Speaker 1: Does brain science need a new grand plan? Is the
Speaker 1: brain less like an assembly line and more like a
Speaker 1: weather system? And if so, what does this mean for
Speaker 1: how we might go about understanding how to think about it,
Speaker 1: and how might AI help us in the near future?
Speaker 1: And what does this have to do with how the
Speaker 1: drug riddle In got its name. Today we'll speak with
Speaker 1: scientists Nicole Rust who's been thinking about these issues. So
Speaker 1: get ready for a great brain stretch. Welcome to Inner
Speaker 1: Cosmos with me David Eagleman. I'm a neuroscientist and an
Speaker 1: author at Stanford, and in these episodes we sailed deeply
Speaker 1: into our three pound universe to understand how we see
Speaker 1: the world and for that matter, how we should view
Speaker 1: the brain. For a very long time now, neuroscience has
Speaker 1: been driven by the hope that if we could just
Speaker 1: zoom in far enough, the brain would finally give up
Speaker 1: its secrets, if we could just do one more electron
Speaker 1: microscope upgrade, or nail one more molecular pathway, or get
Speaker 1: one more brain network labeled and circled in a textbook. Now,
Speaker 1: the approach so far of gathering tons of data has
Speaker 1: delivered real triumphs. We've learned an enormous amount about how
Speaker 1: neurons fire, how circuits form, how chemicals are released and sensed,
Speaker 1: And when you flip open any modern neuroscience textbook, it
Speaker 1: really is a marvel. It's densely packed with discoveries that
Speaker 1: would have been unimaginable a generation ago. But there's an
Speaker 1: uncomfortable question hovering in the background. If we understand so
Speaker 1: much much more than we used to, why do so
Speaker 1: many neuroscience problems remain so stubbornly unsolved? Why do entire
Speaker 1: classes of brain disorders like psychiatric illness or neuroggeneration, or
Speaker 1: disorders of mood and thought continue to resist our best
Speaker 1: efforts And it feels like that's been happening decade after decade.
Speaker 1: Why does it feel sometimes like knowledge is accelerating, but
Speaker 1: meaningful clinical breakthroughs lag behind. These questions force us to
Speaker 1: ask whether the challenge lies in the way we're framing
Speaker 1: the problem. That is, maybe we should be asking whether
Speaker 1: the brain is a different kind of system than the
Speaker 1: metaphors we've relied on. We should be asking whether reductionism,
Speaker 1: which is figuring out all the pieces and parts, can
Speaker 1: ever by itself, fully explain something that evolved to be
Speaker 1: adaptive and live wired, and where you have eighty six
Speaker 1: billion neurons that are like live little creatures, moving and
Speaker 1: adjusting every moment of your life. Every scientific field eventually
Speaker 1: reaches moments like this, moments where success at one scale
Speaker 1: reveals blind spots in another. Fields reach a point where
Speaker 1: accumulating facts is no longer sufficient and what's needed instead
Speaker 1: is a rethinking of first principles. That's the moment neuroscience
Speaker 1: may be in now, and it's why I want to
Speaker 1: talk with today's guest. Nicole Rust is a neuroscientist at
Speaker 1: the University of Pennsylvania, and she has spent years thinking
Speaker 1: deeply about her experiments and data, but also, in more
Speaker 1: recent years thinking about the trajectory of the field itself,
Speaker 1: about how we got here and what assumptions we've inherited,
Speaker 1: and what kinds of questions we might have to ask
Speaker 1: if we want to move forward in a meaningful way.
Speaker 1: She's written a great book about this, called Elusive Cures.
Speaker 1: Nicole is part of a growing group of scientists who
Speaker 1: are stepping back from the daily grind of incremental results
Speaker 1: to ask a simple and hard question, what kind of
Speaker 1: thing is the brain? Really? What would it mean to
Speaker 1: study it on its own terms. So today Nicole and
Speaker 1: I sat down to talk about neuroscience at a crossroads,
Speaker 1: about complexity, what counts as an explanation, and the challenge
Speaker 1: of understanding the most intricate system we've ever encountered. Okay, So, Nicole,
Speaker 1: a few years ago you started working on this idea
Speaker 1: that we need a new grand plan in neuroscience. What
Speaker 1: led you to that conclusion?
Speaker 2: I was hearing concerns from the heads of funding agencies
Speaker 2: and elsewhere that while researchers had been discovering a lot
Speaker 2: of things about the brain, those discoveries hadn't been moving
Speaker 2: the needle in helping individuals with certain classes of disorders.
Speaker 1: So you know, one of the textbooks in our field
Speaker 1: is Principles of Neuroscience. That it just keeps getting fatter
Speaker 1: over the years, absolutely, and it always has struck us
Speaker 1: that if it really were principles, it should be getting thinner.
Speaker 1: But what we just keep doing is a dated dump
Speaker 1: of all the information we're getting. But your point is
Speaker 1: we're not seeing, Ah, here's the clear pathway to solving
Speaker 1: certain problems exactly.
