Latest / What's Your Problem? / The Brain Implant That Could Change Medicine

Transcript

The Brain Implant That Could Change Medicine · What's Your Problem?

0:15
Speaker 1: Pushkin. My guest today is a brain surgeon who also
0:23
Speaker 1: has a PhD in electrical engineering from MIT, which is
0:27
Speaker 1: to say he is extremely well prepared to figure out
0:32
Speaker 1: how to implant electronic devices in people's brains, which is
0:36
Speaker 1: what he's doing, and in fact, as it happens, he's
0:40
Speaker 1: actually been preparing to do this kind of his whole life.
0:44
Speaker 2: You know, I sort of was born into the business.
0:46
Speaker 2: My dad is a neurologist who started on his career
0:48
Speaker 2: as an electrical engineer. You know, electrophysiology, clinical neuroscience and
0:54
Speaker 2: neurology and our surgery. I've been a part of my
0:56
Speaker 2: life forever as far as I can remember. And you know,
1:00
Speaker 2: brain computer interfaces the way we talk about them today
1:03
Speaker 2: didn't exist in the nineteen eighties, but the fundamentals were there,
1:07
Speaker 2: and so that's been percolating in some way forever.
1:18
Speaker 1: I'm Jacob Oldstein and this is What's Your Problem, the
1:21
Speaker 1: show where I talk to people who are trying to
1:23
Speaker 1: make technological progress. My guest today is Ben Rapaport. He's
1:27
Speaker 1: the co founder and chief science officer at Precision Neuroscience.
1:31
Speaker 1: Ben's problem is this, can you build a device that
1:34
Speaker 1: allows someone who is paralyzed to use a computer with
1:38
Speaker 1: only their thoughts and can you do it without sticking
1:42
Speaker 1: needles into their brain. Before he started Precision, Ben was
1:47
Speaker 1: a co founder of Neuralink. Neuralink is probably the best
1:50
Speaker 1: known brain computer interface company, and it was founded in
1:54
Speaker 1: twenty sixteen, right around the moment when modern AI was
1:58
Speaker 1: just emerging. And Ben told me the AI revolution was
2:01
Speaker 1: really what inspired the foundation of Neuralink.
2:05
Speaker 2: The kind of founding principles of neuralink were, you know,
2:09
Speaker 2: here's here's a point in time when we're thinking broadly
2:12
Speaker 2: about how the human brain is going to interact with
2:15
Speaker 2: artificial intelligence. And if breakthrough is an artificial intelligence are
2:20
Speaker 2: scaling at an exponential rate, you know, how's the human
2:23
Speaker 2: brain going to keep up with that? How are we
2:24
Speaker 2: going to keep communicating with artificial intelligence in a way
2:28
Speaker 2: that is feasible and productive.
2:30
Speaker 1: So that's a really different that's not how can we
2:32
Speaker 1: help people who are paralyzed. That's a much more sort
2:36
Speaker 1: of cognitive centric. It's about like the nature of human
2:39
Speaker 1: thought in the context of AI.
2:41
Speaker 2: So that was the that's kind of the raison deetra
2:45
Speaker 2: of neuralink, and it was a little different from a
2:48
Speaker 2: human focused medically oriented focus that Precision has taken. And
2:53
Speaker 2: these different focuses can you know, can and will coexist
2:57
Speaker 2: in an ecosystem in which multiple brain computer interface core
3:01
Speaker 2: technology has become widely available and are the standard to
3:04
Speaker 2: become the standard of care. But it became clear to
3:07
Speaker 2: me that that there was a need to also focus
3:10
Speaker 2: an effort within the community of bring computer interfaces on
3:14
Speaker 2: treating patients with untreatable diseases. That was the origin, you know,
3:18
Speaker 2: brain computer interfaces was really bringing this science and technology
3:21
Speaker 2: to a point where you know, people who today we
3:24
Speaker 2: think have has having really no treatment options, people with
3:28
Speaker 2: paralysis or an ability to speak, for example, from als,
3:32
Speaker 2: you know, really unlocking a world of possibilities for those people.
3:35
Speaker 2: But we really wanted to focus on those applications within
3:39
Speaker 2: bringing computer interfaces, and doing that, in my view, required
3:43
Speaker 2: making a few different design decisions than what we've made
3:47
Speaker 2: at Neuralink. You know, so those were the founding principles
3:50
Speaker 2: of Precision.
3:51
Speaker 1: You leave Neuralink to found Precision. Tell me tell me
3:54
Speaker 1: about what you you know, what you're setting out to
3:57
Speaker 1: create at Precision when you're launching the company, Like, what
3:59
Speaker 1: is it that you want to do and how is
4:01
Speaker 1: it different than what everybody else is doing.
