February 27, 2025 • 29 mins
Ben Rapoport is the co-founder and CSO of Precision Neuroscience. Ben's problem is this: Can you build a device that allows a paralyzed person to use a computer with only their thoughts – without damaging their brain?
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Speaker 1 (00:15):
Pushkin. My guest today is a brain surgeon who also
has a PhD in electrical engineering from MIT, which is
to say he is extremely well prepared to figure out
how to implant electronic devices in people's brains, which is
(00:36):
what he's doing, and in fact, as it happens, he's
actually been preparing to do this kind of his whole life.
Speaker 2 (00:44):
You know, I sort of was born into the business.
My dad is a neurologist who started on his career
as an electrical engineer. You know, electrophysiology, clinical neuroscience and
neurology and our surgery. I've been a part of my
life forever as far as I can remember. And you know,
brain computer interfaces the way we talk about them today
didn't exist in the nineteen eighties, but the fundamentals were there,
(01:07):
and so that's been percolating in some way forever.
Speaker 1 (01:18):
I'm Jacob Oldstein and this is What's Your Problem, the
show where I talk to people who are trying to
make technological progress. My guest today is Ben Rapaport. He's
the co founder and chief science officer at Precision Neuroscience.
Ben's problem is this, can you build a device that
allows someone who is paralyzed to use a computer with
only their thoughts and can you do it without sticking
(01:42):
needles into their brain. Before he started Precision, Ben was
a co founder of Neuralink. Neuralink is probably the best
known brain computer interface company, and it was founded in
twenty sixteen, right around the moment when modern AI was
just emerging. And Ben told me the AI revolution was
really what inspired the foundation of Neuralink.
Speaker 2 (02:05):
The kind of founding principles of neuralink were, you know,
here's here's a point in time when we're thinking broadly
about how the human brain is going to interact with
artificial intelligence. And if breakthrough is an artificial intelligence are
scaling at an exponential rate, you know, how's the human
brain going to keep up with that? How are we
going to keep communicating with artificial intelligence in a way
(02:28):
that is feasible and productive.
Speaker 1 (02:30):
So that's a really different that's not how can we
help people who are paralyzed. That's a much more sort
of cognitive centric. It's about like the nature of human
thought in the context of AI.
Speaker 2 (02:41):
So that was the that's kind of the raison deetra
of neuralink, and it was a little different from a
human focused medically oriented focus that Precision has taken. And
these different focuses can you know, can and will coexist
in an ecosystem in which multiple brain computer interface core
technology has become widely available and are the standard to
(03:04):
become the standard of care. But it became clear to
me that that there was a need to also focus
an effort within the community of bring computer interfaces on
treating patients with untreatable diseases. That was the origin, you know,
brain computer interfaces was really bringing this science and technology
to a point where you know, people who today we
think have has having really no treatment options, people with
(03:28):
paralysis or an ability to speak, for example, from als,
you know, really unlocking a world of possibilities for those people.
But we really wanted to focus on those applications within
bringing computer interfaces, and doing that, in my view, required
making a few different design decisions than what we've made
at Neuralink. You know, so those were the founding principles
(03:50):
of Precision.
Speaker 1 (03:51):
You leave Neuralink to found Precision. Tell me tell me
about what you you know, what you're setting out to
create at Precision when you're launching the company, Like, what
is it that you want to do and how is
it different than what everybody else is doing.
Speaker 2 (04:03):
Yeah. The goal then and is today to build a safe,
scalable brain computer interface that can become the standard of
care in the treatment of patients people with a variety
of diseases of the brain and nervous system that today
are untreatable. That that includes various forms of paralysis and
inability to communicate.
Speaker 1 (04:24):
And tell me about the tell me about the technology, like,
tell me about the thing you're building and how it's
different from what other people are building.
Speaker 2 (04:32):
Our philosophy has been that in order for a brain
computer interface to really work in the real world and
to unlock the potential of this technology for many millions
of people. First, of course, it has to be incredibly safe.
We see the use of the views of the term
minimally invasive a lot, but really in my view, has
to not damage the brain.
Speaker 1 (04:52):
So what does that mean in practice?
Speaker 2 (04:54):
Yeah, the tissue interface with the electrode involves kind of
like little needles of the electrics are little needles and
they penetrate into the brain. And there's been a lot
of innovation in doing it, trying to do that very safely,
But in my view, the most safe version of that
is a version that just kind of caresses the brain
but doesn't penetrate it. And it was at first thought,
(05:16):
you know, certainly when we found a precision, many people
thought that it was not possible to extract high quality
signals from the brain without penetrating, and we and others
have shown that, in fact, it's not only possible to
do but has many advantages. So not that it's the
only way or necessarily better or worse, but from the
standpoint of people who have untreatable diseases and already have
(05:36):
a very low threshold for damage to the brain, not
doing any incremental damage to the brain for us is
very very important. So that was sort of part one
of precision.
Speaker 1 (05:47):
Is there before we get to part two? Is there
a trade off? I mean, do you lose some amount
of sensitivity or resolution? Is that the basic trade off?
Speaker 2 (05:55):
So we always get this question, you know, right, No,
it's absolutely it's a good question, right, And so there's
this false dichotomy. I think that more penetration into the
brain equals higher quality signal, and if you don't do that,
then you somehow sacrifice signal quality. But it's really not
a one dimensional as one dimensional as that if you're
(06:18):
a neuroscientist, then there's a trade off. If you care
about recording from one neuron out of time, and you're
studying the behavior of individual neurons, and you care about that,
then you want what we called intracortical penetrating microelectrodes, the
ones that can come up up close to an individual
neuron and listen to those individual action potentials. And that's
something that neuroscientists care about. So you don't want to
(06:42):
use the same electrodes that we use for a precision
But but if what you care about is is treating
paralysis or sources of communication, what you care about is stable,
high quality signals over a long period of time. And
in that area, arguably, just based on the data, you know,
(07:03):
the cortical surface electrodes that we use at precision are
at least as good, if not better. And I think
you know, I will tell because there's a few of
these different systems that are now out there in the
real world. What's really exciting is that this has come
out of the laboratory, out of animal experiment territory, into
human pilot clinical trials that we and neuralink and synchron
(07:24):
and others are engaged and that's really where it's at.
Speaker 1 (07:27):
So tell me where you are now. I know you've
done some amount of experimental work in people, right what
is the frontier of your work right now?
