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All right, well, welcome to
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another STEIN Fireside Chat.
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I'm Bryan Klassen.
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This is Kai Miller.
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And today we have the pleasure of
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interviewing Cameron McIntyre, who really
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needs no introduction.
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He's kind of a legend in the field of
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DBS, particularly when it
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comes to field modeling.
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And it's our pleasure to
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have him here from Duke.
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And we have, as usual, a Stein that he's
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going to take with him back.
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So thanks for being here.
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Thanks.
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Happy to be here.
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It's great being, you know...
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I've known Kai for forever, and it's
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great getting to know you and, yeah,
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looking forward to talking.
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I'd be interested to hear a little bit
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just about kind of what got you into this
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field in the first place.
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Yeah, I was...
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As a graduate student, I was basically
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working on the physics of electric fields
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and neural activation.
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And it was very basic science-y.
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But, you know, I wanted to be a more
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clinically relevant kind of scientist.
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And so this was in the late 90s.
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And at that time, deep brain stimulation
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was kind of really getting
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off the ground clinically,
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especially in America.
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You know, kind of the first American
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cases were starting to be done.
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And I was like, "Oh, this is it.
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I could do this."
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So I went and did a fellowship at Emory
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and learned, you know,
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kind of like more like
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the clinical practice of DBS.
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And that then gave me ideas of, "Oh, wow,
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we could build like
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these patient-specific
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stimulation computer models and then have
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them be predictive
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tools to use for programming
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or surgical planning or whatever."
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Right?
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And so that's how that
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all kind of got started.
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Got it.
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Did you have a lot of
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competition back then?
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Or were you just sort
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of the pioneer in this?
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There was no competition.
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So actually, you know, there was a guy at
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Medtronic, his name was Roy Testerman.
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He was actually the very first person to
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ever build a finite
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element model of, you know,
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kind of a DBS electric field.
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And he and I had met at a conference.
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And he was a super
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intelligent, super smart engineer guy.
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The added special sauce that I could
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bring to the table was
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more the neuron biophysics,
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right?
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So there's the electric field side of
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things, but then there's also the
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neuro-response characteristic
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and I kind of, you know, he and I, you
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know, I think we were
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peas of the same pod kind
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of thing, but then I was able to kind of
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take it a little bit
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further in terms of clinical,
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you know, kind of applications.
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And your earliest models, I assume
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they're simpler than
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kind of what's come about.
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What other do they take into
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account at the tissue level?
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Yeah.
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So we work on, you know, kind of, well,
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so there's, let's call
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it the clinical practice
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kind of implementation that is primarily
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focused on predicting the
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spread of stimulation of
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axons around the electrode.
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And that's really what the core of what
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the, what people now
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call the VTA or volume of
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tissue activated.
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It's really about, you know, kind of
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axonal spread of
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stimulation, but a pretty simplified
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approximation.
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But then on the science side, we can go,
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you know, into super,
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super detail about, you know,
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kind of the nuances of how, you know,
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neurons, you know, cell bodies,
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dendrites, you know,
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complex axonal arbors, you know, all
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these kinds of things.
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And so, so there's always this big range
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of, you know, kind of
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the simple straightforward
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thing that can be relevant to a clinical,
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you know, kind of analysis.
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And then the like the, you know, 2025
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science of like, what do
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we think we're doing with
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the stimulation, you know, parameters or
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the stimulation, you
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know, kind of new strategies.
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Yeah.
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Okay.
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So there's a phrase, you know, all models
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are wrong, but some are useful, right?
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Your models are very useful, right?
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So we use them, plenty of other
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neurology, neurosurgery teams
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use them around the country.
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And for that reason, you're, you know, a
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celebrity in the deep
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brain stimulation world, right?
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Okay.
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Yeah.
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I've seen you in Korea, I've seen you in
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England, I've seen you in
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Germany, I've seen you all
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over the US.
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You know, like the career that I have had
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the opportunity to have
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is, you know, unbelievable.
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And I'm very appreciative, you know, like
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I don't take it for granted.
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But you know, let me tell you, in 1999 or
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whatever, like that was not the goal.
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Like that wasn't the expectation or the
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aspiration or anything.
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Like it just happened.