4:03
Speaker 2: Yeah. The goal then and is today to build a safe,
4:09
Speaker 2: scalable brain computer interface that can become the standard of
4:12
Speaker 2: care in the treatment of patients people with a variety
4:16
Speaker 2: of diseases of the brain and nervous system that today
4:18
Speaker 2: are untreatable. That that includes various forms of paralysis and
4:22
Speaker 2: inability to communicate.
4:24
Speaker 1: And tell me about the tell me about the technology, like,
4:27
Speaker 1: tell me about the thing you're building and how it's
4:29
Speaker 1: different from what other people are building.
4:32
Speaker 2: Our philosophy has been that in order for a brain
4:34
Speaker 2: computer interface to really work in the real world and
4:38
Speaker 2: to unlock the potential of this technology for many millions
4:42
Speaker 2: of people. First, of course, it has to be incredibly safe.
4:46
Speaker 2: We see the use of the views of the term
4:47
Speaker 2: minimally invasive a lot, but really in my view, has
4:51
Speaker 2: to not damage the brain.
4:52
Speaker 1: So what does that mean in practice?
4:54
Speaker 2: Yeah, the tissue interface with the electrode involves kind of
4:58
Speaker 2: like little needles of the electrics are little needles and
5:01
Speaker 2: they penetrate into the brain. And there's been a lot
5:03
Speaker 2: of innovation in doing it, trying to do that very safely,
5:06
Speaker 2: But in my view, the most safe version of that
5:10
Speaker 2: is a version that just kind of caresses the brain
5:12
Speaker 2: but doesn't penetrate it. And it was at first thought,
5:16
Speaker 2: you know, certainly when we found a precision, many people
5:18
Speaker 2: thought that it was not possible to extract high quality
5:21
Speaker 2: signals from the brain without penetrating, and we and others
5:24
Speaker 2: have shown that, in fact, it's not only possible to
5:26
Speaker 2: do but has many advantages. So not that it's the
5:30
Speaker 2: only way or necessarily better or worse, but from the
5:33
Speaker 2: standpoint of people who have untreatable diseases and already have
5:36
Speaker 2: a very low threshold for damage to the brain, not
5:39
Speaker 2: doing any incremental damage to the brain for us is
5:42
Speaker 2: very very important. So that was sort of part one
5:46
Speaker 2: of precision.
5:47
Speaker 1: Is there before we get to part two? Is there
5:49
Speaker 1: a trade off? I mean, do you lose some amount
5:52
Speaker 1: of sensitivity or resolution? Is that the basic trade off?
5:55
Speaker 2: So we always get this question, you know, right, No,
6:00
Speaker 2: it's absolutely it's a good question, right, And so there's
6:03
Speaker 2: this false dichotomy. I think that more penetration into the
6:08
Speaker 2: brain equals higher quality signal, and if you don't do that,
6:12
Speaker 2: then you somehow sacrifice signal quality. But it's really not
6:15
Speaker 2: a one dimensional as one dimensional as that if you're
6:18
Speaker 2: a neuroscientist, then there's a trade off. If you care
6:22
Speaker 2: about recording from one neuron out of time, and you're
6:24
Speaker 2: studying the behavior of individual neurons, and you care about that,
6:27
Speaker 2: then you want what we called intracortical penetrating microelectrodes, the
6:31
Speaker 2: ones that can come up up close to an individual
6:34
Speaker 2: neuron and listen to those individual action potentials. And that's
6:38
Speaker 2: something that neuroscientists care about. So you don't want to
6:42
Speaker 2: use the same electrodes that we use for a precision
6:46
Speaker 2: But but if what you care about is is treating
6:50
Speaker 2: paralysis or sources of communication, what you care about is stable,
6:55
Speaker 2: high quality signals over a long period of time. And
6:59
Speaker 2: in that area, arguably, just based on the data, you know,
7:03
Speaker 2: the cortical surface electrodes that we use at precision are
7:06
Speaker 2: at least as good, if not better. And I think
7:08
Speaker 2: you know, I will tell because there's a few of
7:10
Speaker 2: these different systems that are now out there in the
7:12
Speaker 2: real world. What's really exciting is that this has come
7:15
Speaker 2: out of the laboratory, out of animal experiment territory, into
7:19
Speaker 2: human pilot clinical trials that we and neuralink and synchron
7:24
Speaker 2: and others are engaged and that's really where it's at.
