#1235 Nicole Rust: Why Neuroscience Hasn’t Solved Brain Disorders―and How We Can Change That

00:00 Ricardo Lopes: Hello, everyone. Welcome to a new episode of the Dissenter. I'm your host, as always, Ricardo Lops, and today I'm joined by Doctor Nicole Rust. She's professor of psychology at the University of Pennsylvania, and today we're talking about her book, Elusive Cures Why Neuroscience Hasn't solved Brain Disorders and How We Can Change that. So, Nicole, welcome to the show. It's a pleasure to everyone.

00:25 Nicole Rust: Oh, Ricardo, thank you so much for having me. It's a pleasure to be here.

00:29 Ricardo Lopes: OK, so first of all, what motivated you to write this book?

00:36 Nicole Rust: So I've been a brain researcher for decades now, and I was hearing increasing calls about the disconnection between the sorts of things that brain researchers like myself had been discovering and the end goals that we need to achieve for society, which include, but are not limited to. TREATMENTS and cures for brain and mental disorders of all types, and what really troubled me is I couldn't spell out how we're going to reach those end goals, right, despite the fact that I have been a brain researcher for decades. And so for me, part of elusive cures was really A personal journey to try to puzzle these things out, but I also reasoned that if I don't have a really clear picture of this through line in my own head, others probably don't as well. And so that's really why it became a book project as opposed to just a personal kind of exploration for me.

01:27 Ricardo Lopes: Right. And I mean, uh, if I read your book correctly, it seems that there's a a disconnect between papers published in neuroscience and the development of new drugs. Why is there such a disconnect?

01:42 Nicole Rust: Yeah, so I think the first thing to emphasize is that it's not that nothing has been happening. So things have been happening. We've had game changing drugs for certain classes of disorders, including things like migraine headaches and even postpartum depression, but there are certain classes of disorders that really have proven to be somewhat formidable in terms of our attempts to understand and treat them. And so these include the neurodegenerative conditions, so things like Alzheimer's and Parkinson's disease, or neuropsychiatric conditions which can include depression, anxiety, and psychosis, and then the neurodevelopmental conditions which include things like autism spectrum disorder as well as forms of intellectual disability. So these are classes of conditions where we have a hard time really understanding what's happening in the brain, for example, when someone falls into a bout of psychosis or a depressive episode, and moreover, how do we intervene with that, right? So when someone wants to shift the brain from a one state into What they might consider a more healthy state, then how do we go about doing that? Those are, those are the just so the causes really of these disorders is what we've had a hard time puzzling out. I think that's really what it boils down to for these conditions.

02:56 Ricardo Lopes: But to develop these drugs, is it enough to understand the brain and how it works?

03:03 Nicole Rust: So that's an important question. Um, LET'S, let's begin by, you know, just, uh, acknowledging that drugs are not always the best form of treatment for every type of disorder. So for example, if someone has a severed spinal cord, right, if you want to treat their paralysis, a drug is probably not the right type of treatment. Uh, BUT under the assumption that you do want to treat a condition with a drug like that is in fact the right way to go about it, then the question is how do you go about finding it? Um. So certainly I think if if the brain is the target of a drug, understanding the brain is probably the most important thing you would want to do. At the same time, just understanding the brain by itself probably misses two things. One is understanding the body, right? So just an obvious example, if something works really well in the brain, but it causes liver toxicity, right, that's not going to, not going to be a good drug. But also understanding the impact on the mind and the behavior of an individual. So another example, if a drug for insomnia causes suicidal ideation, right, that's also a big problem. So When I think about these things, I always think about them in the framework of the brain is really the only route to the mind, right? But it's not always the case that um Understanding what's happening in the brain is the most effective, uh, or, um, best way to understand the mind. So things like suicidal thoughts. We, we, we can't look into a brain and understand, you know, we don't know where those come from, we don't know how those manifest. We can't, we can't look into a brain and determine when one of those is happening. So that's really, uh, I think a target of the mind, really at this point, not the brain.

04:50 Ricardo Lopes: And so, I mean, it's not just a matter of Understanding the brain, but how we go about doing it because there are different frameworks in neuroscience. I've covered different ones on the show and in the book you talk about, yeah, and in the book you talk about what you call the grand plan. So what is that and what is the current, uh, dominant plan or kind of framework that we can find in neuroscience?

05:23 Nicole Rust: Yeah, so, so the grand plan, it's kind of a pithy phrase, right, but intentionally so. So when I say grand plan, I mean it very affectionately as a member of the brain research community, and I think about the grand plan as this broad strokes description about how any era's community of brain researchers as a whole plans to get from wherever they are to those end goals for society. And those end goals come in many forms. So we, in the case of brain research, we want to understand the brain because we're curious. We want to understand ourselves and how we work, and I think that's an admirable goal in and of itself. But it's not our only goal. We also want to understand brains to do things like build things like them. And so this is the whole neuro AI, so trying to understand we are intelligent beings, and so we want to understand how our own intelligence works, so, so we can build things like it. And then understanding the brain to treat brain and mental disorders. So the question is, well, how, how do we plan to do that? In the 1990s there was tremendous enthusiasm around two particular technologies, and that was DNA sequencing. So for the first time we could actually sequence our genes and also noninvasive measures of brain activity. So we could use functional magnetic resonance imaging in order to look and see what's happening inside our brain. And so in the 1990s there was tremendous optimism that if we just sequence genes and image brains we'll figure out the causes of brain and mental conditions, and once we figure out those causes, that will lead us to treatments. And so I would say that's the, that was the grand plan of the 1990s. And unfortunately, It hasn't panned out, so it's not that we haven't learned a tremendous amount with those technologies. We have unquestionably, but I think there's a lot less enthusiasm now that if we just sequence genes and image brains, the causes of these conditions will just be forthcoming. So the new grand plan. IS currently under reevaluation, and one of the big questions, one of the big tasks I gave myself when I was writing Elusive Cures is trying to reformulate what our new grand plan is because the grand plan of today, the emerging grand plan, is a lot less obvious than it was back in the 1990s where there's this kind of universal enthusiasm.

