The future of neuroscience: Karl Deisseroth sheds light on the inner workings of the brain
Guest Karl Deisseroth is a bioengineer and a psychiatrist who has developed two of the most transformational research techniques shaping our understanding of how the brain works — optogenetics, which allows neuroscientists to control brain cells with light, and CLARITY, a way to render the brain’s gray matter transparent yet retain all its intricate wiring for easier study.
There is a vast chasm between neuroscience and psychiatry, Deisseroth says of the reasons he felt compelled to develop technology ahead of pursuing science. It was never a trade-off of one over the other, however. It was simply where we had to go to get better at the science, Deisseroth tells fellow bioengineer and host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast.
Transcript
[00:00:00] Katy: Please give me a warm Stanford welcome to Russ Altman and Karl Deisseroth.
[00:00:12] Russ Altman: Thank you everybody. It's great to see you. I, before we get started a couple of things, uh, this is, um, a live recording of a podcast, but what I'm, we're going to do about a 25 minute normal episode, and after that, we're going to have a little break and then we're going to have a discussion. A wide ranging discussion with Karl about many [00:00:30] many things.
Alright, so, ready? Uh, we'll get going, uh, just about now.
This is Stanford Engineering's The Future of Everything, and I'm your host, Russ Altman. If you enjoy The Future of Everything, please follow or subscribe it wherever you listen to podcasts. This will guarantee that you never miss an episode.
Today, Karl Deisseroth will tell us about technology development for neuroscience, how to [00:01:00] use that technology for discovery in neuroscience, and how we can take that technology and those discoveries and improve the mental health of patients. It's the future of neuroscience.
Before we jump into this episode, I want to ask you all to rate and review the podcast. It will help fellow listeners discover us, and it'll help us improve.
When you think about [00:01:30] the brain, one of the things you might think of is that in 2013, the National Institutes of Health of the United States created a roadmap. It was called Brain 2025, I think. And it was in 2013 or round about there. And it was a roadmap about how we could crack or solve important questions in neuroscience.
Neuroscience, of course, has been making progress for hundreds of years, but there was a sense that there was an opportunity that we needed to take advantage of in [00:02:00] order to accelerate our understanding. And that plan was led to a beautiful document, which I recommend you read. There was actually a follow up five years later, but the thing that I remember about it is one thing they said, the first five years of this effort should be focused on technology development.
We need to create new technologies because we simply don't have what we need. After that. We then need to get serious about solving major problems in neuroscience that have been vexing us for decades. [00:02:30] So, that led to a big push, and in many ways is a beautiful summary of what we're about to talk to with Karl Deisseroth.
Karl is a professor of bioengineering and psychiatry and behavioral sciences at Stanford. His lab does those three things. They develop technologies, they apply them for discovery, and they are driven by the need and the desire to improve the state of mental health care and psychiatric care in all [00:03:00] patients. So, Karl, welcome.
[00:03:02] Karl Deisseroth: Thanks, Russ.
[00:03:03] Russ Altman: And I really want to say that my first question is, how did you respond to these challenges of the Brain Project as it came out just around the time you were, you were still a young and promising scientist? How do you trade off the desire to develop technologies with the desire to just get on with the science.
[00:03:22] Karl Deisseroth: This is an absolutely crucial point because many times people will have a self [00:03:30] image of themselves as, uh, I'm a technology developer, or I'm a pure biologist. Uh, and people have these narratives about themselves, and where they get them is almost random and then they end up being constraining.
And from the very beginning, you know, I came to neuroscience from, I was trained in biochemistry, but I got interested in neuroscience from all the way at the other level, the psychiatric level. [00:04:00] And there's a vast chasm there, and it's very clear that new technologies had to be developed. And so from the very beginning, from my perspective, it was all about... Even if you're basic science, even if you were a pure biologist, you had to develop technologies as a primary goal, honestly.
And so I've tried to instill that in the folks who come through my lab. Um, you know, probably, you know, I think even the people who consider themselves pure biologists who are in my lab, at [00:04:30] least a quarter and up to half of their time is spent on technology development.
It's actually... best that you unify those two threads in the, not in, just in the same lab, in the same person. So for me it was never a trade off or a conflict. In fact it was a way to help move things where it should go. And it was, for us it was, it was essential.
[00:04:52] Russ Altman: So let's take an example, I think one of the things that you're very famous for is technologies associated with optogenetics. So, you have lots of things you can work on. Tell us the story about how the optogenetics technology became clear to you as something worth a big push, as opposed to the many other things you could have [00:05:00] pushed on?