Speaker 2 (07:35):
Yeah, we've now implanted our electrode raise in almost thirty
patients over the last two years. These are pilot studies
across four major medical centers and the US that are
partnering with US, and all of those studies are really
they're temporary placements of the electrodes. So there are studies
that are run in nations who have volunteered to have
(07:57):
the electrodes placed alongside clinical standard electrodes as part of
a under resurgical procedure that they're already undergoing. And we've
been using those opportunities to basically validate the quality of
electroctavity that we can record on those electrodes and to
demonstrate that our algorithms can in fact basically decode intention
(08:18):
and thought as intended by health essentially healthy volunteers.
Speaker 1 (08:23):
So the array itself, like, what does it look like?
Speaker 2 (08:29):
So the brain lives in the skull, so it's a
it is a soft tissue that's kind of jelly like inconsistency,
and so the best way to generally interface with it
is with something also that is soft and flexible. And
the surface of the brain is many of us have
seen in pictures, is curved or undulating, and so our
(08:51):
electrode array is a thin polymer that's many times thinner
even than a human hair. So it's a film, and
embedded in that film are tiny little dots of platinum,
each one connected to it very very very thin platinum wire.
And so that film with the tiny little dots of
(09:12):
platinum inside, can be placed over the brain surface and
it conforms to that curved surface. So each of those
little platinum electrodes touches the surface of the brain at
a very discreete point, and so it can record the
electrical activity from the area of the brain just under
that its touching basically.
Speaker 1 (09:33):
Okay, So in these trials, you put this implant on
a patient's brain and then what.
Speaker 2 (09:42):
So let me describe maybe one of the paradigms that
we use at one of our partner sites. So ian
Cahegus is the neurosurgeon at Penn who's our partner, and
he is a surgeon who specializes in the treatment of
Parkinson's disease. One of the ways of treating Parkinson's disease
is a procedure called deep brain stimulation, in which electrodes
are placed deep within the brain to stimulate those areas
(10:05):
that are responsible for modulating the tremor. Doctor ca Vegas,
among many others, performs these procedures at least a part
of them awake in order to make sure effectively that
the exact right place is being targeted and the brain
doesn't feel pain. And so it's not only possible but
beneficial to do these procedures at least partially awake. So
(10:25):
in those procedures, we take a basically a fifteen minute
window and doctor Ahigis places the precisional electrode directly over
the motor cortex, a portion of the motor cortex that
controls hand movement. And this has provided for us and
for the community, the highest resolution picture of the human
(10:46):
motor cortex and the awake human ever in the history
of the world. So you know, the area of the
brain of the motor cortex that controls hand movement is
about the size of a postage stamp. And critically to
understand is the neurons that are responsible for coordinating those movements.
(11:06):
They all live within a two milimeters your layer of
tissue that's just at the surface of the brain. So
all that critical computation and activity is happening very very
close to the surface. And so it's good.
Speaker 1 (11:20):
For you, good for us method. So so what actually happens.
So you have a patient who's there, you put your
array on the on the portion of their motor cortex
that controls hand movements, and then you say, wiggle your finger.
Speaker 2 (11:34):
Exactly, so we say, we say, we say, basically, you know,
we asked. We walk the patient through making a certain
number of gestures, you know, open hand, close hand, make
a peace sign, and we watch, and I say metaphorically watch,
We watch what the patient is doing, and we watch
what is happening on the surface of the brain. And
and here is you know, where modern machine learning plays
(11:55):
a tremendous role, because this exactly, you know, this is
the AI portion of it, because this is this is
the so called training data. So this this is a
calibration phase in which our algorithms learn what the brain's
signals to the hand look like in a given patient.
So there's a characteristic signature, electrical signature that happens in
(12:16):
the moments before an action is done, and it's a
little bit different in each person, and learning that signature
for that person allows us to recognize when the brain
is telling the hand to make a particular gesture, when
the fingers are supposed to move in a particular way,
when the hand opens and closes, and after about three
to five minutes of training, we then have a trained
(12:37):
algorithm that can recognize not just movement, but the intention
to move. And so we then use the balance of
the time that we have with those patients to ask
the patient to move and validate that we're predicting the
correct movement, and then to imagine movement without moving, and
that too we can accurately predict. And so these procedures
(12:59):
become the basically healthy volunteer test bed for patients who
can't actually move, the paralyzed patients that we'll be treating
within the next couple of years. So that's that's the
nature of this first phase of pilot trials.
Speaker 1 (13:14):
You mentioned that each person is different in terms of
the patterns of neuron activity for each hand motion. In
this context, how different is it like kind of like
a Southern accent versus a New York accent. Is it
like an entirely different language if that kind of metaphor work.
Speaker 2 (13:33):
Yeah, No, that's a perfect metaphor. And it's it's kind
of like that. So you know, you if you're trying
to learn a new language or a dialect, you know
that there are words, and you know that they're spoken
in a particular frequency range, so you kind of know
what to listen for, and you kind of know the cadence.
So when there's a word, you know that's a word,
but you might not know what it means until you
(13:54):
listen in to conversation and you've seen the context.
Speaker 1 (13:58):
So so like pretty different. Like it wouldn't work to
just make a generic algorithm and put her on my brain.
Speaker 2 (14:05):
Because it doesn't work to make a generic algorithm. But
that's an area where there's been a lot of just
fascinating development. And so a good example of this is,
you know, Siri works out of the box for most
people pretty well, right.
Speaker 1 (14:21):
Talk into your eye, right, it works.
Speaker 2 (14:23):
Right, It works pretty well, and then you need to
train it to make it better, and then it listens
to you in the background and gets even better. And
so that that's a good that's a good analogy. So
it is possible for us to build, you know, a
translation algorithm that works somewhat out of the box, but
we build into it a calibration phase that knows something
(14:45):
about the structure of brain signals and how they interact
with and relate to movement or speech. And that's what
basically allows us to use only relatively small amounts of
calibration data. I mean, you know, we can do a
lot with a small amount of calibration data.
Speaker 1 (15:01):
So you're doing a sort of pilot study. Now, when
what's the next big step?