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So yeah.
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Yeah.
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Well, we have got so this is a slide that
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Brian actually made, and I show this to a
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lot of locations.
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And what I say to them is as well, this
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is the magnified
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version of a lead of one of
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the DBS leads.
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And it's got the top part, the bottom
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part, and it's got
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these segmented levels.
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And there's a part we want to stimulate
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in green, a part we want to avoid.
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And it's in orange.
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And then we can basically direct the
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current through the
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different pads in order to more
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or less omit directions of stimulation
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that we want to avoid.
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This is how we
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explain things to patients.
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I think it's very useful for us.
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It's also very useful, I think.
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And actually he does all the
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work when he comes to program.
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Yeah, sure.
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And so sort of move the current
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distribution around.
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And this is my understanding of how we do
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electrical
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stimulation when we plan and try
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to get around side effects
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and move things up and down.
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And now, of course, some of the companies
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have much more with their leads that have
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a lot more context.
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These models are still sort of balls of
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current moving around.
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How similar is this to the way that you
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thought things should work
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when you started translating
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it?
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So this is exactly what the vision that
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Chris Butson and I had.
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So this was now-- this kind of fast
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forward up to maybe the early 2000s.
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2004, I think, was kind of like when the
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original patents were
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submitted on this kind of stuff.
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Was he a grad student?
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No.
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So Chris did his PhD
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at University of Utah.
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And then his postdoc, he was really the
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first person I hired as a
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first year faculty member.
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I was working at the
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Cleveland Clinic at the time.
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And Chris and I knew, OK, this is what
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we're going to try and do.
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And we're going to try
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and build this prototype.
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And let's see if we can do it.
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And Utah, of course, is sort of the
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birthplace of a lot of the best
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bioengineering applications
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that make it into patients.
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I was going to either take a job at the U
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or the Cleveland Clinic.
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Those were kind of like the two jobs I
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was considering at the time.
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So that's how I met Chris was when I was
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out there interviewing.
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And then I thought the clinic was going
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to be a better fit for
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what I wanted to accomplish
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scientifically.
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And so I was able to
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recruit Chris to come with me.
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Nice.
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Nice.
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So you stole him from Utah.
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Exactly.
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He ended up going back there.
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He was a faculty member there for 10
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years probably after he left my lab.
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Yeah.
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And I think he still
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spends some time there.
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Even if it wasn't primarily at Florida.
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Exactly.
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Yeah.
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Cool.
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Do you still interact with him?
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Oh, yeah.
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All the time.
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I mean, just socially.
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We haven't had a collaborative project
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for a long, long time.
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But not for any bad reason.
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When I target for all the standard
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targets, I'm not
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aiming for a white matter.
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The models that you have are mostly
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related to, you said, stimulating axons.
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But you mean axons as they come out of
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cells, not passing axons
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that are in white matter?
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Really the technical nuances of the VTA
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are really the white
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matter passing axons.
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So the way I think about a VTA is that it
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is really about the
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furthest extent you should
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00:08:12,358 -->
00:08:15,328
expect for the stimulation to go.
267
00:08:16,696 -->
00:08:19,832
Part of the area around your electrode.
268
00:08:20,800 -->
00:08:23,502
And usually what you're worried about are
269
00:08:23,502 -->
00:08:26,739
these relatively large diameter fibers of
270
00:08:26,739 -->
00:08:29,642
passage that are not really
271
00:08:29,642 -->
00:08:31,377
associated with your "target."
272
00:08:32,411 -->
00:08:34,313
So now I'm sure most users
273
00:08:34,313 -->
00:08:36,115
don't really understand that.
274
00:08:37,517 -->
00:08:39,185
They're thinking about it of, I'm trying
275
00:08:39,185 -->
00:08:40,319
to get my target
276
00:08:40,319 -->
00:08:42,455
volume or my target nucleus
277
00:08:42,755 -->
00:08:45,258
to be encompassed by this thing.
278
00:08:45,491 -->
00:08:47,493
But the proxy is really
279
00:08:47,493 -->
00:08:48,694
kind of the opposite of that.