[00:05:03] Karl Deisseroth: Well, like a lot of things, you don't really know until you try, uh, if you're getting the green light from nature, and you just have to try it. It's like, you're walking through Manhattan, you know, you know where you're going, but you gotta take the lights that are there. [00:05:30] And there's no way to know until you, you look, uh, and so that's kind of the perspective. And honestly, the, if you want to, what optogenetics is, it's a way of controlling specific cells with light.
You can turn them on or turn them off with light, even in complex behaving mammals doing intricate tasks. So if you have that goal, uh, how do you set about doing it? Well, there's a lot of ways you might turn things on or off. Uh, you might use different ways of delivering energy, [00:06:00] you might use genes that help cells increase or decrease their excitability. And that was how it started. I said, let's try a bunch of different things and see where we get the green light.
[00:06:12] Russ Altman: So the goal, if, tell me if I'm getting this right, the goal was to precisely control individual cells.
[00:06:18] Karl Deisseroth: Yes.
[00:06:18] Russ Altman: And there was a severe need for this because even I know that in the olden days it was plunge electrodes into neural tissue and have a huge, um, electric field applied [00:06:30] that was not very precise.
[00:06:31] Karl Deisseroth: Yeah, and that's for stimulating. It's even worse for inhibiting the technology for inhibiting was scoop out that part of the brain.
[00:06:41] Russ Altman: So tell me a little bit more. Okay, so you say we have to get precise control of neurons.
[00:06:44] Karl Deisseroth: Ya.
[00:06:45] Russ Altman: You already, you indicated to us, you already were creating a checklist of what might work, what might be possible. What made light the winner? And then how do you take a great idea and turn it into something that is a practical tool that can be exported to others?
[00:06:59] Karl Deisseroth: Yeah. [00:07:00] And that's where being in bioengineering was so important because I had not just myself, but people around me, the department, in the air, in the water, was this to not just discover but to create. And that's really what it was. It was, let's discover what might work and then let's put in the years of work to actually make it work.
And so, from the first principles, test something. See where you're getting the [00:07:30] first, you know, clues something might work. And then, uh, put in the substantial effort that came after that, after the first, you know, promising signs to actually build something that's functional. And that, you know, that said in the backdrop of academia is kind of interesting because as a, someone running a lab, you can't always just take the 10 year viewpoint.
You have to think about the [00:08:00] careers and the papers of the people who work with you. And so then you have to set up checkpoints and milestones where the papers can come and the steps that are partway to the goal are clear and discreet. And so the way you know, I approached that was to now we're at a point.
I'll just jump right to the end to give you a perspective of where things are going. Now we can play in single cell control to hundreds or thousands of individually specified cells. We can literally [00:08:30] circle them and say, I want to drive these thousand cells with this particular sequence and rhythm and, but that took until really just this year to get to that point.
So, but early on, what were the achievable steps? There was, oh, let's control cell types first. Let's control cells, all the cells. that have, uh, one genetic type or one anatomical type. The cells that live here and send a connection to there. And so, establishing those early [00:09:00] milestones, which were actually, honestly, great in themselves.
A lot of discoveries can happen that way. Understanding the biology of cells that have certain projections or certain genetic identities that allowed us to make sure the students and the post docs got their papers and kept us going toward the goal and where we are now.
[00:09:17] Russ Altman: So, for people who are not familiar with optogenetics, and I'm going to, this is a big mistake, but I'm going to try to summarize and you will then correct, but basically, you found proteins, channels, that can be put into cells [00:09:30] artificially, these channels are amazing, because they come from widely disparate organisms found on Earth who have the need, basically to sense where there's light, often to then travel towards it to get nutrients or sunshine for living.
Uh, and then you put these into these cells, and then when you expose light to the cells, something which normally doesn't happen in an enclosed skull, you can change the behavior. Is that a fair summary?
[00:09:53] Karl Deisseroth: That's perfect.
[00:09:54] Russ Altman: Okay. So, how did you know about these, um, proteins that, uh, in these weird little floating algae in the Sargasso Sea [00:10:00] or wherever they live.
[00:10:04] Karl Deisseroth: Well, so these proteins is a class of protein called, uh, microbial rhodopsins. And the genes that encode them are called microbial opsins. This family of proteins had been known for about 50 years. Uh, and it was in biochemistry textbooks. Uh, my biochemistry textbook, Lubert Stryer's third edition of biochemistry.
