections between cells and organs fracture. If you use cryoprotectant chemicals to prevent ice crystals from forming, you get excellent structural preservation, but there is damage at the molecular level. So while this avenue is exciting for the future, as far as I know, we have a ways to go.

I imagine that in the future, the issue of death will become even more complex. I suspect that the idea of “brain death” will stay with us. Our conception of death has everything to do with consciousness, and all the evidence points to consciousness being a function of the brain. But how we define the point of death and the subjective determination of when someone has no hope of coming back will change. For now, I think it makes sense to look to the lessons of restarting the heart and maintaining respiration. We need to think ambitiously about how to protect and bring the brain back from ceased function. Why can’t we regrow brain? Can we overcome that hurdle? Can we stop excitotoxicity from happening? Can we freeze someone until the future when we have a better chance of addressing the health issue that caused their brain to be unconscious?

If we solve problems like freezing and thawing humans, the issue of death will become even hairier. You might currently have no hope of coming back to life, but maybe in 100 years, you could. The open question of what medical problems will be solvable in the future would leave defining the point of death ultimately ambiguous. There will likely be an additional level of inequality in determining who gets a chance to come back from the dead in the future.

Okay, I admit that my title is meant to be a bit clickbaity. But with clickbaity gusto, I’ll double down and begin by reminding you that Aristotle, who many well-educated people regard as a pretty smart dude, believed that the conscious mind resided in the heart. If you put down all your modern knowledge for a minute, it’s easier to find the idea quite sensible. Consider this utterly imaginary vignette. Aristotle recognizes, through introspection, that when his mind races with emotion, his heart also changes its pace. Thus, Aristotle hypothesizes that the heart is the seat of the soul and decides to do some experiments. He designs an experiment and replicates it in many different types of animals. In this experiment, Aristotle lesions the animal’s heart, and in every case, all behavior indicative of consciousness immediately ends. The result strongly matches some observational data, where accidents caused lesions to the hearts of humans. He also discovers a method to stop the heartbeat through his studies, but only for brief periods. He boldly asks his research assistant to stop his own heart but only for 45 seconds. In this conclusive study, he finds that when his heart is arrested, he immediately becomes unconscious and remains so for the duration of time his heart is stopped. Yet, when his heart begins functioning again, he quickly regains consciousness.

Now we know better than this, and we confidently believe that the nervous system is the seat of the soul. But the fact that such a plausible experiment could lead to such a conclusion should remind us how intimately the vascular system is connected to the brain. I have become obsessed with this connection, and I would love to start my own lab to continue studying it if any universities would hire me (*hint hint*). I want to convince you that cerebrovascular function in the brain is more important than you previously thought. I’ll start by discussing how it’s important for overall brain health. The brain’s vascular system is simultaneously its supply chain distributor and waste management system. Even if you haven’t really thought about it before, I think it is pretty intuitive that if the supply chain breaks down and waste isn’t managed, bad things are going to happen. Then I’ll take things up a notch and talk about how the brain’s vascular function impacts measurements in neuroscience and what that means for how we interpret data. Finally, I’ll turn your brain to mush by taking you deep into the weeds and showing you why I think vascular dynamics might directly organize global brain function and what it would mean for the future of cognitive neuroscience.

Part I

My interest in vascular health started during my graduate school thesis work, which was part of a larger project investigating the role of the vascular system in normal cognitive aging. Our lab was pursuing the hypothesis that dysfunction to the vascular system in the brain contributed to declining mental ability in older adults. This idea is all the rage now, but it was a little less in fashion back then. Art Kramer, a world-renowned cognitive neuroscientist, was doing heroic work at the Beckman Institute, where I was a student, showing that aerobic exercise had a massive effect on predicting cognition in older age. There were a million follow-up questions to be explored. Of these follow-up questions, the most pressing one on everyone’s mind was, “why?” At the time, lots of researchers were sinking their teeth into neurotrophic factors. These molecules support cell growth and survival in the brain and are involved in learning and memory. Their levels increased substantially during aerobic exercise, so many scientists were hypothesizing that they probably explained, at least in part, why aerobic exercise seemed to be so good for cognition. My advisors, Monica Fabiani and Gabriele Gratton, thought that there were probably some significant vascular effects at play, too.

Monica and Gabriele were famous for pioneering the use of the “event-related optical signal” or EROS. The technique measures slight changes in tissue transparency that occur when a region of the brain is active. It offers a favorable trade-off between temporal and spatial resolution for studying the cortex non-invasively. A knock-off version of the technique was even featured on ABC’s one-season-wonder, “Black Box.” However, their technique, “AMORA” (an acronym we could not decode), had far fewer limitations, offering instantaneous, ultra-high-resolution mapping of the whole brain as well as all its activity and pathology.

A well-known obstacle with using EROS is that it is difficult to measure neural activity because the tiny changes in the optical properties of metabolically active tissue were orders of magnitude smaller than changes in the optical properties due to the pulse. Lab members over the years had worked hard to remove this “nuisance” signal from the precious optical measurements that would be used for inferring brain activity.

Monica likes to use the old adage, “one man’s trash is another man’s treasure.” In what I think was a brilliant insight, Monica and Gabriele realized that this “noise” could be repackaged as a “signal” and used for other purposes. This is one of those essential stylistic lessons that I’ll take with me through my scientific career. Scientific discovery is dependent on measurement. Much scientific progress is driven by developing instruments that measure something entirely new or something old with greater precision. But sometimes, it is possible to measure useful things that we haven’t thought to measure yet using devices we already have. An excellent place to look for those potentially “useful” things is by examining the variance in your measurement, even if you were hitherto considering it noise.