07:41 Ricardo Lopes: Mhm. Yes, uh, and I was talking about frameworks just a minute ago. Uh, WHAT is the molecular neuroscience framework and in the book you also talk about three main problems with this framework and later on you pick on those problems to, uh, tell us the ways we can perhaps tackle them. So tell us about that.

08:03 Nicole Rust: Yeah. So the again goes back to the 1990s. I think that that was a real moment of clarity for brain research and so that's really when the molecular neuroscience framework first emerged. So the kind of the broad strokes description of the brain and that was emerging in that time. WAS the idea that it all begins with genes. And so our genes encode the information that then is translated and transcribed in order to create our brain cells. Our brain cells are then wired up into these circuits, and it's the patterns of those activity that give rise to all of our brain's functions, including. THINGS like seeing and reading and attending and imagining, but also it's dysfunctions. And then there's a single feedback loop in that cycle by which our experiences with the environment feedback to shape how our brains are wired up, and that happens by changing the genes that are expressed. And so this is how learning works. So. To summarize that framework, researchers of the era were really thinking about the brain as kind of a big domino chain, I would say, and the idea was that dysfunction could be attributed to link to broken links somewhere in that chain. So the goal of the arrow was to really find the broken link in the chain and then target it for effects. And so that broken link might be a genetic mutation or a genetic variant that an individual inherits. And so you might want to target that with a drug or a form of gene therapy. It also might be a form of aberrant brain activity that you could go in and target maybe with an invasive or noninvasive brain stimulation. So that was the 1990s era. And there were really 3 problems it turned out to thinking about the brain that way. The first is that this emphasis on these feedforward dominoes really amounts to causes that lead to effects, and what it ignores is the brain is chock full of these feedback loops. So both within the brain, so brain area A sends information to B, B typically sends information back again to A. So you have these big reciprocal loops. But also an emphasis on how extensively we do interact with our environment and how extensively that really shapes how our brains are wired up at all levels. And In a system like that, you have causes that lead to effects that then feed back on themselves as causes, and that's a completely different type of system. And when it goes awry, it need not be because there's a broken domino in a chain. In fact, that's probably, that's more often the wrong way to think about it. So we know a lot about systems like these because they include things like the weather and ecosystems and nuclear reactors. These are what are called complex dynamical systems, and one of the principles of these systems is that they are notoriously hard to shift from one state to another in a predictable way. And that's because any intervention you do in a system like these leads to these big, because of these big feedback loops, you get these big reverberations, and that's really what treatments amount to. So treatments amount to a form of control over a system, right? You're trying to shift it from an unhealthy to a more healthy state. And so that really highlights the formidable challenge that we're up against in our quest to understand what causes brain and mental conditions and also develop treatments, right? It's really this, this really complicated problem that we have to solve. But for the first time in history we're finally starting to tackle it, yeah. So the three big problems that you mentioned, one is ignoring the feedback loops really put us on the search for the wrong types of causes when it comes to these conditions. Also, Oversimplifying the problem led to this, this exquisite targeting of these broken links in a chain, and that can cause reverberations in the system, so side effects of drugs that are somewhat unpredictable if you don't really embrace the complexity. And then the third. The third issue with the molecular neuroscience framework as it was formulated in the 1990s is that it really just even by its founders, it didn't have a good description of how brain activity gives rise to brain function, so things like think. And, and, and the mind really and also behavior and so those were the 33 things I think we really need to fill in to extend beyond this 1990s way of thinking about the brain, the molecular neuroscience framework, in order to be more impactful as brain researchers.

12:51 Ricardo Lopes: So what should replace then the molecular neuroscience framework? Uh uh, YOU refer to the brain as a complex systems, uh, a complex system, so should it be complex systems theory or within complex systems dynamical systems theory or what

