The future of neuroimaging

0:00This is Stanford Engineering's The Future of Everything, and I'm your host, Russ Altman. Since we started this show eight years ago, it's become an archive of amazing and impactful work by my Stanford colleagues. Research is not something that just happens in the lab, and as you'll hear on this show, the research at Stanford can impact areas like health, technology, law, and business, and many other topics that can affect everyday life. We hope you'll tune in to learn more about how research has the potential to help your life and to help the lives of people you care about in your family and your community.

0:32I've written a lot about how we don't really believe you can like read the contents off of a person's mind right now with the methods that we have. Right. You can you can get some information. You can certainly read off like what kinds of things are thinking about. But in terms of like the train of thought from inside your head. Lie detection. Right. Right. Exactly. That kind of stuff just doesn't work right now.

0:56This is Stanford Engineering, the future of everything, and I'm your host, Russ Altman. Thanks for listening. And if you have a moment, please rate and review the podcast. We love to get a 5.0 if we deserve it. Your comments, we read every one of them, and it helps us grow the show. Today, Russ Poldrack will tell us that functional MRI imaging of the brain can do a lot. It can tell us the similarities between humans and also the differences in their brains, and it can potentially detect brain disorders early and help us more quickly identify diagnoses that need to be treated. It's the future of neuroimaging.

1:33Today, we're continuing our feature, which we call the future in a minute. At the end of our discussion, I will ask Russ a few rapid fire questions and he will give us some rapid fire answers. And before we get started, another reminder to rate and review the show. It's hugely important for growing it, for giving feedback to us, and so we can see how well we're doing.

1:59So I think we all know that brains are complicated. And we also know that we can measure our brain with CT scans of the brain and with MRIs of the brain. That often happens if you bonk your head or there's a problem, a seizure, migraine headaches, lots of reasons why you might get an MRI of the brain. But one of the things that researchers can do is not just take single pictures of the brain, but they can take a picture of the brain, like a movie, while the brain is working, while it's thinking about things or while you're performing specific tasks. This is called functional MRI, and it's based on looking where the blood flows because the parts of your brain that are working the hardest are where the blood flows the most.

2:37Well, you can use that then to understand which parts of the brain are involved with which kinds of activities and how the brain coordinates and communicates all the things that are going on in your body at the single moment in time and over time during the trajectory of your activities. Well, Russ Poldrack is a professor of psychology and of psychiatry and behavioral science at Stanford University. He's an expert at neuroimaging and the use of functional MRI to dissect the similarities and differences between different people's brains and how this may all be correlated with disease. Russ, how did you decide to focus your research on neuro and brain imaging?

3:20You know, I started out in cognitive psychology in graduate school, just studying, you know, how people's minds work by measuring how long it takes them to do things, how quickly they learn, stuff like that. And then I came to Stanford actually to do a postdoc and wasn't planning on doing brain imaging. I was planning on studying people with brain disorders. And this was right when functional MRI was kind of up and coming. Stanford was one of the few places that had been doing it in the early 1990s. And I kind of got sucked into it. And in part, it played to my love of playing around with data.

3:56And because the data you get from brain imaging are just so much richer and bigger than what we got from behavioral studies. And so that's how I got pulled in back in the mid-1990s. And then it's, you know, since then, functional MRI has become the primary tool that my lab uses to try to understand sort of, you know, how it is that the brain makes the mind work. Great, great. So you mentioned a couple of things, and I did want to get to kind of a little basic tutorial for people who don't think about neuroimaging every day. What is functional MRI? Can you give us a brief thumbnail about how it works?

4:31And then we can get into how you're using it to ask the research questions that you ask. Sure. Yeah. So when the brain does stuff, neurons, fire action potentials, they do a lot of metabolic activity. And we would love to be able to directly measure that. But without going into a person's skull, we can't do that non-invasively, or at least We couldn't until people figured out that something interesting happens when neurons fire, which is that the brain sends extra blood there. It actually sends more than it needs to send to make up for the oxygen that's used by the brain. And so you can use the fact that basically oxygenated and deoxygenated hemoglobin have slightly different magnetic characteristics.

5:18This was discovered by Linus Pauling back in the 1930s. 1930s, you can build MRI, what we call sequences, basically like programs for the MRI scanner that are sensitive to this slight difference in the magnetic characteristics. And you can use that to basically indirectly image where activity changes in the brain. Wow. Okay. And then this is for, at what resolution? Are we looking at centimeters or inches or millimeters? How fine grain can your measurements get? So the standard measurements that we do, it depends on the strength of the magnetic field. So the standard scanners that we use, which are kind of like the high-end scanners that would be used in a hospital today, the magnet is three Tesla.

