1 00:00:05,240 --> 00:00:09,640 Speaker 1: Does brain science need a new grand plan? Is the 2 00:00:09,680 --> 00:00:13,360 Speaker 1: brain less like an assembly line and more like a 3 00:00:13,400 --> 00:00:16,239 Speaker 1: weather system? And if so, what does this mean for 4 00:00:16,320 --> 00:00:19,960 Speaker 1: how we might go about understanding how to think about it, 5 00:00:20,280 --> 00:00:23,640 Speaker 1: and how might AI help us in the near future? 6 00:00:24,000 --> 00:00:25,520 Speaker 1: And what does this have to do with how the 7 00:00:25,600 --> 00:00:28,240 Speaker 1: drug riddle In got its name. Today we'll speak with 8 00:00:28,320 --> 00:00:31,840 Speaker 1: scientists Nicole Rust who's been thinking about these issues. So 9 00:00:32,000 --> 00:00:37,560 Speaker 1: get ready for a great brain stretch. Welcome to Inner 10 00:00:37,560 --> 00:00:40,879 Speaker 1: Cosmos with me David Eagleman. I'm a neuroscientist and an 11 00:00:40,880 --> 00:00:44,479 Speaker 1: author at Stanford, and in these episodes we sailed deeply 12 00:00:44,520 --> 00:00:48,400 Speaker 1: into our three pound universe to understand how we see 13 00:00:48,440 --> 00:00:51,800 Speaker 1: the world and for that matter, how we should view 14 00:00:52,040 --> 00:01:08,080 Speaker 1: the brain. For a very long time now, neuroscience has 15 00:01:08,120 --> 00:01:10,160 Speaker 1: been driven by the hope that if we could just 16 00:01:10,680 --> 00:01:13,959 Speaker 1: zoom in far enough, the brain would finally give up 17 00:01:14,080 --> 00:01:17,600 Speaker 1: its secrets, if we could just do one more electron 18 00:01:17,640 --> 00:01:22,040 Speaker 1: microscope upgrade, or nail one more molecular pathway, or get 19 00:01:22,080 --> 00:01:26,680 Speaker 1: one more brain network labeled and circled in a textbook. Now, 20 00:01:26,680 --> 00:01:29,880 Speaker 1: the approach so far of gathering tons of data has 21 00:01:30,000 --> 00:01:35,040 Speaker 1: delivered real triumphs. We've learned an enormous amount about how 22 00:01:35,080 --> 00:01:40,040 Speaker 1: neurons fire, how circuits form, how chemicals are released and sensed, 23 00:01:40,560 --> 00:01:44,880 Speaker 1: And when you flip open any modern neuroscience textbook, it 24 00:01:45,080 --> 00:01:50,120 Speaker 1: really is a marvel. It's densely packed with discoveries that 25 00:01:50,440 --> 00:01:55,160 Speaker 1: would have been unimaginable a generation ago. But there's an 26 00:01:55,240 --> 00:01:59,760 Speaker 1: uncomfortable question hovering in the background. If we understand so 27 00:01:59,840 --> 00:02:02,600 Speaker 1: much much more than we used to, why do so 28 00:02:02,760 --> 00:02:09,959 Speaker 1: many neuroscience problems remain so stubbornly unsolved? Why do entire 29 00:02:10,160 --> 00:02:16,560 Speaker 1: classes of brain disorders like psychiatric illness or neuroggeneration, or 30 00:02:16,639 --> 00:02:20,760 Speaker 1: disorders of mood and thought continue to resist our best 31 00:02:20,800 --> 00:02:25,640 Speaker 1: efforts And it feels like that's been happening decade after decade. 32 00:02:25,840 --> 00:02:29,640 Speaker 1: Why does it feel sometimes like knowledge is accelerating, but 33 00:02:30,240 --> 00:02:35,880 Speaker 1: meaningful clinical breakthroughs lag behind. These questions force us to 34 00:02:35,960 --> 00:02:39,760 Speaker 1: ask whether the challenge lies in the way we're framing 35 00:02:39,800 --> 00:02:43,840 Speaker 1: the problem. That is, maybe we should be asking whether 36 00:02:43,919 --> 00:02:47,840 Speaker 1: the brain is a different kind of system than the 37 00:02:47,880 --> 00:02:52,920 Speaker 1: metaphors we've relied on. We should be asking whether reductionism, 38 00:02:53,120 --> 00:02:56,200 Speaker 1: which is figuring out all the pieces and parts, can 39 00:02:56,280 --> 00:03:00,760 Speaker 1: ever by itself, fully explain something that evolved to be 40 00:03:01,320 --> 00:03:05,000 Speaker 1: adaptive and live wired, and where you have eighty six 41 00:03:05,160 --> 00:03:09,880 Speaker 1: billion neurons that are like live little creatures, moving and 42 00:03:09,919 --> 00:03:14,320 Speaker 1: adjusting every moment of your life. Every scientific field eventually 43 00:03:14,440 --> 00:03:19,600 Speaker 1: reaches moments like this, moments where success at one scale 44 00:03:20,080 --> 00:03:24,280 Speaker 1: reveals blind spots in another. Fields reach a point where 45 00:03:24,320 --> 00:03:29,320 Speaker 1: accumulating facts is no longer sufficient and what's needed instead 46 00:03:29,760 --> 00:03:34,760 Speaker 1: is a rethinking of first principles. That's the moment neuroscience 47 00:03:34,840 --> 00:03:38,040 Speaker 1: may be in now, and it's why I want to 48 00:03:38,080 --> 00:03:42,160 Speaker 1: talk with today's guest. Nicole Rust is a neuroscientist at 49 00:03:42,160 --> 00:03:46,280 Speaker 1: the University of Pennsylvania, and she has spent years thinking 50 00:03:46,320 --> 00:03:49,839 Speaker 1: deeply about her experiments and data, but also, in more 51 00:03:49,880 --> 00:03:54,280 Speaker 1: recent years thinking about the trajectory of the field itself, 52 00:03:54,720 --> 00:03:59,360 Speaker 1: about how we got here and what assumptions we've inherited, 53 00:03:59,640 --> 00:04:02,440 Speaker 1: and what kinds of questions we might have to ask 54 00:04:02,840 --> 00:04:05,720 Speaker 1: if we want to move forward in a meaningful way. 55 00:04:06,080 --> 00:04:09,480 Speaker 1: She's written a great book about this, called Elusive Cures. 56 00:04:10,120 --> 00:04:12,960 Speaker 1: Nicole is part of a growing group of scientists who 57 00:04:13,000 --> 00:04:16,640 Speaker 1: are stepping back from the daily grind of incremental results 58 00:04:16,839 --> 00:04:20,919 Speaker 1: to ask a simple and hard question, what kind of 59 00:04:21,000 --> 00:04:24,560 Speaker 1: thing is the brain? Really? What would it mean to 60 00:04:24,720 --> 00:04:28,360 Speaker 1: study it on its own terms. So today Nicole and 61 00:04:28,440 --> 00:04:32,520 Speaker 1: I sat down to talk about neuroscience at a crossroads, 62 00:04:32,800 --> 00:04:37,159 Speaker 1: about complexity, what counts as an explanation, and the challenge 63 00:04:37,160 --> 00:04:47,719 Speaker 1: of understanding the most intricate system we've ever encountered. Okay, So, Nicole, 64 00:04:47,760 --> 00:04:51,599 Speaker 1: a few years ago you started working on this idea 65 00:04:51,680 --> 00:04:55,480 Speaker 1: that we need a new grand plan in neuroscience. What 66 00:04:55,600 --> 00:04:56,800 Speaker 1: led you to that conclusion? 67 00:04:57,240 --> 00:05:03,360 Speaker 2: I was hearing concerns from the heads of funding agencies 68 00:05:04,000 --> 00:05:07,840 Speaker 2: and elsewhere that while researchers had been discovering a lot 69 00:05:07,880 --> 00:05:11,240 Speaker 2: of things about the brain, those discoveries hadn't been moving 70 00:05:11,320 --> 00:05:16,039 Speaker 2: the needle in helping individuals with certain classes of disorders. 71 00:05:17,240 --> 00:05:20,360 Speaker 1: So you know, one of the textbooks in our field 72 00:05:20,480 --> 00:05:23,279 Speaker 1: is Principles of Neuroscience. That it just keeps getting fatter 73 00:05:23,320 --> 00:05:26,960 Speaker 1: over the years, absolutely, and it always has struck us 74 00:05:27,040 --> 00:05:30,599 Speaker 1: that if it really were principles, it should be getting thinner. 75 00:05:31,120 --> 00:05:33,680 Speaker 1: But what we just keep doing is a dated dump 76 00:05:34,160 --> 00:05:36,440 Speaker 1: of all the information we're getting. But your point is 77 00:05:36,760 --> 00:05:40,040 Speaker 1: we're not seeing, Ah, here's the clear pathway to solving 78 00:05:40,040 --> 00:05:42,120 Speaker 1: certain problems exactly. 79 00:05:41,839 --> 00:05:43,039 Speaker 3: For certain conditions. 