Speaker 3: For certain conditions.
Speaker 2: So for some conditions we have been moving the needle
Speaker 2: quite effectively. And so those include things like new drugs
Speaker 2: from ingrained headache or insomnia, epilepsy and pain. But there
Speaker 2: are other classes of conditions that we've been more frustrated with.
Speaker 3: And so yeah, that's the big question.
Speaker 1: So one of the arguments you make in your new
Speaker 1: book is that many of the pharmaceutical treatments that we have,
Speaker 1: for example, were discovered by accidents, So things like pain
Speaker 1: or ADHD or in some cases depression. So tell us
Speaker 1: about that. What's the story there?
Speaker 2: Yes, absolutely, so those stories are wonderful, the serendipitous discoveries
Speaker 2: that happened long ago before we knew much about the
Speaker 2: brain at all. One example is the first antidepressant, which
Speaker 2: was discovered during clinical trials for the lung infecting bacteria tuberculosis.
Speaker 2: So their clinical trials for the drug for TB and
Speaker 2: what they found was the patients were joyous. There's even
Speaker 2: a picture of light in Life magazine of them dancing around.
Speaker 2: They were so happy. So they realized this chemical probably
Speaker 2: has a different purpose. They put it through clinical trials
Speaker 2: and it became our first antidepressant.
Speaker 1: And what was the name of that drug.
Speaker 3: Ipronia is it?
Speaker 1: And so that was totally an accident.
Speaker 3: It was totally an accident.
Speaker 1: And interestingly, you know the history of medical science is
Speaker 1: shot through with these sorts of accidents, really is Yeah,
Speaker 1: tell us about pain medications.
Speaker 2: Pain medications? So are opioid drugs? Those come from ancient
Speaker 2: Mesopotamia where the Mesopotamians were harvesting opium from the poppy plants.
Speaker 2: And our drugs today, like oxycodone, are just a slow
Speaker 2: release form of that drug that we harvested from opium
Speaker 2: in the early nineteen hundreds.
Speaker 1: How do they end up ingesting that?
Speaker 3: I don't know. That's a great question.
Speaker 1: That's a great question.
Speaker 3: How did they figure it out?
Speaker 1: Yeah? Yeah, yeah, okay. So and adhd M.
Speaker 3: That's another great one. Riddlin.
Speaker 2: So, Ridlin was developed in the nineteen forties by a
Speaker 2: chemist who was Swiss, and he was using a technique
Speaker 2: that we call try it and see what happens. We
Speaker 2: don't do that much anymore. But so he synthesized the drug.
Speaker 2: He liked it.
Speaker 3: He gave some to his wife. She liked it too, because.
Speaker 2: It improved her tennis game, and so he named it
Speaker 2: after her. Her name was Rita, and that's why we
Speaker 2: call it Rita Lynn. So another great story of as
Speaker 2: a drug that happened long before we understood much about
Speaker 2: the brain at all and certainly wasn't based on some
Speaker 2: big discovery about the brain that led to a new breakthrough.
Speaker 2: So there are a lot of discoveries like these.
Speaker 3: Yeah.
Speaker 1: Great. So your argument is that several of the drugs
Speaker 1: that we have were totally accidental. And when it comes
Speaker 1: to things that involve science as we typically do it,
Speaker 1: where we say hey, look here's the gene, here's the
Speaker 1: chemical involved, and so on, it's an enormous undertaking. So
Speaker 1: give us a sense of let's say, for insomnia.
Speaker 2: Yes, yes, you're right, when a new discovery leads to
Speaker 2: a new drug, those discovery stories are absolutely epic. So
Speaker 2: one example of that. A drug for insomnia is subarexcent,
Speaker 2: so superreccent. The way it works is it blocks chemicals
Speaker 2: in our brain that actually keep us awake. And so
Speaker 2: the discovery of superreccent dates back to nineteen ninety eight
Speaker 2: when brain researchers discovered these chemicals in our brain the
Speaker 2: first time. They were then linked later to insomnia via
Speaker 2: studying some dogs that had genetically inherited narcolepsy. So these
Speaker 2: dogs fall asleep spontaneously during.
Speaker 1: The day, and this was the chemical erectionin.
Speaker 3: These chemical ereccin exactly.
Speaker 2: And yeah, so they figured out this was a problem
Speaker 2: in the erecxin pathway in the brain. It was then
Speaker 2: linked to human narcilepsy. And once researchers discovered that there
Speaker 2: are these chemicals in our brain that exists to keep
Speaker 2: us awake, the assumption was that at least some of
Speaker 2: us have insomnia because these chemicals are too active. So
Speaker 2: the pharmaceutical industry went wild trying to find chemicals to
Speaker 2: block the effectiveness of these keep you awake, the erecxins
Speaker 2: in the brain. And so Mark then went through to
Speaker 2: try to find such a chemical. They screened two million
Speaker 2: different chemicals to find the right one, and once they
Speaker 2: found a chemical it was effective, they improved it even
Speaker 2: further to increase its efficacy reduce its side effects. So
Speaker 2: Whurexcin then went through clinical trials and merged in twenty
Speaker 2: fourteen as a new drug. So altogether there was a
Speaker 2: sixteen year process from the big discovery about the brain
Speaker 2: the erecsans to this new drug to block their activity.