There's a beautiful page on the photocycle of bacteria rhodopsin and [00:10:30] how when the light hits the protein, a charged particle gets translocated across the membrane. So known for a long time, uh, but as you said, these come from weird species very, very far afield from animals even, certainly mammals, uh, and, uh, People who had, as I had, expressed different proteins from different species and neurons.
[00:11:00] Neurons are very sensitive, they're very vulnerable. This particular kind of protein, it lives in the membrane of the cell, and that itself is a very disruptive part of the cell to pack full of foreign protein. You know, this is the barrier between the inside and outside of the cell. It's very easy to kill a cell by putting too much of the wrong stuff in its membrane. And I knew this, I had done work like this putting in membrane proteins into cells, and I'd seen how toxic it could be [00:11:30] and that was even with mammalian proteins. What about taking something from a single cell microbe billions of years remove from us evolutionarily.
So it was, uh, unlikely to work, extremely unlikely to work. Uh, and, uh, other issues. There were many other issues to not even try it. The currents would be very small. The rate at which charged particles would translocate across the membrane would be very small. Um, there were [00:12:00] questions about how do you get the light in? How do you target the gene to the cell type that you care about? Um, a host of other issues. And if you add them all up, you look at it and say, nah, it's not worth it.
[00:12:12] Russ Altman: Yes, but it was worth it. It was worth it. Okay, so thank you. And so this is great because it kind of shows that In retrospect, everything is fun and games and everything works, but at the time, you were making a big bet and you were putting resources into a project that could have come up [00:12:30] empty.
So, let's, I'm sure one of the things that people are thinking is, this is a great discovery tool. You can do this now, you can study neurons at a precision that was not possible before. Are we ever going to be putting lights into human brains to take advantage of this technology for diagnostic or therapeutic uses?
[00:12:50] Karl Deisseroth: Well, uh, first of all, it's already actually being done, at least into the central nervous system.
[00:12:55] Russ Altman: Hmm.
[00:12:55] Karl Deisseroth: So, of human beings. So, uh, two years ago, [00:13:00] my colleague and sometimes collaborator, Botond Roska, in Switzerland, he was able to take a blind person and make them be able to see again, at least to be able to identify objects on a table and to reach out to them someone who couldn't it was completely blind before and didn't have that level of
[00:13:18] Russ Altman: Had they been a seer previously?
[00:13:20] Karl Deisseroth: Yes.
[00:13:21] Russ Altman: So their brain knew what to do with light signals.
[00:13:23] Karl Deisseroth: Yes. This was someone with retinitis pigmentosa, late onset degenerative disease, so the brain knew [00:13:30] what to do. And Other things that made it more accessible than some harder problems you might imagine the retina is very accessible the retina is used to dealing with light. And It was initially just a simple experiment, just give the person the ability to see what's in front of them, that there is something in front of them.
But still, that's pretty good, and it answered so many questions that human beings, uh, like the rats, mice, fish, worms, monkeys, that it's been [00:14:00] tested in before or can tolerate these microbial proteins perfectly well and they work. And so it is possible to do direct what you might call direct optogenetics in human beings.
Um, and now the question is you get to more mysterious parts of the brain than the retina. You better know what you're doing because you've got to put in a gene and you probably have to get light in somehow. And so probably good to not be doing your guesswork in the human beings. Do the basic discovery in the animals, [00:14:30] uh, make, and work in structures that are ancestral and conserved, uh, phylogenetically, uh, across animal species, and then you might be in good shape.
Uh, so a lot of my work in the lab has been devoted to that sort of work, is saying, you know, as a psychiatrist, I think about, uh, motivation and energy and anxiety and primary survival drives like hunger. When do we not eat when we're hungry? Uh, these are things that all can go horribly wrong in human beings.[00:15:00]
And so I study the cells and the connections that if you intervene, you can change them. And so that's been a thread all the way through. Building that knowledge of what actually matters in the long run may help with direct optogenetics.
But the final thing is, that's not even necessary for there to be a therapeutic impact. You don't ever have to put these genes in people for something to matter for human health. And that's because understanding what matters opens the door [00:15:30] to any kind of treatment. Once you know the cells that matter for causing or correcting a symptom, then it opens the door to looking at the genes those cells express, the molecular targets that might be there for drugs, the ways of accessing their far flung projections across the brain.
And so once you know what matters, the therapy can be anything.