80 00:05:43,480 --> 00:05:46,839 Speaker 2: So for some conditions we have been moving the needle 81 00:05:47,000 --> 00:05:50,960 Speaker 2: quite effectively. And so those include things like new drugs 82 00:05:50,960 --> 00:05:55,320 Speaker 2: from ingrained headache or insomnia, epilepsy and pain. But there 83 00:05:55,320 --> 00:05:58,400 Speaker 2: are other classes of conditions that we've been more frustrated with. 84 00:05:58,480 --> 00:05:59,799 Speaker 3: And so yeah, that's the big question. 85 00:06:00,040 --> 00:06:01,640 Speaker 1: So one of the arguments you make in your new 86 00:06:01,680 --> 00:06:05,599 Speaker 1: book is that many of the pharmaceutical treatments that we have, 87 00:06:05,680 --> 00:06:10,559 Speaker 1: for example, were discovered by accidents, So things like pain 88 00:06:11,240 --> 00:06:14,359 Speaker 1: or ADHD or in some cases depression. So tell us 89 00:06:14,400 --> 00:06:15,680 Speaker 1: about that. What's the story there? 90 00:06:15,800 --> 00:06:21,320 Speaker 2: Yes, absolutely, so those stories are wonderful, the serendipitous discoveries 91 00:06:21,320 --> 00:06:23,320 Speaker 2: that happened long ago before we knew much about the 92 00:06:23,320 --> 00:06:27,120 Speaker 2: brain at all. One example is the first antidepressant, which 93 00:06:27,279 --> 00:06:32,600 Speaker 2: was discovered during clinical trials for the lung infecting bacteria tuberculosis. 94 00:06:33,000 --> 00:06:35,720 Speaker 2: So their clinical trials for the drug for TB and 95 00:06:35,760 --> 00:06:38,360 Speaker 2: what they found was the patients were joyous. There's even 96 00:06:38,400 --> 00:06:41,000 Speaker 2: a picture of light in Life magazine of them dancing around. 97 00:06:41,000 --> 00:06:43,599 Speaker 2: They were so happy. So they realized this chemical probably 98 00:06:43,600 --> 00:06:45,920 Speaker 2: has a different purpose. They put it through clinical trials 99 00:06:46,160 --> 00:06:48,640 Speaker 2: and it became our first antidepressant. 100 00:06:48,839 --> 00:06:50,400 Speaker 1: And what was the name of that drug. 101 00:06:50,240 --> 00:06:50,960 Speaker 3: Ipronia is it? 102 00:06:51,360 --> 00:06:53,400 Speaker 1: And so that was totally an accident. 103 00:06:53,520 --> 00:06:54,599 Speaker 3: It was totally an accident. 104 00:06:54,720 --> 00:06:57,760 Speaker 1: And interestingly, you know the history of medical science is 105 00:06:57,760 --> 00:07:01,080 Speaker 1: shot through with these sorts of accidents, really is Yeah, 106 00:07:01,120 --> 00:07:03,839 Speaker 1: tell us about pain medications. 107 00:07:03,920 --> 00:07:08,160 Speaker 2: Pain medications? So are opioid drugs? Those come from ancient 108 00:07:08,279 --> 00:07:13,400 Speaker 2: Mesopotamia where the Mesopotamians were harvesting opium from the poppy plants. 109 00:07:14,040 --> 00:07:17,160 Speaker 2: And our drugs today, like oxycodone, are just a slow 110 00:07:17,240 --> 00:07:21,080 Speaker 2: release form of that drug that we harvested from opium 111 00:07:21,120 --> 00:07:22,360 Speaker 2: in the early nineteen hundreds. 112 00:07:22,360 --> 00:07:23,760 Speaker 1: How do they end up ingesting that? 113 00:07:24,000 --> 00:07:25,720 Speaker 3: I don't know. That's a great question. 114 00:07:25,920 --> 00:07:26,600 Speaker 1: That's a great question. 115 00:07:26,720 --> 00:07:27,840 Speaker 3: How did they figure it out? 116 00:07:27,920 --> 00:07:32,160 Speaker 1: Yeah? Yeah, yeah, okay. So and adhd M. 117 00:07:32,240 --> 00:07:33,760 Speaker 3: That's another great one. Riddlin. 118 00:07:34,480 --> 00:07:37,560 Speaker 2: So, Ridlin was developed in the nineteen forties by a 119 00:07:37,680 --> 00:07:41,440 Speaker 2: chemist who was Swiss, and he was using a technique 120 00:07:41,480 --> 00:07:44,080 Speaker 2: that we call try it and see what happens. We 121 00:07:44,120 --> 00:07:47,119 Speaker 2: don't do that much anymore. But so he synthesized the drug. 122 00:07:47,560 --> 00:07:48,200 Speaker 2: He liked it. 123 00:07:48,520 --> 00:07:52,160 Speaker 3: He gave some to his wife. She liked it too, because. 124 00:07:51,920 --> 00:07:54,600 Speaker 2: It improved her tennis game, and so he named it 125 00:07:54,640 --> 00:07:56,720 Speaker 2: after her. Her name was Rita, and that's why we 126 00:07:56,800 --> 00:08:00,400 Speaker 2: call it Rita Lynn. So another great story of as 127 00:08:00,480 --> 00:08:03,160 Speaker 2: a drug that happened long before we understood much about 128 00:08:03,160 --> 00:08:05,200 Speaker 2: the brain at all and certainly wasn't based on some 129 00:08:05,280 --> 00:08:07,600 Speaker 2: big discovery about the brain that led to a new breakthrough. 130 00:08:07,760 --> 00:08:09,200 Speaker 2: So there are a lot of discoveries like these. 131 00:08:09,280 --> 00:08:09,760 Speaker 3: Yeah. 132 00:08:09,800 --> 00:08:13,080 Speaker 1: Great. So your argument is that several of the drugs 133 00:08:13,120 --> 00:08:17,040 Speaker 1: that we have were totally accidental. And when it comes 134 00:08:17,080 --> 00:08:21,520 Speaker 1: to things that involve science as we typically do it, 135 00:08:21,520 --> 00:08:23,720 Speaker 1: where we say hey, look here's the gene, here's the 136 00:08:23,760 --> 00:08:28,880 Speaker 1: chemical involved, and so on, it's an enormous undertaking. So 137 00:08:29,000 --> 00:08:32,440 Speaker 1: give us a sense of let's say, for insomnia. 138 00:08:32,559 --> 00:08:36,440 Speaker 2: Yes, yes, you're right, when a new discovery leads to 139 00:08:36,480 --> 00:08:40,680 Speaker 2: a new drug, those discovery stories are absolutely epic. So 140 00:08:40,760 --> 00:08:44,280 Speaker 2: one example of that. A drug for insomnia is subarexcent, 141 00:08:45,000 --> 00:08:49,000 Speaker 2: so superreccent. The way it works is it blocks chemicals 142 00:08:49,000 --> 00:08:52,720 Speaker 2: in our brain that actually keep us awake. And so 143 00:08:53,000 --> 00:08:56,480 Speaker 2: the discovery of superreccent dates back to nineteen ninety eight 144 00:08:56,559 --> 00:09:00,120 Speaker 2: when brain researchers discovered these chemicals in our brain the 145 00:09:00,160 --> 00:09:04,840 Speaker 2: first time. They were then linked later to insomnia via 146 00:09:05,240 --> 00:09:09,240 Speaker 2: studying some dogs that had genetically inherited narcolepsy. So these 147 00:09:09,240 --> 00:09:11,400 Speaker 2: dogs fall asleep spontaneously during. 148 00:09:11,240 --> 00:09:13,800 Speaker 1: The day, and this was the chemical erectionin. 149 00:09:13,520 --> 00:09:15,160 Speaker 3: These chemical ereccin exactly. 150 00:09:15,480 --> 00:09:18,280 Speaker 2: And yeah, so they figured out this was a problem 151 00:09:18,320 --> 00:09:20,679 Speaker 2: in the erecxin pathway in the brain. It was then 152 00:09:20,760 --> 00:09:25,120 Speaker 2: linked to human narcilepsy. And once researchers discovered that there 153 00:09:25,120 --> 00:09:27,160 Speaker 2: are these chemicals in our brain that exists to keep 154 00:09:27,240 --> 00:09:28,959 Speaker 2: us awake, the assumption was that at least some of 155 00:09:29,080 --> 00:09:32,280 Speaker 2: us have insomnia because these chemicals are too active. So 156 00:09:32,320 --> 00:09:36,000 Speaker 2: the pharmaceutical industry went wild trying to find chemicals to 157 00:09:36,160 --> 00:09:39,880 Speaker 2: block the effectiveness of these keep you awake, the erecxins 158 00:09:39,880 --> 00:09:42,880 Speaker 2: in the brain. And so Mark then went through to 159 00:09:42,920 --> 00:09:47,280 Speaker 2: try to find such a chemical. They screened two million 160 00:09:47,320 --> 00:09:49,840 Speaker 2: different chemicals to find the right one, and once they 161 00:09:49,840 --> 00:09:53,080 Speaker 2: found a chemical it was effective, they improved it even 162 00:09:53,160 --> 00:09:56,920 Speaker 2: further to increase its efficacy reduce its side effects. So 163 00:09:57,080 --> 00:10:01,040 Speaker 2: Whurexcin then went through clinical trials and merged in twenty 164 00:10:01,120 --> 00:10:04,400 Speaker 2: fourteen as a new drug. So altogether there was a 165 00:10:04,480 --> 00:10:07,480 Speaker 2: sixteen year process from the big discovery about the brain 166 00:10:07,920 --> 00:10:12,119 Speaker 2: the erecsans to this new drug to block their activity. 