Speaker 1: And what kind of money is involved in that?
Speaker 3: It was about a billion dollars.
Speaker 2: Yeah, and that's about as quick as has ever happened
Speaker 2: from a big discovery to a new therapy.
Speaker 3: Yeah. So it's absolutely epic.
Speaker 1: Got it. So many discoveries are accidental. Ones that aren't
Speaker 1: accidental are epic in terms of the amount of time
Speaker 1: and money they take. So where does that put us
Speaker 1: in modern neuroscience research. Let's jump to nineteen ninety eight
Speaker 1: when Eric Candell wrote a paper suggesting, look, here's the
Speaker 1: framework by which we should think about these things.
Speaker 3: Yeah.
Speaker 2: So in Eric Kendall's nineteen ninety eight paper, he was
Speaker 2: really channeling the ethos of an era of brain research
Speaker 2: that followed on excitement around two big new technologies, our
Speaker 2: ability to sequence genes and image the human brain non
Speaker 2: invasively with techniques such as functional nandecoresonance imaging.
Speaker 3: And Yeah, he laid out.
Speaker 2: A proposal of the new intellectual framework, as he called it. So,
Speaker 2: in Kendell's framework, it all begins with genes. Our genes
Speaker 2: are the code that is used to make our brain cells,
Speaker 2: which are wired into these circuits, and it's the activation
Speaker 2: of those circuits that give rise to all of mental
Speaker 2: function and in term behavior, Kendell suggested that there's one
Speaker 2: big feedback loop, so our behavior, our interactions with the
Speaker 2: world feedback to shape how our brains are wired up.
Speaker 3: That's learning.
Speaker 2: And Kendell focused on this big arrow from how the
Speaker 2: brain gives rise to the mind as the great challenge
Speaker 2: for psychologists and biologists to delineate the relationship between those
Speaker 2: two things.
Speaker 1: And the arrow is pointing from genes, two circuits exsolately, Yes,
Speaker 1: experience and behavior.
Speaker 2: Okay, so yeah, to summarize this idea about the brain
Speaker 2: and the type of thing it is, it's really set
Speaker 2: up as a big chain of causes that lead to effects.
Speaker 2: And the notion then is that when the brain becomes dysfunctional,
Speaker 2: when you have some type of disorder, it's a broken.
Speaker 3: Link in the chain.
Speaker 2: It might be a mutated gene that leads to a
Speaker 2: disorder that you might want to target with a drug,
Speaker 2: or it might be a part of the brain has
Speaker 2: aberrant activity which you could then target with stimulation.
Speaker 3: So this era of brain research I.
Speaker 2: Like to call find the broken link in the chain
Speaker 2: so we can go in and target it for a fix.
Speaker 2: And that example that we just talked about super excent
Speaker 2: it was very much of that type of find the
Speaker 2: broken link in the chain target it for a fixed
Speaker 2: type of approach that led to that big discovery.
Speaker 1: Right, So sometimes that works, and that probably felt like
Speaker 1: real progress. I'm sure when Eric Candell no Bel laureate
Speaker 1: wrote this paper in ninety eight, he felt like, Hey,
Speaker 1: we're really simplifying this and getting this straight how one
Speaker 1: thing leads to another. But when you take a look
Speaker 1: at what's going on in the field, you think that's
Speaker 1: somehow not sufficient.
Speaker 2: Absolutely so, there are certain classes of disorders that have
Speaker 2: really proven to be somewhat impenetrable using that type of
Speaker 2: find the broken link in a chain approach. What's then example,
Speaker 2: So they include our psychiatric conditions like depression and anxiety
Speaker 2: and schizophrenia. So those are all cases in which we
Speaker 2: do have therapies, but they don't work for everyone. And
Speaker 2: many of those therapies date to pre date our understanding
Speaker 2: of the brain, so they were discovered serendipitously. Also our
Speaker 2: neurodegenerative conditions like Alzheimer's and Parkinson's and als, where we
Speaker 2: do have some treatments in some cases, for example Parkinson's,
Speaker 2: but we don't have ways to slow down the degeneration
Speaker 2: that's happening in the brain that's leading to the decline.
Speaker 1: In other words, when we look at all these disorders,
Speaker 1: we think, wow, this is really somehow more complicated. And
Speaker 1: why because when we look for, let's say, a gene
Speaker 1: for schizophrenia, what do we find.