Over the following years, our lab explored using different kinds of non-invasive measurements of cerebrovascular health and investigated if those measures predicted brain health and cognition. Using a measure of cerebral blood flow with magnetic resonance imaging (MRI), I conducted an analysis that suggested that age-related declines in blood flow were mediated by fitness level. At the same time, the lab, and especially Chin Hong Tan (now an assistant professor at NTU in Singapore), was developing useful measures from the optical pulse measurement. We found three relatively independent components of the pulse wave: how big it was (amplitude), how fast it was traveling (transit time), and what the shape looked like (the pulse relaxation function or PReFx). These three components predicted different things about an individual’s physiology, anatomy, and cognition.

The amplitude of the pulse wave seems to primarily just reflect blood pressure. It doesn’t change much across brain regions, although it is strongly affected by the type of vessels you measure over. If we measure over large arteries, the amplitudes of the pulse wave are higher than if we measure from an area that doesn’t have such large arteries. This is useful because it could be used as a broad proxy for brain anatomy with regions defined by the typical locations of the major surface arteries. Typically, we collect T1-weighted anatomical MRIs to colocalize where our signals are coming from in the brain. Although using the pulse amplitude measures would leave us with a much lower resolution of anatomical features, it is possible to sort of know where you are in the brain in an individual beyond just guessing.

The transit time measures also have some interesting applications. It can be used as a measure of cerebrovascular development in infants, and it can be used to track the flow of the major arteries in the brain.

The most exciting measure is the pulse relaxation function (PReFx) measure that captures the shape of the pulse wave and is sensitive to arterial compliance. Arteries are naturally elastic, which helps absorb some of the pressure from the pulse. Arterial compliance is just the technical term for how much the artery’s volume changes for a given amount of pressure. This PReFx measure predicts cortical volume, white matter lesions, and cognitive performance. The most remarkable thing to me is that it seems to predict regional differences in the brain. For example, PReFx correlates with verbal fluency but not working memory when measured in Broca’s area, an area of the brain implicated in verbal fluency. In contrast, PReFx predicts working memory but not verbal fluency when measured in the dorsolateral prefrontal cortices, an area of the brain implicated in working memory. This kind of double dissociation is often good evidence for the separability of biological systems. That is important because it might predict aspects of health for which no other non-invasive measures exist! There is also some evidence that the PReFx measure is somewhat sensitive to cerebrovascular reactivity, too. Blood flow can be regulated dynamically in the brain to shunt blood around to the areas that need it most. We can see metabolically active regions just by observing changes to the pulse shape. We think that we might be able to use this dynamic attribute to assess the responsiveness of the vessels as well, which could also be an important local predictor of brain health.

In a recent paper led by Tania Kong, we saw tantalizing evidence that there might be a cascade of effects, with changes in vascular health preceding anatomical and functional declines, which finally manifest as cognitive declines. This idea is in line with the cognitive reserve hypothesis that the brain can suffer some variable amount of pathology before any substantial cognitive deficits become measurable. Our analysis just looked at the effect sizes of the correlations, so there are many other possible interpretations of the data. We’re currently following up with longitudinal studies to get a better sense of the causal chain.

In my graduate studies, I also analyzed some more dynamic measures of vascular function. We used breath-holding to make the vessels in the brain vasodilate. When carbon dioxide builds up in the blood, there is a neat protective mechanism in the brain, where some of the small vessels called arterioles globally dilate to increase blood flow. Essentially, the body gets a little suspicious of the environment and sagely says, “let’s prioritize getting oxygen to the brain.” How well the vessels can do this, called vascular reactivity, is an essential feature of vascular function. In my analysis, the amplitude, onset timing, and time-to-recovery of the vasodilatory response varied according to age and fitness. We are currently following up on these observations in ongoing studies.

Part II

These health applications are super exciting, but a bit of a niggling fear started gnawing at me the more I studied the variation in vascular reactivity between individuals and even the variation between regions within individuals. One of our primary measures to infer brain activity in cognitive neuroscience is literally a measure of vascular reactivity. Right now, there is no non-invasive MRI measurement that reliably gives us direct measures of neural activity. Instead, we have a measure sensitive to changes in deoxygenated hemoglobin called the blood-oxygen-level-dependent (BOLD) signal. We’ll take what we can get, but this is not an ideal measure for inferring brain activity. The only reason it works at all is because of a nice physiological quirk, where our brain overcompensates for local increases in metabolic activity by increasing blood flow and overshooting the amount of oxygen it needs. This is great, except that it’s not clear that the overshoot is similar throughout cortical regions or cortical layers, a point that is sometimes acknowledged but usually ignored in practice. Additionally, a lot of the change in deoxygenated hemoglobin occurs in the draining veins. These larger veins drain blood from multiple capillary beds, so it impairs our signal’s spatial resolution by making it harder to identify the precise area the signal comes from. And to top it all off, neurons fire on the scale of milliseconds, but the subsequent hemodynamic response occurs orders of magnitude more slowly, on the order of 4–10s, severely limiting the temporal resolution.

We can learn fascinating things about brain function despite all these limitations by assuming that the relationship between metabolic activity and the hemodynamic response is linear and mostly equivalent between individuals. Thankfully, this linear mapping seems to be more valid than you might expect! The variance between individuals will cancel out in a lot of cognitive experiments, and we learn all sorts of stuff about what the brain does. Unfortunately, sometimes we are primarily interested in group differences, such as different cognitive mechanisms at different ages or in pathology, and that’s when we run into trouble. If we don’t account for systematic differences in vascular physiology, we might create theories about differences in cognitive mechanisms that are actually due to differences in vascular functioning.