6:04That's the measurement of the magnetic field. At three Tesla, we can generally measure about two millimeters. Okay. And we're constrained not so much by, we could make the pixels smaller, we call voxels, because they're 3D pixels. We could make them smaller, but the problem is that the mechanism that we're measuring, this blood flow mechanism, the blood actually has to kind of go downstream a bit of the actual capillary bed before there's enough for us to see it. And so at 3T, that is a millimeter or two. At higher field strength, you can actually do better because you can see things happening closer to the capillaries.

6:46And so we're starting to collaborate now with people at Stanford. There's a new seven Tesla MRI system that we're hoping to do. And then you can get down to about a millimeter, depending on how you want to do it. Okay. So to summarize, what I think we've learned is that you're measuring what part of the brain is kind of working because it's using up more blood. And that is the whole brain. You're looking at the whole brain. And so I guess, and you'll tell me if I'm wrong, and I have heard you speak a couple of times, that what we're doing then is we ask a study participant to do some sort of cognitive test.

7:21Think about this, do this activity, or are you sad or are you not sad? And then we see what parts of the brain are active, and that allows you to create these associations between parts of the brain and types of activity. So is that generally the idea? That's one way that we do things. Yeah, so that's the classic way when I started doing functional MRI, you know, 30 years ago, almost. We would give people tasks to do, and we still do that. You know, they sit in the MRI scanner, they're lying there with buttons and, you know, visual presentation. We do things like having them, you know, make actions and then occasionally stop themselves or change what they're doing.

8:00We're interested in how people kind of control their behavior. There's another thing that you can do, which is simply have them lay there and do nothing and collect data. And then look at kind of what's correlated with what over time, because you're taking a picture every roughly, you know, one and a half to two seconds. OK. And so this is what we call resting functional MRI. And this actually has turned out to be super useful in understanding structure in the brain that we didn't really know about until the early 2000s. People started doing this. It turns out that there's this set of we now call them kind of like, you know, large scale networks in the brain, depending on who you ask, maybe 13, 15 of them, maybe 20, that engage at the same time and that engage in relation to the same types of things.

8:48So for example, there's one network we call the salience network, which involves some places in the parietal lobe and some places in the frontal lobe that anytime something surprising happens, that whole network kind of turns on. Ah, okay. So good. So that is actually super exciting. And I wanted to ask, now tell me what are some of the exciting things that you're doing? I know that you even mentioned in the introduction that you had an interest in what went wrong in the brain and mental illness and other diseases of the brain. So how are you approaching it? What are the questions that you're asking?

9:21Yeah, I mean, the big questions that we're asking right now in the lab are, there's a couple of directions I'll talk about. One is sort of how it goes back to this idea of, you know, how we control our behavior. We think that, you know, a number of different mental health disorders and neurological disorders involve deficits in the ability to, for example, stop yourself from thinking something or stop yourself from doing something. So we've, one of the things that my lab has studied the most is how we stop ourselves. So, and we do this in a really simple way. We have you doing a task where you usually press a button but occasionally you get a signal that says, oh, don't press the button.

10:01We call it a stop signal task. And we, a long time ago, first kind of worked out the circuitry in the brain, in the human brain that allows you to stop. And a lot of the work we're doing now is trying to better understand how that relates to other aspects of control. For example, if you are doing one task and then you have to change to doing another task, or if you have a habit that you're trying to override, you know, those different aspects of control. So we're trying to understand to what degree they involve sort of, you know, overlapping versus separate circuitry in the human brain. Yeah. So you mentioned, tying that back to your previous comment, you described that there are these networks in the brain.

10:40I think you called it organization. You said the brain is organized and it's organized into these, I guess, sets of cells that communicate with each other a lot. What are the kinds of, so I assume that you're looking at those networks when you're asking these questions. And so is it that all 15 of them are, or 20 or whatever it is, are interacting? Or do you find that certain of them are quiet during some tasks, but they get more active at others? And it just sounds very complicated because, you know, we all, even we know from intuition that our brain has a lot of things going on at any given moment.

11:15So how do you figure out what you can ascribe to the task that you've asked them to do versus they're just trying to keep the body moving and, you know, maybe their mind is wandering to other things because they're sitting in a big tube, it's making a lot of noise. How do you do all that? Yeah. Well, one, you know, we, I think you're, you're exactly right that the brain is a complicated thing. So there's not just 15 things going on at once in the brain, there's, you know, probably billions of things. And in part, you know, we have to take kind of a hierarchical approach, right? We, you know, we look at the brain at a relatively, I would say mesoscopic kind of middle scale view, right down to maybe a millimeter up to like these networks are often, you know, centimeters.