Speaker 2 (15:08):
So I want to be careful about what I say
before it happens. But we do anticipate being able to
in the very near future extend what are now short
duration file with studies that last only the span of
time that we have access to the brain within a
standard androsurgical procedure, which is relatively short. We anticipate having
(15:30):
ways of extending that with regulatory approval, to hopefully many
days and weeks within the calendar year. And then, of course,
this is all in the service of permanent implants that
wirelessly communicate with the outside world, and that will be
the basis of our pivotal clinical trial a couple of
years hence.
Speaker 1 (15:51):
Still to come. On the show, Ben and I discussed
the possibility of using brain computer interfaces in healthy people,
also the meaning of consciousness. Just before the break, Ben
mentioned that pivotal clinical trial that they're building up to,
(16:13):
and so I asked him what exactly they're going to
be doing in that trial.
Speaker 2 (16:17):
So the first clinical application is going to be for
the treatment of severe paralysis, okay, And the device will
be an implant that has the electrodes on the brain
and an implant within the chestwell that provides power and
data transfer to the outside world to communicate with the
external devices like a computer. And that system will allow,
for example, a person with a spinal cord injury really
(16:39):
to hold the desk job that will allow them to
operate effectively a word processing program, email, serve the internet,
have a zoom conversation, operate and expel a sales spreadsheet,
use PowerPoint, have the ability to re enter the workforce
with a level of personal and economic self sufficiency that
allows them to, you know, certain freedoms that they don't have,
(17:01):
and that our core to being a part of modern society.
That is, for us a major goal. Number one, I'm
quite sure that past the technolog becomes provenly safe and effective,
that other disorders and conditions that are perhaps less dramatic,
you know, will benefit from this and in other forms
of technology. And part three is there's a lot that
(17:23):
I'm sure that we're not even imagining right now. You know,
the brain computer interface, at least the precision system is
really in some ways a platform technology because it's it
translates the wet and difficult to access, delicate, you know,
biological signals of the brain into robust digital bitstreams and
allows us to compute on them in a scalable way.
(17:46):
The brain computer interface is not a substitute for a
keyboard in a mouse. It's not a substitute for a
gestural interface or a voice interface. It's another kind of
interface with the brain, just like it was would have
been impossible to predict based on the keyboard, a loan,
or the graphical user interface alone, all of the different
applications that have emerged. I think, as long as we
(18:08):
build a safe, reliable interface and make that responsibly available
kind of skuy's the limit. And I can't even hazard
to guess at some of the things that will come next.
So I think there's a there's a whole generation of
discovery and innovation waiting to happen after we get this
across the line into patients to become standard of care.
Speaker 1 (18:27):
Could you imagine it being used in healthy people for
you know, the computer and the brain application.
Speaker 2 (18:35):
Yeah, I could eventually, in a sense, I would love
that to be the case.
Speaker 1 (18:38):
I think I'm ambivalent about that one. Tell me why
you'd love that to be.
Speaker 2 (18:43):
The case, Well, because it will have meant that we've.
Speaker 1 (18:46):
Well, yes, it'll mean your thing works really well, it
is wildly safe.
Speaker 2 (18:50):
Yes, that's true, right, Yeah, So we build with that
goal in mind in a way, right, just because in
order for something to be accepted by an eble body
person who has zero risk tolerance, right and basically only downside,
if something goes wrong or doesn't work properly, you need
it to work just in a bulletproof way. That's that's
the kind of system that we're trying to engineer.
Speaker 1 (19:12):
Yes, from that point of view, it makes perfect sense.
Then if that is true, then then you have built
a wildly safe and effective device exactly.
Speaker 2 (19:22):
So, if you and I were having this conversation and
you said to me, gosh, I would love to write.
I mean that would mean that all those doubts had
been erased. And in order to erase those doubts, we
have to prove certain things to the world, and that's
that's really our job.
Speaker 1 (19:35):
Would you would you want if you were healthy? Would
you want to have your device in your brain? If
it were safe and effective?
Speaker 2 (19:43):
I would have to do certain things that that the
device can't do yet, yeah, rush, but I wouldn't definitely
wouldn't rerule it out when we get there. And I
mean it's like sometimes with technology, it's it's hard to
wrap your mind around what's going to happen in a generation,
right too little kids and we're always talking about like
should the kids actually get to use an iPhone?
Speaker 1 (20:02):
Hold out for as long as you can so, right,
because so it's not exactly a choice, right, that's the
that's the thing you think, like, oh an iPhone?
Speaker 2 (20:10):
That's my point. By the way, I'm very very permissive
and me too.
Speaker 1 (20:15):
You know what finished me was COVID, Like we held
out really strong and then COVID hit did ason.
Speaker 2 (20:21):
So but the reason I bring that up is that,
you know, like our parents could not even have conceived
of even that question, right.
Speaker 1 (20:28):
Yes, But I mean the other way to think about
that is, like, you know, I'm pro progress and pro technology,
but like having kids makes me wish iPhones didn't exist, right,
makes me wish like, sure, give them a flip phone
so they can text their friends that call me if
something goes wrong. But well, you know, I don't know.
But on the other hand, I make podcasts for a little.
Speaker 2 (20:49):
It's an interesting discussion, right, and you know, we sometimes joke,
but somehow kids are born now knowing how to swipe
and navigate the phone interface. Right. So my point is
that in twenty years it's going to be a different conversation.
There's a lot of kids of people in the company,
and they know what we're doing, you know, like girls
know what we're doing, and their view and the technology
(21:09):
is different. They see it as something that exists, and
when you're bored into it, you have kind of a
different sense of what's okay and what's normal. And that's
the generation that's growing up today is going to grow
up with bring computer interfaces just being a normal thing.
Speaker 1 (21:24):
Yeah, maybe your grandkids will feel about bring computer interfaces
the way your kids feel about iPhones.
Speaker 2 (21:30):
It's going to happen faster than that.
Speaker 1 (21:36):
We'll be back in a minute with the lightning rim
tell me about the metabolic factors limiting performance in marathon runners.
Speaker 2 (21:54):
Okay, right, so that was a paper that I wrote
now more than a decade ago. So I'm a dedicated
marathon runner and I run forty something marathons over twenty
plus years. There's a longer story, which we don't have
time for now, is to I wrote that paper.
Speaker 1 (22:10):
Give me what's the short version of that story of
why you wrote the paper.
Speaker 2 (22:13):
The short version is it shouldn't be metabolically possible to
run a marathon because interest everybody. Everybody thinks the paradox
is that you know, you can't eat enough pasta to
get through twenty six miles.