167 00:10:12,400 --> 00:10:13,880 Speaker 1: And what kind of money is involved in that? 168 00:10:13,960 --> 00:10:15,559 Speaker 3: It was about a billion dollars. 169 00:10:15,720 --> 00:10:18,920 Speaker 2: Yeah, and that's about as quick as has ever happened 170 00:10:19,040 --> 00:10:21,240 Speaker 2: from a big discovery to a new therapy. 171 00:10:21,480 --> 00:10:22,760 Speaker 3: Yeah. So it's absolutely epic. 172 00:10:22,960 --> 00:10:26,200 Speaker 1: Got it. So many discoveries are accidental. Ones that aren't 173 00:10:26,240 --> 00:10:29,480 Speaker 1: accidental are epic in terms of the amount of time 174 00:10:29,520 --> 00:10:32,000 Speaker 1: and money they take. So where does that put us 175 00:10:32,240 --> 00:10:36,640 Speaker 1: in modern neuroscience research. Let's jump to nineteen ninety eight 176 00:10:36,840 --> 00:10:40,920 Speaker 1: when Eric Candell wrote a paper suggesting, look, here's the 177 00:10:41,000 --> 00:10:43,079 Speaker 1: framework by which we should think about these things. 178 00:10:43,440 --> 00:10:43,720 Speaker 3: Yeah. 179 00:10:43,960 --> 00:10:46,280 Speaker 2: So in Eric Kendall's nineteen ninety eight paper, he was 180 00:10:46,280 --> 00:10:49,160 Speaker 2: really channeling the ethos of an era of brain research 181 00:10:49,280 --> 00:10:53,240 Speaker 2: that followed on excitement around two big new technologies, our 182 00:10:53,280 --> 00:10:57,320 Speaker 2: ability to sequence genes and image the human brain non 183 00:10:57,360 --> 00:11:01,280 Speaker 2: invasively with techniques such as functional nandecoresonance imaging. 184 00:11:02,080 --> 00:11:04,320 Speaker 3: And Yeah, he laid out. 185 00:11:04,120 --> 00:11:09,280 Speaker 2: A proposal of the new intellectual framework, as he called it. So, 186 00:11:09,480 --> 00:11:12,640 Speaker 2: in Kendell's framework, it all begins with genes. Our genes 187 00:11:12,640 --> 00:11:15,079 Speaker 2: are the code that is used to make our brain cells, 188 00:11:15,280 --> 00:11:18,160 Speaker 2: which are wired into these circuits, and it's the activation 189 00:11:18,240 --> 00:11:20,520 Speaker 2: of those circuits that give rise to all of mental 190 00:11:20,520 --> 00:11:24,520 Speaker 2: function and in term behavior, Kendell suggested that there's one 191 00:11:24,600 --> 00:11:28,000 Speaker 2: big feedback loop, so our behavior, our interactions with the 192 00:11:28,040 --> 00:11:30,480 Speaker 2: world feedback to shape how our brains are wired up. 193 00:11:30,480 --> 00:11:31,120 Speaker 3: That's learning. 194 00:11:32,120 --> 00:11:36,080 Speaker 2: And Kendell focused on this big arrow from how the 195 00:11:36,080 --> 00:11:38,920 Speaker 2: brain gives rise to the mind as the great challenge 196 00:11:39,240 --> 00:11:43,000 Speaker 2: for psychologists and biologists to delineate the relationship between those 197 00:11:43,040 --> 00:11:43,520 Speaker 2: two things. 198 00:11:43,559 --> 00:11:47,120 Speaker 1: And the arrow is pointing from genes, two circuits exsolately, Yes, 199 00:11:47,120 --> 00:11:48,240 Speaker 1: experience and behavior. 200 00:11:48,280 --> 00:11:51,000 Speaker 2: Okay, so yeah, to summarize this idea about the brain 201 00:11:51,040 --> 00:11:52,640 Speaker 2: and the type of thing it is, it's really set 202 00:11:52,720 --> 00:11:55,040 Speaker 2: up as a big chain of causes that lead to effects. 203 00:11:55,880 --> 00:12:00,000 Speaker 2: And the notion then is that when the brain becomes dysfunctional, 204 00:12:00,040 --> 00:12:02,559 Speaker 2: when you have some type of disorder, it's a broken. 205 00:12:02,280 --> 00:12:03,000 Speaker 3: Link in the chain. 206 00:12:03,160 --> 00:12:05,480 Speaker 2: It might be a mutated gene that leads to a 207 00:12:05,520 --> 00:12:07,640 Speaker 2: disorder that you might want to target with a drug, 208 00:12:08,000 --> 00:12:09,800 Speaker 2: or it might be a part of the brain has 209 00:12:09,840 --> 00:12:12,959 Speaker 2: aberrant activity which you could then target with stimulation. 210 00:12:13,640 --> 00:12:15,480 Speaker 3: So this era of brain research I. 211 00:12:15,480 --> 00:12:17,959 Speaker 2: Like to call find the broken link in the chain 212 00:12:18,080 --> 00:12:19,839 Speaker 2: so we can go in and target it for a fix. 213 00:12:20,360 --> 00:12:23,079 Speaker 2: And that example that we just talked about super excent 214 00:12:23,120 --> 00:12:25,200 Speaker 2: it was very much of that type of find the 215 00:12:25,240 --> 00:12:27,199 Speaker 2: broken link in the chain target it for a fixed 216 00:12:27,240 --> 00:12:44,400 Speaker 2: type of approach that led to that big discovery. 217 00:12:44,600 --> 00:12:47,320 Speaker 1: Right, So sometimes that works, and that probably felt like 218 00:12:47,400 --> 00:12:51,160 Speaker 1: real progress. I'm sure when Eric Candell no Bel laureate 219 00:12:51,320 --> 00:12:53,760 Speaker 1: wrote this paper in ninety eight, he felt like, Hey, 220 00:12:54,080 --> 00:12:58,280 Speaker 1: we're really simplifying this and getting this straight how one 221 00:12:58,280 --> 00:13:01,079 Speaker 1: thing leads to another. But when you take a look 222 00:13:01,160 --> 00:13:03,439 Speaker 1: at what's going on in the field, you think that's 223 00:13:03,480 --> 00:13:05,079 Speaker 1: somehow not sufficient. 224 00:13:05,360 --> 00:13:08,320 Speaker 2: Absolutely so, there are certain classes of disorders that have 225 00:13:08,520 --> 00:13:12,760 Speaker 2: really proven to be somewhat impenetrable using that type of 226 00:13:12,840 --> 00:13:16,079 Speaker 2: find the broken link in a chain approach. What's then example, 227 00:13:16,280 --> 00:13:20,079 Speaker 2: So they include our psychiatric conditions like depression and anxiety 228 00:13:20,200 --> 00:13:23,520 Speaker 2: and schizophrenia. So those are all cases in which we 229 00:13:23,559 --> 00:13:26,560 Speaker 2: do have therapies, but they don't work for everyone. And 230 00:13:26,600 --> 00:13:29,679 Speaker 2: many of those therapies date to pre date our understanding 231 00:13:29,720 --> 00:13:32,960 Speaker 2: of the brain, so they were discovered serendipitously. Also our 232 00:13:33,000 --> 00:13:38,040 Speaker 2: neurodegenerative conditions like Alzheimer's and Parkinson's and als, where we 233 00:13:38,120 --> 00:13:41,920 Speaker 2: do have some treatments in some cases, for example Parkinson's, 234 00:13:42,000 --> 00:13:44,800 Speaker 2: but we don't have ways to slow down the degeneration 235 00:13:44,880 --> 00:13:47,560 Speaker 2: that's happening in the brain that's leading to the decline. 236 00:13:47,720 --> 00:13:49,839 Speaker 1: In other words, when we look at all these disorders, 237 00:13:49,880 --> 00:13:53,679 Speaker 1: we think, wow, this is really somehow more complicated. And 238 00:13:53,720 --> 00:13:56,280 Speaker 1: why because when we look for, let's say, a gene 239 00:13:56,320 --> 00:13:58,199 Speaker 1: for schizophrenia, what do we find. 240 00:13:58,480 --> 00:14:03,920 Speaker 2: Absolutely so, in the case of schizophrenia, it's very rare 241 00:14:04,000 --> 00:14:09,079 Speaker 2: to have a single gene variation or mutation that leads 242 00:14:09,120 --> 00:14:12,800 Speaker 2: to the disorder. More likely, well, now that we've sequenced 243 00:14:12,840 --> 00:14:16,280 Speaker 2: lots of genes, we know that it's variation in hundreds 244 00:14:16,320 --> 00:14:19,680 Speaker 2: of genes that are tied to the condition. So if 245 00:14:19,680 --> 00:14:23,000 Speaker 2: one identical twin has schizophrenia, the chances of the other 246 00:14:23,080 --> 00:14:26,600 Speaker 2: identical twin having schizophrenia they're fifty percent. It's not one 247 00:14:26,640 --> 00:14:29,600 Speaker 2: hundred percent, it's fifty percent. So there is a big 248 00:14:29,680 --> 00:14:32,440 Speaker 2: genetic component to all of this, but there are also 249 00:14:32,560 --> 00:14:36,040 Speaker 2: environmental effects and other issues at. 250 00:14:35,920 --> 00:14:39,640 Speaker 1: Play, and these intertwine in ways that are super complex. 