Speaker 2: Absolutely so, in the case of schizophrenia, it's very rare
Speaker 2: to have a single gene variation or mutation that leads
Speaker 2: to the disorder. More likely, well, now that we've sequenced
Speaker 2: lots of genes, we know that it's variation in hundreds
Speaker 2: of genes that are tied to the condition. So if
Speaker 2: one identical twin has schizophrenia, the chances of the other
Speaker 2: identical twin having schizophrenia they're fifty percent. It's not one
Speaker 2: hundred percent, it's fifty percent. So there is a big
Speaker 2: genetic component to all of this, but there are also
Speaker 2: environmental effects and other issues at.
Speaker 1: Play, and these intertwine in ways that are super complex.
Speaker 1: As a side note, you know, the first gene pulled
Speaker 1: for a major disease was for hunting tins and it
Speaker 1: was a gene and if you have that gene, you're
Speaker 1: going to die of hunting tins unless you dive something
Speaker 1: else first, Yes, and everyone thought this is great, We're
Speaker 1: going to figure out the gene that goes with every disease,
Speaker 1: and it turned out to be much more complicated.
Speaker 2: Yeah, And even now, thirty years later, we still don't
Speaker 2: have an effect of treatment for Huntingtons, although fingers crossed,
Speaker 2: it looks like maybe there might be one on the
Speaker 2: way in clinical trials, but it's taken over thirty years
Speaker 2: to get there, even when we knew exactly what the problem.
Speaker 1: Was, right, Okay, So but for something like schizphrenia or
Speaker 1: major depressive disorder, we're looking at something that's much more complicated.
Speaker 1: We can't even find a single gene for it. As
Speaker 1: you said, we find hundreds of genes. So where does
Speaker 1: that put us?
Speaker 2: Yeah, so I think researchers are definitely waking up to
Speaker 2: the idea that this idea about the brain as a big,
Speaker 2: long chain is probably oversimplified. The human brain is often
Speaker 2: held up as the most complex thing in the entire universe,
Speaker 2: and this chain of causes the lead to effects, it's
Speaker 2: not very complicated. So we might ask ourselves, well, what's
Speaker 2: so complicated about the brain and what are we missing
Speaker 2: in this chain?
Speaker 3: Like idea.
Speaker 1: And by the way, it still might be causes leading
Speaker 1: to effects, right, but it's the feedback loops at every
Speaker 1: stage exactly.
Speaker 2: That's the complexity that we're beginning to embrace. So causes
Speaker 2: that lead to effects that feed back on themselves as causes.
Speaker 2: The brain is not like a chain in this type
Speaker 2: of idea. It's a whole different type of thing.
Speaker 1: Right. So your analogy that you make in the book,
Speaker 1: which is wonderful, is like a weather system, right, So
Speaker 1: unpack that for us.
Speaker 2: Yes, so you can think about these complex systems that
Speaker 2: have many interdependent parts, and the weather is a terrific
Speaker 2: example of that, and you can think about when a
Speaker 2: complex system goes awry, it's a lot like a weather
Speaker 2: breaking out into a storm.
Speaker 1: Yeah.
Speaker 2: We know a lot about systems like these because we've
Speaker 2: studied them quite extensively. And one thing we know about
Speaker 2: them is they're very, very hard to perturb in ways
Speaker 2: that you would want to shift them out of their storms,
Speaker 2: which in the case of the brain, would be shifting
Speaker 2: the brain from a less healthy to a more healthy state.
Speaker 1: And one second tangent in your book, you tell us
Speaker 1: a wonderful story about Johnny von Neuman and weather I
Speaker 1: had no idea tell us then.
Speaker 3: Yeah.
Speaker 2: So in the nineteen forties, the end goal of weather
Speaker 2: research really was to control the weather. Researchers wanted to
Speaker 2: not just dissipate hurricanes, which is a worthy goal in
Speaker 2: and of itself, but they also even wanted to weaponize
Speaker 2: the weathers.
Speaker 1: This was the US government that, This was.
Speaker 3: The US government.
Speaker 2: Yeah, so they were very interested in funding weather research
Speaker 2: with that end goal. Part of von Neumann's development of
Speaker 2: the first computers was explicitly in the end goal, first
Speaker 2: predict the weather, then learn how to control it.
Speaker 3: And so researchers tried that out and it didn't work out.
Speaker 1: So well, and it still hasn't worked out.
Speaker 3: It still hasn't worked out.
Speaker 1: And why it's because the weather's so complicated.
Speaker 2: It's so complicated, and it is a system because you
Speaker 2: have these big feedback loops, right, any type of intervention
Speaker 2: you try to do will reverberate in unexpected ways, and
Speaker 2: so that's what makes these systems really really difficult to control.
Speaker 1: So when we look at something like major depressive disorder,
Speaker 1: the temptation to say, look, if we could just find
Speaker 1: the gene. There is no theugen, but if we could
Speaker 1: just do this pharmaceutical here there, we can solve this,
Speaker 1: and that has proved to be ineffective precisely because of
Speaker 1: the complexity of the system here.