11:57Obviously, there are people, you know, in neuroscience who look at things down at the molecular level, the cellular level. So we're taking a particular view where there are going to be fewer things going on. And one of the interesting things that we know is that within those, say, 15 networks, there's substructure as well. There are individual areas, there may be hundreds of them in the brain that we know make up those larger scale areas. And they will also show differences, even though they might together move around, say, when you see something surprising, different ones are going to react in slightly different ways.

12:33And so we don't just analyze things at the level of those large scale networks. We analyze them at many different levels down to the millimeter level. And we do it, you know, there's various ways that we can ask these questions. One is, you know, how much activity is there in, you know, in those particular things. Another interesting question that, or an interesting direction that's come about is asking for a particular individual, how much of their brain is sort of assigned to each of those networks? Yeah. So, you know, before you go on, I wanted to ask, are those networks the same for everybody?

13:10And are they in the same spots? So, sort of, yeah. So, pretty much everybody has all the networks. And until about 15 years ago, we didn't really know. We knew that everybody kind of had all the networks, but we never had enough data from any individual to really map out what's the fine grain structure there. And so, I actually did a study on myself. Yes, I wanted to ask you about that. That's very exciting. 2012, 2013, where, you know, I was I was interested in this kind of resting state functional MRI approach, but we hadn't really done it in my lab. We had always taken that other kind of task based approach.

13:53And I didn't really trust the data because we didn't have very much data from any individual. So I basically said, one, I wanted to know just when you have enough data, what does it look like? And also, how does it change over time? So and, you know, I was director of an imaging center in Austin. I had a scanner that I could easily get into regularly. So I basically started scanning myself generally twice a week when I was not traveling or something. Every Tuesday and Thursday morning at 7.30 a.m., I would get into the MRI scanner for 30 minutes and we'd collect some data on it. And what instructions did you give yourself about what to think about or what to do?

14:26We collected 10 minutes of data with me just sitting there resting. And for the first few times, it was like just me trying not to have a panic attack because I didn't really like getting into an MRI scanner. But then by the end, I was like almost falling asleep. It became almost like my daily meditation. But for those 10 minutes, I basically just sat there with my eyes closed and just thought about whatever I want to think about. Then I usually also did some kind of other measurements, either task measurements or other types of MRI measurements. And then actually every Tuesday, I would also go get a blood draw to do some various like biological analyses.

15:04We salute you for your dedication to the cause. So what do you learn from these experiments? It's a hugely, I mean, you are able, and I assume that this is all ethical and that somebody approved this even doing to yourself. What kind of things do you learn about the resting state? And how does that relate to what somebody else might yield if you had me do it twice a week for half an hour? Yeah. I'll just mention there's a bunch of interesting ethics questions about self-experimentation. At University of Texas, they basically said, we don't consider this research. We will not even consider it as an IRB protocol.

15:43You just go do it yourself. So other people differ. Yes. But OK, so what did we learn? The first thing we learned is that when you have enough data, you can very reliably characterize not just these large scale networks, but you can do what we call parcellation, where you find like small patches of the of the cerebral cortex that are kind of talking to each other more intensely by which I mean they're correlated with each other more than they are to other parts of the brain. And so when we took those existing methods, and I ended up collaborating with a group at Washington University in St. Louis who had been developing these methods.

16:21When we took their methods, we found about 600 of those little centimeter to two centimeter patches across my brain. And when we took about a few hours of data and did it and then took a different few hours of data and did it again, we saw very similar results. And so that told us that these things are reliable. Now, are these patches in the same network that you told us about before, or are they talking to other networks? They're mostly, so you can think about it, like each network is kind of broken into these little parcels, but different ones of them have different degrees of communication.

16:57And there's all these sort of like network theory models about hubs and participation and all this sort of stuff you can use to try to understand that. And we did some of that work. The really interesting thing was that, so we got all this data on me, we did the parcellation, and for each of the individual parcels, you can ask, which network is it talking to most strongly? And most of them kind of looked like what you would expect, but some of them were in weird places. So like in, you know, we traditionally expected the middle of the prefrontal cortex is general, was generally thought of as being part of this thing we call the default mode network, which is a network that's mostly active when you're just sitting there not doing anything.

17:39It actually turns down when you start doing a cognitive task. It's like an idol. Exactly. Right. Yeah. And within that part of the brain, there was a little spot that was connected to the salience network that I mentioned. And nobody had seen that before because they were taking a small amount of data from a bunch of people and averaging them together. And so we actually didn't know, does Russ Poldeck just have a weird brain or is everyone like this? So a group at WashU led by Nico Dozenbach went and did a study. They called them midnight scan clubs because they were getting in the scanner at midnight because the time was cheap.