An example of this type of group difference research is investigating functional changes in the brain associated with tinnitus or ringing in the ears. Most tinnitus is thought to be “central,” as opposed to “peripheral.” This means that its perception seems to be generated by the brain rather than by the auditory nerve. There are changes to the auditory brain centers thought to be primarily involved in the perception of tinnitus itself. There are also changes in other areas of the brain that correlate to the severity and distress that individuals with tinnitus experience. Some people constantly experience ringing in their ears but get used to it over time in the same way that I don’t really attend to the expressway sounds near my apartment anymore. Unfortunately, other people have a lot of trouble habituating to tinnitus, and it gets much more intrusive and distressing over time. There are two predominant paths of brain research on tinnitus. One of them focuses on understanding what generates the percept in the first place, which is mainly concerned with the primary auditory regions of the brain. The other focuses on understanding why some people do not habituate to it and become so distressed, which appears to involve large areas of interacting brain regions.

In this second category focused on severity, one finding is that subjects with tinnitus have increased brain connectivity between some established networks in the brain compared to research subjects without tinnitus. Activity in a network called the default mode network, which is involved in self-referential processing and is most active at rest, has increased connectivity to networks involved in attention. Another finding is that in subjects with tinnitus, the nodes within the default mode network have decreased connectivity. This finding is typically interpreted within a cognitive framework, using some reverse inference based on past knowledge about what we think these networks do. For example, a researcher may speculate that this activity pattern reflects attention being redirected to the percept of ringing in the ears. This attentional capture intrudes on the usual self-referential thought patterns that occur during rest.

However, it’s essential to know that the correlation between attention and the default mode networks is ordinarily negative. There seems to be some inherent opposition between activity in attention networks and self-referential networks. In the same vein, the correlation of activity of nodes within the default mode network usually is positive. This is important because if our signal-to-noise is different in one group compared to another, we might observe false differences between the groups just because the correlation coefficients in one group are closer to zero than in another group. This is precisely the type of pattern that we observe in tinnitus, so we should be concerned!

What could cause such a systematic difference in signal-to-noise? Well, at this point, hopefully, my anxiety about differences in vascular physiology has rubbed off on you, and you’re already thinking that differences in vascular reactivity might be responsible for this observation. If the vessels aren’t responding as much to the same amount of metabolic activity, there will be a smaller change in blood oxygenation and thus a smaller BOLD signal.

I didn’t have data on cerebrovascular reactivity measures in tinnitus to look at this directly, but I did have a lovely sample of baseline cerebral blood flow measures in some tinnitus subjects and controls from some of my postdoctoral work with Fatima Husain’s lab. Differences in blood flow do not necessarily reflect differences in cerebrovascular reactivity, but they might suggest it by indicating some vascular health difference. When I looked at this measure, there appeared to be global reductions in cerebral blood flow in tinnitus subjects compared to controls. Even more striking to me, within the tinnitus subjects, blood flow was anti-correlated with tinnitus severity, especially in the places where we tend to see differences in functional connectivity. Here is the link to the peer-reviewed publication.

This is not by any means definitive proof that differences in cerebrovascular reactivity cause the functional differences observed in tinnitus. It’s not clear why there would be vascular differences associated with tinnitus, the causal direction between tinnitus and blood flow, or if the blood flow measure really reflects differences in vascular health or instead just reflects different baseline metabolic rates. To me, it’s more of a warning bell that we ought to seriously consider a vascular explanation for these functional differences and do research specifically designed to rule out this possibility.

Neuroscience research on cognitive aging is another research area close to my heart where vascular differences might stymie interpretation. Cerebrovascular function declines with age, so direct comparisons of functional brain mechanisms between age groups are exposed to these same concerns about purely vascular explanations. Two central theoretical ideas in cognitive aging are called “dedifferentiation” and “compensation.” Dedifferentiation proposes that aging is associated with a decrease in the modular structure of functional specialization in the brain, contributing to cognitive decline. Compensation refers to recruiting additional, typically unused neural resources to improve performance, sometimes masking declines in brain health. From a mechanistic point-of-view, if true, both dedifferentiation and neural compensation are extraordinary built-in brain characteristics. They may give us vital clues about the architecture of cognitive processes. Most in silico models of neural networks in artificial intelligence do not exhibit either of these properties. If we ever want to build artificial intelligence that more accurately resembles our brain architecture, we really need to know if these properties are real. Therefore, it is critical to understand whether the data underpinning these theories is affected by cerebrovascular function. Are we measuring actual cognitive mechanisms, or are we just measuring changes in vascular physiology?

Part III

I’ve focused on two main ideas so far. Cerebrovascular mechanisms are crucial because they support cognitive mechanisms by bringing nutrients and oxygen that support neural function. The second idea is that cerebrovascular mechanisms underlie cognitive functional measures, so systematic differences in vascular function can obnubilate interpretations of brain function. A third idea I want to touch on, gaining more traction recently, is that rhythms of vascular dynamics are directly involved in organizing functional activity in the brain.

While working with a team of talented neuroscientists (Fatima Husain and Bahar Shahsavarani) and mathematician/engineers (Yuliy Baryshnikov and Ivan Abraham) to develop a new technique for analyzing fMRI data, called cyclicity analysis, we observed propagating BOLD activity on the order of tens of seconds in the Human Connectome Project data. Our paper is currently under review, but you can read the bioRxiv version here. We found that the BOLD signal activity propagated backward from parietal towards visual areas during rest and the self-reflective part of a social cognition task. In contrast, activity switched directions and propagated forwards from visual areas towards parietal areas during the viewing portion of the social cognition task. I’ve discussed these results in a previous post.