280
00:08:48,961 -->
00:08:51,998
But once again, things become useful in
281
00:08:51,998 -->
00:08:53,199
clinical contexts for
282
00:08:53,199 -->
00:08:54,734
sometimes different reasons than
283
00:08:54,734 -->
00:08:57,303
what the original science is about.
284
00:08:58,237 -->
00:08:59,639
I mean, it's not a bad thing.
285
00:08:59,639 -->
00:09:02,375
Like I said, it is a very, very useful
286
00:09:02,375 -->
00:09:05,044
clinical proxy to accelerate
287
00:09:05,044 -->
00:09:08,180
an important kind of clinical
288
00:09:08,214 -->
00:09:08,748
need.
289
00:09:09,215 -->
00:09:10,449
That's what Brian tells me.
290
00:09:13,653 -->
00:09:19,158
That's what over the years, from a
291
00:09:19,158 -->
00:09:20,259
scientific perspective,
292
00:09:20,259 -->
00:09:21,193
there's many other things that
293
00:09:21,193 -->
00:09:22,962
we would like to be able to characterize.
294
00:09:23,629 -->
00:09:25,965
But a VTA kind of model is not really
295
00:09:25,965 -->
00:09:27,266
appropriate for that.
296
00:09:28,034 -->
00:09:29,769
Not the right neural elements, not the
297
00:09:29,769 -->
00:09:31,504
right freeze relative
298
00:09:31,504 -->
00:09:33,072
electrode, all these things.
299
00:09:33,639 -->
00:09:36,442
A lot of great science going on in terms
300
00:09:36,442 -->
00:09:38,477
of kind of simulating
301
00:09:38,477 -->
00:09:40,980
how electric fields kind
302
00:09:40,980 -->
00:09:43,482
of activate different things around our
303
00:09:43,482 -->
00:09:44,717
deep brain stimulation electrodes.
304
00:09:45,418 -->
00:09:47,653
But unfortunately, maybe the clinical
305
00:09:47,653 -->
00:09:50,556
neurology literature isn't
306
00:09:50,556 -->
00:09:55,394
as detail oriented as I would
307
00:09:55,394 -->
00:09:55,861
like.
308
00:09:56,696 -->
00:10:00,566
Maybe people use these techniques to do
309
00:10:00,566 -->
00:10:02,435
analyses that are a little bit flawed.
310
00:10:03,536 -->
00:10:03,769
Okay?
311
00:10:04,103 -->
00:10:04,303
Yeah.
312
00:10:08,874 -->
00:10:10,142
Sounds like I could steer you into
313
00:10:10,142 -->
00:10:10,943
something you might regret.
314
00:10:10,977 -->
00:10:12,011
Yeah, yeah, yeah.
315
00:10:12,011 -->
00:10:13,813
Let's keep it politically correct.
316
00:10:14,480 -->
00:10:17,016
But anyway, that's kind of like, that's
317
00:10:17,016 -->
00:10:18,584
part of what, if you
318
00:10:18,584 -->
00:10:19,952
want to be a scientist that
319
00:10:19,952 -->
00:10:22,154
is doing something that is going to be
320
00:10:22,154 -->
00:10:23,990
relevant to not just 10
321
00:10:23,990 -->
00:10:25,191
other people in the world that
322
00:10:25,191 -->
00:10:29,362
care about your one thing, you have to
323
00:10:29,362 -->
00:10:31,197
recognize that you need to build
324
00:10:31,197 -->
00:10:32,698
something that is generally
325
00:10:33,332 -->
00:10:35,534
applicable or generally useful to a much,
326
00:10:35,534 -->
00:10:37,003
much broader spectrum of people.
327
00:10:38,070 -->
00:10:39,705
And you're going to lose control of it
328
00:10:39,705 -->
00:10:44,644
once it's not in your little lab anymore.
329
00:10:44,944 -->
00:10:46,612
And when it's out in the wild, wild
330
00:10:46,612 -->
00:10:47,613
things are going to happen.
331
00:10:48,214 -->
00:10:49,382
And that's okay.
332
00:10:50,516 -->
00:10:53,419
I don't get upset about it from the
333
00:10:53,419 -->
00:10:54,553
perspective of like, "Oh
334
00:10:54,553 -->
00:10:56,522
my gosh, this is stupid."