7:27
Speaker 1: So tell me where you are now. I know you've
7:30
Speaker 1: done some amount of experimental work in people, right what
7:34
Speaker 1: is the frontier of your work right now?
7:35
Speaker 2: Yeah, we've now implanted our electrode raise in almost thirty
7:40
Speaker 2: patients over the last two years. These are pilot studies
7:44
Speaker 2: across four major medical centers and the US that are
7:47
Speaker 2: partnering with US, and all of those studies are really
7:51
Speaker 2: they're temporary placements of the electrodes. So there are studies
7:54
Speaker 2: that are run in nations who have volunteered to have
7:57
Speaker 2: the electrodes placed alongside clinical standard electrodes as part of
8:01
Speaker 2: a under resurgical procedure that they're already undergoing. And we've
8:05
Speaker 2: been using those opportunities to basically validate the quality of
8:09
Speaker 2: electroctavity that we can record on those electrodes and to
8:12
Speaker 2: demonstrate that our algorithms can in fact basically decode intention
8:18
Speaker 2: and thought as intended by health essentially healthy volunteers.
8:23
Speaker 1: So the array itself, like, what does it look like?
8:29
Speaker 2: So the brain lives in the skull, so it's a
8:32
Speaker 2: it is a soft tissue that's kind of jelly like inconsistency,
8:38
Speaker 2: and so the best way to generally interface with it
8:41
Speaker 2: is with something also that is soft and flexible. And
8:45
Speaker 2: the surface of the brain is many of us have
8:47
Speaker 2: seen in pictures, is curved or undulating, and so our
8:51
Speaker 2: electrode array is a thin polymer that's many times thinner
8:56
Speaker 2: even than a human hair. So it's a film, and
8:59
Speaker 2: embedded in that film are tiny little dots of platinum,
9:04
Speaker 2: each one connected to it very very very thin platinum wire.
9:09
Speaker 2: And so that film with the tiny little dots of
9:12
Speaker 2: platinum inside, can be placed over the brain surface and
9:16
Speaker 2: it conforms to that curved surface. So each of those
9:21
Speaker 2: little platinum electrodes touches the surface of the brain at
9:25
Speaker 2: a very discreete point, and so it can record the
9:28
Speaker 2: electrical activity from the area of the brain just under
9:31
Speaker 2: that its touching basically.
9:33
Speaker 1: Okay, So in these trials, you put this implant on
9:38
Speaker 1: a patient's brain and then what.
9:42
Speaker 2: So let me describe maybe one of the paradigms that
9:45
Speaker 2: we use at one of our partner sites. So ian
9:48
Speaker 2: Cahegus is the neurosurgeon at Penn who's our partner, and
9:52
Speaker 2: he is a surgeon who specializes in the treatment of
9:54
Speaker 2: Parkinson's disease. One of the ways of treating Parkinson's disease
9:58
Speaker 2: is a procedure called deep brain stimulation, in which electrodes
10:01
Speaker 2: are placed deep within the brain to stimulate those areas
10:05
Speaker 2: that are responsible for modulating the tremor. Doctor ca Vegas,
10:09
Speaker 2: among many others, performs these procedures at least a part
10:13
Speaker 2: of them awake in order to make sure effectively that
10:16
Speaker 2: the exact right place is being targeted and the brain
10:18
Speaker 2: doesn't feel pain. And so it's not only possible but
10:22
Speaker 2: beneficial to do these procedures at least partially awake. So
10:25
Speaker 2: in those procedures, we take a basically a fifteen minute
10:29
Speaker 2: window and doctor Ahigis places the precisional electrode directly over
10:34
Speaker 2: the motor cortex, a portion of the motor cortex that
10:37
Speaker 2: controls hand movement. And this has provided for us and
10:41
Speaker 2: for the community, the highest resolution picture of the human
10:46
Speaker 2: motor cortex and the awake human ever in the history
10:50
Speaker 2: of the world. So you know, the area of the
10:53
Speaker 2: brain of the motor cortex that controls hand movement is
10:55
Speaker 2: about the size of a postage stamp. And critically to
11:01
Speaker 2: understand is the neurons that are responsible for coordinating those movements.
11:06
Speaker 2: They all live within a two milimeters your layer of
11:10
Speaker 2: tissue that's just at the surface of the brain. So
11:14
Speaker 2: all that critical computation and activity is happening very very
11:17
Speaker 2: close to the surface. And so it's good.
11:20
Speaker 1: For you, good for us method. So so what actually happens.
11:24
Speaker 1: So you have a patient who's there, you put your
11:27
Speaker 1: array on the on the portion of their motor cortex
11:30
Speaker 1: that controls hand movements, and then you say, wiggle your finger.