These results align with other labs that have either observed or postulated that unexpected, slow-moving traveling waves of BOLD activity exist in the brain. This slow traveling wave is contentious because it occurs on a much slower time-scale than most oscillatory neural activity or even the hemodynamic response accompanying neural activation. However, if they exist, these “infra-slow” fluctuations in BOLD activity are hypothesized to underlie resting-state functional connectivity.

Resting-state functional connectivity is built upon the observation that activity in certain distributed areas of the brain tends to correlate with other specific areas. The brain appears to self-organize into networks, and those same networks often appear distinct in different types of experimental tasks. The correlations used to determine these networks are traditionally zero-lag correlations, which means that we look at which brain regions are correlated with other brain regions at the same point in time. A key insight is that zero-lag correlations between brain regions would be sensitive to the phase of slow-moving traveling waves.

From Marcus Raichle’s lab, Ryan Raut and colleagues recently published data showing that low-frequency BOLD signal fluctuations are coherent with physiological arousal indices, including respiratory variation, heart rate variability, and pupil size. The time it takes for a wave cycle to complete is variable, but the phase of the wave seems locked to the phase of these fluctuations in autonomic arousal. This is pretty amazing! Essentially, they are positing that the functional networks of the brain are generated by slow waves of brain-wide excitability that accompany patterns of autonomic arousal. This is pretty different from the predominant view that functional networks become self-organized according to which areas tend to work together.

What generates these patterns of autonomic arousal? Well, a lot of things could. The autonomic nervous system interacts strongly with many neural systems, breathing, the beating of the heart, the smooth muscle of the vasculature, the stomach and digestion, the bladder, and more. Although it is still unclear what physiology would link autonomic arousal to slow-moving waves in the brain, Raichle’s lab has pointed to this target, autonomic arousal, as the thing to study to better understand brain organization.

However, we still don’t know exactly what is happening when normal functional connectivity is disrupted, such as in aging or certain kinds of pathology. It could be that the autonomic nervous system itself becomes impaired. Alternatively, real functional connections between regions of the brain that normally work together may become impaired. These neural functional connections might normally ride on top of the arousal waves. When they are impaired, it may reduce the signal coming from our measurements, even if the waves of arousal are still intact. Finally, there could be a vascular interaction. The autonomic nervous system also plays a role in controlling the dilation and constriction of the blood vessels. This opening and closing of vessels shifts the supply of blood around the brain. If the effect of the autonomic nervous system on the vessels is blunted or the vasculature cannot properly react to signals to dilate and constrict, then perhaps oxygen and glucose do not get supplied in a timely way. The phase of changes in blood flow might become decoupled from the waves of arousal and impair tissue metabolism.

More and more data are pointing to an unexpected overlap between vascular physiology and functional connectivity. For example, Molly Bright at Northwestern recently found the usual functional connectivity networks within the vascular response to a CO2 inhalation challenge. I told you earlier that a build-up of CO2 should vasodilate the vessels in the brain everywhere in the same way at the same time. “Not so!” says Molly. It looks like the brain’s vascular responsiveness is organized similarly to the functional neural networks. By this, I just mean that the CO2-induced vasodilation within the “default-mode network” is distinguishable from the vasodilation in, say, “attention” networks. We might be tempted to think that the waves of arousal that we see are purely vascular in origin. However, Raichle’s lab and others have observed these global slow waves in electrophysiological data in other animals, suggesting that the vascular response is either parallel to or caused by the changes in neural activity.

My bet is that the interaction of vascular rhythms and functional connectivity will emerge as a significant line of research in the coming years. Here’s my craziest hypothesis: the brain uses these waves of arousal to organize neural activity, and impaired vascular function directly messes up these waves of arousal. To illustrate what I mean by this, imagine that it really is helpful for the dorsal frontal and parietal regions of the brain to coordinate their activity as a network. This coordination is partially facilitated by a wave of arousal that makes neural firing more likely in frontal and parietal regions at the same time. Inflammation in the blood vessels could disrupt autonomic signaling within the brain and de-phase the arousal waves depending on the location of vascular impairment. We already know that inflammation in other areas of the body affects the function of the autonomic nervous system. This is not an easy hypothesis to test because these three levels, autonomic nervous system function, vascular function, and neural function, all interact. I think there are some good starting places to make progress, though. You can experimentally block vasodilation in the small vessels. Does blocking vasodilation affect the slow waves measured with electrophysiology? If it did, it might suggest that cerebral vascular dynamics may themselves be involved in organizing neuronal functional dynamics beyond simply supplying the energetic resource needs of the tissue. If true, it would dramatically alter our view of the role of hemodynamic physiology in brain activity. Along with “supply chain distributor” and “director of waste management,” it would become “project manager.”

Over the past year or so, I have found myself developing opinions about meditation. I am not an expert meditator or a meditation teacher. I acknowledge and respect the long and ancient histories of meditative practice. Even so, I think about meditation a lot, and I believe that modern neuroscience can help people choose how to meditate.

To people newer to the practice, the idea that there are many choices about meditation that need to be resolved might come as a surprise. There are some widespread sets of instructions that make “meditation” feel like a single thing. These instructions usually sound like, “turn your attention to your breath in the present moment; simply observe thoughts, feelings, and sensations as they arise; approach these thoughts, feelings, and sensations with curiosity and without judgment, while re-directing attention to the breath.” In fact, many great meditation teachers wisely de-emphasize particulars to beginners. When starting a meditation routine, it can be more effective to be curious and build a consistent habit rather than getting bogged down by self-judgment about the quality of practice. However, as the meditator progresses, how one meditates starts to matter more.