335
00:10:56,756 -->
00:11:00,192
But I do sometimes get irritated that
336
00:11:00,192 -->
00:11:02,962
people are doing things
337
00:11:02,962 -->
00:11:04,497
that are technically flawed
338
00:11:04,697 -->
00:11:08,567
in a very inappropriate way and then
339
00:11:08,567 -->
00:11:10,569
being publicly rewarded
340
00:11:10,569 -->
00:11:12,772
with publications that should
341
00:11:12,772 -->
00:11:13,673
not be published.
342
00:11:14,540 -->
00:11:14,640
Yeah.
343
00:11:14,640 -->
00:11:15,474
And that's high profile.
344
00:11:15,708 -->
00:11:15,975
Exactly.
345
00:11:16,742 -->
00:11:17,910
That's very frustrating.
346
00:11:18,310 -->
00:11:18,511
Yeah.
347
00:11:18,878 -->
00:11:22,815
So at this conference that you were at
348
00:11:22,815 -->
00:11:23,449
and we were at with
349
00:11:23,449 -->
00:11:25,351
you recently, you brought
350
00:11:25,351 -->
00:11:27,987
up this idea of glial encapsulation.
351
00:11:28,754 -->
00:11:29,555
So that's this idea
352
00:11:29,555 -->
00:11:31,023
that you put a lead in.
353
00:11:32,224 -->
00:11:33,159
Hopefully I've got it
354
00:11:33,159 -->
00:11:34,393
exactly where we want it.
355
00:11:34,493 -->
00:11:36,028
We do testing with stimulation and
356
00:11:36,028 -->
00:11:36,862
electrophysiology to
357
00:11:36,862 -->
00:11:37,563
confirm where we want.
358
00:11:38,097 -->
00:11:38,898
We get optimal
359
00:11:38,898 -->
00:11:40,399
stimulation effect that we think.
360
00:11:40,399 -->
00:11:43,669
And then we deliver electrical current.
361
00:11:43,669 -->
00:11:46,939
And what I usually tell patients is my
362
00:11:46,939 -->
00:11:48,240
goal is to not need
363
00:11:48,240 -->
00:11:49,642
these steering capabilities
364
00:11:49,642 -->
00:11:52,711
upfront because I want to not have side
365
00:11:52,711 -->
00:11:53,846
effects that are that close.
366
00:11:54,480 -->
00:11:57,016
And something that I wonder is early on
367
00:11:57,016 -->
00:11:57,516
you may start
368
00:11:57,516 -->
00:11:58,918
stimulating in different directions
369
00:11:59,819 -->
00:12:00,820
using the directionality.
370
00:12:01,187 -->
00:12:02,988
But if you want to stimulate later, if
371
00:12:02,988 -->
00:12:03,989
you have encapsulation
372
00:12:03,989 -->
00:12:09,095
like that, do you at some
373
00:12:09,095 -->
00:12:11,764
point do you model a general field or do
374
00:12:11,764 -->
00:12:12,731
you see there sort of
375
00:12:12,731 -->
00:12:13,899
being for lack of everything
376
00:12:14,100 -->
00:12:15,401
I think of hydration
377
00:12:15,401 -->
00:12:16,502
shells around protein.
378
00:12:16,502 -->
00:12:17,236
So it's like a different thing.
379
00:12:17,536 -->
00:12:21,474
But basically having a fluid space
380
00:12:21,474 -->
00:12:25,611
between the neuro pill
381
00:12:25,611 -->
00:12:27,680
and your lead and then you
382
00:12:27,713 -->
00:12:29,348
stimulate out and do you think that the
383
00:12:29,348 -->
00:12:29,949
way that the current
384
00:12:29,949 -->
00:12:31,383
spreads or that these spatial
385
00:12:31,383 -->
00:12:33,753
distributions should be mapped
386
00:12:33,753 -->
00:12:35,788
differently if we have a
387
00:12:35,788 -->
00:12:36,822
patient we're trying to salvage
388
00:12:36,822 -->
00:12:37,957
because they're having too much side
389
00:12:37,957 -->
00:12:39,091
effect as their disease
390
00:12:39,091 -->
00:12:40,059
is increased and we've had
391
00:12:40,059 -->
00:12:41,594
to increase our current that we deliver.