Speaker 1 (22:24):
Uh, just like if you do the math, there's not
enough energy stored in the body.
Speaker 2 (22:28):
If you do the simple math. There seems to be
a paradox that you can't you can't eat enough pasta
to run the marathon. Right, everybody thinks you got to
run eat passive before you run the marathon. It turns
out that can't really eat enough pasta to run a marathon.
So how is it even possible, okay, And the reason
it's possible is that you're burning some fat as you go.
And then everybody knows that there's this phenomenon of hitting
the wall where you know, many runners collapse or have
(22:51):
a major impact at some point, you know, along the way,
usually about two thirds the way through the race, where
they just can't keep going or it can't keep going
at the same pace that they started the race. And
why does that happen. That happens because they're not burning
carbohydrates as the fuel substrate, or they can't burn them
at the same rate that they started the race. So
how do you not hit the wall? How do you
(23:12):
avoid that phenomenon? And basically you need to run at
a pace that basically burns both fuel substrates fat and
the carbohydrate at a rate that basically you just exhaust
your carbohydrates stores at mile twenty six point two. So
that's one of the core rate limiting metabolic factors in marathon.
Speaker 1 (23:32):
And that, I mean, so, what was it that you
figured out that got published in whatever it was Plus.
Speaker 2 (23:39):
Yes, I figured that out, and I figured out how
to model that mathematically.
Speaker 1 (23:45):
And did it affect the way people run?
Speaker 2 (23:48):
Marathons well effected the way I run marathons?
Speaker 1 (23:50):
And how did you change your running strategy based on
your own research?
Speaker 2 (23:55):
I learned how to pace myself in a more quantitative way,
and I learned how to have a structure. My pre
race died and my training diet in a way that
was much better than I had in the years before that.
Speaker 1 (24:07):
Did you get faster?
Speaker 2 (24:08):
I got sick dificantly faster.
Speaker 1 (24:10):
Yeah.
Speaker 2 (24:10):
I run a bunch of some three hour marathons around
the time I figured that all out.
Speaker 1 (24:14):
That is a very fast marathon.
Speaker 2 (24:16):
And for a period of time, I don't know if
it's still the case, but maybe embarrassingly that was my
It still is I think my only single author paper,
and for a period of time it was most cited paper.
Speaker 1 (24:29):
Well, you know, Einstein's most cited paper is the one
where he describes entanglement and basically says, this proves that
quantum is not a complete description of reality because there's
no way it could be true and he was wrong.
Speaker 2 (24:41):
Right, can't aspire to that necessarily, But anyway.
Speaker 1 (24:46):
What's one tip that comes out of that? Like, do
I is there like a model I could plug in?
I ran my first marathon this year. I did not
know about your paper. Is there something you can tell me,
just qualitatively from it that I'm doing wrong?
Speaker 2 (24:59):
Yeah, take a look. There's a little formula there basically
that allows the average person to estimate their optimal marathon pace.
Speaker 1 (25:08):
Boston ma On or New York Marathon. What do you
like better?
Speaker 2 (25:11):
Well, you know, I'm a native. I've run both many times.
I've run Boston for the last twenty four years consecutively,
and I've run New York. I think, I forget now
how many times more than ten? And I love them both,
And I'm not going to go I'm not going to
say in public which one I love more. But they're
very different. They're very different, and yeah that's all. That's all.
(25:34):
That's all I'll say. But they are wonderful races and
a lot of special things about both.
Speaker 1 (25:41):
What is one thing we don't understand about the brain
that you wish we understood?
Speaker 2 (25:47):
So the question of what is consciousness? I think has
been a big one in philosophy and neuroscience for a
long long time. Right, you know, I think that the
tools of bring computer interfaces are probably have already given,
but certainly we'll be giving us in the next couple
of years ways to answer that in a really rigorous
and quantitative way. And not just that, but I think
(26:09):
to have an impact in disorders of consciousness, and so
I think that's an area where rank of beer interfaces
are going to have perhaps a surprisingly major impact.
Speaker 1 (26:20):
What's a disorder of consciousness? I don't think I know
that phrase, like help me, what does that mean?
Speaker 2 (26:25):
Well, you know, I think many people are familiar with
the koma, right, so people who are alive but not
compismentus in the in the ways that you and I
are when we're talking. That's just a dramatic example of that.
Speaker 1 (26:37):
Has the work you've done, I mean, either as a
as a brand surgeon or as in developing brain computer interfaces,
how has that changed the way you think about consciousness?
Speaker 2 (26:48):
If it has, I'm not sure it has yet, but
at least not in a race I want to talk about
in public. But I mean, watch this space carefully.
Speaker 1 (26:58):
Say one more thing about that. That's it's very intriguing
to me. I feel like there's something you're thinking that
you're not saying.
Speaker 2 (27:05):
I think a lot of it is public, and I
think in a really really interesting way. So i'd highlight
some recent work or recently published work by Negoschiff and
others demonstrating that some people who seem to be in
a minimally conscious state actually have the ability to communicate
if you give them the tools to do so, and
that just has profound implications for the diagnosis of certain
(27:30):
types of severe brain injury, for prognosticating, you know, the
subsequent course of people who have such injuries, and all
kinds of philosophical, ethical, and really just most importantly practical
aspects of how do we take care of people with
that kind of severe brain injury, many of whom pose
tremendously difficult questions to family and caregivers who can't predict
(27:52):
what's going to happen next and can't communicate with their
loved ones. And there's always this question in such situations,
you know, is that the person we knew still there?
And will that person come back, so to speak or not?
And answering that question, this is one aspect of getting
at what is consciousness and how does it flunctually and
(28:14):
how do we quantify it, and how do we read
or restore it when it's lost or damaged. So, you know,
that has been the realm of philosophy for most of
human history, and I think it is very exciting for
me now that's that's changed in the last several years.
And I do think that the technology of Breaker Beeterer
(28:37):
interfaces is going to have an impact in making some
of the discoveries that have come to light actionable.
Speaker 1 (28:49):
Ben Rappaport is the co founder and chief science officer
at Precision Neuroscience. Today's show was produced by Gabriel hunter Cheng.