11:34
Speaker 2: Exactly, so we say, we say, we say, basically, you know,
11:37
Speaker 2: we asked. We walk the patient through making a certain
11:40
Speaker 2: number of gestures, you know, open hand, close hand, make
11:43
Speaker 2: a peace sign, and we watch, and I say metaphorically watch,
11:47
Speaker 2: We watch what the patient is doing, and we watch
11:49
Speaker 2: what is happening on the surface of the brain. And
11:52
Speaker 2: and here is you know, where modern machine learning plays
11:55
Speaker 2: a tremendous role, because this exactly, you know, this is
11:59
Speaker 2: the AI portion of it, because this is this is
12:02
Speaker 2: the so called training data. So this this is a
12:04
Speaker 2: calibration phase in which our algorithms learn what the brain's
12:09
Speaker 2: signals to the hand look like in a given patient.
12:12
Speaker 2: So there's a characteristic signature, electrical signature that happens in
12:16
Speaker 2: the moments before an action is done, and it's a
12:19
Speaker 2: little bit different in each person, and learning that signature
12:23
Speaker 2: for that person allows us to recognize when the brain
12:27
Speaker 2: is telling the hand to make a particular gesture, when
12:29
Speaker 2: the fingers are supposed to move in a particular way,
12:31
Speaker 2: when the hand opens and closes, and after about three
12:34
Speaker 2: to five minutes of training, we then have a trained
12:37
Speaker 2: algorithm that can recognize not just movement, but the intention
12:42
Speaker 2: to move. And so we then use the balance of
12:44
Speaker 2: the time that we have with those patients to ask
12:47
Speaker 2: the patient to move and validate that we're predicting the
12:50
Speaker 2: correct movement, and then to imagine movement without moving, and
12:55
Speaker 2: that too we can accurately predict. And so these procedures
12:59
Speaker 2: become the basically healthy volunteer test bed for patients who
13:05
Speaker 2: can't actually move, the paralyzed patients that we'll be treating
13:09
Speaker 2: within the next couple of years. So that's that's the
13:11
Speaker 2: nature of this first phase of pilot trials.
13:14
Speaker 1: You mentioned that each person is different in terms of
13:19
Speaker 1: the patterns of neuron activity for each hand motion. In
13:25
Speaker 1: this context, how different is it like kind of like
13:28
Speaker 1: a Southern accent versus a New York accent. Is it
13:30
Speaker 1: like an entirely different language if that kind of metaphor work.
13:33
Speaker 2: Yeah, No, that's a perfect metaphor. And it's it's kind
13:36
Speaker 2: of like that. So you know, you if you're trying
13:39
Speaker 2: to learn a new language or a dialect, you know
13:42
Speaker 2: that there are words, and you know that they're spoken
13:45
Speaker 2: in a particular frequency range, so you kind of know
13:47
Speaker 2: what to listen for, and you kind of know the cadence.
13:49
Speaker 2: So when there's a word, you know that's a word,
13:52
Speaker 2: but you might not know what it means until you
13:54
Speaker 2: listen in to conversation and you've seen the context.
13:58
Speaker 1: So so like pretty different. Like it wouldn't work to
14:01
Speaker 1: just make a generic algorithm and put her on my brain.
14:05
Speaker 2: Because it doesn't work to make a generic algorithm. But
14:09
Speaker 2: that's an area where there's been a lot of just
14:11
Speaker 2: fascinating development. And so a good example of this is,
14:15
Speaker 2: you know, Siri works out of the box for most
14:18
Speaker 2: people pretty well, right.
14:21
Speaker 1: Talk into your eye, right, it works.
14:23
Speaker 2: Right, It works pretty well, and then you need to
14:25
Speaker 2: train it to make it better, and then it listens
14:28
Speaker 2: to you in the background and gets even better. And
14:31
Speaker 2: so that that's a good that's a good analogy. So
14:33
Speaker 2: it is possible for us to build, you know, a
14:36
Speaker 2: translation algorithm that works somewhat out of the box, but
14:40
Speaker 2: we build into it a calibration phase that knows something
14:45
Speaker 2: about the structure of brain signals and how they interact
14:48
Speaker 2: with and relate to movement or speech. And that's what
14:53
Speaker 2: basically allows us to use only relatively small amounts of
14:57
Speaker 2: calibration data. I mean, you know, we can do a
14:59
Speaker 2: lot with a small amount of calibration data.
15:01
Speaker 1: So you're doing a sort of pilot study. Now, when
15:06
Speaker 1: what's the next big step?