13:09 Nicole Rust: exactly? Yeah, I think, I think, uh, one, you know, another characteristic of these systems is that they're really hard to intuit through. And so more so, you know, domino chains, even if they're really, really elaborate and there are many, many higher, many, many stages, you still can kind of puzzle through them often. In the case of these systems where you have these big feedback loops, it becomes a system that's really hard to puzzle through. So I would argue that we definitely need more emphasis on formulating explicit computational mathematical computer models of the system. And those are models that incorporate, you know, these big feedback loops. So yes, very much we're in the, in the world of dynamical systems and complex systems, appreciating, you know, their emergent surprising properties that are often hard to predict from just understanding their parts in isolation. It's not, it's not magical. It's just that their parts interact in ways that are really complicated with the big time lags, and so that's why they're hard to predict. And the other key component of the brain, and this is really what makes the brain different than, say, the weather, we have a lot to learn from the weather, I think, but what makes the brain different from the weather is it's an adaptive system. And so adaptation, I think of as our brain's superpower, right? If we put you in a new culture, you'll learn the language. That's kind of a slow form of adaptation, but our brains have all sorts of ways to adapt to changing conditions like see a tiger and it will trigger us to run. It kicks our whole brain and body into a fight or flight kind of response. And so we have all these different ways to adapt, and a lot of that adaptation relies on these big feedback loops, but that means that the brain is really able to control and manipulate itself effectively. And so I think that that's a huge component of thinking about the brain. So shifting away from thinking about the brain. IS a domino chain of causes that lead to effects and thinking about it as a complex adaptive system where causes lead to effects and then feedback to manipulate themselves again as causes.

15:17 Ricardo Lopes: And by the way, apart from what you mentioned there of the importance of going beyond the domino chain account of the brain or the domino chain metaphor, one of the most dominant metaphors that I guess we could say we still have in neuroscience is the computer metaphor. So what are the limitations with that kind of metaphor?

15:41 Nicole Rust: So the the computer metaphor, it's one I've worked on a lot throughout my career, and so the, the metaphor, it's really targeted at one of the three big problems with the molecular neuroscience framework, and that is filling the gap between brain activity and brain function. And so when we think about the brain as a computer, I think the most dominant metaphor in brain research has been a serial computer. So this is the idea that the brain is taking information in through its senses. So you have your visual system, your auditory system, your touch system. And then it's processing that incoming information through a series of modules. So vision would process the visual information, hearing would be processed by the hearing system, and then they would converge into other modules like memory and attention and decision making. But the general idea is that of the brain as the serial computer metaphor is that you have these different modules and they each do a job and then they pass the answer on to the next module and then your brain decides ultimately what to do. So that's the brain as the computer metaphor and it's been very Instructive for us to try to figure out how certain things work like how is it that the light patterns falling on our eye, which are a little bit like pixels on a computer screen, how are those transformed into a percept of the identity of the objects that you're looking at or the equivalent for the auditory system or how do we remember the things that we see, those sorts of questions. The issue with that metaphor ultimately is that if you think through the brain as computing things, like computing the labels of objects in a picture or something. It's hard to see how that metaphor jumps to a lot of the conditions that that happen, a lot of ways that the brain goes awry. So things like insomnia, that doesn't really seem like a deficit in computation or anorexia or epilepsy, all of these issues don't feel like deficits in computation. They really feel like deficits in something like the brain has become uncalibrated or imbalanced, and it's really hard to link that metaphor of the brain as a computer with ideas about dysfunction, right? It's that that link, I think, isn't obvious or clear. Mhm.

18:02 Ricardo Lopes: In the book you explore different kinds of brain-related conditions and I would like to go through some illustrative examples like psychosis, depression, and schizophrenia. But just before we get into that, how are brain drugs developed? I mean, what are the steps that scientists, researchers, and drug developers take in the development of these kinds of drugs?

18:29 Nicole Rust: It can happen in a few different ways, and there are, and I'll contrast two of them. One is the idealized way. And so this is when a big discovery about the brain leads to a novel treatment that we've never had before, right? And when that happens, that's epic, and that's really what that's often what's called the bench to bedside story. So the idea is that there's a discovery at the research bench that then leads to a new treatment that we can deploy clinically. And I'd be happy to walk through an example of exactly how that happens when it happens. So that's one way. And then there's another way that this happens, and that is more serendipitously. Uh, WHERE researchers kind of stumble on one thing, often while looking for another, and it turns out that the second way is how most of our therapies to treat brain and mental disorders have been developed. Um, SO, and, and they're, they're, they're, they're still important, and when I think about this, I just think about all hands on deck. People are suffering. It doesn't matter how we get to this endpoint, we just have to get there. But I think when we're trying to also troubleshoot, it's important to Um, recognize the reality as opposed to an idealized narrative of how this happens. But let's walk through the idealized way that this happens, because these, it does happen, and when it happens, it's, it's, it's just awe inspiring and epic. And so I can walk through a drug called Suvaexin. So Suvaexin is a drug that's designed to treat insomnia. And it works through a pathway called the orexin pathway. So erexins are a chemical in the brain that helps brain cells communicate with one another, and it's involved in sleep. Actually, it's involved in wakefulness and sleep. And so the discovery of erein dates back to investigations of dogs and what causes a genetically inherited form of dog narcolepsy. So these are dogs that like humans, in the middle of the day, if they get overexcited, they would fall asleep. And so while that is not true of humans, human narcolepsy is not genetically inherited. The idea was, well, if we study these dogs, maybe we can understand what's causing narcolepsy. And so researchers, this was an era before we had the high throughput genetic sequencing we have today, and so it took them a very long time. But they finally arrived at this, this, the discovery that there was this orexin receptor in the brain that was mutated in the dog colony. Next, it was linked to human narcolepsy. Now, in humans, like I said, it's not genetically inherited. Rather, it is a form of neurodegeneration, so probably a neuroimmune attack attack. So the neurons that produce this chemical are degenerated in individuals with narcolepsy. So once they had the information from the dogs, they then could link it to the humans, and the way that this works, it's really fascinating. So at the time that researchers discovered this, they knew that there were chemicals in the brain that kind of make us go to sleep like melatonin you've probably heard of, but they didn't realize. ARE also chemicals in the brain that keep us awake, and that's what aexin does. And so if aexin binds to its receptor that keeps us awake and so if that receptor is mutated and it can't bind, then that's what was causing the narcolepsy. And so this discovery set the pharmaceutical industry off on this big hunt for compounds, drugs that could block the binding of aexin to its receptor, so it couldn't maintain this wakefulness with the idea that maybe this is what's happening in some individuals with insomnia. They just have too much binding and so we need to block it from binding. And so a number of different pharmaceutical companies were competing to find these drugs. The drug was ultimately developed by Merck. So Merck in the United States screened, I think, 2 million compounds to find one that would block the errein receptor in just the right way. They even, once they screened it and found one chemical compound they thought was the best, they even refined it to make a better one. Then they passed it through clinical trials. It took about 10 years for the pharmaceutical industry to go from the discovery to the creation of Suvaexent as an approved drug. So just as an overview, right, this starts with decades of researchers discovering things about the brain and this new chemical they didn't even realize existed, linking that to human, a human brain disorder, narcolepsy. And then the pharmaceutical industry developing things for another 10 years at the cost of about a billion dollars to create Superexcent. So the whole, you know, bench to bedside arc, it's absolutely epic when it happens, absolutely.