251 00:14:39,880 --> 00:14:42,880 Speaker 1: As a side note, you know, the first gene pulled 252 00:14:42,880 --> 00:14:45,840 Speaker 1: for a major disease was for hunting tins and it 253 00:14:45,880 --> 00:14:47,840 Speaker 1: was a gene and if you have that gene, you're 254 00:14:47,840 --> 00:14:49,560 Speaker 1: going to die of hunting tins unless you dive something 255 00:14:49,560 --> 00:14:52,280 Speaker 1: else first, Yes, and everyone thought this is great, We're 256 00:14:52,280 --> 00:14:54,440 Speaker 1: going to figure out the gene that goes with every disease, 257 00:14:54,480 --> 00:14:56,640 Speaker 1: and it turned out to be much more complicated. 258 00:14:56,800 --> 00:14:59,440 Speaker 2: Yeah, And even now, thirty years later, we still don't 259 00:14:59,440 --> 00:15:03,240 Speaker 2: have an effect of treatment for Huntingtons, although fingers crossed, 260 00:15:03,240 --> 00:15:04,640 Speaker 2: it looks like maybe there might be one on the 261 00:15:04,640 --> 00:15:07,600 Speaker 2: way in clinical trials, but it's taken over thirty years 262 00:15:08,120 --> 00:15:10,720 Speaker 2: to get there, even when we knew exactly what the problem. 263 00:15:10,480 --> 00:15:14,800 Speaker 1: Was, right, Okay, So but for something like schizphrenia or 264 00:15:14,840 --> 00:15:18,360 Speaker 1: major depressive disorder, we're looking at something that's much more complicated. 265 00:15:18,360 --> 00:15:21,160 Speaker 1: We can't even find a single gene for it. As 266 00:15:21,200 --> 00:15:23,600 Speaker 1: you said, we find hundreds of genes. So where does 267 00:15:23,640 --> 00:15:24,280 Speaker 1: that put us? 268 00:15:24,640 --> 00:15:28,400 Speaker 2: Yeah, so I think researchers are definitely waking up to 269 00:15:28,440 --> 00:15:32,440 Speaker 2: the idea that this idea about the brain as a big, 270 00:15:32,440 --> 00:15:35,840 Speaker 2: long chain is probably oversimplified. The human brain is often 271 00:15:35,880 --> 00:15:38,680 Speaker 2: held up as the most complex thing in the entire universe, 272 00:15:39,200 --> 00:15:42,440 Speaker 2: and this chain of causes the lead to effects, it's 273 00:15:42,480 --> 00:15:45,920 Speaker 2: not very complicated. So we might ask ourselves, well, what's 274 00:15:45,920 --> 00:15:49,840 Speaker 2: so complicated about the brain and what are we missing 275 00:15:50,080 --> 00:15:50,760 Speaker 2: in this chain? 276 00:15:50,880 --> 00:15:51,400 Speaker 3: Like idea. 277 00:15:52,000 --> 00:15:54,280 Speaker 1: And by the way, it still might be causes leading 278 00:15:54,280 --> 00:15:57,400 Speaker 1: to effects, right, but it's the feedback loops at every 279 00:15:57,480 --> 00:15:58,680 Speaker 1: stage exactly. 280 00:15:58,920 --> 00:16:02,640 Speaker 2: That's the complexity that we're beginning to embrace. So causes 281 00:16:02,680 --> 00:16:06,200 Speaker 2: that lead to effects that feed back on themselves as causes. 282 00:16:06,600 --> 00:16:08,520 Speaker 2: The brain is not like a chain in this type 283 00:16:08,520 --> 00:16:10,400 Speaker 2: of idea. It's a whole different type of thing. 284 00:16:10,760 --> 00:16:13,600 Speaker 1: Right. So your analogy that you make in the book, 285 00:16:13,600 --> 00:16:17,440 Speaker 1: which is wonderful, is like a weather system, right, So 286 00:16:17,840 --> 00:16:18,880 Speaker 1: unpack that for us. 287 00:16:19,240 --> 00:16:22,200 Speaker 2: Yes, so you can think about these complex systems that 288 00:16:22,240 --> 00:16:25,960 Speaker 2: have many interdependent parts, and the weather is a terrific 289 00:16:26,080 --> 00:16:29,560 Speaker 2: example of that, and you can think about when a 290 00:16:29,720 --> 00:16:33,160 Speaker 2: complex system goes awry, it's a lot like a weather 291 00:16:33,280 --> 00:16:35,240 Speaker 2: breaking out into a storm. 292 00:16:35,920 --> 00:16:36,360 Speaker 1: Yeah. 293 00:16:36,760 --> 00:16:38,760 Speaker 2: We know a lot about systems like these because we've 294 00:16:38,760 --> 00:16:41,640 Speaker 2: studied them quite extensively. And one thing we know about 295 00:16:41,640 --> 00:16:45,520 Speaker 2: them is they're very, very hard to perturb in ways 296 00:16:45,560 --> 00:16:49,760 Speaker 2: that you would want to shift them out of their storms, 297 00:16:49,800 --> 00:16:51,880 Speaker 2: which in the case of the brain, would be shifting 298 00:16:51,920 --> 00:16:54,240 Speaker 2: the brain from a less healthy to a more healthy state. 299 00:16:54,600 --> 00:16:57,040 Speaker 1: And one second tangent in your book, you tell us 300 00:16:57,040 --> 00:16:59,680 Speaker 1: a wonderful story about Johnny von Neuman and weather I 301 00:16:59,800 --> 00:17:01,320 Speaker 1: had no idea tell us then. 302 00:17:01,480 --> 00:17:01,920 Speaker 3: Yeah. 303 00:17:02,000 --> 00:17:05,840 Speaker 2: So in the nineteen forties, the end goal of weather 304 00:17:05,880 --> 00:17:09,840 Speaker 2: research really was to control the weather. Researchers wanted to 305 00:17:09,880 --> 00:17:12,800 Speaker 2: not just dissipate hurricanes, which is a worthy goal in 306 00:17:12,840 --> 00:17:15,160 Speaker 2: and of itself, but they also even wanted to weaponize 307 00:17:15,160 --> 00:17:15,600 Speaker 2: the weathers. 308 00:17:15,960 --> 00:17:17,600 Speaker 1: This was the US government that, This was. 309 00:17:17,520 --> 00:17:18,240 Speaker 3: The US government. 310 00:17:18,280 --> 00:17:21,200 Speaker 2: Yeah, so they were very interested in funding weather research 311 00:17:21,240 --> 00:17:25,399 Speaker 2: with that end goal. Part of von Neumann's development of 312 00:17:25,640 --> 00:17:30,320 Speaker 2: the first computers was explicitly in the end goal, first 313 00:17:30,400 --> 00:17:32,600 Speaker 2: predict the weather, then learn how to control it. 314 00:17:34,640 --> 00:17:38,720 Speaker 3: And so researchers tried that out and it didn't work out. 315 00:17:38,560 --> 00:17:40,119 Speaker 1: So well, and it still hasn't worked out. 316 00:17:40,240 --> 00:17:41,120 Speaker 3: It still hasn't worked out. 317 00:17:41,200 --> 00:17:43,440 Speaker 1: And why it's because the weather's so complicated. 318 00:17:43,520 --> 00:17:46,480 Speaker 2: It's so complicated, and it is a system because you 319 00:17:46,520 --> 00:17:49,760 Speaker 2: have these big feedback loops, right, any type of intervention 320 00:17:49,880 --> 00:17:53,000 Speaker 2: you try to do will reverberate in unexpected ways, and 321 00:17:53,040 --> 00:17:56,160 Speaker 2: so that's what makes these systems really really difficult to control. 322 00:17:56,400 --> 00:17:58,920 Speaker 1: So when we look at something like major depressive disorder, 323 00:17:59,280 --> 00:18:01,520 Speaker 1: the temptation to say, look, if we could just find 324 00:18:01,720 --> 00:18:04,040 Speaker 1: the gene. There is no theugen, but if we could 325 00:18:04,160 --> 00:18:06,719 Speaker 1: just do this pharmaceutical here there, we can solve this, 326 00:18:06,840 --> 00:18:11,320 Speaker 1: and that has proved to be ineffective precisely because of 327 00:18:11,400 --> 00:18:13,520 Speaker 1: the complexity of the system here. 328 00:18:13,800 --> 00:18:15,359 Speaker 3: Yes, absolutely, and so. 329 00:18:15,280 --> 00:18:17,280 Speaker 1: One example that you talked about in the book was 330 00:18:17,560 --> 00:18:22,480 Speaker 1: emotions research and the one hundred years' war that's happened there. 