It was edited by Lyddya jene Kott and engineered by
Sarah Brumer. You can email us at problem at Pushkin
dot FM. I'm Jacob Goldstein and we'll be back next
week with another episode of What's Your Problem MHM.
Pushkin. My guest today is a brain surgeon who also
has a PhD in electrical engineering from MIT, which is
to say he is extremely well prepared to figure out
how to implant electronic devices in people's brains, which is
(00:36):
what he's doing, and in fact, as it happens, he's
actually been preparing to do this kind of his whole life.
Speaker 2 (00:44):
You know, I sort of was born into the business.
My dad is a neurologist who started on his career
as an electrical engineer. You know, electrophysiology, clinical neuroscience and
neurology and our surgery. I've been a part of my
life forever as far as I can remember. And you know,
brain computer interfaces the way we talk about them today
didn't exist in the nineteen eighties, but the fundamentals were there,
(01:07):
and so that's been percolating in some way forever.
Speaker 1 (01:18):
I'm Jacob Oldstein and this is What's Your Problem, the
show where I talk to people who are trying to
make technological progress. My guest today is Ben Rapaport. He's
the co founder and chief science officer at Precision Neuroscience.
Ben's problem is this, can you build a device that
allows someone who is paralyzed to use a computer with
only their thoughts and can you do it without sticking
(01:42):
needles into their brain. Before he started Precision, Ben was
a co founder of Neuralink. Neuralink is probably the best
known brain computer interface company, and it was founded in
twenty sixteen, right around the moment when modern AI was
just emerging. And Ben told me the AI revolution was
really what inspired the foundation of Neuralink.
Speaker 2 (02:05):
The kind of founding principles of neuralink were, you know,
here's here's a point in time when we're thinking broadly
about how the human brain is going to interact with
artificial intelligence. And if breakthrough is an artificial intelligence are
scaling at an exponential rate, you know, how's the human
brain going to keep up with that? How are we
going to keep communicating with artificial intelligence in a way
(02:28):
that is feasible and productive.
Speaker 1 (02:30):
So that's a really different that's not how can we
help people who are paralyzed. That's a much more sort
of cognitive centric. It's about like the nature of human
thought in the context of AI.
Speaker 2 (02:41):
So that was the that's kind of the raison deetra
of neuralink, and it was a little different from a
human focused medically oriented focus that Precision has taken. And
these different focuses can you know, can and will coexist
in an ecosystem in which multiple brain computer interface core
technology has become widely available and are the standard to
(03:04):
become the standard of care. But it became clear to
me that that there was a need to also focus
an effort within the community of bring computer interfaces on
treating patients with untreatable diseases. That was the origin, you know,
brain computer interfaces was really bringing this science and technology
to a point where you know, people who today we
think have has having really no treatment options, people with
(03:28):
paralysis or an ability to speak, for example, from als,
you know, really unlocking a world of possibilities for those people.
But we really wanted to focus on those applications within
bringing computer interfaces, and doing that, in my view, required
making a few different design decisions than what we've made
at Neuralink. You know, so those were the founding principles
(03:50):
of Precision.
Speaker 1 (03:51):
You leave Neuralink to found Precision. Tell me tell me
about what you you know, what you're setting out to
create at Precision when you're launching the company, Like, what
is it that you want to do and how is
it different than what everybody else is doing.
Speaker 2 (04:03):
Yeah. The goal then and is today to build a safe,
scalable brain computer interface that can become the standard of
care in the treatment of patients people with a variety
of diseases of the brain and nervous system that today
are untreatable. That that includes various forms of paralysis and
inability to communicate.
Speaker 1 (04:24):
And tell me about the tell me about the technology, like,
tell me about the thing you're building and how it's
different from what other people are building.
Speaker 2 (04:32):
Our philosophy has been that in order for a brain
computer interface to really work in the real world and
to unlock the potential of this technology for many millions
of people. First, of course, it has to be incredibly safe.
We see the use of the views of the term
minimally invasive a lot, but really in my view, has
to not damage the brain.
Speaker 1 (04:52):
So what does that mean in practice?
Speaker 2 (04:54):
Yeah, the tissue interface with the electrode involves kind of
like little needles of the electrics are little needles and
they penetrate into the brain. And there's been a lot
of innovation in doing it, trying to do that very safely,
But in my view, the most safe version of that
is a version that just kind of caresses the brain
but doesn't penetrate it. And it was at first thought,
(05:16):
you know, certainly when we found a precision, many people
thought that it was not possible to extract high quality
signals from the brain without penetrating, and we and others
have shown that, in fact, it's not only possible to
do but has many advantages. So not that it's the
only way or necessarily better or worse, but from the
standpoint of people who have untreatable diseases and already have
(05:36):
a very low threshold for damage to the brain, not
doing any incremental damage to the brain for us is
very very important. So that was sort of part one
of precision.
Speaker 1 (05:47):
Is there before we get to part two? Is there
a trade off? I mean, do you lose some amount
of sensitivity or resolution? Is that the basic trade off?
Speaker 2 (05:55):
So we always get this question, you know, right, No,
it's absolutely it's a good question, right, And so there's
this false dichotomy. I think that more penetration into the
brain equals higher quality signal, and if you don't do that,
then you somehow sacrifice signal quality. But it's really not
a one dimensional as one dimensional as that if you're
(06:18):
a neuroscientist, then there's a trade off. If you care
about recording from one neuron out of time, and you're
studying the behavior of individual neurons, and you care about that,
then you want what we called intracortical penetrating microelectrodes, the
ones that can come up up close to an individual
neuron and listen to those individual action potentials. And that's
something that neuroscientists care about. So you don't want to
(06:42):
use the same electrodes that we use for a precision
But but if what you care about is is treating
paralysis or sources of communication, what you care about is stable,
high quality signals over a long period of time. And
in that area, arguably, just based on the data, you know,
(07:03):
the cortical surface electrodes that we use at precision are
at least as good, if not better. And I think
you know, I will tell because there's a few of
these different systems that are now out there in the
real world. What's really exciting is that this has come
out of the laboratory, out of animal experiment territory, into
human pilot clinical trials that we and neuralink and synchron
(07:24):
and others are engaged and that's really where it's at.
Speaker 1 (07:27):
So tell me where you are now. I know you've
done some amount of experimental work in people, right what
is the frontier of your work right now?
