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a lot of what we do in neuroscience just focuses on the patient. And even that's even probably an

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overstatement. It's really focused on the person's brain. So we don't think about that the brain's

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sitting in this person's body, the person's in this environment, the person has to talk to their

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loved ones, they have to interact with them and so on and so forth. And it's like, well, if we

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took that model and thought about how do we take them out of the MRI facility and put them into

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having dinner with their loved ones, how do we actually study that in a way that's useful?

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I'm John Fox, Director of the Dal Monte Institute for Neuroscience at the University of Rochester.

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And I'm absolutely delighted to welcome you to another episode of Neuroscience Perspectives.

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We've taken the podcast on the road to the Society for Neuroscience Conference here in lovely,

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beautiful, sunny Chicago. And I'm thrilled to be joined by Dr. Randi McIntosh, Professor and

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Director of the Institute for Neuroscience and Neurotechnology at Simon Fraser University,

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one of Canada's preeminent academic institutions in Burnaby and Vancouver, Canada. He is also one

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of the founders of the Virtual Brain, an open source neuroscience platform that simulates the

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human brain and offers insight into how it works and how disorders like stroke, epilepsy, or

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neurodegenerative diseases impact its function. Randi, thank you for taking the time to come to

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join us today. Happy to be here. Let's dive into your research. We'll come back to a bit to the

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computational stuff. You're really interested in the development of the brain really across

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the lifespan and the sort of functional architecture of the brain. Where does that even come from? How

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did you decide to study this? Well, that's a long story. I think the motivation really came from my

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foray initially when I was a postdoc, was doing some work on aging at NIH. And then when I was

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in Toronto, I was in a geriatric research facility called Baycrest. And there, obviously, the focus

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is on aging. It occurred to me, it's not terribly surprising that a lot of what happens to you when

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you get older is influenced by what happened to you when you were younger. Anybody who's an athlete

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knows that if you got a busted knee when you were 20, you try and go skiing when you're 50, of course.

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It's like, my knee's sore. What's going on? But certainly it's going to happen with the brain as

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well. So a lot of the approaches that one uses to understand that trajectory requires you to really

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have that sort of lifespan perspective. The challenge, of course, is that we really don't have

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data sets that cover that whole span. It's usually longitudinal studies typically are short,

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relatively speaking. So we had to find a way to kind of pull these data together. That's kind of

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the motivation behind doing aging and development, but then also trying to find a computational

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platform like Virtual Brain that allows you to sort of stitch these things together to make sense

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of it all. So this idea of sort of building a computational representation of a biological

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structure in silico, in a computer, where does that come from? Tell the lay viewer out there

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why you would expect to be able to really model a human brain in a computer.

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Yeah. I mean, it's not like we're trying to build a completely accurate representation of the brain.

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That would be otherwise we'd just build another brain. What we're trying to do is understand

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what are the critical pieces that we can simulate that allow us to understand something important

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about it. I think the great analogy is something like, for example, flight simulators, where

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the whole aeronautics industry really had a huge boon when they improve their ability to simulate

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the consequences of certain design considerations for wings, for example, that sort. We can do

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something similar in the brain. So we know that the areas are connected in a certain way. Does

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that have any relevance? If we change the connections and then try and simulate the activity

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that comes from that, does that look anything different in terms of what we see in real data

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sets? And you can go back and forth between them and say, if I model the critical features that I

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think are important for capturing dementia, for example, for capturing the evolution of language,

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for instance, what are those critical features? And I can test my hypotheses in the simulation

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itself. And the nice thing in simulation is that you can do the experiments without having to

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actually paper scans and so on and so forth. So it's a very efficient way to test your

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speculations and then try and incorporate the empirical validity of that validation.

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The same way that you test wing designs and come up with a small set of candidates for

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wings that actually work and say, well, this one's the one I'm going to put onto this particular

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plane. And it's great because that way you can avoid having to actually test it and have the

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planes crashing, which you don't want to have happen, of course. And all the work and effort

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that goes into performing human experimentation too. But is it one of these cases then where you

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can make some predictions from the virtual brain and then take it back into the field and say,

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does this actually happen in reality? Precisely. I mean, there's some papers recently,

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not from my group necessarily, but one in development looking at the change, for example,

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of the balance of excitation inhibition, which is a big thing in the brain, and how that evolves as

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we develop. And the model shows that there are certain things that should happen. It's never

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been really shown, but those people went back and sort of doing some pharmacological experiments

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to show that in fact, yeah, that's actually what we see empirically. So it's a great way of

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validating and testing and generating hypotheses for that matter as well.