331 00:18:22,520 --> 00:18:23,719 Speaker 1: So explain to us what that is. 332 00:18:24,720 --> 00:18:27,880 Speaker 2: Yes, So, our ability to understand what's happening in many 333 00:18:27,880 --> 00:18:31,000 Speaker 2: of the psychiatric conditions comes down to wanting to be 334 00:18:31,040 --> 00:18:34,359 Speaker 2: able to measure an emotion, say, in the brain, and 335 00:18:34,400 --> 00:18:36,720 Speaker 2: that's proven to be very difficult to try to do. 336 00:18:37,760 --> 00:18:37,840 Speaker 1: So. 337 00:18:38,000 --> 00:18:41,760 Speaker 2: Researchers for over one hundred years have been arguing about 338 00:18:42,160 --> 00:18:44,919 Speaker 2: what types of things are emotions in the brain and 339 00:18:44,920 --> 00:18:48,240 Speaker 2: how are they organized. It might be that different emotions 340 00:18:48,320 --> 00:18:52,720 Speaker 2: like fear and disgust and happiness, they might be organized 341 00:18:52,760 --> 00:18:54,720 Speaker 2: in kind of their own little compartments in the brain, 342 00:18:54,840 --> 00:18:56,719 Speaker 2: kind of like our sensory systems where we have one 343 00:18:56,720 --> 00:18:58,919 Speaker 2: part of our brain for vision and another part for hearing, 344 00:18:59,720 --> 00:19:03,359 Speaker 2: Or might be that they're much more intermingled in the 345 00:19:03,400 --> 00:19:06,800 Speaker 2: brain such that and a lot like color vision. Right, 346 00:19:06,840 --> 00:19:09,680 Speaker 2: So color vision, we have one visual system and there's 347 00:19:09,720 --> 00:19:13,000 Speaker 2: a continuous space in our brain upon which we put 348 00:19:13,080 --> 00:19:16,960 Speaker 2: labels like cyan and red and magenta. We don't really 349 00:19:17,000 --> 00:19:19,800 Speaker 2: know how emotions are organized in our brain, whether they're 350 00:19:19,840 --> 00:19:24,040 Speaker 2: more like these compartments or more continuously, but understanding that 351 00:19:24,160 --> 00:19:26,560 Speaker 2: is one of the keys to trying to measure an 352 00:19:26,560 --> 00:19:28,320 Speaker 2: emotion in the brain. You have to figure out where 353 00:19:28,359 --> 00:19:29,520 Speaker 2: is it that you want to look and how is 354 00:19:29,560 --> 00:19:30,600 Speaker 2: it going to be reflected there? 355 00:19:31,400 --> 00:19:34,200 Speaker 1: Yeah, and so that's led to this one hundred years 356 00:19:34,240 --> 00:19:36,399 Speaker 1: war because there are people on both sides of this argument. 357 00:19:36,440 --> 00:19:38,600 Speaker 1: It's either separate or it's spectral. 358 00:19:38,760 --> 00:19:39,520 Speaker 3: Absolutely, yeah. 359 00:19:39,560 --> 00:19:41,399 Speaker 1: And so we're looking at things like emotions and we 360 00:19:41,440 --> 00:19:44,399 Speaker 1: all want an explanation for it. But the question is 361 00:19:44,440 --> 00:19:46,359 Speaker 1: what will it take for us to be able to 362 00:19:46,520 --> 00:19:47,560 Speaker 1: answer something like that. 363 00:19:47,920 --> 00:19:52,040 Speaker 2: It will take an appreciation that the way that emotions 364 00:19:52,080 --> 00:19:54,200 Speaker 2: manifest in the brain is not going to be simple 365 00:19:54,240 --> 00:19:56,840 Speaker 2: and straightforward. It's not going to be an individual neuron 366 00:19:57,000 --> 00:20:00,960 Speaker 2: that's activated when we feel aggression or sadness. 367 00:20:01,040 --> 00:20:01,920 Speaker 3: It's going to take. 368 00:20:02,320 --> 00:20:05,800 Speaker 2: Embracing these ideas that in the brain, if brain area 369 00:20:05,880 --> 00:20:08,639 Speaker 2: a sense information to b be send information back to 370 00:20:08,680 --> 00:20:11,680 Speaker 2: A again, and so we expect emotions to be reflected 371 00:20:11,920 --> 00:20:14,919 Speaker 2: in ways that kind of reverberate and dynamically evolve in 372 00:20:14,960 --> 00:20:17,240 Speaker 2: the brain as opposed to snapshots. 373 00:20:16,640 --> 00:20:17,639 Speaker 3: That you could take a picture of. 374 00:20:17,920 --> 00:20:20,280 Speaker 1: That's a really simple way, and in fact, it might 375 00:20:20,320 --> 00:20:22,879 Speaker 1: not even be that we can talk about in let's 376 00:20:22,960 --> 00:20:24,560 Speaker 1: cut two one hundred years from now, that we can 377 00:20:24,560 --> 00:20:28,600 Speaker 1: talk about area A and area B, right, because in 378 00:20:28,640 --> 00:20:32,480 Speaker 1: a sense, the whole brain is spectral in the sense 379 00:20:32,560 --> 00:20:35,240 Speaker 1: of you've got eighty six billion neurons that are all 380 00:20:35,240 --> 00:20:38,359 Speaker 1: doing their things, but they don't have border walls between them. 381 00:20:38,720 --> 00:20:41,160 Speaker 1: So what we do as neuroscientists as we say, oh, 382 00:20:41,200 --> 00:20:44,240 Speaker 1: this area seems to be involved in blah, but boy, 383 00:20:44,359 --> 00:20:46,760 Speaker 1: these things are spread out. 384 00:20:46,880 --> 00:20:47,800 Speaker 3: How do you think about that? 385 00:20:47,840 --> 00:20:49,960 Speaker 2: When you think about the brain in terms of how 386 00:20:50,200 --> 00:20:54,120 Speaker 2: compartmentalized is it? How much is everything everywhere all at once? 387 00:20:54,680 --> 00:20:56,320 Speaker 2: What's your take on that? You've thought a lot about it? 388 00:20:56,359 --> 00:20:59,159 Speaker 1: I know, I mean, you know, so starting with the 389 00:20:59,200 --> 00:21:03,240 Speaker 1: experiments of Carl Lashly whatever last century, as you all know, 390 00:21:04,119 --> 00:21:07,080 Speaker 1: Lashley was trying to figure out where is a memory stored? 391 00:21:07,119 --> 00:21:10,040 Speaker 1: So he trained little mice to run a maze and 392 00:21:10,080 --> 00:21:14,639 Speaker 1: then he would cut parts of the brain to see, Okay, 393 00:21:14,680 --> 00:21:17,480 Speaker 1: where is that memory stored? So if I take all 394 00:21:17,480 --> 00:21:19,399 Speaker 1: these rats and I cut different parts. Where can I 395 00:21:19,400 --> 00:21:21,560 Speaker 1: find the memory store? And what he found is that 396 00:21:21,920 --> 00:21:26,399 Speaker 1: none of the experiments yielded anything because the memory is 397 00:21:26,400 --> 00:21:29,200 Speaker 1: somehow stored in a distributed manner. It's more like cloud 398 00:21:29,240 --> 00:21:31,879 Speaker 1: computing rather than here's my hard drive and you've just 399 00:21:31,920 --> 00:21:35,040 Speaker 1: broken the hard drive. And so that was one of 400 00:21:35,119 --> 00:21:37,720 Speaker 1: the first examples of Wow, we're looking at a big 401 00:21:37,720 --> 00:21:41,520 Speaker 1: complex system here where stuff is really distributed in ways 402 00:21:41,600 --> 00:21:44,480 Speaker 1: that's hard for us as humans to say, oh, yeah, 403 00:21:44,560 --> 00:21:48,600 Speaker 1: you're just restoring zeros and ones there. It's a very 404 00:21:48,600 --> 00:21:52,080 Speaker 1: different sort of thing. Every attempt we've made to compartmentalize 405 00:21:52,119 --> 00:21:56,399 Speaker 1: the brain doesn't seem to hold that well over time. 406 00:21:56,720 --> 00:22:00,399 Speaker 1: We still do find temptations say, look, this is the 407 00:22:00,480 --> 00:22:02,919 Speaker 1: visual cortex and this is auditory and so on, and 408 00:22:02,960 --> 00:22:06,800 Speaker 1: that's mostly true. But even embedded in here, you've got many, 409 00:22:06,840 --> 00:22:12,080 Speaker 1: many neurons that are reaching across long distances to talk 410 00:22:12,119 --> 00:22:15,400 Speaker 1: to other areas. And you know, when we look at 411 00:22:15,400 --> 00:22:19,040 Speaker 1: baby's brains, we find, you know, there are neurons and 412 00:22:19,080 --> 00:22:22,760 Speaker 1: the auditory cortex that are that are activating the visual 413 00:22:22,760 --> 00:22:25,960 Speaker 1: cortex when they're sound, and in the visual cortext they 414 00:22:25,960 --> 00:22:28,760 Speaker 1: are activating the auditory cortex when their site and as 415 00:22:28,800 --> 00:22:32,520 Speaker 1: we grow, those things start talking less to each other, 416 00:22:32,600 --> 00:22:36,200 Speaker 1: but they're still there. And