Speaker 2 (07:35):
Yeah, we've now implanted our electrode raise in almost thirty
patients over the last two years. These are pilot studies
across four major medical centers and the US that are
partnering with US, and all of those studies are really
they're temporary placements of the electrodes. So there are studies
that are run in nations who have volunteered to have
(07:57):
the electrodes placed alongside clinical standard electrodes as part of
a under resurgical procedure that they're already undergoing. And we've
been using those opportunities to basically validate the quality of
electroctavity that we can record on those electrodes and to
demonstrate that our algorithms can in fact basically decode intention
(08:18):
and thought as intended by health essentially healthy volunteers.
Speaker 1 (08:23):
So the array itself, like, what does it look like?
Speaker 2 (08:29):
So the brain lives in the skull, so it's a
it is a soft tissue that's kind of jelly like inconsistency,
and so the best way to generally interface with it
is with something also that is soft and flexible. And
the surface of the brain is many of us have
seen in pictures, is curved or undulating, and so our
(08:51):
electrode array is a thin polymer that's many times thinner
even than a human hair. So it's a film, and
embedded in that film are tiny little dots of platinum,
each one connected to it very very very thin platinum wire.
And so that film with the tiny little dots of
(09:12):
platinum inside, can be placed over the brain surface and
it conforms to that curved surface. So each of those
little platinum electrodes touches the surface of the brain at
a very discreete point, and so it can record the
electrical activity from the area of the brain just under
that its touching basically.
Speaker 1 (09:33):
Okay, So in these trials, you put this implant on
a patient's brain and then what.
Speaker 2 (09:42):
So let me describe maybe one of the paradigms that
we use at one of our partner sites. So ian
Cahegus is the neurosurgeon at Penn who's our partner, and
he is a surgeon who specializes in the treatment of
Parkinson's disease. One of the ways of treating Parkinson's disease
is a procedure called deep brain stimulation, in which electrodes
are placed deep within the brain to stimulate those areas
(10:05):
that are responsible for modulating the tremor. Doctor ca Vegas,
among many others, performs these procedures at least a part
of them awake in order to make sure effectively that
the exact right place is being targeted and the brain
doesn't feel pain. And so it's not only possible but
beneficial to do these procedures at least partially awake. So
(10:25):
in those procedures, we take a basically a fifteen minute
window and doctor Ahigis places the precisional electrode directly over
the motor cortex, a portion of the motor cortex that
controls hand movement. And this has provided for us and
for the community, the highest resolution picture of the human
(10:46):
motor cortex and the awake human ever in the history
of the world. So you know, the area of the
brain of the motor cortex that controls hand movement is
about the size of a postage stamp. And critically to
understand is the neurons that are responsible for coordinating those movements.
(11:06):
They all live within a two milimeters your layer of
tissue that's just at the surface of the brain. So
all that critical computation and activity is happening very very
close to the surface. And so it's good.
Speaker 1 (11:20):
For you, good for us method. So so what actually happens.
So you have a patient who's there, you put your
array on the on the portion of their motor cortex
that controls hand movements, and then you say, wiggle your finger.
Speaker 2 (11:34):
Exactly, so we say, we say, we say, basically, you know,
we asked. We walk the patient through making a certain
number of gestures, you know, open hand, close hand, make
a peace sign, and we watch, and I say metaphorically watch,
We watch what the patient is doing, and we watch
what is happening on the surface of the brain. And
and here is you know, where modern machine learning plays
(11:55):
a tremendous role, because this exactly, you know, this is
the AI portion of it, because this is this is
the so called training data. So this this is a
calibration phase in which our algorithms learn what the brain's
signals to the hand look like in a given patient.
So there's a characteristic signature, electrical signature that happens in
(12:16):
the moments before an action is done, and it's a
little bit different in each person, and learning that signature
for that person allows us to recognize when the brain
is telling the hand to make a particular gesture, when
the fingers are supposed to move in a particular way,
when the hand opens and closes, and after about three
to five minutes of training, we then have a trained
(12:37):
algorithm that can recognize not just movement, but the intention
to move. And so we then use the balance of
the time that we have with those patients to ask
the patient to move and validate that we're predicting the
correct movement, and then to imagine movement without moving, and
that too we can accurately predict. And so these procedures
(12:59):
become the basically healthy volunteer test bed for patients who
can't actually move, the paralyzed patients that we'll be treating
within the next couple of years. So that's that's the
nature of this first phase of pilot trials.
Speaker 1 (13:14):
You mentioned that each person is different in terms of
the patterns of neuron activity for each hand motion. In
this context, how different is it like kind of like
a Southern accent versus a New York accent. Is it
like an entirely different language if that kind of metaphor work.
Speaker 2 (13:33):
Yeah, No, that's a perfect metaphor. And it's it's kind
of like that. So you know, you if you're trying
to learn a new language or a dialect, you know
that there are words, and you know that they're spoken
in a particular frequency range, so you kind of know
what to listen for, and you kind of know the cadence.
So when there's a word, you know that's a word,
but you might not know what it means until you
(13:54):
listen in to conversation and you've seen the context.
Speaker 1 (13:58):
So so like pretty different. Like it wouldn't work to
just make a generic algorithm and put her on my brain.
Speaker 2 (14:05):
Because it doesn't work to make a generic algorithm. But
that's an area where there's been a lot of just
fascinating development. And so a good example of this is,
you know, Siri works out of the box for most
people pretty well, right.
Speaker 1 (14:21):
Talk into your eye, right, it works.
Speaker 2 (14:23):
Right, It works pretty well, and then you need to
train it to make it better, and then it listens
to you in the background and gets even better. And
so that that's a good that's a good analogy. So
it is possible for us to build, you know, a
translation algorithm that works somewhat out of the box, but
we build into it a calibration phase that knows something
(14:45):
about the structure of brain signals and how they interact
with and relate to movement or speech. And that's what
basically allows us to use only relatively small amounts of
calibration data. I mean, you know, we can do a
lot with a small amount of calibration data.
Speaker 1 (15:01):
So you're doing a sort of pilot study. Now, when
what's the next big step?
Speaker 2 (15:08):
So I want to be careful about what I say
before it happens. But we do anticipate being able to
in the very near future extend what are now short
duration file with studies that last only the span of
time that we have access to the brain within a
standard androsurgical procedure, which is relatively short. We anticipate having
(15:30):
ways of extending that with regulatory approval, to hopefully many
days and weeks within the calendar year. And then, of course,
this is all in the service of permanent implants that
wirelessly communicate with the outside world, and that will be
the basis of our pivotal clinical trial a couple of
years hence.