[00:15:53] Russ Altman: Fantastic. Okay, so this leads really nicely into what I wanted to spend a few minutes talking about, which is, You are an actual [00:16:00] practicing psychiatrist.
[00:16:01] Karl Deisseroth: Yes.
[00:16:01] Russ Altman: You care deeply about the problems that your patients have. And these are complex problems you were just referring. You know, people think of depression, bipolar, um, schizophrenia. Terrible diseases with a huge morbidity in, uh, in the population. But there are many more subtle dysfunctions that you see in your patients. And by the way, they're beautifully described in your book called Projections. And I want to know, and we've established in the last few minutes, the kind of great engineering and discovery that [00:16:30] you've been able to do.
But I know that a lot of this is informed by your clinical practice. So can we go to the other end of the extreme and tell us, how do you approach The art of medicine as you go into a room with a patient, um, with knowing that you have this amazing set of capabilities back at the lab, but still looking at a patient who needs help now. So tell me about that experience.
[00:16:54] Karl Deisseroth: Well, it's, first of all, it's, I've kept doing it, uh, just because it's such an [00:17:00] experience. It matters. It's become part of my identity, uh, you know, how I see myself, but it's, I'll be honest, particularly when, you know, I do both inpatient and outpatient work, so outpatient's in a clinic where someone comes in who you know pretty well and you've been seeing them for years and they come in every month or so and that's great to really know the person and you can really help them best.
But then the inpatient work, that's acute emergency room psychiatry where people are the most vulnerable, most broken [00:17:30] down, uh, need the most help, and things are most confusing, chaotic, lack of information, life threatening, uh, you know, comorbid conditions are present, other diseases that are present at the same time.
And I do that for about one week a year, and I have to admit, when that week comes close, I start to get kind of anxious myself, um. Because I haven't done the acute stuff for a year, and what if I'm not [00:18:00] up to the job, you know, what if things, uh, are, have advanced in some way? Of course I stay current, I, uh, but I, there's always some question, have I not stayed current enough?
But that week is always a completely invigorating, transformative period for me, and I come out the other side, uh, just new energy, new perspective, and I come back to the lab with just [00:18:30] such joy, uh, that each year it's like rebooting myself. It's quite remarkable to see. So that's one thing is it's it actually matters psychologically, motivationally for me.
But, it also really helps the science, because I, you know, many people who are working in a lab, who are interested in disease process, somebody might be interested in autism or they [00:19:00] might be interested in schizophrenia, but they haven't had a lot of direct exposure to patients. I think it really helps to be able to go and talk to, you know, your advisor and say, what is the symptom really like, what is it?
Feel like what really matters to the patient, uh, instead of just looking at a list in a book and saying, Oh, okay, uh, repetitive behavior, okay. It matters a lot to think about how this actually manifests in human beings. And so I try to to share that real world perspective with my students as much [00:19:30] as possible.
Um, so it helps in that regard. We design a lot of our experiments, keeping in mind what really matters, uh, challenges people with, uh, psychiatric, uh, disorders, uh, what they encounter and to what extent can we. Um, study those things in the lab, focusing on conserved ancestral circuits and behaviors.
[00:19:53] Russ Altman: So I, that, that's great.
And I mentioned the, uh, the BRAIN project that was kicked off about 10 years [00:20:00] ago and I talked about how it was going to be technology development. But then they were going to start looking at the big problems and I know you've thought about this a lot. And in fact there was a report five years later, and I did look at it in preparing for our conversation.
But then I said, nah, I'd like to hear Karl's take on what are the most pressing questions in neuroscience that are within reach now, that may not have been in reach ten years ago because of this technology development.
[00:20:24] Karl Deisseroth: Yeah, this was a moment that I remember this very well, the [00:20:30] 2013 moment when the group was put together, uh, from the White House and so I was very happy to play a role in that group.
Uh, there were a few of us who organized workshops over the course of a year. We integrated feedback from neuroscientists and engineers from across the world. And there was a clear sense that some things were missing. Uh, and that the wind was at our back at that moment and so it was something to pay attention to.[00:21:00]
One was the, what we've alluded to a couple times, is the cell types. What is the parts list, uh, effectively in the brain? Um, and, you know, with other organs, like the heart, it's easier to appreciate the parts list, right? You've got your cardiac myocytes, the muscle cells that contract and they drive the contraction of the heart. And then you've got your pacemaker cells that time the rhythm. And then you've got your blood vessel cells in the heart that send the [00:21:30] blood around the heart. And that's great. The brain, it's, there are hundreds if not thousands of cell types in the brain. They're all intermixed with each other.