if you go, let's say blind, 417 00:22:36,280 --> 00:22:39,040 Speaker 1: at some point those neurons sitting in an auditory cortex 418 00:22:39,119 --> 00:22:41,680 Speaker 1: will start We'll start taking over that territory right away, 419 00:22:41,720 --> 00:22:45,560 Speaker 1: because those cross connections are all sitting there. I love 420 00:22:45,600 --> 00:22:49,040 Speaker 1: the fact that you're pursuing this because it is a 421 00:22:49,080 --> 00:22:54,320 Speaker 1: system that we have always been tempted to simplify and say, Okay, look, 422 00:22:54,359 --> 00:22:56,000 Speaker 1: it's probably going to be this. And by the way, 423 00:22:56,040 --> 00:23:00,200 Speaker 1: this is the wonderful thing about science is saying hey, hey, 424 00:23:00,800 --> 00:23:03,040 Speaker 1: there's going to be a way to really simplify this, 425 00:23:03,119 --> 00:23:06,280 Speaker 1: and that's where we get progress. And yet we've attempted 426 00:23:06,280 --> 00:23:07,520 Speaker 1: to oversimplify here. 427 00:23:07,760 --> 00:23:11,480 Speaker 2: Absolutely, Yeah, I completely agree with that. Yeah, but I 428 00:23:11,480 --> 00:23:13,639 Speaker 2: think we're also ready for the first time in history 429 00:23:13,680 --> 00:23:16,560 Speaker 2: to take on the complexity like we've never been able 430 00:23:16,560 --> 00:23:18,600 Speaker 2: to do this before. So it is an exciting era 431 00:23:18,720 --> 00:23:21,240 Speaker 2: for brain research to build on this oversimplification. 432 00:23:21,520 --> 00:23:26,199 Speaker 1: That's right. And so you've been looking at other systems 433 00:23:27,480 --> 00:23:30,639 Speaker 1: and other scientific voices from the last fifty years that 434 00:23:30,680 --> 00:23:33,400 Speaker 1: have suggested things. So what do you see as possible 435 00:23:33,400 --> 00:23:34,399 Speaker 1: ways forward there. 436 00:23:34,760 --> 00:23:35,520 Speaker 3: Yes, so. 437 00:23:36,920 --> 00:23:40,560 Speaker 2: There's been a long thread through brain research. It's been 438 00:23:40,560 --> 00:23:44,720 Speaker 2: more of an undercurrent than the most dominant idea that 439 00:23:45,440 --> 00:23:48,000 Speaker 2: the way we should be thinking about the brain is 440 00:23:48,119 --> 00:23:51,120 Speaker 2: something much more akin to the weather, a dynamical system 441 00:23:51,520 --> 00:23:55,520 Speaker 2: where we're interested in how it evolves in time in 442 00:23:55,600 --> 00:23:58,560 Speaker 2: terms of things like it's patterns of activity and how 443 00:23:59,160 --> 00:24:03,399 Speaker 2: it is structured, not just as a computer, but something 444 00:24:03,440 --> 00:24:09,320 Speaker 2: that's continuously adapting to change. And these ideas date back 445 00:24:09,359 --> 00:24:12,919 Speaker 2: to Norbert Reener in cybernetics in the nineteen forties and 446 00:24:12,960 --> 00:24:17,480 Speaker 2: there's been an undercurrent of them throughout history and brain research, 447 00:24:17,920 --> 00:24:21,000 Speaker 2: including John Hopfield's Nobel Prize on he won in twenty 448 00:24:21,040 --> 00:24:24,160 Speaker 2: twenty four for physics for these ideas based on work 449 00:24:24,160 --> 00:24:25,760 Speaker 2: that he did in the nineteen eighties. 450 00:24:25,480 --> 00:24:26,919 Speaker 1: And tell us about cybernetics. 451 00:24:27,040 --> 00:24:34,119 Speaker 2: Cybernetics was this idea that the brain exists to control 452 00:24:34,280 --> 00:24:37,240 Speaker 2: the body and interact with the environment and a big 453 00:24:37,320 --> 00:24:38,119 Speaker 2: feedback loop. 454 00:24:38,440 --> 00:24:40,040 Speaker 3: That was the gist of cybernetics. 455 00:24:40,400 --> 00:24:43,600 Speaker 1: Yeah, and so that was Norbert Veener and other people 456 00:24:43,640 --> 00:24:46,400 Speaker 1: have built on that idea of having dynamic systems, lots 457 00:24:46,400 --> 00:24:50,120 Speaker 1: of feedback loops and so where do you see that 458 00:24:50,440 --> 00:24:54,240 Speaker 1: moving forward. So if we think today, okay, look, let's 459 00:24:54,240 --> 00:24:56,320 Speaker 1: think of the brain as a very complicated system with 460 00:24:56,320 --> 00:24:58,840 Speaker 1: lots of feedback. How do you tackle something like that. 461 00:24:58,840 --> 00:25:01,280 Speaker 2: Well, there are a couple of different things are really important. 462 00:25:01,960 --> 00:25:07,320 Speaker 2: One is because these types of systems are so integrated, 463 00:25:07,800 --> 00:25:10,560 Speaker 2: you have to measure all their parts at the same time. 464 00:25:10,680 --> 00:25:13,040 Speaker 2: You can't measure their parts one at a time, And 465 00:25:13,119 --> 00:25:15,280 Speaker 2: for the first time in history, we're able to do that. 466 00:25:15,359 --> 00:25:17,800 Speaker 2: Twenty years ago, when I was recording from brain cells 467 00:25:17,840 --> 00:25:19,880 Speaker 2: and looking at their activity, I was able to look 468 00:25:19,880 --> 00:25:23,320 Speaker 2: at one at a time. Today we can record from 469 00:25:23,720 --> 00:25:27,159 Speaker 2: one million brain cells simultaneously in a mouse. 470 00:25:27,200 --> 00:25:28,040 Speaker 3: It's remarkable. 471 00:25:28,440 --> 00:25:31,600 Speaker 2: That's exactly the type of data that you need in 472 00:25:31,720 --> 00:25:34,639 Speaker 2: order to understand how all these brain cells are interacting 473 00:25:34,640 --> 00:25:37,960 Speaker 2: with one another. We also have to build these really 474 00:25:37,960 --> 00:25:39,560 Speaker 2: complicated models to make. 475 00:25:39,400 --> 00:25:40,920 Speaker 3: Sense of these dynamical systems. 476 00:25:40,960 --> 00:25:44,160 Speaker 2: Again, causes lead to effects that feedback on themselves as causes. 477 00:25:44,359 --> 00:25:47,800 Speaker 2: These are not things you can think through and try 478 00:25:47,840 --> 00:25:51,400 Speaker 2: to reason through. You need computers in order to do this, 479 00:25:51,840 --> 00:25:54,760 Speaker 2: and for the first time in history, we have artificial 480 00:25:54,800 --> 00:25:57,760 Speaker 2: intelligence of a type that can actually help us sift 481 00:25:57,760 --> 00:26:01,359 Speaker 2: through and make sense of this data. And build computer 482 00:26:01,440 --> 00:26:05,399 Speaker 2: programs that rival something as complicated as the types of 483 00:26:05,440 --> 00:26:07,400 Speaker 2: things that we can do. So it's a really exciting 484 00:26:07,440 --> 00:26:12,600 Speaker 2: era those two technologies, biotechnology and artificial intelligence coming together 485 00:26:12,720 --> 00:26:15,840 Speaker 2: in order to enable us to really embrace this type 486 00:26:15,840 --> 00:26:29,960 Speaker 2: of complexity. 487 00:26:30,280 --> 00:26:33,760 Speaker 1: Give us a sense of, for example, David Anderson's lab 488 00:26:33,800 --> 00:26:37,280 Speaker 1: at Caltech, how he looks at this giant data and 489 00:26:37,280 --> 00:26:39,520 Speaker 1: figures out, hey, here's a way to capture what's going on. 490 00:26:39,760 --> 00:26:42,760 Speaker 2: Yeah, that's a great example, and it's so relevant to 491 00:26:43,320 --> 00:26:45,200 Speaker 2: a problem that we've really been struggling with, and that 492 00:26:45,320 --> 00:26:46,960 Speaker 2: is how do we measure an emotion in the brain. 493 00:26:47,840 --> 00:26:51,040 Speaker 2: So in David's lab, he is really interested in the 494 00:26:51,200 --> 00:26:58,960 Speaker 2: evolutionarily ancient emotions like aggression, fighting, or feeding, and he 495 00:26:59,480 --> 00:27:01,280 Speaker 2: looks into a part of the brain that we know 496 00:27:01,440 --> 00:27:05,160 Speaker 2: is involved, the hypothalamus. And we know it's involved because 497 00:27:05,200 --> 00:27:08,480 Speaker 2: if you naturally, if you put two male mice together. 498 00:27:08,240 --> 00:27:09,960 Speaker 3: They'll fight. They're aggressive. 