23:20 Ricardo Lopes: Mhm. Yeah, that is really, really fascinating. So tell us now about some examples of conditions in the, in your book. You go through several of them, but tell us about some of the history behind the development of treatments for conditions like psychosis and depression.

23:39 Nicole Rust: Yeah, so psychosis is, is an interesting example of how things happened a little bit differently. Um, SO, Rewinds back to the 1940s, 1950s. This field hadn't actually figured out that brain cells communicate via chemical transmission. They were still working all of that out, and that's when the first antipsychotic was discovered, and the way it was discovered was not based on information about how the brain works, but rather it was a company in Europe who was developing antihistamines. To alleviate allergies, and as many of us know, if you've ever taken an antihistamine, it can make you tired. So they found that one of their antihistamines made people particularly tired. And so someone intuited, you know, maybe this would actually help with someone who is very agitated and psychotic. And so they just gave it a try. And it was this first patient. It was in the 1950s, and the effects on this patient were almost immediate and calming. And so this was an individual who was going through a severe psychotic episode. In this era. We didn't have a lot of good ways to treat individuals who are going through extreme bouts of psychosis, immediately calming. And so that was chloropromazine. Yeah. Fast forward today, we now have, I think, 26 antipsychotics in total. But they all work in the same way as this first antipsychotic that was discovered serendipitously. That is not to say that we haven't relied on new discoveries about the brain in order to create these new drugs we have. But it turns out that these refinements into drugs, which are better because often they have fewer side effects, for example, they don't work in a different way than this first serendipitously discovered drug, so they all bind to the same. Types of receptors in the brain, they bind with different affinities, and that's what can lead to different, different, um different side effect profiles like the new generation. Maybe they'll cause less weight gain, for example, because they bind better to the right receptors and not to the wrong ones. So that's an example of refinement, and, and it's a that's what a lot, about 70% of brain drug development happens through refinement of this type. So it's not a new drug that works in a new way, but rather it's a drug that maybe works better in the best cases, but it works in the same way as an original drug.

26:16 Ricardo Lopes: Uh, IN the case of the depression, because I mean, many people nowadays, at least the ones that are diagnosed, suffer from depression. Uh, IT'S one of the major causes of disability, morbidity in the world. So what, what, what, how have treatments for depression been developed?

26:40 Nicole Rust: Yeah, so depression is a is a mixed case in these in this story about bench to bedside. So the first antidepressants were completely serendipitous. So researchers were doing clinical trials for the drug for the lung infecting disease tuberculosis, and what they noticed is that individuals during the clinical Trials were really happy. There was a news article reporting that they were dancing around and joyous, and so they realized that it probably has some sort of mood uplifting capability, and so they put it through clinical trials. So that was a very serendipitously discovered drug and again before we really understood much about what's happening in the brain at all. On the other hand, there are the class of, for example, selective serotonin reuptake inhibitors, SSRIs, that many individuals, these are a temporary class of drugs. They include things like Prozac. Those drugs were based on a Nobel Prize winning discovery that when brain cells, which again they release chemicals to communicate with one another, after they release their chemicals, then they'll go through a process of kind of scavenging them, so reuptaking them. And the SSRIs, they block that reuptake in order to maintain those chemicals in the, so the brain cells communicate a little bit more strongly for a little bit longer. So those are the SSRIs, and so that those really did follow from a big discovery about the brain that then led to a new, a new type of therapy. So in that case, it's been, it's been quite mixed. On the other hand, the SSRIs, it's a huge class, right? And so there was the first Prozac and then many other drugs, and all those drugs again, they, they often work pretty much the same way. So many refinements on that on that first big discovery, but with different side effect protofiles and importantly, Some of those drugs within that class will work for some individuals but not for others, and other drugs will work for some individuals but not for others. So we still don't have a really great understanding about how to match individuals with antidepressants, and I know that that's a big push in the psychiatric community right now, and it's so important, right, to get that right.