15:08
Speaker 2: So I want to be careful about what I say
15:11
Speaker 2: before it happens. But we do anticipate being able to
15:16
Speaker 2: in the very near future extend what are now short
15:19
Speaker 2: duration file with studies that last only the span of
15:24
Speaker 2: time that we have access to the brain within a
15:26
Speaker 2: standard androsurgical procedure, which is relatively short. We anticipate having
15:30
Speaker 2: ways of extending that with regulatory approval, to hopefully many
15:34
Speaker 2: days and weeks within the calendar year. And then, of course,
15:37
Speaker 2: this is all in the service of permanent implants that
15:41
Speaker 2: wirelessly communicate with the outside world, and that will be
15:45
Speaker 2: the basis of our pivotal clinical trial a couple of
15:48
Speaker 2: years hence.
15:51
Speaker 1: Still to come. On the show, Ben and I discussed
15:53
Speaker 1: the possibility of using brain computer interfaces in healthy people,
15:58
Speaker 1: also the meaning of consciousness. Just before the break, Ben
16:09
Speaker 1: mentioned that pivotal clinical trial that they're building up to,
16:13
Speaker 1: and so I asked him what exactly they're going to
16:15
Speaker 1: be doing in that trial.
16:17
Speaker 2: So the first clinical application is going to be for
16:19
Speaker 2: the treatment of severe paralysis, okay, And the device will
16:23
Speaker 2: be an implant that has the electrodes on the brain
16:26
Speaker 2: and an implant within the chestwell that provides power and
16:29
Speaker 2: data transfer to the outside world to communicate with the
16:33
Speaker 2: external devices like a computer. And that system will allow,
16:36
Speaker 2: for example, a person with a spinal cord injury really
16:39
Speaker 2: to hold the desk job that will allow them to
16:41
Speaker 2: operate effectively a word processing program, email, serve the internet,
16:46
Speaker 2: have a zoom conversation, operate and expel a sales spreadsheet,
16:50
Speaker 2: use PowerPoint, have the ability to re enter the workforce
16:54
Speaker 2: with a level of personal and economic self sufficiency that
16:58
Speaker 2: allows them to, you know, certain freedoms that they don't have,
17:01
Speaker 2: and that our core to being a part of modern society.
17:04
Speaker 2: That is, for us a major goal. Number one, I'm
17:07
Speaker 2: quite sure that past the technolog becomes provenly safe and effective,
17:13
Speaker 2: that other disorders and conditions that are perhaps less dramatic,
17:17
Speaker 2: you know, will benefit from this and in other forms
17:20
Speaker 2: of technology. And part three is there's a lot that
17:23
Speaker 2: I'm sure that we're not even imagining right now. You know,
17:26
Speaker 2: the brain computer interface, at least the precision system is
17:30
Speaker 2: really in some ways a platform technology because it's it
17:33
Speaker 2: translates the wet and difficult to access, delicate, you know,
17:37
Speaker 2: biological signals of the brain into robust digital bitstreams and
17:43
Speaker 2: allows us to compute on them in a scalable way.
17:46
Speaker 2: The brain computer interface is not a substitute for a
17:48
Speaker 2: keyboard in a mouse. It's not a substitute for a
17:51
Speaker 2: gestural interface or a voice interface. It's another kind of
17:56
Speaker 2: interface with the brain, just like it was would have
17:59
Speaker 2: been impossible to predict based on the keyboard, a loan,
18:02
Speaker 2: or the graphical user interface alone, all of the different
18:05
Speaker 2: applications that have emerged. I think, as long as we
18:08
Speaker 2: build a safe, reliable interface and make that responsibly available
18:13
Speaker 2: kind of skuy's the limit. And I can't even hazard
18:16
Speaker 2: to guess at some of the things that will come next.
18:18
Speaker 2: So I think there's a there's a whole generation of
18:21
Speaker 2: discovery and innovation waiting to happen after we get this
18:24
Speaker 2: across the line into patients to become standard of care.
18:27
Speaker 1: Could you imagine it being used in healthy people for
18:31
Speaker 1: you know, the computer and the brain application.
18:35
Speaker 2: Yeah, I could eventually, in a sense, I would love
18:37
Speaker 2: that to be the case.
18:38
Speaker 1: I think I'm ambivalent about that one. Tell me why
18:42
Speaker 1: you'd love that to be.
18:43
Speaker 2: The case, Well, because it will have meant that we've.
18:46
Speaker 1: Well, yes, it'll mean your thing works really well, it
18:48
Speaker 1: is wildly safe.