499 00:27:10,920 --> 00:27:13,639 Speaker 2: If you stimulate the hypothalamus of a mouse, even if 500 00:27:13,640 --> 00:27:16,200 Speaker 2: they're all alone, it will cause that type of aggression. 501 00:27:16,880 --> 00:27:20,119 Speaker 2: And if a mouse has damage to their hypothalamus, they 502 00:27:20,160 --> 00:27:23,960 Speaker 2: won't be aggressive anymore. So we know the hypothalamus is 503 00:27:24,000 --> 00:27:27,080 Speaker 2: definitely involved in mouse aggression. But if you look at 504 00:27:27,119 --> 00:27:29,520 Speaker 2: the activity of the brain cells in that part of 505 00:27:29,560 --> 00:27:32,720 Speaker 2: the hypothalamus, it really just doesn't make any sense because 506 00:27:32,760 --> 00:27:35,480 Speaker 2: not very many of them are active when the mice 507 00:27:35,520 --> 00:27:38,760 Speaker 2: are aggressive, and even the brain cells that are activated 508 00:27:38,840 --> 00:27:41,440 Speaker 2: during aggression they do all sorts of other things as well, 509 00:27:41,800 --> 00:27:45,359 Speaker 2: So you really can't look in the hypothalamus and understand 510 00:27:45,640 --> 00:27:48,159 Speaker 2: why is it that this part of the brain is 511 00:27:48,200 --> 00:27:49,760 Speaker 2: so important for aggression. 512 00:27:50,040 --> 00:27:51,879 Speaker 1: In other words, it's not like the cells turn on 513 00:27:52,480 --> 00:27:53,159 Speaker 1: and then turn off. 514 00:27:53,240 --> 00:27:56,399 Speaker 2: Okay, yep, yeah, It's just not an obvious answer. And 515 00:27:56,480 --> 00:28:00,960 Speaker 2: so these researchers in this group they started to shift 516 00:28:01,000 --> 00:28:03,359 Speaker 2: to this new way of thinking about the brain, not 517 00:28:03,520 --> 00:28:05,679 Speaker 2: as a big chain, but again as one of these 518 00:28:05,680 --> 00:28:08,359 Speaker 2: systems with these big feedback loops. So they shifted to 519 00:28:08,359 --> 00:28:11,040 Speaker 2: this new way of thinking about activity and the hypothalamus 520 00:28:11,600 --> 00:28:16,480 Speaker 2: that is a lot like a landscape of hills and valleys, 521 00:28:16,800 --> 00:28:19,600 Speaker 2: where at any one point in time, the activity of 522 00:28:19,600 --> 00:28:23,600 Speaker 2: the hypothalamus is somewhere on that landscape, and where it 523 00:28:23,800 --> 00:28:27,720 Speaker 2: falls where it ends up, determines how aggressive the mouse 524 00:28:27,760 --> 00:28:28,080 Speaker 2: will be. 525 00:28:28,440 --> 00:28:31,520 Speaker 1: So you're measuring all the cells and you're representing it 526 00:28:31,560 --> 00:28:33,119 Speaker 1: as a point on the landscape. 527 00:28:33,320 --> 00:28:34,120 Speaker 3: Yes, that's right. 528 00:28:34,480 --> 00:28:38,400 Speaker 2: So at any one point in time, the activity and 529 00:28:38,400 --> 00:28:42,160 Speaker 2: the hypothalamus will be somewhere on this landscape, and where 530 00:28:42,160 --> 00:28:45,560 Speaker 2: it ends up falling in the valley along this long 531 00:28:45,640 --> 00:28:49,680 Speaker 2: line determines how aggressive the mouse will be. At one 532 00:28:49,800 --> 00:28:52,040 Speaker 2: end of the valley, that will translate into a mouse 533 00:28:52,080 --> 00:28:54,720 Speaker 2: that's not going to be aggressive, perhaps because what they've 534 00:28:54,720 --> 00:28:56,920 Speaker 2: seen is maybe a female mouse or not a mouse 535 00:28:56,920 --> 00:28:57,280 Speaker 2: at all. 536 00:28:57,960 --> 00:28:59,080 Speaker 3: On the other end. 537 00:28:59,000 --> 00:29:01,760 Speaker 2: Of the valley, that's where the population ends up sitting, 538 00:29:02,040 --> 00:29:05,479 Speaker 2: that will cause the mouse to be aggressive. And they 539 00:29:05,480 --> 00:29:08,520 Speaker 2: could see that this was true, not just by doing 540 00:29:09,000 --> 00:29:12,080 Speaker 2: observational work where they observe what's happening in the hypothalamus, 541 00:29:12,360 --> 00:29:15,000 Speaker 2: but they actually could use this new generation of tools 542 00:29:15,040 --> 00:29:18,840 Speaker 2: where they could causally perturb the system and confirm that 543 00:29:18,840 --> 00:29:21,640 Speaker 2: that indeed was causing the mice to be aggressive. 544 00:29:21,800 --> 00:29:25,560 Speaker 1: Amazing. So instead of looking at a particular cell or 545 00:29:25,560 --> 00:29:27,360 Speaker 1: a group of cells and trying to think through it, 546 00:29:27,840 --> 00:29:30,720 Speaker 1: you have to take all the cells and collapse that 547 00:29:30,840 --> 00:29:33,920 Speaker 1: high dimensional activity onto a point on a landscape, and 548 00:29:33,960 --> 00:29:36,400 Speaker 1: then you can start describing what that landscape is doing. 549 00:29:36,560 --> 00:29:40,440 Speaker 2: Absolutely, and the big shift here is that that landscape 550 00:29:40,800 --> 00:29:44,120 Speaker 2: can't be shaped by a big chain of causes. 551 00:29:44,120 --> 00:29:45,000 Speaker 3: It lead to effects. 552 00:29:45,840 --> 00:29:49,320 Speaker 2: The formation of the landscape depends on thinking about the 553 00:29:49,360 --> 00:29:51,360 Speaker 2: brain as having these big feedback loops in it. 554 00:29:51,840 --> 00:29:55,920 Speaker 1: Yeah, you know, it's funny. Even in any neuroscience textbook, 555 00:29:56,440 --> 00:29:58,800 Speaker 1: you know you have sell A talks to sell B. 556 00:29:59,440 --> 00:30:01,360 Speaker 1: And of course, so we know that every cell in 557 00:30:01,400 --> 00:30:03,400 Speaker 1: the cortex is talking to you about ten thousand of 558 00:30:03,440 --> 00:30:06,200 Speaker 1: its neighbors, and lots of these are very complicated feedback loops, 559 00:30:06,240 --> 00:30:09,520 Speaker 1: and of course you have excitatory and inhibitory neurons, and 560 00:30:09,640 --> 00:30:13,200 Speaker 1: so straight away, I think any clever student looks at 561 00:30:13,200 --> 00:30:15,640 Speaker 1: this and says, wait a minute, something is something is 562 00:30:15,680 --> 00:30:19,320 Speaker 1: crazy here to think about? Oh a does s? And 563 00:30:19,400 --> 00:30:22,800 Speaker 1: yet our textbooks still read that way because we don't 564 00:30:22,880 --> 00:30:27,160 Speaker 1: know how to teach in a way where we're saying, look, 565 00:30:27,400 --> 00:30:30,760 Speaker 1: start from square one, we're going to talk about dynamical systems. 566 00:30:31,360 --> 00:30:33,800 Speaker 1: So how would you think about revising the way we 567 00:30:33,880 --> 00:30:34,760 Speaker 1: teach neuroscience. 568 00:30:35,280 --> 00:30:40,520 Speaker 2: That's a really important question. Back in the nineteen forties 569 00:30:40,560 --> 00:30:45,200 Speaker 2: and fifties, we used to have an ecology food chains, 570 00:30:45,600 --> 00:30:48,200 Speaker 2: and then at some point they became food webs because 571 00:30:48,240 --> 00:30:52,400 Speaker 2: we realized that these ecological systems. There are these complex 572 00:30:52,480 --> 00:30:55,360 Speaker 2: dynamical systems with these big feedback loops in them, and 573 00:30:55,400 --> 00:30:58,560 Speaker 2: so we started to teach starting from elementary school, we 574 00:30:58,600 --> 00:31:01,719 Speaker 2: started to teach ecology differently, and so, yeah, I very 575 00:31:01,800 --> 00:31:03,560 Speaker 2: much think that that's what we need to start doing 576 00:31:03,680 --> 00:31:06,440 Speaker 2: in brain research as well, is starting from the beginning 577 00:31:06,960 --> 00:31:10,480 Speaker 2: teaching about the brain as a system chuck full of 578 00:31:10,480 --> 00:31:13,360 Speaker 2: these feedback loops and what are all of the consequences 579 00:31:13,360 --> 00:31:13,600 Speaker 2: of that. 580 00:31:13,920 --> 00:31:17,880 Speaker 1: Yeah, And even if dynamical systems science as we understand 581 00:31:17,880 --> 00:31:20,480 Speaker 1: it now turns out not to be the full picture, 582 00:31:20,520 --> 00:31:21,680 Speaker 1: at least we're getting closer. 583 00:31:21,960 --> 00:31:22,480 Speaker 3: Absolutely. 