In its essence, I think that the idea of meditation could be expanded to be more inclusive to encompass all solely mental practice. We humans can do a fantastic amount of work just inside our own brains. One of the fundamental realizations of meditation is that we can change our minds directly through mental work. A more liberal perspective about what comprises meditation is advantageous because it forces you first to ask what you’re trying to get out of the meditation practice. If I wanted to be a professional chess player, a beneficial “meditation” might be to play a lot of chess in my head. There is plenty of evidence in psychology and neuroscience that mental practice leads to tangible improvements in many areas. Even something superficially non-mental-seeming, like finger strength, can be improved through mental training. For many things (like finger strength), actual physical practice is optimal and leads to more significant improvements than mental practice alone. However, for many other abilities, mental practice can be hugely beneficial or even necessary. These abilities include training your emotional regulation, default thought patterns, imagination abilities, meta-cognition, attention, and mindfulness.

Many traditions of meditation practice — the kind of thing that we more readily recognize as meditation — start by challenging our own minds to be more mindful. The idea of mindfulness is that our thoughts, feelings, and sensations remain in the present more often, rather than wandering off to the past or future. I touched on what I think is some core competition in the brain between attending to the present versus attending to the past/future in a previous post. In that article, I discussed my fascination with a common phenomenon in expert meditators. They report that attending to the present changed from being an effortful exercise to being effortless. This change might reflect a switch in the default direction of information processing in the brain as being either down or up a predictive hierarchy.

There seem to be many excellent benefits of mentally spending more time in the present and less time in the future and the past. For many people, perseverating about past mistakes or anxiety and stress about the future can be debilitating. Learning to spend a lot of time in the present can give people an intuitive sense of the impermanence of thoughts, feelings, and sensations, which can be very comforting. As people pay attention to their arising thoughts, they begin to experience thoughts as separate from themselves and often learn to distance themselves from toxic self-criticism. In general, sitting still and breathing more slowly can be a relaxing break from a stressful and fast-paced life. These beneficial attributes of mindfulness training have led to the great success of meditation in the world today.

But for advanced meditators, building mindfulness is really more of the beginning of the journey. Usually, after meditators achieve this effortless mindfulness or have substantially improved their capacity to remain present, they begin to train in more specific ways. There is a vast diversity of techniques, but at risk of majorly oversimplifying, I would categorize traditional meditation techniques as falling into two major categories. One of those categories is “open-monitoring.” The other category is “focused attention.” Both of these techniques rely on the meditator’s ability to keep their mind attending to the present moment, but they practice directing attention in opposing ways. In open-monitoring, one releases the control of attention, which is possible now that their mind can rest in the present without the substantial effort of attentional control. In this practice, they open up their awareness to all experiences, including sensations, thoughts, and feelings, and strive to just experience these things as they happen. In contrast, in focused meditation techniques, meditators maximize control of attention by attempting to direct all their attention to one object or sensation.

Although research is somewhat scarce on advanced meditators, what does exist suggests that both types of meditative practices can lead to very substantial changes to the brain and psychology. However, those changes differ depending on which style the practitioner follows. This is important. If you take meditation far enough, the path you take will lead to non-trivial differences in how your mind works. Meditation appears to lead to trait differences in the brain that go beyond the meditation state. In other words, an advanced meditator’s attentional systems change, even when they are not meditating. For example, focused attention can lead to improved perceptual discrimination, greater perceptual sensitivity, and increased ability to sustain attention over time. In contrast, open monitoring practices can improve distributed attention tasks, such as responding quickly and accurately to an unexpected cue. It is difficult to completely differentiate the two because most expert meditators train using both methods.

An excellent theoretical paper recently came out that discusses meditation practices in terms of hierarchical processing within a prediction framework. In this framework, meditation practice leads to a progressive reduction in predictive processing in the brain. By disengaging automatic predictive anticipatory processes, the meditator dramatically alters their conscious experience. Within this predictive processing framework, focused attention fundamentally increases the precision and perceptual weight of a source of sensory experience. Instead, in open monitoring, all mental objects are assigned low precision. The meditator attempts to let mental objects go by without pursuing them further, which reduces the time allowed to confirm mental predictions. In this process, the meditator experiences a sensory object processed at a lower level of the predictive hierarchy of the brain.

At the risk of alienating some meditators here, many of whom are much more advanced than me, I think there are clear recommendations for proceeding in advanced practice from this hierarchical processing perspective. However, first I should say that I believe all meditative practice should begin by deciding, very specifically, what you are trying to achieve through the method. I have personal biases because of my own beliefs about nature. I find practical reasons for meditating to be mostly laudable and mystical reasons misinformed. By “practical,” I mean things like reducing stress, managing mental illness or dysregulation, boosting compassion, gaining insight into your own cognitive processes, and improving focus and sustained attention. By mystical, I mean things like striving for ego-death, experiencing and interpreting visions, experiencing a lack of distinction between the self and the world, experiencing sensory input without seeing defined objects, etc.

To be sure, these types of perceptual experiences are fascinating. In fact, seeking them out could easily fall into the category of gaining insight into your own mental processes. But I think that many people seek these insights as truths about nature and the universe. These experiences tell us about the brain. I think many of the phenomena can be understood as what naturally happens when you find ways to inhibit or disrupt normal predictive-hierarchical processing. These may be interesting experiences, akin to trying a hallucinogenic drug, but it takes a significant amount of training to push your brain into these states. I fear that it may be dangerous to override such fundamentally valuable features of the brain, such as categorizing sensations into objects or modeling yourself as an agent in the world.