18:11So 10 of them scanned themselves a bunch and found that it turns out that everybody has these little function, we call them functional variants now. But they're in different places. So when you average a bunch of people together, they kind of wash out. Okay. So that does answer my question about whether everybody's the same. The answer is that maybe at a high level, as you said, the network functionality is there, but at a lower level, exactly how your brain implements that communication can vary. And do we know, is that a function of genetics or how you were raised or who knows? We don't know.

18:45And that's a really important question that I think we'd love to answer. I mean, there's some data showing that these functional variants are heritable. So there's presumably there's some part of the genetic plan that relates to wiring. See if you can get your father and mother into an MRI for a couple of times a week, right? Right. I know. Yeah. But the one really interesting thing is that, and we just learned this in the last couple of years, there was a paper a couple of years ago that showed that basically did the same thing on people who had been diagnosed with depression and imaged them a bunch and saw that they had more of the brain associated with the salience network.

19:27like substantially more. It's a small sample, but they use some other data sets to try to validate it. And I was a little bit skeptical about it, but we, working with Carolyn Rodriguez in the psychiatry department at Stanford, did a study where we brought in people who have obsessive compulsive disorder symptoms, scanned them 10 times over a few months, and saw that they show the same phenomenon. They show this pretty marked expansion of the salience network. So this is potentially very profound because now you're hitting upon people who have perhaps even diagnosed psychiatric or other brain diseases.

20:08And now you can maybe, of course, the implication is maybe this is a diagnostic tool, but it also could be used to track the efficiency of treatments. Like, are the treatments working? Is the network behaving more the way we might expect? Is that a direction? Have I imputed too much or is that a direction that we're going in? Well, it's interesting because it's actually like that nature paper from 2024 showed that the size of the salience network didn't actually track with symptoms or with remission. So it almost seems more like a trait that might kind of give one a higher probability of having depression.

20:49But what I think the greater excitement is about, you know, we know that most people who come in and get treated for depression, the treatments work about a third of the time, right? The first time they have to try a bunch of different treatments. And some people, you know, really never find a treatment that works for them. The question is, could we use something like these data to do a better job of predicting what particular treatment is going to be useful? And then potentially, if it's like a brain stimulation treatment to target that. This is Russ Altman from The Future of Everything. I'm speaking with Russ Poldrack from Stanford University.

21:21We've been talking about the basics of functional MRI and brain imaging, how it works and what you can learn. And I wanted to move to a discussion of reproducibility and open science. But before that, I want to remind you that at the end of my conversation with Russ, we'll pause and we'll do future in a minute where I'll ask him some rapid fire questions and he'll give me some rapid fire answers. So Russ, in the area of brain imaging, there's been a lot of discussion and even in the lay press and certainly in the scientific press about reproducibility of the results. And you've just described what sounded like very careful studies that you did both on yourself and on participants.

21:56What is the reproducibility issue and how is the field responding to it? Yeah. So, you know, and brain imaging isn't unique in this spec. I think a lot of scientific fields have been struggling with this. You know, what we noticed over the last couple of decades was that, you know, it wasn't always the case that when somebody reported a result, that that result could be reproduced by someone else. And this was especially true in studies looking at, you know, differences between groups like, you know, depressed versus healthy individuals or at kind of correlations with, you know, personality features.

22:32Although you did tell me in the first segment about you did your study on yourself and then you had your 10 colleagues who were able to reproduce it.

22:42So Go ahead. Please continue. Yeah. Yeah. And I'll just point out that the move to collecting so much more data, I think, is what got us there. Yeah. So so I think, you know, we a lot of us, you know, I was writing papers, you know, 10 years ago about the challenges of, you know, what we needed to do to make the result, the research more reproducible. And we we felt like there were two moves that one could make. One is much larger samples, just as in, for example, genetics. People used to look at genetic variants and their effects in 20 people. Now they look at it in 100 ,000 people because they knew that the small studies were not reproducible.

23:21Similarly, there's been a move in brain imaging to work with much larger data sets that have helped try to make the results more robust. So just to clarify, is that more participants or more data on each participant or both? Yeah. So we would love it to be both. It's hard to do both. So, for example, there's a study called the Adolescent Brain Cognitive Development Study that's imaging about 10 ,000 kids repeatedly over time. And they collect about an hour of brain imaging data per kid per session. And that's just because you couldn't get kids to come back that often. But you have thousands of kids, so you can at least start to pull out some, especially, you know, things that looking at correlations with, you know, various cognitive features across people.

24:11So you can either kind of go broad like that and just have large samples and kind of use the power of large numbers to wash out, you know, the noise of the tiny samples. or you can go deep. And that's, you know, there's a, in, since my study, basically, there's been a move within the brain imaging literature to, to go much deeper. And it's, it's really led to a number of like, you know, really incredible new findings. I mentioned the one about depression. There's others about like kind of new aspects of brain structure that we didn't know about before. But those are the two directions that the field has gone to try to address this.