18:50
Speaker 2: Yes, that's true, right, Yeah, So we build with that
18:52
Speaker 2: goal in mind in a way, right, just because in
18:55
Speaker 2: order for something to be accepted by an eble body
18:58
Speaker 2: person who has zero risk tolerance, right and basically only downside,
19:03
Speaker 2: if something goes wrong or doesn't work properly, you need
19:06
Speaker 2: it to work just in a bulletproof way. That's that's
19:09
Speaker 2: the kind of system that we're trying to engineer.
19:12
Speaker 1: Yes, from that point of view, it makes perfect sense.
19:15
Speaker 1: Then if that is true, then then you have built
19:18
Speaker 1: a wildly safe and effective device exactly.
19:22
Speaker 2: So, if you and I were having this conversation and
19:23
Speaker 2: you said to me, gosh, I would love to write.
19:25
Speaker 2: I mean that would mean that all those doubts had
19:27
Speaker 2: been erased. And in order to erase those doubts, we
19:31
Speaker 2: have to prove certain things to the world, and that's
19:33
Speaker 2: that's really our job.
19:35
Speaker 1: Would you would you want if you were healthy? Would
19:39
Speaker 1: you want to have your device in your brain? If
19:41
Speaker 1: it were safe and effective?
19:43
Speaker 2: I would have to do certain things that that the
19:46
Speaker 2: device can't do yet, yeah, rush, but I wouldn't definitely
19:49
Speaker 2: wouldn't rerule it out when we get there. And I
19:50
Speaker 2: mean it's like sometimes with technology, it's it's hard to
19:53
Speaker 2: wrap your mind around what's going to happen in a generation,
19:56
Speaker 2: right too little kids and we're always talking about like
19:59
Speaker 2: should the kids actually get to use an iPhone?
20:02
Speaker 1: Hold out for as long as you can so, right,
20:05
Speaker 1: because so it's not exactly a choice, right, that's the
20:07
Speaker 1: that's the thing you think, like, oh an iPhone?
20:10
Speaker 2: That's my point. By the way, I'm very very permissive
20:13
Speaker 2: and me too.
20:15
Speaker 1: You know what finished me was COVID, Like we held
20:17
Speaker 1: out really strong and then COVID hit did ason.
20:21
Speaker 2: So but the reason I bring that up is that,
20:23
Speaker 2: you know, like our parents could not even have conceived
20:26
Speaker 2: of even that question, right.
20:28
Speaker 1: Yes, But I mean the other way to think about
20:30
Speaker 1: that is, like, you know, I'm pro progress and pro technology,
20:34
Speaker 1: but like having kids makes me wish iPhones didn't exist, right,
20:39
Speaker 1: makes me wish like, sure, give them a flip phone
20:41
Speaker 1: so they can text their friends that call me if
20:43
Speaker 1: something goes wrong. But well, you know, I don't know.
20:46
Speaker 1: But on the other hand, I make podcasts for a little.
20:49
Speaker 2: It's an interesting discussion, right, and you know, we sometimes joke,
20:52
Speaker 2: but somehow kids are born now knowing how to swipe
20:54
Speaker 2: and navigate the phone interface. Right. So my point is
20:58
Speaker 2: that in twenty years it's going to be a different conversation.
21:02
Speaker 2: There's a lot of kids of people in the company,
21:04
Speaker 2: and they know what we're doing, you know, like girls
21:07
Speaker 2: know what we're doing, and their view and the technology
21:09
Speaker 2: is different. They see it as something that exists, and
21:13
Speaker 2: when you're bored into it, you have kind of a
21:15
Speaker 2: different sense of what's okay and what's normal. And that's
21:18
Speaker 2: the generation that's growing up today is going to grow
21:21
Speaker 2: up with bring computer interfaces just being a normal thing.
21:24
Speaker 1: Yeah, maybe your grandkids will feel about bring computer interfaces
21:28
Speaker 1: the way your kids feel about iPhones.
21:30
Speaker 2: It's going to happen faster than that.
21:36
Speaker 1: We'll be back in a minute with the lightning rim
21:48
Speaker 1: tell me about the metabolic factors limiting performance in marathon runners.
21:54
Speaker 2: Okay, right, so that was a paper that I wrote
21:58
Speaker 2: now more than a decade ago. So I'm a dedicated
22:01
Speaker 2: marathon runner and I run forty something marathons over twenty
22:05
Speaker 2: plus years. There's a longer story, which we don't have
22:07
Speaker 2: time for now, is to I wrote that paper.
22:10
Speaker 1: Give me what's the short version of that story of
22:12
Speaker 1: why you wrote the paper.