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Yeah. You know, it's interesting because in the field, right, there are folks that come

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to neuroscience from a computational background, a math background, and people who come to it from

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biology and pharmacology. And then there's a very huge group of people who come to it from the

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cognitive sciences, right? They want to understand the brain. And many of those people, and I'm going

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to paint in a very broad stroke here, you know, might be a little mathaphobic. They didn't, they

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came through the, you know, interest from a psychology perspective. Would you go so far as

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to say that really there's no way forward in understanding the brain without having

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computational models, or is that too extreme?

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I don't think...

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I'm not trying to get you in trouble with all the psychology.

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No, no, no. I mean, my pitch isn't psychology, sorry. I come from that lineage. And I have

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those battles a lot. Certainly like my, when I was in Toronto, the courses I taught were mainly

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multivariate stats in the psychology department. So, you know, half the classes absolutely hated

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me for how dare you put up equations. So I do think it's important to have a computational

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perspective. And it's important to realize that these computational models are great tools.

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Right.

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I don't think it's fair to expect all of the psychologists to learn dynamical systems theory

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and be able to derive the equations, but it is worthwhile to be open to what they show

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and to at least be able to have a conversation about them.

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Right. At an intuitive level.

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Yeah. And what that means though, is that it's on the other side, the people who are

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the maths that are doing this, the applied mathematicians, the physicists and so on,

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have to learn how to converse with the psychologist and learn how to do the math.

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With the psychologist and make analogies or just show the equations in a way that's

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tractable so they can have a conversation and then build the models jointly, for example.

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Yeah. Maybe even take that one step further. And the end of the day, right, our duty is to the

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people who pay the taxes to pay for the, so we need to be able to communicate this

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for sure.

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Folks who aren't even in the field in a way that's understandable.

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And you typically find that that way of trying to communicate actually works really well for

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communicating to your colleagues as well. Because it's not going down the lowest common denominator,

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but it's actually trying to make it tractable. And that exercise, that capacity is one that

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needs to be developed better in science.

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Right. Yeah. You know, I'm just thinking, you were certainly one of the pioneers of not the

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person who really introduced structural equation modelling to what we do and that. And brought a

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lot of very good mathematical tools into the space. There's right now we're in the middle of this AI

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revolution. It's as if AI showed up a couple of years ago. And yet, you know, I think if people

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in our field, you know, we've been talking about these same mathematical procedures for 20, 25

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years. Are we at a discontinuity now? Did something happen two years ago or is it just that it's

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finally emerged into the public sphere?

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I think it's become easier to use the tools and there's now a critical corpus of students,

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critical number of students and scientists that are using these tools that are interested in

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applying it to the brain. Historically, it was interesting when I was in Toronto again, that a

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lot of people who were developing these technologies, the algorithms for AI and machine

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learning and so on, they did it a lot of times without thinking about the brain, without

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actually talking to someone who was actually doing the wet science. So for them, it didn't really

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matter if that's how the brain did it. This does, this learns face recognition really well. So we

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really cared about, but now I think there's that linkage that's been formed. And that's really what

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I think changed things is that all of a sudden the gates to open and I've got people going back and

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forth who are really facile in the computer science underlying AI and people who are willing to take

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that step to apply it to their own data sets. There is a course and you probably know this as

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well as I do that, there's an assumption it's almost going to be a panacea for us. And I think

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it's important to make sure that the realistic aspects of AI machine learning are conveyed to

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people who think it's going to solve all the problems. And that's where the problems come in,

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I think is that over promise, under deliver kind of thing. Yeah, exactly. Yeah. It's very interesting

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just to think that the dialogue, the narrative in the media about AI is so extreme. Yeah, unfortunate.

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Yeah. And yet again, like these mechanisms, convolution nets, all this stuff, it's really

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been around for decades and the neurosciences trying to solve the problems that we think about

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every day. And again, as you say, there's a real risk of expecting too much too quickly. Yeah.

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Yeah. It's going to be an evolution, there's no question that all techniques do that.

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Depending on how far you're removed from the actual work done at ML and AI is we need to be

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careful of not putting too much of a spin in either direction on that. And that's where that

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tempering, the enthusiasm, but kind of saying this is a great tool because it can do things that we

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could not do 20 years ago, but it's not going to solve your problem. It's not going to write your thesis,

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it's not going to solve your problem for you. You have to actually still think about what's going on.

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You and I had a conversation about this over the last few weeks,

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thinking of the traditional way in which experimentation is done in human beings.