392
00:12:42,228 -->
00:12:42,895
Is that something that
393
00:12:42,895 -->
00:12:43,963
should be taken into account?
394
00:12:45,197 -->
00:12:47,666
Well that's one of the benefits of the
395
00:12:47,666 -->
00:12:48,901
constant current devices
396
00:12:48,901 -->
00:12:50,803
now is that really this whole
397
00:12:50,803 -->
00:12:52,138
impedance thing shouldn't
398
00:12:52,138 -->
00:12:53,339
make that big of a difference.
399
00:12:54,006 -->
00:12:56,142
I think we've proven that both in the
400
00:12:56,142 -->
00:12:58,244
biophysics and in experimental
401
00:12:58,244 -->
00:13:00,279
measurements and in real
402
00:13:00,279 -->
00:13:00,880
life practice.
403
00:13:01,213 -->
00:13:03,415
So I'm not worried about it that much.
404
00:13:04,817 -->
00:13:09,922
I think that once again a VTA and a
405
00:13:09,922 -->
00:13:10,923
clinical programming
406
00:13:10,923 -->
00:13:12,992
environment is a simple thing
407
00:13:12,992 -->
00:13:13,926
that's meant to get you
408
00:13:13,926 -->
00:13:15,060
going in the right direction.
409
00:13:15,060 -->
00:13:18,197
Whereas if you want to really do the full
410
00:13:18,197 -->
00:13:19,498
on science experiment
411
00:13:19,498 -->
00:13:20,666
and you want to dissect
412
00:13:21,600 -->
00:13:25,204
how one neuro population might respond to
413
00:13:25,204 -->
00:13:26,238
your stimulation
414
00:13:26,238 -->
00:13:27,773
parameters set compared to
415
00:13:27,773 -->
00:13:30,276
another nearby neuro population that
416
00:13:30,276 -->
00:13:31,477
might have very different
417
00:13:31,477 -->
00:13:33,546
biophysical characteristics
418
00:13:33,546 -->
00:13:37,016
to it then all of these nuances of how
419
00:13:37,016 -->
00:13:38,851
you build up that model
420
00:13:38,851 -->
00:13:40,052
or how you build up that
421
00:13:40,052 -->
00:13:42,421
anatomy kind of stimulation
422
00:13:42,421 -->
00:13:43,889
they make a huge difference.
423
00:13:44,323 -->
00:13:46,425
And that's where we're at this really
424
00:13:46,425 -->
00:13:49,061
interesting time I feel
425
00:13:49,061 -->
00:13:50,996
like in our study of deep brain
426
00:13:51,063 -->
00:13:53,632
stimulation because we've answered a lot
427
00:13:53,632 -->
00:13:54,600
of the easy questions.
428
00:13:55,034 -->
00:13:56,802
Now we want to start answering these much
429
00:13:56,802 -->
00:13:58,204
more complex questions.
430
00:14:01,807 -->
00:14:05,811
And so if you want to be asking much more
431
00:14:05,811 -->
00:14:06,946
complex questions
432
00:14:06,946 -->
00:14:09,215
about networks or parallel
433
00:14:09,215 -->
00:14:11,984
stimulation streams of kind of neural
434
00:14:11,984 -->
00:14:14,520
activity you really need
435
00:14:14,520 -->
00:14:15,788
to be thinking about like
436
00:14:15,788 -->
00:14:18,157
okay is the model that I'm using actually
437
00:14:18,157 -->
00:14:19,058
appropriate for the
438
00:14:19,058 -->
00:14:20,526
questions that I'm asking.
439
00:14:21,026 -->
00:14:22,394
That's where there's you know kind of
440
00:14:22,394 -->
00:14:24,129
disconnect I think and you
441
00:14:24,129 -->
00:14:26,398
know kind of the use cases.