584 00:31:22,720 --> 00:31:26,680 Speaker 1: Yeah. And Nicole, despite the limitations and where neuroscience research 585 00:31:26,720 --> 00:31:28,240 Speaker 1: has gone, you're very optimistic. 586 00:31:28,360 --> 00:31:29,880 Speaker 3: Tell us why absolutely. 587 00:31:31,000 --> 00:31:33,480 Speaker 2: When I started to write this book, I actually wasn't 588 00:31:33,520 --> 00:31:35,280 Speaker 2: sure where it would lead, and I started from a 589 00:31:35,320 --> 00:31:38,560 Speaker 2: place of kind of confusion and even a little bit 590 00:31:38,560 --> 00:31:42,840 Speaker 2: of pessimism because I could see that there were these 591 00:31:42,840 --> 00:31:47,640 Speaker 2: certain conditions for which we were get a little bit stuck. 592 00:31:48,640 --> 00:31:51,120 Speaker 2: On the other side of writing the book, I'm unequivocally 593 00:31:51,160 --> 00:31:55,800 Speaker 2: optimistic about the future of our field for the conditions 594 00:31:55,800 --> 00:31:58,520 Speaker 2: like the psychiatric conditions and their degenerative conditions. 595 00:31:58,720 --> 00:31:59,640 Speaker 1: And why it's. 596 00:31:59,520 --> 00:32:02,120 Speaker 2: Because I see that the changes that need to happen 597 00:32:02,400 --> 00:32:05,880 Speaker 2: are already happening in our fields. Right we were oversimplifying 598 00:32:05,920 --> 00:32:08,240 Speaker 2: the brain. We were treating it like this chain of 599 00:32:08,320 --> 00:32:10,800 Speaker 2: causes that lead to effects, and it was just a 600 00:32:10,840 --> 00:32:14,160 Speaker 2: massive oversimplification of the most complex thing in the entire 601 00:32:14,240 --> 00:32:14,920 Speaker 2: known universe. 602 00:32:15,280 --> 00:32:17,840 Speaker 3: But now researchers are starting to embrace. 603 00:32:17,800 --> 00:32:21,360 Speaker 2: This important type of complexity that we can again for 604 00:32:21,400 --> 00:32:24,280 Speaker 2: the first time in history, because we have new biotechnology, 605 00:32:24,480 --> 00:32:27,240 Speaker 2: we have artificial intelligence. For the first time, we're really 606 00:32:27,320 --> 00:32:31,080 Speaker 2: to study the brain in this way, and I am 607 00:32:31,280 --> 00:32:34,160 Speaker 2: very excited about the idea that that will be the 608 00:32:34,240 --> 00:32:37,920 Speaker 2: key to unlocking progress for all of the millions, billions 609 00:32:37,960 --> 00:32:40,360 Speaker 2: actually of individuals who are suffering from these conditions. 610 00:32:45,280 --> 00:32:48,640 Speaker 1: That was my interview with Nicole Rust. This conversation circled 611 00:32:48,640 --> 00:32:51,200 Speaker 1: around the idea that the brain may not be the 612 00:32:51,320 --> 00:32:54,959 Speaker 1: kind of object we once hoped it was. For a 613 00:32:55,040 --> 00:32:59,800 Speaker 1: long time, neuroscience advanced under a parsimonious assumption that if 614 00:32:59,800 --> 00:33:02,840 Speaker 1: we can you could just identify the right pieces, the 615 00:33:02,920 --> 00:33:06,520 Speaker 1: right links in the chain, the story would come into focus. 616 00:33:07,120 --> 00:33:10,280 Speaker 1: Genes lead to proteins. Proteins built cells, cells form circuits, 617 00:33:10,360 --> 00:33:14,480 Speaker 1: Circuits generate thoughts and motions and behavior fix the broken link, 618 00:33:14,800 --> 00:33:19,280 Speaker 1: and the system heals. Sometimes that strategy works, but there 619 00:33:19,320 --> 00:33:23,880 Speaker 1: are entire domains where it doesn't, where no single gene 620 00:33:23,960 --> 00:33:29,040 Speaker 1: or molecule or brain region carries the explanatory weight that 621 00:33:29,080 --> 00:33:32,160 Speaker 1: we wanted to. Gradually, it's become clear to us that 622 00:33:32,400 --> 00:33:36,840 Speaker 1: most disorders don't behave like oh, there's a broken part, 623 00:33:37,160 --> 00:33:41,200 Speaker 1: but instead you have altered states of a whole system. 624 00:33:41,600 --> 00:33:43,840 Speaker 1: That means you can't just swap out apart. You have 625 00:33:43,920 --> 00:33:47,920 Speaker 1: to figure out if it's possible to nudge a complex 626 00:33:48,360 --> 00:33:54,880 Speaker 1: landscape that realization slash. That admission changes a lot, because 627 00:33:54,920 --> 00:33:58,120 Speaker 1: it reveals that the brain is more like a dynamic 628 00:33:58,280 --> 00:34:03,400 Speaker 1: environment shaped by feedback loops and continual self adjustment. It's 629 00:34:03,440 --> 00:34:06,960 Speaker 1: a system that can settle into values of activity that 630 00:34:07,000 --> 00:34:09,640 Speaker 1: are hard to escape. And by the way, it's a 631 00:34:09,640 --> 00:34:14,000 Speaker 1: system whose behavior depends not just on what's out there 632 00:34:14,040 --> 00:34:17,240 Speaker 1: in front of it now, but often on many things 633 00:34:17,480 --> 00:34:20,920 Speaker 1: that have interacted with it throughout its lifetime. So this 634 00:34:21,120 --> 00:34:25,879 Speaker 1: reframing has consequences for how we do experiments, for one, 635 00:34:26,239 --> 00:34:31,040 Speaker 1: but also it explains why some breakthroughs arrive accidentally while 636 00:34:31,080 --> 00:34:35,080 Speaker 1: others require decades of effort. It sheds light on why 637 00:34:35,400 --> 00:34:40,040 Speaker 1: prediction is hard, why control is even harder, and why 638 00:34:40,360 --> 00:34:46,400 Speaker 1: treating brain disorders sometimes resembles influencing the weather in Nicole's analogy, 639 00:34:46,800 --> 00:34:49,799 Speaker 1: more than it resembles fixing an engine. But although this 640 00:34:49,920 --> 00:34:52,960 Speaker 1: might seem like a pessimistic story, it is in fact 641 00:34:53,000 --> 00:34:56,759 Speaker 1: an optimistic one because for the first time, we might 642 00:34:56,800 --> 00:35:01,680 Speaker 1: actually have the tools to take this complexity seriously. We 643 00:35:01,760 --> 00:35:05,360 Speaker 1: can measure vast populations of neurons that once, we can 644 00:35:05,840 --> 00:35:09,800 Speaker 1: model systems that evolve in time. We can leverage artificial 645 00:35:09,840 --> 00:35:14,319 Speaker 1: intelligence to help us see patterns that are invisible to 646 00:35:14,440 --> 00:35:18,719 Speaker 1: our intuition alone. In other words, neuroscience may finally be 647 00:35:18,920 --> 00:35:24,320 Speaker 1: growing into the kind of science the brain requires. Every 648 00:35:24,400 --> 00:35:28,799 Speaker 1: mature field eventually has to let go of its simplest metaphors. 649 00:35:29,040 --> 00:35:35,360 Speaker 1: Physics moved beyond clockwork, universes, ecology moved from food chains 650 00:35:35,480 --> 00:35:39,560 Speaker 1: to food webs, and now neuroscience may be moving beyond 651 00:35:39,880 --> 00:35:45,520 Speaker 1: linear causality towards something richer and stranger and closer to 652 00:35:45,600 --> 00:35:49,360 Speaker 1: the truth. The challenge ahead is about learning how to 653 00:35:49,440 --> 00:35:53,759 Speaker 1: think in dynamic landscapes instead of static links, and if 654 00:35:53,800 --> 00:35:56,360 Speaker 1: we get that right, the payoff is going to be 655 00:35:56,960 --> 00:36:01,640 Speaker 1: new ways of helping the millions of people whose lives 656 00:36:01,719 --> 00:36:05,280 Speaker 1: are shaped by brains that have settled into difficult states, 657 00:36:05,520 --> 00:36:08,600 Speaker 1: and that's where the next era of neuroscience is going 658 00:36:08,680 --> 00:36:20,040 Speaker 1: to really begin. Go to eagleman dot com slash podcast 659 00:36:20,080 --> 00:36:23,080 Speaker 1: for more information and to find further reading. Join the 660 00:36:23,080 --> 00:36:26,279 Speaker 1: weekly discussions on my substack and check out Subscribe to 661 00:36:26,440 --> 00:36:29,360 Speaker 1: Inner Cosmos on YouTube for videos of each episode and 662 00:36:29,400 --> 00:36:32,719 Speaker 1: to leave comments until next time. I'm David Eagleman and 663 00:36:32,760 --> 00:36:34,440 Speaker 1: this is Inner Cosmos.