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We put them in an isolated, electrically shielded booth or we have them doing something

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in a magnet and they're highly unnatural. And we do it for control, right? So we can really turn

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the dials in a very specific way and an experiment to control everything as best we can. But you've

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really started to think about getting out into the environment and measuring people in the real world,

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doing what it is they do actually in reality. Tell us a little bit about that and where your

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thinking is going. Yeah, the motivation for that really came from getting back to my

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years in Toronto. And you probably know this as well, when you're in a hospital facility and you

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kind of see what people are actually faced with when they're faced with different kinds of neurological

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aspects, stroke, dementia, and so on and so forth. So there's them and then there's the families and

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then there's the extended family and all having to deal with the challenges that are around that.

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A lot of what we do in neuroscience just focuses on the patient and even that's even the patient

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is probably an overstatement. It's really focused on the person's brain. So we don't think about

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that the brain sitting in this person's body, the person's in this environment, the person has to

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talk to their loved ones, they have to interact with them and so on and so forth. And it's like,

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well, if we took that model and thought about how do we take them out of the MRI facility and put

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them into having dinner with their loved ones, how do we actually study that in a way that's useful?

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There's all this thing about collaborative cognition that's been out for quite a while where

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all of a sudden the person with their families seems to do better than if they're doing the

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regular cognitive assessment. They shouldn't be able to do that and all of a sudden they're

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remembering things. And that's the kind of thing that's kind of puzzling because we don't have a

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way of studying that because we want to do it in a scanner. But what if we did it in the person's

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kitchen or went to their community? And that was the motivation. I didn't get a lot of traction

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primarily because there's a lot of other things going on. In BC and especially in Simon Fraser

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University, there is this notion that the university is part of the community. And if that's true,

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if we really aspire to be part of the community, let's bring the community in. Let's work with them

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to start designing our studies. So I said, let's actually put our money where our mouths are and

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try and build a platform that we can take out into the different communities in and around Vancouver,

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take it into the interior BC and talk to the communities about what they think is important

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for brain health, for brain resilience. What do they think are the most pressing issues?

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Work with them on trying to design some studies, have wearable devices that can measure not just

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brain, but body and get those integrated in a ways that we can now model with AI and virtual brain,

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but also use the communities as our collaborators. And I think that's going to really push

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the neuroscience in a direction that we need to go because it does integrate the person in the

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environment and the environmental issues that we deal with as well. But it also then allows the

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community to feel like they're actually part of the enterprise. And that's the important thing for me.

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Yeah, fantastic. And we're sort of at this historical nexus to where the sensors, high bandwidth,

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easy, deployable, cheap sensors are now available to us. And I mean, I think neuroscience has always

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been very good at adopting these technologies. So the ability is there now, maybe it wasn't there

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20 years ago where everything was heavy and lugubrious and difficult and we didn't have the

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bandwidth or computing power. Yeah. Yeah. And now it's you can have it in your back pocket.

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You know, I love what you just said about to the listening to the community at Rochester.

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Rochester is actually the home of what they call the biopsychosocial model of medicine. And this was

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the thought that you stopped thinking about a person as the disease that they walk through

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the door with, but as a person that was embedded in a family and a community and a culture and a

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medical history. And so I think, you know, you've literally restated that very beautifully.

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And we're in a position now to really measure the biopsychosocial context in which somebody lives.

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Yeah. And it's a complicated problem. I use the word complicated in the sense of

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getting back to some more discussion earlier about the math underlying these things,

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that it's going to be an enormous challenge to find a way to integrate these data in the

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way that can be used. Because you think about, again, when we're talking about brain, we kind

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of, we can measure neurons, we can measure populations of neurons, we can make multi-scale

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models, at least putatively. But now we're talking about bringing us in a body. So we have these

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interactions between the visceral and the metabolomics, the motor system and how the

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motor interacts. And that becomes another level of modeling that we really have to kind of think

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about how to do well. Extraordinary complexity, but the essence of actually being a healthy

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human being, a lot of interactivity in the other systems. You know, and I suppose another challenge

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will be doing it equitably and making sure that we get it to people who are in less technologically

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rich parts of the country. Definitely. This is certainly a big consideration for folks,

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for example, with disabilities, movement disabilities, who can't come to the mothership,

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you know, to the main university to get an MRI scan and that. So there's real hope there

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that these sort of mobile technologies that you can push out into the community might have

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much greater reach than our current model. I'm hoping so. And again, it's also like the

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communities, like the indigenous communities, First Nations communities are obviously

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skeptical of these kinds of things. But I think there's a great opportunity there to

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work with them as partners on these things to really understand their perspective on things.