Speaker 3: Yes, absolutely, and so.
Speaker 1: One example that you talked about in the book was
Speaker 1: emotions research and the one hundred years' war that's happened there.
Speaker 1: So explain to us what that is.
Speaker 2: Yes, So, our ability to understand what's happening in many
Speaker 2: of the psychiatric conditions comes down to wanting to be
Speaker 2: able to measure an emotion, say, in the brain, and
Speaker 2: that's proven to be very difficult to try to do.
Speaker 1: So.
Speaker 2: Researchers for over one hundred years have been arguing about
Speaker 2: what types of things are emotions in the brain and
Speaker 2: how are they organized. It might be that different emotions
Speaker 2: like fear and disgust and happiness, they might be organized
Speaker 2: in kind of their own little compartments in the brain,
Speaker 2: kind of like our sensory systems where we have one
Speaker 2: part of our brain for vision and another part for hearing,
Speaker 2: Or might be that they're much more intermingled in the
Speaker 2: brain such that and a lot like color vision. Right,
Speaker 2: So color vision, we have one visual system and there's
Speaker 2: a continuous space in our brain upon which we put
Speaker 2: labels like cyan and red and magenta. We don't really
Speaker 2: know how emotions are organized in our brain, whether they're
Speaker 2: more like these compartments or more continuously, but understanding that
Speaker 2: is one of the keys to trying to measure an
Speaker 2: emotion in the brain. You have to figure out where
Speaker 2: is it that you want to look and how is
Speaker 2: it going to be reflected there?
Speaker 1: Yeah, and so that's led to this one hundred years
Speaker 1: war because there are people on both sides of this argument.
Speaker 1: It's either separate or it's spectral.
Speaker 3: Absolutely, yeah.
Speaker 1: And so we're looking at things like emotions and we
Speaker 1: all want an explanation for it. But the question is
Speaker 1: what will it take for us to be able to
Speaker 1: answer something like that.
Speaker 2: It will take an appreciation that the way that emotions
Speaker 2: manifest in the brain is not going to be simple
Speaker 2: and straightforward. It's not going to be an individual neuron
Speaker 2: that's activated when we feel aggression or sadness.
Speaker 3: It's going to take.
Speaker 2: Embracing these ideas that in the brain, if brain area
Speaker 2: a sense information to b be send information back to
Speaker 2: A again, and so we expect emotions to be reflected
Speaker 2: in ways that kind of reverberate and dynamically evolve in
Speaker 2: the brain as opposed to snapshots.
Speaker 3: That you could take a picture of.
Speaker 1: That's a really simple way, and in fact, it might
Speaker 1: not even be that we can talk about in let's
Speaker 1: cut two one hundred years from now, that we can
Speaker 1: talk about area A and area B, right, because in
Speaker 1: a sense, the whole brain is spectral in the sense
Speaker 1: of you've got eighty six billion neurons that are all
Speaker 1: doing their things, but they don't have border walls between them.
Speaker 1: So what we do as neuroscientists as we say, oh,
Speaker 1: this area seems to be involved in blah, but boy,
Speaker 1: these things are spread out.
Speaker 3: How do you think about that?
Speaker 2: When you think about the brain in terms of how
Speaker 2: compartmentalized is it? How much is everything everywhere all at once?
Speaker 2: What's your take on that? You've thought a lot about it?
Speaker 1: I know, I mean, you know, so starting with the
Speaker 1: experiments of Carl Lashly whatever last century, as you all know,
Speaker 1: Lashley was trying to figure out where is a memory stored?
Speaker 1: So he trained little mice to run a maze and
Speaker 1: then he would cut parts of the brain to see, Okay,
Speaker 1: where is that memory stored? So if I take all
Speaker 1: these rats and I cut different parts. Where can I
Speaker 1: find the memory store? And what he found is that
Speaker 1: none of the experiments yielded anything because the memory is
Speaker 1: somehow stored in a distributed manner. It's more like cloud
Speaker 1: computing rather than here's my hard drive and you've just
Speaker 1: broken the hard drive. And so that was one of
Speaker 1: the first examples of Wow, we're looking at a big
Speaker 1: complex system here where stuff is really distributed in ways
Speaker 1: that's hard for us as humans to say, oh, yeah,
Speaker 1: you're just restoring zeros and ones there. It's a very
Speaker 1: different sort of thing. Every attempt we've made to compartmentalize
Speaker 1: the brain doesn't seem to hold that well over time.