28:50 Ricardo Lopes: And do you think that the kind of approach to the brain you described earlier based on complex systems theory could help us figure out how these uh treatments for depression exactly work and how we can improve them?

29:08 Nicole Rust: Absolutely, and this is something that's, that's really near and dear to my heart. um, SO before writing a list of cures, I used to study the visual system and I used to study memory. After zooming out, I decided where to dive back in, and I decided to dive back in on research and mood and depression myself. So this is something very, very near and dear to my heart. Yeah, one of, so let's just start with what we know and what we don't. We understand how those drugs are operating at the level of molecules as I just described. They, they interact with these molecules, they block the reuptake, but we really don't understand how changes in the amount of these chemicals. Improves mood or changes mood, right? So there's a big gap there, and part of it is because is that there's a lag between, so these drugs often take weeks or even months to kick in, and their chemical effects seem to happen pretty quickly in the brain. And so one of the big questions, so it can't just be that it's this increase in chemicals in the brain that then is alleviating mood, you know, alleviating depression. There's something else going on there, and that's, that's one of the big missing pieces. When I, as a researcher, look at, look at what's going on, I note that we still don't really understand how things like mood are reflected in patterns of brain activity, and that's, that's something we really need to understand to figure out in order to bridge this gap between what's happening in our molecules and what's happening in terms of brain function. And mood, we need to figure out how do the molecules change the patterns of activity and then how do the patterns of brain activity give rise to this experience that we have as mood. And it turns out that mood is more difficult to study than many other brain functions. And so this is what I'm, I'm very excited and committed to jumping in to try to help figure out is how do we bridge that gap.

30:59 Ricardo Lopes: OK, so let me ask you about one more condition before we, we move on to other topics. Uh, HOW do we tend to approach schizophrenia?

31:09 Nicole Rust: Schizophrenia, yeah, schizophrenia has had, um. So many evolutions in the way that researchers think about it. In the late 1800s, early 1900s, there were researchers like Eugen Bleuler, Eugen Bleuler, it's hard for me to say that one, who coined the term schizophrenia, and he was actually quite progressive if you go back and read his work. So he was very adamant that schizophrenia had biological causes. There's something happening in the brain that's causing it. And that we should, we should treat individuals with the condition in a limited way, so they should definitely be part of our communities, and we should, they should be hospitalized, but only when needed, right? But and minimally so. So we want to help them, but we also want to help them as a community. Unfortunately, the 1900s then was peppered with some less progressive and important ideas about schizophrenia, which included institutionalization in the United States. We didn't have good treatments before the discovery of the antipsychotics, and so patients were sometimes subject to horrible treatments like lobotomy and other types of things. And there was also an era in which even after the antipsychotics were discovered, That there were ideas that schizophrenia was a disorder that followed from traumatic experiences during childhood. This was the idea of the schizophrenogenic mother. So the idea that mothers were a little bit too domineering and they were the causes of their children's schizophrenia, and The idea was that the treatment, the cure involved a form of psychodynamic therapy that really tried to root out these traumatic childhood experiences, and it was a therapy to help individuals kind of recover. That idea, um, It had had some, some, um, a lot of, I guess, enthusiasm about it behind it for a while and some research. That research was really debunked in the 1970s and 1980s, and by the mid 1980s there was kind of universal appreciation that this is, this is not, this is not about traumatic childhood experiences and bad parenting. So today, the way we think about schizophrenia is that it, it is likely to be a genetically inherited partially neurodevelopmental disorder, but not due to these traumatic childhood parenting experiences. So the first piece of evidence that we have is that if one identical twin has schizophrenia, the chances of the other identical twin having it are only 50%. But they are 50%, so that tells us there's something probably genetic, there's something genetically inherited about schizophrenia. These two share the same genes. They share the same womb environments largely, right? And they have a lot of shared experiences. But so that's telling us that it's not all about genes. There's something else going on. We also know that um There's an increased incidence of schizophrenia when children are in the womb during a traumatic period like a famine, so that can cause an increased incidence of schizophrenia. So that tells us there's probably something about traumatic experiences, but more neurodevelopmental experiences, not, not necessarily like psychological development. So we think about it as some type of neurodevelopmental disorder. It's nobody really understands exactly why it manifests for the first time in late adolescence around 1718, 19, mid-20s. So that's still a big mystery. And there are a number of other big mysteries about not only the causes of schizophrenia, but also how it manifests in the adult brain. So that's kind of an evolution in how we're thinking about it with emphasis that that we've, I think, Gotten, we, we've gone full circle. I think back to the early 1900s. Um, WE do have some treatments that can help, but they don't work for everyone. Uh, AND we still have a lot of work to do in order to really understand schizophrenia.

35:29 Ricardo Lopes: And by the way, I also wanted to ask you about Alzheimer's. I mean, why has it been so hard for us to have an understanding of what causes it, how it develops, and also in terms of developing treatments for Alzheimer's.