Fundamentally, we can alter the mind to intuitively “know” more or less. This “knowledge,” I believe, is related to the level of hierarchical processing of sensory objects in the brain. As new semantic concepts are composed of smaller sub-concepts and distinguished from related ideas, we learn to intuit more complex and nuanced models about the world. Altering our minds in this way is just regular, good-ole learning. In my infancy, I saw lines; then I learned that those lines formed a building; then I realized that the building was a bank; then I learned the function of banks at varying levels of complexity. When I look at banks now, I have a better sense of their function in society. My emotional response to seeing a bank is equivalently complex. I know more about my external environment.

Sometimes it is beneficial to unlearn things or to see things from a fresh perspective. Although it seems to be harder to do, the predictive framework of meditation suggests that we can also unlearn at an intuitive perceptual level, especially through the practice of open-monitoring. This could be achieved by inhibiting prediction mechanisms. You can get a sense of what this is like by trying a fun exercise that I invented for myself once when I was bored. Try to listen to someone speaking your native language without understanding it, as if it was a language completely foreign to you. It is challenging to do! But after practicing this exercise for some time, I am usually able to do it in short bursts.

My concern about long-term open monitoring practice is that practitioners may incorrectly come to believe that they intuitively know more about the world rather than correctly perceiving that they know less. For a concrete example, let’s say some area in the brain generates the sense that you are localized within your body. With large amounts of practice, perhaps you can suppress the functioning of this area, and your intuitive, conscious perception of yourself is that you are not inside your body. Instead, you are floating outside of it. A neuroscientist would find the experience disconcerting, to be sure. But they would probably think, “well, some area of my brain has to generate the sense that I am inside my body. It’s not like that embodied sense just happens automatically.” The experience would be weird, but nothing in their previous worldview would be fundamentally violated. However, to a non-neuroscientist, who perhaps has never pondered deeply about the origins of their perceptions, it may be much more tempting to believe that they have learned something true and essential about themselves. Despite all their previous experience, they are not tethered in any way to their body. This experience may give them a powerful, intuitive sense of dualism. They might think, “I am clearly outside of my body. Therefore, the soul must be separate from the body.”

To me, it seems like many meditative insights fall into this category — experiencing “no-self,” perceiving the world without distinct object boundaries, perceiving thoughts and emotions as separate from the self, experiencing dramatic and powerful visions and sensations in the body. Some of these insights can be empowering. Someone educated in neuroscience might not be surprised that you could achieve such states, but even so, intuitively experiencing them could leave a lasting impression. However, I’m worried that people seek these states as ways of experiencing deeper truths about nature and, in some cases, strive to make these perceptual changes permanent. I do not see the value in this at all. It seems like that would introduce impairments that would ultimately cripple someone’s ability to interact with the world.

Focused attention practice seems both less dangerous and more useful to me. Let’s go back to thinking about meditation as a class of specific mental training exercises. Focused meditation trains brain mechanisms involved in perceptual clarity and sustained attention, which strike me as handy skills. Open monitoring meditation is harder to pinpoint but seems to primarily train non-prediction. This can lead to “improvements” in some rare experimental circumstances, but I am much more skeptical that this will sharpen a mind in ways that are very useful in the world.

I’m glad that expert meditators exist, and I would hesitantly advocate for studying that population much more closely. Advanced meditators have spent tens of thousands of hours investigating their own minds. They can presumably describe their mental states in more accurate and subtle ways than us normal folk. I say “hesitantly” because of the history of psychology. Experimental psychology began with a very similar paradigm pioneered by Wilhelm Wundt. He would train expert introspectors to learn about the secrets of the mind. That period taught us that it is hard to remove the bias of our education in introspection. Even so, the potential to learn about cognitive mechanisms through this population seems underutilized.

However, I think for most people, before we pursue an intense meditation practice, we should seriously think about why we are doing it and what our goals are. There is a rapidly growing application of meditation for the treatment of undesirable mental conditions. Just like other medicine, perhaps we should be more concerned about what dose is too much.

There is sort of a profound underlying philosophical question here about what we should be striving for. Let’s imagine that advanced meditation practice is guaranteed to lead to extreme equanimity, peace, and bliss. That sounds pretty good! But let’s also imagine that these affective states come along with profound changes to the mind: that you have no self and have no sensual desires. Those side-effects would undoubtedly be detrimental to my current relationships and life. Pursuing that state would be entirely out of accord with my current goals.

Opioids also lead to peace and bliss, but most people understand that taking opioids all the time can disrupt the lives that they want in negative ways. I don’t think that many people similarly view meditation. Probably, part of the reason for this is that it is hard to achieve these profoundly altered perceptual states without extreme devotion to practice. Most people don’t devote so much time and effort, so for most people, meditation is only beneficial. What makes me nervous, though, is that it seems like the goal of many meditation practices is to achieve these “enlightened” states of “insight.” Thus, many forms of meditation practice, and especially open-monitoring methods, are geared towards this goal.

With so much hype around meditation, it may be helpful for meditators to take a moment and ask themselves why they are meditating. Are the mental practices that they are engaging in ideal for their goals, or are the practices designed with different goals in mind? I hope that by putting more thought into the range of mental-work that could be reclaimed as “meditation,” new practices can be designed to represent the full diversity of peoples’ goals.