They're all tangled up in this massive wiring. Um. And we only had the broadest categorization of cell types. We knew there were excitatory cells, and we knew there were inhibitory cells, and we knew there were dopamine cells and serotonin cells, but we didn't know [00:22:00] fundamentally much more than that. So that was one goal.
And once we knew the cell types, then we could bring in tools like optogenetics and say, What happens if you turn up or turn down the cell type in this context? And that was one, probably the first major goal of the Brain initiative was to get that parts list. And then once we had the parts list to start bringing the optogenetic tools, which we'd been developing along the way to bear on those parts.
[00:22:29] Russ Altman: So I [00:22:30] know that one of the things that they talk about and that you talk about, you mentioned it even earlier in our conversation, is this idea of causality. That, um, perhaps too often people were looking at the correlation of certain phenomenon in the brain to certain behaviors or certain illnesses. Uh, and I think that you implied before that there's something very, um, concrete about knowing that a certain cell is driving the normal or the abnormal behavior.
[00:22:59] Karl Deisseroth: Yeah, this, the [00:23:00] brain is so fast and so interconnected, and we've actually done experiments like this. You can ask a simple question, like, of all the cells in the brain, about what fraction are correlated with the simplest possible action you could imagine.
And we did this experiment, published it a couple years ago in 2019, recording, effectively, from tens of thousands of cells across the brain, electrically, while a thirsty mouse went for a sip of water. Simplest possible [00:23:30] thing you can imagine. And more than half of all the neurons across the brain correlated with that action.
[00:23:39] Russ Altman: We're all thirsty.
[00:23:43] Karl Deisseroth: Yeah, or something, uh, but, and that, this was every structure, you know, structures that weren't even visual, you know, parts of the brain you wouldn't imagine that would be involved. So, right away you've got... But the brain is [00:24:00] so fast, so interconnected, that everything ends up being correlated, more or less.
So that's where causality is so important, is to ask what, what matters. By the way, I don't mean to dismiss at all how amazing it is that this information gets around. I think that's very important. I think every part of the brain needs to know what's being planned by another part, so it can make sense of then the new information it's getting and isn't surprised by it.
I think the brain is read into a lot of things, even if those parts that are getting information are not causally [00:24:30] involved in the immediate action. But if you care about what matters for something specific, maybe it's a psychiatric disease symptom, or maybe it's a memory, uh, or maybe it's a healthy adaptive drive that we care about like parenting.
Uh, these things, to know what actually matters, you have to bring in a causal sort of data set.
[00:24:53] Russ Altman: So let me ask you about that, because we've been talking about like, okay, and I know it is true that in some cases you find one or a small [00:25:00] group of cells that really seems to be driving the thing of interest.
But, um, of course, there's going to be cases where it's an ensemble of cells, maybe even distant in the brain. That, and how are we gonna get at that when you're gonna need to do your optogenetic, um, queries, uh, perhaps over several tell cell types in drastically different parts of the brain and meet maybe even with very exquisite timing.
So what is all that? How's that gonna happen?
[00:25:28] Karl Deisseroth: Well, uh, we're getting [00:25:30] there and in some ways. We are there now. Uh, so at least in, uh, mice, for example, and in, uh, zebrafish, small organisms where we can access many parts of the brain, uh, at once. Uh, we can now, uh, reach into many different regions simultaneously, see what's happening, cause things to happen, uh, and have millisecond timing resolution synchrony, or asynchrony, as we like. So, uh, [00:26:00] we're now there, uh, in principle, and in some studies, it's already starting to be done. There's work done, uh, both here and around the world, people doing things like recording from cerebellum and frontal cortex at the same time, for example. You know, front and back of the brain.
[00:26:18] Russ Altman: One thing that strikes me is that means that there is an almost infinite number of combinations of cells that you could look at. So do we need or do we have a theory [00:26:30] for which cells actually should be working together so that our search space of which combinations can be brought into a manageable range?
[00:26:38] Karl Deisseroth: So this is very, very important. And this is, uh, was another key pillar of the brain Initiative because we realized this issue that, uh, very clearly, we needed theory guidance, uh, to deal with this combinatorial explosion you're alluding to. And it hadn't been as serious a problem before because we [00:27:00] didn't have the technological abilities to make it a problem.