442
00:14:27,700 -->
00:14:30,236
Do you think that so right now you know
443
00:14:30,236 -->
00:14:31,470
we deliver the most
444
00:14:31,470 -->
00:14:32,438
common thing would be something
445
00:14:32,438 -->
00:14:34,840
like 130 Hertz square wave stimulation
446
00:14:34,840 -->
00:14:37,009
regular impulses that are
447
00:14:37,009 -->
00:14:38,711
given through spatial pattern
448
00:14:39,078 -->
00:14:40,579
that's determined based on how it
449
00:14:40,579 -->
00:14:41,614
improves patient symptoms
450
00:14:41,614 -->
00:14:43,282
initially and then where
451
00:14:43,282 -->
00:14:44,750
would where they would get side effects.
452
00:14:45,517 -->
00:14:47,219
Do you think that there's a role for
453
00:14:47,219 -->
00:14:48,354
starting to change
454
00:14:48,354 -->
00:14:51,357
patterning and we'll talk to some
455
00:14:51,357 -->
00:14:52,291
of the folks about that later.
456
00:14:52,958 -->
00:14:55,461
But also having approaches where you
457
00:14:55,461 -->
00:14:57,963
deliberately try to deposit
458
00:14:57,963 -->
00:15:02,101
charge in one region and sync
459
00:15:02,201 -->
00:15:04,003
charge in another region with the shape
460
00:15:04,003 -->
00:15:05,104
of the current that you deliver.
461
00:15:05,604 -->
00:15:09,041
In other words can you shape the recovery
462
00:15:09,041 -->
00:15:10,476
of charge around the
463
00:15:10,476 -->
00:15:13,245
lead in time not just
464
00:15:13,245 -->
00:15:13,746
in space.
465
00:15:15,014 -->
00:15:16,882
The pulses that we typically use are so
466
00:15:16,882 -->
00:15:18,517
short I'm not sure that
467
00:15:18,517 -->
00:15:19,952
would make a giant difference
468
00:15:19,952 -->
00:15:21,720
you know in terms of the biophysics but
469
00:15:21,720 -->
00:15:23,222
the train you know kind
470
00:15:23,222 -->
00:15:24,189
of the let's just talk
471
00:15:24,189 -->
00:15:25,958
about like you know maybe the pulse
472
00:15:25,958 -->
00:15:27,960
trains and you know you
473
00:15:27,960 -->
00:15:29,662
expanding our opportunity
474
00:15:29,795 -->
00:15:32,264
to explore that space like yeah like
475
00:15:32,264 -->
00:15:33,399
that's what I'm really
476
00:15:33,399 -->
00:15:34,967
excited about for the like
477
00:15:34,967 -->
00:15:36,969
the next generation of you know kind of
478
00:15:36,969 -->
00:15:38,270
some of the science experiments.
479
00:15:39,238 -->
00:15:42,374
You know we can use that to one you know
480
00:15:42,374 -->
00:15:43,575
just better understand
481
00:15:43,575 -->
00:15:46,745
the role of frequency you
482
00:15:46,745 -->
00:15:49,081
know the generic role of frequency in the
483
00:15:49,081 -->
00:15:49,782
therapeutic effects
484
00:15:49,782 -->
00:15:50,916
of DBS which is always
485
00:15:50,916 -->
00:15:51,717
you know kind of one of
486
00:15:51,717 -->
00:15:53,118
these like little mysteries.
487
00:15:55,020 -->
00:15:57,823
But you know not just the simple concept
488
00:15:57,823 -->
00:15:58,824
of you know low
489
00:15:58,824 -->
00:16:00,292
frequency versus high frequency
490
00:16:00,292 -->
00:16:03,262
but really this concept of you know how
491
00:16:03,262 -->
00:16:05,531
to different patterns of either
492
00:16:05,531 -->
00:16:06,799
artificial neuroactivity
493
00:16:06,999 -->
00:16:08,667
that we're inducing because of our
494
00:16:08,667 -->
00:16:10,936
stimulus or you know the
495
00:16:10,936 -->
00:16:12,438
underlying you know kind of
496
00:16:12,438 -->
00:16:14,873
stochastic neural firing that you just
497
00:16:14,873 -->
00:16:16,709
have and you know in a normal
498
00:16:16,709 -->
00:16:18,277
physiological circuit
499
00:16:18,277 -->
00:16:20,913
and can we figure out how to marry those
500
00:16:20,913 -->
00:16:22,147
two worlds together to
501
00:16:22,147 -->
00:16:23,515
actually like communicate
502
00:16:23,849 -->
00:16:25,517
with this circuit that we're modulating
503
00:16:25,517 -->
00:16:26,552
as opposed to just
504
00:16:26,552 -->
00:16:28,921
basically trying to blow it
505
00:16:28,921 -->
00:16:29,621
up right.