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And that there's some, you know, some of the conversations I've had have been extremely

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illuminating to really get a perspective that it's almost that the perspective they bring is

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that perspective that there is the person is part of this broader community, broader environment.

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So if we have a way of actually incorporating that into our, how we do our science, that can

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really make a big difference, not just to us, but also to them. Fantastic. Fantastic. Let's talk a

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bit about Randy McIntosh. Let's talk about the five year old. Oh, okay. You know, so what were

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you like growing up? Where does the passion for science come from? I think I saw a story about

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you and your brother coming up with experiments and being just very inquisitive young man.

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He was motivated by Ivo Knievel. Ivo Knievel, that's what I saw. Ivo Knievel was a motorcycle

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daredevil. He used to jump over incredible things. And one of the ones was the Snake River Canyon,

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which was, I don't remember when exactly it was, but, and he had a motorcycle that was basically

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a rocket. So my brother and I tried to recreate that and ended up putting my brother in a box and

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then setting it on fire. Unfortunately, we got him out of it before it got too bad. But trying to

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build a rocket. My brother and our version of that was to get the Ivo Knievel action, man. We didn't

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go any further than that. Oh, that's wonderful. We should have prototyped it first, I guess.

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There you go. So you almost set your brother on fire. That's how it started. Yeah. I think he's

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forgiven me. I think he's forgiven me, right? But yeah, so I started reading quite early.

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I don't, my mom says I was around four and I started reading psychopedias and I was just,

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this thirst for knowledge was always there. What I was interested in was quite diverse,

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everything from dinosaurs to climate to like different languages to history and so on. So the

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science has always been something that's really been driven. It's been this, I guess, an innate

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curiosity that was always there. And it never really changed even in the high school days when

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I was doing the rebellious things, there was still a scientist that was there. Certainly when I

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transitioned to university, there was a brief moment where I thought I wanted to be a lawyer,

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a very brief moment after my first year pre-loss, like, yeah, that's not a good idea. So

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I stayed with science after that and ended up being neuroscience. It was partly from

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some work in high school, but also partly from taking some classes in my hometown on Lethbridge,

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where it was really sort of inspired by the classes I had with people like Rob Sutherland and

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Ian Wishaw in particular. So again, again, we hear of people who come in contact with very specific

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individuals who really just float their boat, get the intellectual juices from it.

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Yeah, yeah, yeah. And Rob was a new faculty at that point in time, so he was extremely

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interactive as well because you could see the enthusiasm for what he was trying to do.

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And it's that enthusiasm for that path of discovery that really kind of grabbed me. It's like, wow,

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I could really see myself doing this.

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I could really see myself doing this. And then you did your original undergraduate

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university up in Canada in Calgary and then headed down to Texas for your PhD.

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Right. What brought you to Texas?

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It was really the person I wanted to work with. So in Calgary, I started working with a technology

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called autoradiographic 2-deoxyglucose, which is basically a predecessor of the imaging stuff

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that we're doing now with humans, but I was doing that with rats. And there was a guy in Texas

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named Francisco Gonzalez Lima who was using this method 2-DG to make it easier to spay

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in a behavioral sense. So when it was first used, it was kind of like what happens to

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glucose metabolism if we give an animal ketamine, if we give it a stroke. But there were very

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few people doing like what happens if an animal learns, for example, that an auditory stimulus is

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an aversive signal, a classical conditioning kind of thing. And he was doing that and was like,

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I want to do that. So I sent him a letter, handwritten letter, and I got a letter back

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about two weeks later saying, yeah, come on down. So at that point he was in Texas,

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saying I'm in university, which is at the medical school there. And then a couple of years later,

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he got a position in Austin. And you moved to Austin with him.

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I moved to University of Texas at Austin, which was...

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Pretty good. Good place to land for you, I happen to know because of your passion and love for music.

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Right? So music's another big component of your life.

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Definitely. Definitely. Yeah. I was one of these...

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I can't tell you the number of people who've sat in that spot. Music is highly,

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highly overrepresented in the neurosciences or in the sciences. I think it's amazing.

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Yeah. It's interesting because I don't know if people think about it as a separate thing.

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I mean, certainly the process of creating music is different than analyzing data and thinking about

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the brain, but I don't know that they're different enough so that they're considered

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almost separate activities. Right.

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That a lot of... And we can test this, of course. A lot of the processes,

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the underlying biological process should be similar. It could be very much the case

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that a lot of the fluidity in terms of the mental processes could be similar.