Speaker 1 (15:51):
Still to come. On the show, Ben and I discussed
the possibility of using brain computer interfaces in healthy people,
also the meaning of consciousness. Just before the break, Ben
mentioned that pivotal clinical trial that they're building up to,
(16:13):
and so I asked him what exactly they're going to
be doing in that trial.
Speaker 2 (16:17):
So the first clinical application is going to be for
the treatment of severe paralysis, okay, And the device will
be an implant that has the electrodes on the brain
and an implant within the chestwell that provides power and
data transfer to the outside world to communicate with the
external devices like a computer. And that system will allow,
for example, a person with a spinal cord injury really
(16:39):
to hold the desk job that will allow them to
operate effectively a word processing program, email, serve the internet,
have a zoom conversation, operate and expel a sales spreadsheet,
use PowerPoint, have the ability to re enter the workforce
with a level of personal and economic self sufficiency that
allows them to, you know, certain freedoms that they don't have,
(17:01):
and that our core to being a part of modern society.
That is, for us a major goal. Number one, I'm
quite sure that past the technolog becomes provenly safe and effective,
that other disorders and conditions that are perhaps less dramatic,
you know, will benefit from this and in other forms
of technology. And part three is there's a lot that
(17:23):
I'm sure that we're not even imagining right now. You know,
the brain computer interface, at least the precision system is
really in some ways a platform technology because it's it
translates the wet and difficult to access, delicate, you know,
biological signals of the brain into robust digital bitstreams and
allows us to compute on them in a scalable way.
(17:46):
The brain computer interface is not a substitute for a
keyboard in a mouse. It's not a substitute for a
gestural interface or a voice interface. It's another kind of
interface with the brain, just like it was would have
been impossible to predict based on the keyboard, a loan,
or the graphical user interface alone, all of the different
applications that have emerged. I think, as long as we
(18:08):
build a safe, reliable interface and make that responsibly available
kind of skuy's the limit. And I can't even hazard
to guess at some of the things that will come next.
So I think there's a there's a whole generation of
discovery and innovation waiting to happen after we get this
across the line into patients to become standard of care.
Speaker 1 (18:27):
Could you imagine it being used in healthy people for
you know, the computer and the brain application.
Speaker 2 (18:35):
Yeah, I could eventually, in a sense, I would love
that to be the case.
Speaker 1 (18:38):
I think I'm ambivalent about that one. Tell me why
you'd love that to be.
Speaker 2 (18:43):
The case, Well, because it will have meant that we've.
Speaker 1 (18:46):
Well, yes, it'll mean your thing works really well, it
is wildly safe.
Speaker 2 (18:50):
Yes, that's true, right, Yeah, So we build with that
goal in mind in a way, right, just because in
order for something to be accepted by an eble body
person who has zero risk tolerance, right and basically only downside,
if something goes wrong or doesn't work properly, you need
it to work just in a bulletproof way. That's that's
the kind of system that we're trying to engineer.
Speaker 1 (19:12):
Yes, from that point of view, it makes perfect sense.
Then if that is true, then then you have built
a wildly safe and effective device exactly.
Speaker 2 (19:22):
So, if you and I were having this conversation and
you said to me, gosh, I would love to write.
I mean that would mean that all those doubts had
been erased. And in order to erase those doubts, we
have to prove certain things to the world, and that's
that's really our job.
Speaker 1 (19:35):
Would you would you want if you were healthy? Would
you want to have your device in your brain? If
it were safe and effective?
Speaker 2 (19:43):
I would have to do certain things that that the
device can't do yet, yeah, rush, but I wouldn't definitely
wouldn't rerule it out when we get there. And I
mean it's like sometimes with technology, it's it's hard to
wrap your mind around what's going to happen in a generation,
right too little kids and we're always talking about like
should the kids actually get to use an iPhone?
Speaker 1 (20:02):
Hold out for as long as you can so, right,
because so it's not exactly a choice, right, that's the
that's the thing you think, like, oh an iPhone?
Speaker 2 (20:10):
That's my point. By the way, I'm very very permissive
and me too.
Speaker 1 (20:15):
You know what finished me was COVID, Like we held
out really strong and then COVID hit did ason.
Speaker 2 (20:21):
So but the reason I bring that up is that,
you know, like our parents could not even have conceived
of even that question, right.
Speaker 1 (20:28):
Yes, But I mean the other way to think about
that is, like, you know, I'm pro progress and pro technology,
but like having kids makes me wish iPhones didn't exist, right,
makes me wish like, sure, give them a flip phone
so they can text their friends that call me if
something goes wrong. But well, you know, I don't know.
But on the other hand, I make podcasts for a little.
Speaker 2 (20:49):
It's an interesting discussion, right, and you know, we sometimes joke,
but somehow kids are born now knowing how to swipe
and navigate the phone interface. Right. So my point is
that in twenty years it's going to be a different conversation.
There's a lot of kids of people in the company,
and they know what we're doing, you know, like girls
know what we're doing, and their view and the technology
(21:09):
is different. They see it as something that exists, and
when you're bored into it, you have kind of a
different sense of what's okay and what's normal. And that's
the generation that's growing up today is going to grow
up with bring computer interfaces just being a normal thing.
Speaker 1 (21:24):
Yeah, maybe your grandkids will feel about bring computer interfaces
the way your kids feel about iPhones.
Speaker 2 (21:30):
It's going to happen faster than that.
Speaker 1 (21:36):
We'll be back in a minute with the lightning rim
tell me about the metabolic factors limiting performance in marathon runners.
Speaker 2 (21:54):
Okay, right, so that was a paper that I wrote
now more than a decade ago. So I'm a dedicated
marathon runner and I run forty something marathons over twenty
plus years. There's a longer story, which we don't have
time for now, is to I wrote that paper.
Speaker 1 (22:10):
Give me what's the short version of that story of
why you wrote the paper.
Speaker 2 (22:13):
The short version is it shouldn't be metabolically possible to
run a marathon because interest everybody. Everybody thinks the paradox
is that you know, you can't eat enough pasta to
get through twenty six miles.