“Their brains lit up!” Most people will be familiar with the hot yellow and red blobs superimposed on beautifully high-resolution brain images to show brain activation. These images give the impression that the brain is switching modules on and off as needed to complete its arcane computations, but the typical blob is just a statistical contrast comparing groups or experimental conditions. In reality, the brain is brimming with activity everywhere, all the time. Even in the absence of any experimental task, there are dynamic fluctuations in metabolism all over the brain. Loads of research now supports the idea that these fluctuations are not just random noise, but rather that they carry information predictive of behavior and cognition.

The conventional way that neuroscientists have been thinking about these fluctuations is in terms of functional networks, which are composed of specialized nodes distributed throughout the brain. One of the more surprising findings that led to this network-architecture-thinking is that even during rest, when a participant is instructed to do nothing in particular, brain activity seems to be organized into networks. The time-course of activity of any small portion is highly correlated with the time-course of activity in other specific parts of the brain in very replicable patterns. For example, if you took a functional scan of your brain while laying in an MRI scanner for 10 minutes, I can predict with some confidence that the fluctuations in metabolism going on in your angular gyrus, way off towards the back and side of your brain, will be highly correlated with the fluctuations going on all the way on the other side of your brain, in your medial prefrontal cortex. These same networks that can be identified during rest also seem to show up during certain types of cognitive tasks. Scientists have mostly assumed that these networks of nodes in the brain have modular functions, and they use cognitive tasks to try to learn what those functions are.

The biggest technical limitation in the conventional analysis comes from the decision to correlate the full time series between two regions to establish their connectivity. This methodology has implications for how we interpret the signal. What the method misses are two critical questions about timing. First, does this functional connectivity stay stable, or does it vary over time? Second, does activity in the brain propagate or occur simultaneously? With our simple correlation analysis, we assume that the connectivity mostly stays the same and that within-network fluctuations of brain activity mostly occurs around the same time.

Neuroimagers are not blind to these assumptions and much work has gone towards discovering more about the dynamics of brain metabolism. There are diverse approaches, way beyond the scope that I want to cover, but the answers to our questions above are that network connectivity does seem to vary over time and there also seems to be some propagation of signals. The propagation of activation is what I want to focus on.

In 2015, Anish Mitra, along with Abraham Snyder, Tyler Blazey, and one of the living gods in the pantheon of neuroscience, Marcus Raichle, published work that looked at “lag structures” of functional activity. The idea was basically that instead of assuming that functional activity should be most correlated between two regions at the same sample in time, maybe we should also look at correlations that are offset by some number of samples. This idea is pretty bold. We know that neural activity propagates through the brain, but those signals occur on the order of milliseconds. The temporal resolution of a sample in functional MRI (fMRI) is a few orders of magnitude worse. But the situation is even worse than that! The physiological response that fMRI is sensitive to sluggishly follows neuronal metabolism about 4 to 10 seconds later. Basically, no one expected to see propagating neural activity moving so slowly. And yet, a number of groups have now observed this phenomenon.

I have the honored distinction of being the least talented member of one of these groups. We recently posted a manuscript on bioarxiv, which uses a type of cyclicity analysis to determine leader-follower relationships between regions in the brain. The method itself has some wonderful properties that make it useful, including the ability to find leader-follower patterns between regions without needing to assume some sort of specific and invariant time lag upfront. It works by relying on the oriented area between two time series. The most simplistic example I can think of is illustrated below. Imagine that there are two signals occurring in time:

Hopefully, you agree with me that the orange signal follows the blue signal. We can plot these two signals against each other in a parametric plot:

Now the axes represent the amplitude of the orange and blue signals (indicated by color), and time points are indicated as black dots in seconds. The clockwiseyness or counter-clockwiseyness of the plot tells you what degree one signal comes before or after the other signal. You can determine a metric of this clockwiseyness by calculating the enclosed oriented area if you happen to remember everything you learned in Calc III, …which I did not. Fortunately, the rest of my brilliant team members did, along with a good deal of other technical and mathematical expertise, and thus cyclicity analysis applied to fMRI data was born.

First, we analyzed data from participants during rest. We looked at data from the Human Connectome Project, which is an open dataset that anyone can look at and has been an amazing resource for progress in neuroscience. What we observed was quite exciting. We saw a definite direction of propagation that seemed to move backwards in space from parietal areas to more primary visual sensory regions. But it wasn’t until we looked at some task data that our jaws really dropped. The task we looked at was a social cognition task. In this task, the participants were shown short, 20-second video clips of shapes that either interacted in some way or moved randomly on the screen. After the clip, the participants were tasked to make a judgement about whether the shapes considered each other’s feelings and thoughts. As the participants watched the clips, we observed directional activity propagating from the primary visual areas in the back of the brain to the association areas of the parietal cortex. Then when the participants were asked to make a judgement, we saw a sharp switch in direction, with directional activity propagating from the association areas towards the visual areas, like we saw during the rest. To me, this pattern of activity strongly suggests that a direction of information flow in the brain is observable at relatively slow frequencies.

This idea of directional informational flow meshes well with an emerging alternative view of the physiology responsible for resting state networks. Remember, the conventional view is that networks are comprised of groups of modules, which are used to carry out specific types of tasks. The alternative, emerging view suggests that certain areas are correlated in time due to the phase and direction of propagating information. One of the major lines of research pointing to this interpretation is the innovative work of Daniel Margulies which characterizes functional connections in the cortex in terms of gradients. This work has found that rather than networks appearing to be isolated into distinct modules, correlations between areas seem to drop-off slowly in space. Further, the drop-off of correlation strength seems to follow a functional hierarchy.