Speaker 1: We still do find temptations say, look, this is the
Speaker 1: visual cortex and this is auditory and so on, and
Speaker 1: that's mostly true. But even embedded in here, you've got many,
Speaker 1: many neurons that are reaching across long distances to talk
Speaker 1: to other areas. And you know, when we look at
Speaker 1: baby's brains, we find, you know, there are neurons and
Speaker 1: the auditory cortex that are that are activating the visual
Speaker 1: cortex when they're sound, and in the visual cortext they
Speaker 1: are activating the auditory cortex when their site and as
Speaker 1: we grow, those things start talking less to each other,
Speaker 1: but they're still there. And if you go, let's say blind,
Speaker 1: at some point those neurons sitting in an auditory cortex
Speaker 1: will start We'll start taking over that territory right away,
Speaker 1: because those cross connections are all sitting there. I love
Speaker 1: the fact that you're pursuing this because it is a
Speaker 1: system that we have always been tempted to simplify and say, Okay, look,
Speaker 1: it's probably going to be this. And by the way,
Speaker 1: this is the wonderful thing about science is saying hey, hey,
Speaker 1: there's going to be a way to really simplify this,
Speaker 1: and that's where we get progress. And yet we've attempted
Speaker 1: to oversimplify here.
Speaker 2: Absolutely, Yeah, I completely agree with that. Yeah, but I
Speaker 2: think we're also ready for the first time in history
Speaker 2: to take on the complexity like we've never been able
Speaker 2: to do this before. So it is an exciting era
Speaker 2: for brain research to build on this oversimplification.
Speaker 1: That's right. And so you've been looking at other systems
Speaker 1: and other scientific voices from the last fifty years that
Speaker 1: have suggested things. So what do you see as possible
Speaker 1: ways forward there.
Speaker 3: Yes, so.
Speaker 2: There's been a long thread through brain research. It's been
Speaker 2: more of an undercurrent than the most dominant idea that
Speaker 2: the way we should be thinking about the brain is
Speaker 2: something much more akin to the weather, a dynamical system
Speaker 2: where we're interested in how it evolves in time in
Speaker 2: terms of things like it's patterns of activity and how
Speaker 2: it is structured, not just as a computer, but something
Speaker 2: that's continuously adapting to change. And these ideas date back
Speaker 2: to Norbert Reener in cybernetics in the nineteen forties and
Speaker 2: there's been an undercurrent of them throughout history and brain research,
Speaker 2: including John Hopfield's Nobel Prize on he won in twenty
Speaker 2: twenty four for physics for these ideas based on work
Speaker 2: that he did in the nineteen eighties.
Speaker 1: And tell us about cybernetics.
Speaker 2: Cybernetics was this idea that the brain exists to control
Speaker 2: the body and interact with the environment and a big
Speaker 2: feedback loop.
Speaker 3: That was the gist of cybernetics.
Speaker 1: Yeah, and so that was Norbert Veener and other people
Speaker 1: have built on that idea of having dynamic systems, lots
Speaker 1: of feedback loops and so where do you see that
Speaker 1: moving forward. So if we think today, okay, look, let's
Speaker 1: think of the brain as a very complicated system with
Speaker 1: lots of feedback. How do you tackle something like that.
Speaker 2: Well, there are a couple of different things are really important.
Speaker 2: One is because these types of systems are so integrated,
Speaker 2: you have to measure all their parts at the same time.
Speaker 2: You can't measure their parts one at a time, And
Speaker 2: for the first time in history, we're able to do that.
Speaker 2: Twenty years ago, when I was recording from brain cells
Speaker 2: and looking at their activity, I was able to look
Speaker 2: at one at a time. Today we can record from
Speaker 2: one million brain cells simultaneously in a mouse.
Speaker 3: It's remarkable.
Speaker 2: That's exactly the type of data that you need in
Speaker 2: order to understand how all these brain cells are interacting
Speaker 2: with one another. We also have to build these really
Speaker 2: complicated models to make.
Speaker 3: Sense of these dynamical systems.
Speaker 2: Again, causes lead to effects that feedback on themselves as causes.
Speaker 2: These are not things you can think through and try
Speaker 2: to reason through. You need computers in order to do this,
Speaker 2: and for the first time in history, we have artificial
Speaker 2: intelligence of a type that can actually help us sift
Speaker 2: through and make sense of this data. And build computer
Speaker 2: programs that rival something as complicated as the types of
Speaker 2: things that we can do. So it's a really exciting
Speaker 2: era those two technologies, biotechnology and artificial intelligence coming together
Speaker 2: in order to enable us to really embrace this type
Speaker 2: of complexity.
Speaker 1: Give us a sense of, for example, David Anderson's lab
Speaker 1: at Caltech, how he looks at this giant data and
Speaker 1: figures out, hey, here's a way to capture what's going on.
Speaker 2: Yeah, that's a great example, and it's so relevant to
Speaker 2: a problem that we've really been struggling with, and that
Speaker 2: is how do we measure an emotion in the brain.
Speaker 2: So in David's lab, he is really interested in the
Speaker 2: evolutionarily ancient emotions like aggression, fighting, or feeding, and he
Speaker 2: looks into a part of the brain that we know
Speaker 2: is involved, the hypothalamus. And we know it's involved because
Speaker 2: if you naturally, if you put two male mice together.