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And this could be why music actually has such an appeal for people who are thinking about

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high thoughts, like either would they be scientists or whether they be other people who are also in

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that creative genre that use these other things to continue to engage broader networks for

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their own enjoyment, but also potentially to also help think through problems.

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I had somebody say to me one time, one of the things that I was thinking about was

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somebody say to me one time, one of the things that she got out of music was that in the sciences

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and what we do, that there's a tremendous delayed gratification component to it.

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You start something today and you'll be very lucky if you see something useful or something

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that will give you a little bit of a buzz for two or three years and often longer.

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And she went home and she played some music and the product was right there.

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And that was something that she needed to tie it through.

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This is very true. This is very true. There's a gratification you get from

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playing music or even listening to music that's harder to get from

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getting your paper accepted in nature.

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It's a long day.

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Tell us, with your trainees, and well, I often ask this question,

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if given the Gestalt, the Zeitgeist in 2024 for a youngster coming through graduate school,

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do you have pearls of wisdom, advice about the world that they're entering into in the sciences?

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Would you do it again today if you had it to do over?

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Oh yeah, no question.

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As I said, I mean, science is deeply ingrained in who I am.

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So I don't think about my career choice as being a job. And that was something that I

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really found peculiar in talking to some of my other colleagues who treated science as a job.

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And this is true for other professions, whether it be art, you can think about other professions,

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medical doctor, for example, that you don't shut things off at five o'clock.

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And I think that's one of the things in science that it's going to sound smug and maybe a little

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bit elitist, I guess, but it's really don't go into science if it's not a passion.

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Yeah.

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Don't think about it as like, my job is to be a scientist because it doesn't stop at five o'clock

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on Friday. Some of my insights come to me on Sunday mornings, unfortunately.

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So I gotta write that down. So I'm up at four o'clock in the morning on a Sunday trying to

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get this thing written down. And that's the way, I mean, musicians do that as well, right?

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Yeah, of course.

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Musicians get inspiration or artists do the same kind of thing. And I think that's the

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attraction of being in this field. And it's for me, I feel blessed that I actually get to do this.

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Yeah.

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I'm kind of stunned sometimes that I get paid to do this. Wow. And that's, I think, one of the

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things that people going through the system now really have to think about is that, is this

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something that really I think of as a passion or is it something I'm doing just because this is

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what you do?

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Yeah.

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So going through bachelor's, going through your PhD, going through your postdoc, if you're just

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doing this because those are the steps, it's probably the wrong reason for doing it. And stop

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at some point and think about, is that really what I want to do? I've been quite deliberate

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with my students to have those conversations a number of times and be very frank with them.

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I'm not going to think less highly of you if you decide I want to go into industry, for example.

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Right.

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Because that's a good choice and maybe that's actually what you want to do and maybe be

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successful there. Try it.

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Yeah.

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Why not? There's no penalty really. You may take a pay cut. Actually, most people go into industry,

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get a pay raise.

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Yeah, they pay raise.

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But it's really allowing that opportunity to explore where they want to go and supporting

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them for that. And that's my job is to really support their journey so that they're making

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the decision when they finish their PhD that they're going to be doing something that they're

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going to be passionate about when they're 60 or 70 years old as well.

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Right. Right. No off switch. In the end of the day, it's a living breathing thing. It's part

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and parcel of your very being to be on the whole time. And I appreciate that too. It's not a job.

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It's a privilege to get paid to do it.

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For sure. For sure. Yeah. Like when we moved to Vancouver, we have a place now on Vancouver

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Island as well, which is a great retreat. But when I go there, it's not like I shut everything off.

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It's a different perspective.

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Yeah.

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No question. But it's not like I...

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That's an important point of being a scientist too, right? Which is it's one thing to be in the lab

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all the time and doing experiments. But the quiet time and the thoughtful time is where

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a lot of the work gets done. And that's the part maybe that doesn't feel like work.

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Yeah.

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Where you're riding your bike and...

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Well, the virtual brain history actually came not because Victor, you're sitting in a lab,

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you were sitting in a pub having a conversation about it. And all of a sudden inspiration came

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from that. And he and I do a lot of running together. So when we are on runs, we have these

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great conversations that are kind of meandering and all of a sudden, oh, we should stop and record

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this.

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Yeah. So you could have been talking about the football game or you're talking about this.

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It's just...

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Right. Yeah. Yeah. Yeah. For sure.

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Superb. Randy, thanks so much for taking the time with us.

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Pleasure. I enjoyed it.

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It's really great to have you here. Looking forward to seeing your work.

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Yeah. Thank you.

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Over the years to come.

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Take care.