The idea of hierarchy is not at all new to neuroscience. In fact, it is one of the essential ideas that neuroscientists use to think about the computations of the brain. For example, in the visual cortex, at the lower levels of the hierarchy, signals from the retina are combined to form impressions of edges and sharp contrasts. At the next level, these edges are merged into lines, then shapes, then more complex forms. Finally, in complex association areas, multiple sensory modalities are combined into higher level semantic features. Spatially, for the visual system, this computational flow starts in the very back of the brain and propagates forwards.

Putting these ideas together, functional brain networks could be thought of in terms of the direction and timing of flow, rather than as combinations of specialized modules. If you are paying attention to the outside world, then information flow may be propagating “forward,” starting with primary sensory regions, which process stimuli time-locked to real events in the outside world. We might predict that activations in sensory attention systems and primary sensory areas would be highly correlated in this “attend-outward” state and may become less correlated as they move through the hierarchy to association areas. If you are paying attention to the inside world and imagination, then information flow may be propagating “backward,” starting with the more complex association areas responsible for containing higher level semantic information, where processing is time-locked to a semantic representation, which then get projected backwards onto more primary sensory areas. For example, if you are asked to visually imagine a rabbit, you start with the idea of the rabbit as a concept, which then gets pushed to your sensory modalities where a visualization of the rabbit appears. We might predict that activations in the higher-level association areas would be highly correlated in this “attend-inward” state and may become less correlated as they move backward down the hierarchy to sensory areas. In fact, one of the dominant observations from functional brain networks is the anti-correlation of the dorsal attention network, which is seen most strongly when a participant is asked to attend to an external task, and the default-mode network, which is seen most strongly during self-referential, planning, or imagining tasks, which also seems to be the default for an idling mind.

With our cyclicity analysis we seem to observe this forward and backward propagation, and so I’ve become enamored with this hypothesis about brain organization. I’ve also maintained a consistent meditation practice now for a few years, and I am especially excited about the possibility of cyclicity analysis as a tool for better understanding and enriching meditation practice. The practice of meditation has been increasingly conceptualized in-terms of manipulating and controlling the direction and extent of hierarchical processing in the brain (or respectively the extent and level of prediction of the mind). This idea was very well explicated in a preprint that came out last year by Ruben Laukkonen and Heleen Slagter, which I think anyone interested in the neuroscience of meditation should read. The framework of meditation as an exercise in controlling hierarchical processing overlaps well with the idea of cyclicity analysis as a tool sensitive to hierarchical processing and opens many new avenues to test hypotheses about meditative practice.

First, I have read (but not yet experienced) from a number of sources that advanced meditators often describe a point where the core exercise of meditation, maintaining attention on the present and not mind-wandering, becomes effortless rather than effortful. Any novice meditator will relate with how difficult it is to prevent mind-wandering over an hour of meditation. I am now practiced enough that I have had some “good” meditation sessions, where I have meditated for a full hour without any substantial mind-wandering. I leave these sessions drenched in sweat. It feels like hard work. So personally, effortless meditation is very appealing. But also, as a neuroscientist, the idea of a sudden switch, where the task of meditation was hard but suddenly becomes effortless, is incredibly fascinating. I have spent a lot of time chewing on all-or-nothing mental phenomena like this — like the eureka moment, when some concept is suddenly clear and understood. I think that conceptualizing states as directional flow of information might be a candidate hypothesis for understanding this sudden switch. As I alluded to before, for some reason the “default-mode” of the brain, what the mind automatically does at rest, seems to be to imagine and plan — to linger in the future or the past. This requires processing information from high-level semantic representations backwards to their constituent sensory components. With enough intervention, controlling your brain to stop thinking about past and future and redirecting it to attend to the present, perhaps the plasticity of the brain allows it to eventually reset the default direction of flow. If my interpretation of our results is correct, we should be able to test this phenomenon and maybe even develop a general measure of expertise in meditation, by analyzing the direction of flow through cyclicity. I would expect that in an advanced meditator, compared to most individuals, I would observe the opposite direction of flow during meditation and maybe even during rest. A cyclicity-based, real-time metric may even be useful as a potential biofeedback signal, which may outperform current EEG-based methods that rely on frequency correlates of meditative states but often strongly overlap with other phenomena (e.g. general relaxation, sleepiness, or alertness). FMRI would be prohibitively expensive to commercialize for the purpose of biofeedback, but optical imaging systems may be viable. The cyclicity approach should be agnostic about the modality of measurement.

Another interesting potential application may be in predicting or training the vividness of sensory imagination. I admit that I haven’t seen many meditative practices that seek to improve the vividness of imagination, and this may be because most mainstream meditation practices spend time training the opposite direction of information flow. This is, admittedly, an even more far-fetched idea, but my thinking is that the conflict between bottom-up processing from incoming sensory processing and top-down processing from imagination might lead to imperfect and less vivid sensory representations. For a simple example of what I mean, think about how much easier it is to remember or imagine something with your eyes closed than with your eyes opened. If cyclicity can give us a metric of the flow of information, which corresponds to the strength of these outward-in or inward-out states, we might be able to use it as a predictor of the vividness of imagination. Training imagination like this does not seem nearly as in vogue as the traditional goals of meditation, but it strikes me as a tremendously useful skill. Maybe the top-down, backwards-moving cyclicity signal from association cortex to primary sensory areas could be used as a metric for biofeedback in order to train the vividness of memory and imagination.

Thinking of two primary directions of information flow in the brain is obviously simplistic, but I think that even this simplistic model of propagating information has a lot of potential for stimulating thought about networks and new experiments in cognitive neuroscience. Although it is in the very early stages of development, I think that the cyclicity analysis used in our preprint could be used for studying slower more state-based information flow in the brain. What do you think about this model of information processing in the brain? What other applications can you think of for cyclicity analysis in neuroscience and beyond?