Speaker 3: They'll fight. They're aggressive.
Speaker 2: If you stimulate the hypothalamus of a mouse, even if
Speaker 2: they're all alone, it will cause that type of aggression.
Speaker 2: And if a mouse has damage to their hypothalamus, they
Speaker 2: won't be aggressive anymore. So we know the hypothalamus is
Speaker 2: definitely involved in mouse aggression. But if you look at
Speaker 2: the activity of the brain cells in that part of
Speaker 2: the hypothalamus, it really just doesn't make any sense because
Speaker 2: not very many of them are active when the mice
Speaker 2: are aggressive, and even the brain cells that are activated
Speaker 2: during aggression they do all sorts of other things as well,
Speaker 2: So you really can't look in the hypothalamus and understand
Speaker 2: why is it that this part of the brain is
Speaker 2: so important for aggression.
Speaker 1: In other words, it's not like the cells turn on
Speaker 1: and then turn off.
Speaker 2: Okay, yep, yeah, It's just not an obvious answer. And
Speaker 2: so these researchers in this group they started to shift
Speaker 2: to this new way of thinking about the brain, not
Speaker 2: as a big chain, but again as one of these
Speaker 2: systems with these big feedback loops. So they shifted to
Speaker 2: this new way of thinking about activity and the hypothalamus
Speaker 2: that is a lot like a landscape of hills and valleys,
Speaker 2: where at any one point in time, the activity of
Speaker 2: the hypothalamus is somewhere on that landscape, and where it
Speaker 2: falls where it ends up, determines how aggressive the mouse
Speaker 2: will be.
Speaker 1: So you're measuring all the cells and you're representing it
Speaker 1: as a point on the landscape.
Speaker 3: Yes, that's right.
Speaker 2: So at any one point in time, the activity and
Speaker 2: the hypothalamus will be somewhere on this landscape, and where
Speaker 2: it ends up falling in the valley along this long
Speaker 2: line determines how aggressive the mouse will be. At one
Speaker 2: end of the valley, that will translate into a mouse
Speaker 2: that's not going to be aggressive, perhaps because what they've
Speaker 2: seen is maybe a female mouse or not a mouse
Speaker 2: at all.
Speaker 3: On the other end.
Speaker 2: Of the valley, that's where the population ends up sitting,
Speaker 2: that will cause the mouse to be aggressive. And they
Speaker 2: could see that this was true, not just by doing
Speaker 2: observational work where they observe what's happening in the hypothalamus,
Speaker 2: but they actually could use this new generation of tools
Speaker 2: where they could causally perturb the system and confirm that
Speaker 2: that indeed was causing the mice to be aggressive.
Speaker 1: Amazing. So instead of looking at a particular cell or
Speaker 1: a group of cells and trying to think through it,
Speaker 1: you have to take all the cells and collapse that
Speaker 1: high dimensional activity onto a point on a landscape, and
Speaker 1: then you can start describing what that landscape is doing.
Speaker 2: Absolutely, and the big shift here is that that landscape
Speaker 2: can't be shaped by a big chain of causes.
Speaker 3: It lead to effects.
Speaker 2: The formation of the landscape depends on thinking about the
Speaker 2: brain as having these big feedback loops in it.
Speaker 1: Yeah, you know, it's funny. Even in any neuroscience textbook,
Speaker 1: you know you have sell A talks to sell B.
Speaker 1: And of course, so we know that every cell in
Speaker 1: the cortex is talking to you about ten thousand of
Speaker 1: its neighbors, and lots of these are very complicated feedback loops,
Speaker 1: and of course you have excitatory and inhibitory neurons, and
Speaker 1: so straight away, I think any clever student looks at
Speaker 1: this and says, wait a minute, something is something is
Speaker 1: crazy here to think about? Oh a does s? And
Speaker 1: yet our textbooks still read that way because we don't
Speaker 1: know how to teach in a way where we're saying, look,
Speaker 1: start from square one, we're going to talk about dynamical systems.
Speaker 1: So how would you think about revising the way we
Speaker 1: teach neuroscience.
Speaker 2: That's a really important question. Back in the nineteen forties
Speaker 2: and fifties, we used to have an ecology food chains,
Speaker 2: and then at some point they became food webs because
Speaker 2: we realized that these ecological systems. There are these complex
Speaker 2: dynamical systems with these big feedback loops in them, and
Speaker 2: so we started to teach starting from elementary school, we
Speaker 2: started to teach ecology differently, and so, yeah, I very
Speaker 2: much think that that's what we need to start doing
Speaker 2: in brain research as well, is starting from the beginning
Speaker 2: teaching about the brain as a system chuck full of
Speaker 2: these feedback loops and what are all of the consequences
Speaker 2: of that.
Speaker 1: Yeah, And even if dynamical systems science as we understand
Speaker 1: it now turns out not to be the full picture,
Speaker 1: at least we're getting closer.
Speaker 3: Absolutely.