35:47 Nicole Rust: Yeah, yeah, Alzheimer's is a really tricky one, and the answer ultimately I'm going to come to is like we really don't know. I open my book with a story, a very moving story about a woman named Carol Jennings. So Carol Jennings was a woman. She was in the UK and she was convinced that her family had a genetically inherited form of Alzheimer's disease during a time in which That that was not thought to exist. Today we know that there are kind of three classes of Alzheimer's. There are, there's a form like Carol Jennings' family has. It's very rare, but if you inherit certain genes, it will lead to Alzheimer's later in life with certainty, and that's the form of Carolle Jennings' family. There's another pile of Alzheimer's in which if you inherit certain genes, it increases the probability that you'll, you'll develop Alzheimer's later in life, but it does not insure it. And then there's another class of Alzheimer's which we can't find any sort of genetic link. It happens and we have no idea why. So there are 3 different classes. In terms of if we are able to cure any of these, we should be able to cure the ones where we actually can point to the cause and say that gene is mutated and we should be able to cure this with certainty. It turns out that that gene is a gene for a protein called amyloid. And going back to the early 1900s, Alzheimer's himself, Dr. Alzheimer, he identified that in the brains of individuals with Alzheimer's, their brains are full of these clumps of protein. Later we figured out those are clumps of amyloid. And so when researchers made the discovery in Carolle Jennings' family that these, that that not only they already knew that these clumps of protein exist in the patients with Alzheimer's, when they discovered in her family that the mutation in this protein caused the disease. They reasoned that those clumps of protein must be toxic. So the idea was it's the accumulation of those toxic clumps that then triggers the neurodegeneration that then triggers the cognitive decline associated with Alzheimer's. So this became the amyloid hypothesis and It has to be something wrong with Alzheimer's, at least in families like Carol's, right? So, and what I think of is just an absolute heroic efforts, right? And just like a sure tour de force, researchers came up with drugs to clear these amyloid plaques from the brain. It's remarkable that they were able to do it, and those drugs have been just approved in the last few years. So that's remarkable. The tragic end to the story so far is that they don't actually slow the cognitive decline, at least to the way that everyone would hope that they would, even in families like Carol's where we know that amyloid causes the disease. So, so they do slow it, but It's in a in a disease that's that's thought to last around 8 to 12 years, it might extend the course of the disease, slow it down by 8 months. So you can see they're not the magic bullet that we're hoping for. And so the new idea is that it's, it's might be that those drugs were given too late. It might still be that Alzheimer's triggers events, but it might be that once those events are triggered, you can't stop. You know, they're just like the beginning of a big domino chain or a big cascade, and then once, once things have started, you, you can't slow it down by just going back to that original cause. That's kind of the current thinking behind Alzheimer's right now. Some individuals think it's another protein called tau that maybe is accumulating, and that's the problem. Other individuals are really starting to embrace this idea of the brain as a big complex dynamic system of the type I'm talking about that would cause something like a triggering cause, causes this huge kind of cascade, including the originators of the original hypothesis, this amyloid hypothesis, and an individual who actually made the discovery about Carol's family. His name is John Hardy, and he's in the UK. So why haven't we been made more progress with Alzheimer's? Nobody really knows, but I, but the suspicions I've just conveyed, I think, are the current suspicions that it's not as simple as find the broken domino in the chain and target for, target it for a fix. That's clear. At least we've been targeting the wrong domino minimally, but yeah, what the right answer is going to be is still unclear.

40:21 Ricardo Lopes: So when it comes to uncovering the causes behind brain dysfunction and how to treat it, how does this new approach based on the brain as a complex dynamic system change how researchers go about finding those causes of brain dysfunction?

40:43 Nicole Rust: Um, Yeah, I mean there there are a couple of different things. I would say. One is that it's something I said before, but it's really hard to puzzle through these complicated systems, and so I think there's a much more, a much larger emphasis on creating models of the system as opposed to just trying to intuit how things work. Another big change is that the parts of a complex dynamical system because of these big feedback loops, they interact, and that's what gives rise to their kind of surprising emergent properties. But what that means is you can't study the parts one at a time. You have to study them all at the same time, and so it really changes the measurements that we make. So flipping it on its head, being a little bit more systematic about it, we have to change how we measure things. We have to measure everything at the same time. And so what does that mean? Instead of measuring one thing across many different individuals, we need to measure a lot of things about the same individuals, right, in order to really understand the causes of many of these disorders. So that's measurement. We also want to make models of these systems so we can Puzzle through the what's going wrong, and we also want to use those models to puzzle through how can we meaningfully, once we figure out what's going wrong, how do we meaningfully shift the system back to health. And in engineering, they would call that a control problem and control in complex dynamical systems, we know it is very difficult. So yeah, it changes every step of the scientific process, right? It makes, it makes the whole process much more computational, much more mathematical, but we have to change every step measurement, description, and modeling and control. We have to change how we think about it. Fortunately, all of these things I'm talking about are not. It's this, so I want to emphasize, when I wrote Elusive Cures, this isn't about me, you know, coming in and swooping in as some genius, right, telling everyone how to fix these things. What I was really doing in Elusive Cures was tapping into the genius of this era that's already happening. And all the shifts of all the technologies of all the things that brain researchers are trying out, I could really see how this particular shift amongst a community who's already doing these things was really going to matter for this end goal of of of societally impacts to society that will be actually meaningful and beneficial. And so these changes are already happening and it's really, really exciting.