Speaker 1 (22:24):
Uh, just like if you do the math, there's not
enough energy stored in the body.
Speaker 2 (22:28):
If you do the simple math. There seems to be
a paradox that you can't you can't eat enough pasta
to run the marathon. Right, everybody thinks you got to
run eat passive before you run the marathon. It turns
out that can't really eat enough pasta to run a marathon.
So how is it even possible, okay, And the reason
it's possible is that you're burning some fat as you go.
And then everybody knows that there's this phenomenon of hitting
the wall where you know, many runners collapse or have
(22:51):
a major impact at some point, you know, along the way,
usually about two thirds the way through the race, where
they just can't keep going or it can't keep going
at the same pace that they started the race. And
why does that happen. That happens because they're not burning
carbohydrates as the fuel substrate, or they can't burn them
at the same rate that they started the race. So
how do you not hit the wall? How do you
(23:12):
avoid that phenomenon? And basically you need to run at
a pace that basically burns both fuel substrates fat and
the carbohydrate at a rate that basically you just exhaust
your carbohydrates stores at mile twenty six point two. So
that's one of the core rate limiting metabolic factors in marathon.
Speaker 1 (23:32):
And that, I mean, so, what was it that you
figured out that got published in whatever it was Plus.
Speaker 2 (23:39):
Yes, I figured that out, and I figured out how
to model that mathematically.
Speaker 1 (23:45):
And did it affect the way people run?
Speaker 2 (23:48):
Marathons well effected the way I run marathons?
Speaker 1 (23:50):
And how did you change your running strategy based on
your own research?
Speaker 2 (23:55):
I learned how to pace myself in a more quantitative way,
and I learned how to have a structure. My pre
race died and my training diet in a way that
was much better than I had in the years before that.
Speaker 1 (24:07):
Did you get faster?
Speaker 2 (24:08):
I got sick dificantly faster.
Speaker 1 (24:10):
Yeah.
Speaker 2 (24:10):
I run a bunch of some three hour marathons around
the time I figured that all out.
Speaker 1 (24:14):
That is a very fast marathon.
Speaker 2 (24:16):
And for a period of time, I don't know if
it's still the case, but maybe embarrassingly that was my
It still is I think my only single author paper,
and for a period of time it was most cited paper.
Speaker 1 (24:29):
Well, you know, Einstein's most cited paper is the one
where he describes entanglement and basically says, this proves that
quantum is not a complete description of reality because there's
no way it could be true and he was wrong.
Speaker 2 (24:41):
Right, can't aspire to that necessarily, But anyway.
Speaker 1 (24:46):
What's one tip that comes out of that? Like, do
I is there like a model I could plug in?
I ran my first marathon this year. I did not
know about your paper. Is there something you can tell me,
just qualitatively from it that I'm doing wrong?
Speaker 2 (24:59):
Yeah, take a look. There's a little formula there basically
that allows the average person to estimate their optimal marathon pace.
Speaker 1 (25:08):
Boston ma On or New York Marathon. What do you
like better?
Speaker 2 (25:11):
Well, you know, I'm a native. I've run both many times.
I've run Boston for the last twenty four years consecutively,
and I've run New York. I think, I forget now
how many times more than ten? And I love them both,
And I'm not going to go I'm not going to
say in public which one I love more. But they're
very different. They're very different, and yeah that's all. That's all.
(25:34):
That's all I'll say. But they are wonderful races and
a lot of special things about both.
Speaker 1 (25:41):
What is one thing we don't understand about the brain
that you wish we understood?
Speaker 2 (25:47):
So the question of what is consciousness? I think has
been a big one in philosophy and neuroscience for a
long long time. Right, you know, I think that the
tools of bring computer interfaces are probably have already given,
but certainly we'll be giving us in the next couple
of years ways to answer that in a really rigorous
and quantitative way. And not just that, but I think
(26:09):
to have an impact in disorders of consciousness, and so
I think that's an area where rank of beer interfaces
are going to have perhaps a surprisingly major impact.
Speaker 1 (26:20):
What's a disorder of consciousness? I don't think I know
that phrase, like help me, what does that mean?
Speaker 2 (26:25):
Well, you know, I think many people are familiar with
the koma, right, so people who are alive but not
compismentus in the in the ways that you and I
are when we're talking. That's just a dramatic example of that.
Speaker 1 (26:37):
Has the work you've done, I mean, either as a
as a brand surgeon or as in developing brain computer interfaces,
how has that changed the way you think about consciousness?
Speaker 2 (26:48):
If it has, I'm not sure it has yet, but
at least not in a race I want to talk about
in public. But I mean, watch this space carefully.
Speaker 1 (26:58):
Say one more thing about that. That's it's very intriguing
to me. I feel like there's something you're thinking that
you're not saying.
Speaker 2 (27:05):
I think a lot of it is public, and I
think in a really really interesting way. So i'd highlight
some recent work or recently published work by Negoschiff and
others demonstrating that some people who seem to be in
a minimally conscious state actually have the ability to communicate
if you give them the tools to do so, and
that just has profound implications for the diagnosis of certain
(27:30):
types of severe brain injury, for prognosticating, you know, the
subsequent course of people who have such injuries, and all
kinds of philosophical, ethical, and really just most importantly practical
aspects of how do we take care of people with
that kind of severe brain injury, many of whom pose
tremendously difficult questions to family and caregivers who can't predict
(27:52):
what's going to happen next and can't communicate with their
loved ones. And there's always this question in such situations,
you know, is that the person we knew still there?
And will that person come back, so to speak or not?
And answering that question, this is one aspect of getting
at what is consciousness and how does it flunctually and
(28:14):
how do we quantify it, and how do we read
or restore it when it's lost or damaged. So, you know,
that has been the realm of philosophy for most of
human history, and I think it is very exciting for
me now that's that's changed in the last several years.
And I do think that the technology of Breaker Beeterer
(28:37):
interfaces is going to have an impact in making some
of the discoveries that have come to light actionable.
Speaker 1 (28:49):
Ben Rappaport is the co founder and chief science officer
at Precision Neuroscience. Today's show was produced by Gabriel hunter Cheng.
It was edited by Lyddya jene Kott and engineered by
Sarah Brumer. You can email us at problem at Pushkin
dot FM. I'm Jacob Goldstein and we'll be back next
week with another episode of What's Your Problem MHM.
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