Speaker 1: Yeah. And Nicole, despite the limitations and where neuroscience research
Speaker 1: has gone, you're very optimistic.
Speaker 3: Tell us why absolutely.
Speaker 2: When I started to write this book, I actually wasn't
Speaker 2: sure where it would lead, and I started from a
Speaker 2: place of kind of confusion and even a little bit
Speaker 2: of pessimism because I could see that there were these
Speaker 2: certain conditions for which we were get a little bit stuck.
Speaker 2: On the other side of writing the book, I'm unequivocally
Speaker 2: optimistic about the future of our field for the conditions
Speaker 2: like the psychiatric conditions and their degenerative conditions.
Speaker 1: And why it's.
Speaker 2: Because I see that the changes that need to happen
Speaker 2: are already happening in our fields. Right we were oversimplifying
Speaker 2: the brain. We were treating it like this chain of
Speaker 2: causes that lead to effects, and it was just a
Speaker 2: massive oversimplification of the most complex thing in the entire
Speaker 2: known universe.
Speaker 3: But now researchers are starting to embrace.
Speaker 2: This important type of complexity that we can again for
Speaker 2: the first time in history, because we have new biotechnology,
Speaker 2: we have artificial intelligence. For the first time, we're really
Speaker 2: to study the brain in this way, and I am
Speaker 2: very excited about the idea that that will be the
Speaker 2: key to unlocking progress for all of the millions, billions
Speaker 2: actually of individuals who are suffering from these conditions.
Speaker 1: That was my interview with Nicole Rust. This conversation circled
Speaker 1: around the idea that the brain may not be the
Speaker 1: kind of object we once hoped it was. For a
Speaker 1: long time, neuroscience advanced under a parsimonious assumption that if
Speaker 1: we can you could just identify the right pieces, the
Speaker 1: right links in the chain, the story would come into focus.
Speaker 1: Genes lead to proteins. Proteins built cells, cells form circuits,
Speaker 1: Circuits generate thoughts and motions and behavior fix the broken link,
Speaker 1: and the system heals. Sometimes that strategy works, but there
Speaker 1: are entire domains where it doesn't, where no single gene
Speaker 1: or molecule or brain region carries the explanatory weight that
Speaker 1: we wanted to. Gradually, it's become clear to us that
Speaker 1: most disorders don't behave like oh, there's a broken part,
Speaker 1: but instead you have altered states of a whole system.
Speaker 1: That means you can't just swap out apart. You have
Speaker 1: to figure out if it's possible to nudge a complex
Speaker 1: landscape that realization slash. That admission changes a lot, because
Speaker 1: it reveals that the brain is more like a dynamic
Speaker 1: environment shaped by feedback loops and continual self adjustment. It's
Speaker 1: a system that can settle into values of activity that
Speaker 1: are hard to escape. And by the way, it's a
Speaker 1: system whose behavior depends not just on what's out there
Speaker 1: in front of it now, but often on many things
Speaker 1: that have interacted with it throughout its lifetime. So this
Speaker 1: reframing has consequences for how we do experiments, for one,
Speaker 1: but also it explains why some breakthroughs arrive accidentally while
Speaker 1: others require decades of effort. It sheds light on why
Speaker 1: prediction is hard, why control is even harder, and why
Speaker 1: treating brain disorders sometimes resembles influencing the weather in Nicole's analogy,
Speaker 1: more than it resembles fixing an engine. But although this
Speaker 1: might seem like a pessimistic story, it is in fact
Speaker 1: an optimistic one because for the first time, we might
Speaker 1: actually have the tools to take this complexity seriously. We
Speaker 1: can measure vast populations of neurons that once, we can
Speaker 1: model systems that evolve in time. We can leverage artificial
Speaker 1: intelligence to help us see patterns that are invisible to
Speaker 1: our intuition alone. In other words, neuroscience may finally be
Speaker 1: growing into the kind of science the brain requires. Every
Speaker 1: mature field eventually has to let go of its simplest metaphors.
Speaker 1: Physics moved beyond clockwork, universes, ecology moved from food chains
Speaker 1: to food webs, and now neuroscience may be moving beyond
Speaker 1: linear causality towards something richer and stranger and closer to
Speaker 1: the truth. The challenge ahead is about learning how to
Speaker 1: think in dynamic landscapes instead of static links, and if
Speaker 1: we get that right, the payoff is going to be
Speaker 1: new ways of helping the millions of people whose lives
Speaker 1: are shaped by brains that have settled into difficult states,
Speaker 1: and that's where the next era of neuroscience is going
Speaker 1: to really begin. Go to eagleman dot com slash podcast
Speaker 1: for more information and to find further reading. Join the
Speaker 1: weekly discussions on my substack and check out Subscribe to
Speaker 1: Inner Cosmos on YouTube for videos of each episode and
Speaker 1: to leave comments until next time. I'm David Eagleman and
Speaker 1: this is Inner Cosmos.