43:20 Ricardo Lopes: And how do we determine causality then within the framework you present in the book and perhaps as a sort of illustrative example, tell us about causality in the case of opioid dependence and addiction.

43:36 Nicole Rust: Yeah, so when you have a domino chain, causality is pretty straightforward, right? Causes lead to effects, there's some cause that's broken, right? Go and find a broken domino and fix it. Causality is not so straightforward when you have these big feedback loops, right? So you have causes to effects that feedback, the time lags, and so even the notion of of of cause and effect really starts to break down. And you also have this additional complication that we have this notion of levels of complexity or levels of organization, right? So we know that genes encode the stuff that brain cells are made up of and the brain activity is what gives rise to the functions of our brains. So we have these different levels. So in the case of opioid dependence, you could think about its causes in a lot of different ways, and it really depends on who you ask. So if you asked a brain researcher what causes opioid dependence and addiction, they'll talk about how the opioids, especially the synthetic opioids like fentanyl, they're hijacking the brain circuits and they're leading those motivational drug seeking circuits toward drugs. But if you talk to a geneticist, they'll say, but that's not quite right because there's a genetic predisposition. It's about 50% of opioid addiction and dependence depends on the genes that you inherit. And if you ask someone like a social worker, they'll say no, it's about poverty and trauma and experience that then lead to opioid dependence and addiction. And in the US, where we're experiencing the opioid epidemic epidemic where we've seen deaths due to overdose increase 7.5fold since 2000, that's really tragic. Lawyers will point to the pharmaceutical industry and say, you know, no, it's because those increases are because they were pushing drugs. The pharmaceutical industries were pushing drugs in the hunt for profits, right? So you have all these different ways of enumerating the causes of opioid dependence and addiction, and one of the big questions is how do all these causes fit together? Should we think about them as they're linked across different levels? It's like a one cause, but that doesn't seem quite right. Um, SO researchers, uh, In the US who have focused on trying to really target the opioid epidemic. HAVE started to build these complex dynamical systems models which try to not exactly all the causes I listed, but they try to build causes, they try to build models of what's happening within a community and use those models in order to develop therapeutic interventions. So those models will include things like the rate at which individuals who are prescribed an opioid will actually become dependent or addicted. Also, Individuals who are coming in dependence and addiction from exposure on the street, traumatic events that happen to them, the rate of relapse and recovery under these different scenarios where they have different access to different types of interventions, and they use these big complex models in order to tease apart what types of interventions could we use to actually achieve a certain end goal which might be twofold decrease in deaths within, say, say 3 years. And the tragic story behind behind a lot of this work is that the amount of intervention that would be required for these models is a huge astronomical investment in terms of how many therapists we would need and how many, given, given what we have access to, but at least it helps us point in the right direction, right? It helps us. Not try to invest in, in, in things that won't work, and it really kind of, I think, um solidifies, quantifies what what needs to be done so we can meaningfully do it, um, yeah, teasing together these causes.

47:39 Ricardo Lopes: And so within this new framework, how do we go from understanding brain dysfunction to developing cures and treatments?

47:52 Nicole Rust: Yeah, that's a, that's a really good question. I would say there are 22 primary ways. One is the, it's kind of the equivalent of the classic bench to bedside story. So we want to understand a brain function in a lot of detail. We want to build really complicated models of it, and we want to infer from those models what's causing the dysfunction. And how do we treat it? And so this is an approach that has been quite successful for certain types of cancer like leukemia, where there's a form of leukemia that is controlled. It's this very complicated genetic network. So there are 60 genes and they're all connected together in 140 different ways, and it's impossible to just look at this network and say, oh yeah, that's what's causing the leukemia. But what researchers can do when they have a big complex dynamical systems model like that when they've modeled the whole thing is they can then go through what's called a process of model reduction and so they can take out of the model the things that don't matter for cancer, leaving behind a more simple model that they can, they can tease apart and then infer how to treat and how to stop it. So that's one approach. THAT the brain is really, really complicated. It has 86 billion neurons in it. And so, you know, we're not going to be able to model it in all of its detail, but we can get pretty detailed models of things, right, in terms of certain things and focus on the levels that we think matter. So that's one form is model in great detail, reduce those models to figure out the causes, and then target those causes for a treatment. And that approach has also been taken in Parkinson's disease in exciting ways where it's really embracing the complexity of a big, complex dynamical system and then trying to figure out how to treat it. There's another way, another approach, and it's more akin to the serendipitous approach, and that is based on partial knowledge of how something works. Take your best guess at what How we might influence the system to change it from disordered to a more ordered state and let's try and I think we want to do this too because individuals out there are suffering, right? They're suffering from conditions and so we need to give some of these things a try, even in the absence of these, this really complete. Kind of detail. So I would say those are the two parallel um processes uh that, that, that can go from understanding to, to treatments.

50:19 Ricardo Lopes: Mhm. In the, at the beginning of our conversation, we talked about the plan. So what is the new grand plan then?