22:13
Speaker 2: The short version is it shouldn't be metabolically possible to
22:16
Speaker 2: run a marathon because interest everybody. Everybody thinks the paradox
22:20
Speaker 2: is that you know, you can't eat enough pasta to
22:22
Speaker 2: get through twenty six miles.
22:24
Speaker 1: Uh, just like if you do the math, there's not
22:27
Speaker 1: enough energy stored in the body.
22:28
Speaker 2: If you do the simple math. There seems to be
22:30
Speaker 2: a paradox that you can't you can't eat enough pasta
22:33
Speaker 2: to run the marathon. Right, everybody thinks you got to
22:34
Speaker 2: run eat passive before you run the marathon. It turns
22:36
Speaker 2: out that can't really eat enough pasta to run a marathon.
22:39
Speaker 2: So how is it even possible, okay, And the reason
22:42
Speaker 2: it's possible is that you're burning some fat as you go.
22:45
Speaker 2: And then everybody knows that there's this phenomenon of hitting
22:47
Speaker 2: the wall where you know, many runners collapse or have
22:51
Speaker 2: a major impact at some point, you know, along the way,
22:54
Speaker 2: usually about two thirds the way through the race, where
22:55
Speaker 2: they just can't keep going or it can't keep going
22:58
Speaker 2: at the same pace that they started the race. And
23:01
Speaker 2: why does that happen. That happens because they're not burning
23:04
Speaker 2: carbohydrates as the fuel substrate, or they can't burn them
23:08
Speaker 2: at the same rate that they started the race. So
23:11
Speaker 2: how do you not hit the wall? How do you
23:12
Speaker 2: avoid that phenomenon? And basically you need to run at
23:16
Speaker 2: a pace that basically burns both fuel substrates fat and
23:20
Speaker 2: the carbohydrate at a rate that basically you just exhaust
23:23
Speaker 2: your carbohydrates stores at mile twenty six point two. So
23:28
Speaker 2: that's one of the core rate limiting metabolic factors in marathon.
23:32
Speaker 1: And that, I mean, so, what was it that you
23:34
Speaker 1: figured out that got published in whatever it was Plus.
23:39
Speaker 2: Yes, I figured that out, and I figured out how
23:43
Speaker 2: to model that mathematically.
23:45
Speaker 1: And did it affect the way people run?
23:48
Speaker 2: Marathons well effected the way I run marathons?
23:50
Speaker 1: And how did you change your running strategy based on
23:53
Speaker 1: your own research?
23:55
Speaker 2: I learned how to pace myself in a more quantitative way,
23:59
Speaker 2: and I learned how to have a structure. My pre
24:02
Speaker 2: race died and my training diet in a way that
24:04
Speaker 2: was much better than I had in the years before that.
24:07
Speaker 1: Did you get faster?
24:08
Speaker 2: I got sick dificantly faster.
24:10
Speaker 1: Yeah.
24:10
Speaker 2: I run a bunch of some three hour marathons around
24:12
Speaker 2: the time I figured that all out.
24:14
Speaker 1: That is a very fast marathon.
24:16
Speaker 2: And for a period of time, I don't know if
24:17
Speaker 2: it's still the case, but maybe embarrassingly that was my
24:20
Speaker 2: It still is I think my only single author paper,
24:24
Speaker 2: and for a period of time it was most cited paper.
24:29
Speaker 1: Well, you know, Einstein's most cited paper is the one
24:32
Speaker 1: where he describes entanglement and basically says, this proves that
24:36
Speaker 1: quantum is not a complete description of reality because there's
24:38
Speaker 1: no way it could be true and he was wrong.
24:41
Speaker 2: Right, can't aspire to that necessarily, But anyway.
24:46
Speaker 1: What's one tip that comes out of that? Like, do
24:48
Speaker 1: I is there like a model I could plug in?
24:50
Speaker 1: I ran my first marathon this year. I did not
24:52
Speaker 1: know about your paper. Is there something you can tell me,
24:56
Speaker 1: just qualitatively from it that I'm doing wrong?
24:59
Speaker 2: Yeah, take a look. There's a little formula there basically
25:02
Speaker 2: that allows the average person to estimate their optimal marathon pace.
25:08
Speaker 1: Boston ma On or New York Marathon. What do you
25:10
Speaker 1: like better?
25:11
Speaker 2: Well, you know, I'm a native. I've run both many times.
25:14
Speaker 2: I've run Boston for the last twenty four years consecutively,
25:19
Speaker 2: and I've run New York. I think, I forget now
25:21
Speaker 2: how many times more than ten? And I love them both,
25:25
Speaker 2: And I'm not going to go I'm not going to
25:27
Speaker 2: say in public which one I love more. But they're
25:29
Speaker 2: very different. They're very different, and yeah that's all. That's all.