Um, but, heh, but then, uh, all of a sudden we had this ability and the theory hadn't caught up. The theorists were who are great, many of them we collaborate with and are dear friends of mine. But that they, we were not able to be guided by theory all of a sudden, uh, given where the technology had brought us.
And so that's been a very important part of the Brain initiative. Uh, and it's really been wonderful, honestly, [00:27:30] to see the new generation of theorists that have been, uh, you know, fostered.
[00:27:36] Russ Altman: So, in the last couple of minutes, I want to go back to your book, Projections. Uh, it's great. I've read it.
Um, and I've read it more than once. And, um, not even in preparation for this conversation. Uh, why would you write a book? And, um, tell about, tell me about, Any kind of knock on effects that this has had? Is it what you expected? Was it easier, harder than you [00:28:00] expected? And what has been the reception been, other than from Russ, who clearly is a fan?
[00:28:05] Karl Deisseroth: Well, I'm glad you read it. I'm glad you liked it. That's why I wrote it. Not for you. But, uh, the immediate stimulus right around the time I started writing it in earnest was I wanted to communicate with everybody. I wanted to share with everybody. Both the inner worlds of psychiatric [00:28:30] patients, which I think are mysterious and opaque to many people, understandably.
I wanted to share that perspective, and so, and I thought about that hard, how to do that, and in each chapter deals with a different psychiatric disorder, and each chapter is written, uh, in a, colored by the, what I know about the inner worlds of these patients. So the chapter on mania is written in a, uh, an over [00:29:00] exuberant, uh, style.
The chapter on, on schizophrenia is written in a fragmented style. And so I wanted to help use the language to, as well as the content to illustrate what was going on. Just to share, just so people knew. Another goal was to bring the science. I wanted everybody to know where the science had brought us to.
To know that the basic science and engineering has been good. It's been helpful. People who [00:29:30] don't necessarily have any exposure to science or engineering in their everyday life. I thought it would be great for them to see what's happening. So that was the immediate precipitant, but I've always loved writing.
I've always loved words. That was actually what led me to think about the brain first. And, uh, I'd been writing all along the way. When I saw a patient, I would write about the patient. When I saw words or phrases that to me captured [00:30:00] an emotion, well, a feeling, I would write those down. And then, so in some ways the book is 20 years of that.
So and bringing it together, uh, was hard because I wanted to, I had, I set some ground rules for myself. I wanted to be. No matter how creative I wanted to get with language, which I did, but I set a rule I had to be absolutely grounded in the science, like [00:30:30] I couldn't stray even an iota from rock solid truth.
And so that was the fundamental balance in writing it. How do I allow literary flights and stay anchored firmly in, in science. And so I threading that was a challenge. But what I do know is I didn't I offend any scientist, which was very important as far as I know, uh, both, both the scientist and the literary folks, uh, seem to like it.
[00:30:58] Russ Altman: Have you gotten feedback from the, [00:31:00] uh, from the world?
[00:31:02] Karl Deisseroth: So I've got amazing feedback from people from all over the world, um, you know, for example, uh, things I didn't expect, uh, because a lot of this was me trying to capture the inner worlds of these people based on my experiences with them. A lot of these are very mysterious still to me, having treated many people with these disorders.
For example, there's a chapter on borderline [00:31:30] personality disorder. And there's a chapter on eating disorders. And these are very mysterious, even to this day. I don't claim to understand them, but I've seen many patients with them. I know psychiatry still doesn't understand them in a fundamental way. Um, but I knew something about these patients and I was able to try to capture their, something about the interstate in the best way I could. And I, from both sets of patients, borderline and eating disorder patients, I got [00:32:00] spontaneous emails from people, uh, you know, thanking me for, uh, you know, people saying that you've captured it, you know, better than I've heard anybody capture.
And for me, just to be able to show that was so important to get because, honestly, I didn't know. I was doing my best, but I didn't know. And for the people who really were suffering to say that was, for me, it made it all worthwhile as hard as it was.
[00:32:24] Russ Altman: Fantastic. Well, I want to thank Karl Deisseroth for this, the last few minutes.
That was the [00:32:30] future of neuroscience. You've been listening to The Future of Everything with Russ Altman. If you enjoy the podcast, please subscribe. Please rate and review. We have more than 200 episodes in the back archives. Please listen to them. You can connect with me on Twitter @RBAltman, or X, or on threads @RussBAltman, and you can follow Stanford Engineering @StanfordENG.
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