50:29 Nicole Rust: The new grand plan, yeah, so, so is it that the grand plan of the 1990s was, it's very pithy, right? So it's, if you think about the brain as a domino chain, it would be find the broken domino and fix it. That would be the grand plan of the 1990s. So when you get these more complex systems, then the grand plan becomes less pithy. It's, it's a lot less simple, but I would argue it is understand the brain is a complex adaptive system. And then create models to infer how it works, how it becomes dysfunctional, and how to intervene to shift the system from Uh, a condition, uh, a less healthy condition to a more healthy one. So I would say that that is the new emerging grand plan.

51:17 Ricardo Lopes: And how do you look then at the future of brain research? Are you optimistic about it?

51:24 Nicole Rust: I'm unequivocally optimistic, and I'll I'll just share that part of what really motivated me to write Elusive Cures was pessimism, right? I just didn't understand, even though I was in the field for 2020 years, I didn't understand how the research That my community was doing would translate to the societal end goals, and that left me frustrated and because I couldn't pinpoint it, right, it just left me with this pessimism. If I can't see the through line, then it's really hard for me to be optimistic that there's one there. On the other side of writing Elusive gear, so this process of writing it, right, really created required me to zoom out and think about brain and mind research as a whole and all the brain's functions and its dysfunctions. And it really changed the way I think about the type of thing that the brain is, but I now can very clearly describe why the brain is so complicated, why we've had such a hard time with these certain classes of disorders, and moreover, I can describe what it is that we're going to do about it. And one of the big developments that's leading to my community thinking about the brain this way as a complex adaptive system as opposed to a domino chain, it's really about the development of new technology, and it's two types of technology. One is biotechnology. So I mentioned earlier to study a complex system, you have to measure everything at the same time. 20 years ago when I was recording from neurons as a trainee, I could record from one or two neurons at a time. Today we can record from a million neurons at the same time in a mouse, and that allows us to make measurements we've never been able to make before. So that's one big revolution. So changes in what we can measure. But also the emergence of AI has been a game changer. So for the first time in history we can actually fit models that are brainlike. So a long time ago we had limited computational power. We had limited ability to build these models. And so we could just deal with little toy kind of illustrations of what the brain might be a little bit like, but nothing that rivaled the complexity of the brain. And now with AI we can actually train models to do brain-like things, and that's leading us to new insights into how these brain-like things can actually manifest. And it's just a whole different conceptual way of thinking about the brain. So yeah, unequivocally optimistic about, about the future of Brain research to really help contribute and dissolve these disconnections between what we're discovering and these kind of most formidable disorders. We won't know until we know and so of course we can't make any promises, but I for one. Very optimistic and it's, it's also what's led me to be so excited about what's happening. I want to redirect my entire research program from studying memory to studying mood where I see there's this huge unmet need and I think we're finally ready for the first time in history to tackle it. So I'm very excited.

54:34 Ricardo Lopes: Just one last question then, since we are in the age of artificial intelligence, do you think that it can play an important role here?

54:44 Nicole Rust: Absolutely, hands down, but perhaps not in the way that um some are trying to sell it. So I don't think that we're going to develop artificial scientists that and in the book I described, you know, where we say, OK, we hit the button and we say go, go cure depression, and then all the brain researchers go to the beach and we come back a few hours later, and then we look at, you know, the AI's cure depression for us. I don't think it's going to be like that at all. Um, RIGHT now, AI is offering two things to the brain research community, and there are two really important things. The first are tools. We have tools that we've never had before, and they include things like um there's an AI called Alphafold, which can tell us about the three dimensional structures of proteins given their DNA sequences. We've never had that before. And so AI can finally offer this to us. And once you know the three dimensional structure of a protein, that can help you develop a drug in order to target it and so on. So that's an example of a tool. Another example of tools are image processing. So we're really interested in how the detailed connectivity of the brain, we can take pictures of it, and AI can analyze those pictures and reconstruct this three dimensional structure of how the brain is all connected together. So that's a pile of tools, and those are tools we've never had before, and they just speed up the discovery process enormously, enormously, because before we had people who had to, like, you know, make all of those inferences or reconstruct those proteins. It took years. Now in AI, it takes some seconds, which is like, so everything is just being sped up. So that's one way tools. And another is what I mentioned. AI is allowing us to create these models that are brain-like that we've never had before. And once we have those models, we can infer how intelligence, how brains, how language works. We can extract principles that we've never had before. So AI is very much contributing in these two ways at the same time. I'm a little bit dubious that we're going to replace scientists anytime soon, right? So I think scientists are using these things as tools, and humans are still very much in the loop, and I see them being in the loop in an important way very much in the future. But AI is game changing in terms of the tools that are facilitating scientific discovery. Yeah, absolutely.

57:00 Ricardo Lopes: Great, so the book is again Elusive Cures Why Neuroscience Hasn't solved Brain Disorders and How We Can Change that. I'm leaving a link to it in the description of the interview. And Nicole, just before we go, apart from the book, where can people find your work on the internet?

57:18 Nicole Rust: Yeah, you could go to my website, Nicole Crust.com. Um, SO I have summaries of everything there as well as, uh, when that will link you to, to other things I do like my, my lab at the University of Pennsylvania.

57:31 Ricardo Lopes: Great. So thank you so much for taking the time to come on the show. It's been a real pleasure to

57:36 Nicole Rust: talk with you. Oh, thank you so much, Ricardo. It's been a, it's been a great conversation. I appreciate it. Thank you.

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