25:34
Speaker 2: That's all I'll say. But they are wonderful races and
25:37
Speaker 2: a lot of special things about both.
25:41
Speaker 1: What is one thing we don't understand about the brain
25:44
Speaker 1: that you wish we understood?
25:47
Speaker 2: So the question of what is consciousness? I think has
25:50
Speaker 2: been a big one in philosophy and neuroscience for a
25:54
Speaker 2: long long time. Right, you know, I think that the
25:57
Speaker 2: tools of bring computer interfaces are probably have already given,
26:00
Speaker 2: but certainly we'll be giving us in the next couple
26:02
Speaker 2: of years ways to answer that in a really rigorous
26:06
Speaker 2: and quantitative way. And not just that, but I think
26:09
Speaker 2: to have an impact in disorders of consciousness, and so
26:13
Speaker 2: I think that's an area where rank of beer interfaces
26:15
Speaker 2: are going to have perhaps a surprisingly major impact.
26:20
Speaker 1: What's a disorder of consciousness? I don't think I know
26:23
Speaker 1: that phrase, like help me, what does that mean?
26:25
Speaker 2: Well, you know, I think many people are familiar with
26:27
Speaker 2: the koma, right, so people who are alive but not
26:31
Speaker 2: compismentus in the in the ways that you and I
26:33
Speaker 2: are when we're talking. That's just a dramatic example of that.
26:37
Speaker 1: Has the work you've done, I mean, either as a
26:40
Speaker 1: as a brand surgeon or as in developing brain computer interfaces,
26:44
Speaker 1: how has that changed the way you think about consciousness?
26:48
Speaker 2: If it has, I'm not sure it has yet, but
26:52
Speaker 2: at least not in a race I want to talk about
26:53
Speaker 2: in public. But I mean, watch this space carefully.
26:58
Speaker 1: Say one more thing about that. That's it's very intriguing
27:01
Speaker 1: to me. I feel like there's something you're thinking that
27:04
Speaker 1: you're not saying.
27:05
Speaker 2: I think a lot of it is public, and I
27:07
Speaker 2: think in a really really interesting way. So i'd highlight
27:11
Speaker 2: some recent work or recently published work by Negoschiff and
27:14
Speaker 2: others demonstrating that some people who seem to be in
27:18
Speaker 2: a minimally conscious state actually have the ability to communicate
27:22
Speaker 2: if you give them the tools to do so, and
27:24
Speaker 2: that just has profound implications for the diagnosis of certain
27:30
Speaker 2: types of severe brain injury, for prognosticating, you know, the
27:34
Speaker 2: subsequent course of people who have such injuries, and all
27:37
Speaker 2: kinds of philosophical, ethical, and really just most importantly practical
27:41
Speaker 2: aspects of how do we take care of people with
27:44
Speaker 2: that kind of severe brain injury, many of whom pose
27:47
Speaker 2: tremendously difficult questions to family and caregivers who can't predict
27:52
Speaker 2: what's going to happen next and can't communicate with their
27:55
Speaker 2: loved ones. And there's always this question in such situations,
27:59
Speaker 2: you know, is that the person we knew still there?
28:03
Speaker 2: And will that person come back, so to speak or not?
28:07
Speaker 2: And answering that question, this is one aspect of getting
28:11
Speaker 2: at what is consciousness and how does it flunctually and
28:14
Speaker 2: how do we quantify it, and how do we read
28:17
Speaker 2: or restore it when it's lost or damaged. So, you know,
28:22
Speaker 2: that has been the realm of philosophy for most of
28:26
Speaker 2: human history, and I think it is very exciting for
28:30
Speaker 2: me now that's that's changed in the last several years.
28:34
Speaker 2: And I do think that the technology of Breaker Beeterer
28:37
Speaker 2: interfaces is going to have an impact in making some
28:40
Speaker 2: of the discoveries that have come to light actionable.
28:49
Speaker 1: Ben Rappaport is the co founder and chief science officer
28:53
Speaker 1: at Precision Neuroscience. Today's show was produced by Gabriel hunter Cheng.
28:58
Speaker 1: It was edited by Lyddya jene Kott and engineered by
29:01
Speaker 1: Sarah Brumer. You can email us at problem at Pushkin
29:05
Speaker 1: dot FM. I'm Jacob Goldstein and we'll be back next
29:08
Speaker 1: week with another episode of What's Your Problem MHM.