506
00:16:29,621 -->
00:16:32,758
That to me that's the mechanism of DBS is
507
00:16:32,758 -->
00:16:34,560
you know blowing up
508
00:16:34,560 -->
00:16:35,561
the circuit that you're
509
00:16:35,561 -->
00:16:36,095
stimulating.
510
00:16:36,462 -->
00:16:37,863
You're turning an injury on and off.
511
00:16:38,297 -->
00:16:38,497
Exactly.
512
00:16:38,564 -->
00:16:38,764
Yeah.
513
00:16:38,831 -->
00:16:41,867
And so you know but we're you know
514
00:16:41,867 -->
00:16:43,402
learning so much all the
515
00:16:43,402 -->
00:16:45,904
time you know maybe coming
516
00:16:45,938 -->
00:16:47,239
from all these kind of
517
00:16:47,239 -->
00:16:49,775
crazy BCI type things right.
518
00:16:49,775 -->
00:16:52,244
We're learning the language if you will
519
00:16:52,244 -->
00:16:52,845
maybe we don't
520
00:16:52,845 -->
00:16:54,213
understand it because it's all in
521
00:16:54,213 -->
00:16:56,682
some giant artificial neural net of
522
00:16:56,682 -->
00:16:59,051
decoding but we are learning
523
00:16:59,051 -->
00:17:00,619
something about the signaling
524
00:17:00,652 -->
00:17:03,222
patterns and what they mean and if we can
525
00:17:03,222 -->
00:17:05,524
turn that on its head and then figure out
526
00:17:05,557 -->
00:17:07,893
how to actually like send information
527
00:17:07,893 -->
00:17:10,195
into the system so a
528
00:17:10,195 -->
00:17:11,997
computer brain interface as
529
00:17:11,997 -->
00:17:13,031
opposed to a brain
530
00:17:13,031 -->
00:17:14,066
computer interface right.
531
00:17:14,767 -->
00:17:17,202
Like we're not that far away from you
532
00:17:17,202 -->
00:17:18,137
know being able to have
533
00:17:18,137 -->
00:17:19,505
some logical experiments
534
00:17:19,571 -->
00:17:21,940
conducted to make that happen which is
535
00:17:21,940 -->
00:17:22,941
going to be super fun.
536
00:17:23,542 -->
00:17:26,412
And do you think that I mean something
537
00:17:26,412 -->
00:17:28,313
that has gained a lot of
538
00:17:28,313 -->
00:17:29,548
attention over the last
539
00:17:30,716 -->
00:17:33,252
you know decade primarily is this ability
540
00:17:33,252 -->
00:17:34,453
to insert genes into
541
00:17:34,453 -->
00:17:35,454
cells and turn them on
542
00:17:35,454 -->
00:17:36,188
and off with light.
543
00:17:36,622 -->
00:17:38,323
So you're capturing specific classes of
544
00:17:38,323 -->
00:17:39,691
cells and something that
545
00:17:39,691 -->
00:17:41,727
I wondered is given that
546
00:17:41,727 -->
00:17:44,163
we have a heterogeneous population of
547
00:17:44,163 -->
00:17:44,997
neurons that we're
548
00:17:44,997 -->
00:17:46,365
interacting with regardless of
549
00:17:46,799 -->
00:17:48,534
which structure that we're in can we
550
00:17:48,534 -->
00:17:50,436
pattern the stimulation to
551
00:17:50,436 -->
00:17:51,870
select certain cell types.
552
00:17:52,237 -->
00:17:52,438
Yeah.
553
00:17:53,005 -->
00:17:55,674
I mean yeah like you know we you know and
554
00:17:55,674 -->
00:17:56,742
avoid the viral
555
00:17:56,742 -->
00:17:57,576
construct and all those things
556
00:17:57,643 -->
00:17:59,211
you need to optogenetics but can we just
557
00:17:59,211 -->
00:17:59,778
do that with the
558
00:17:59,778 -->
00:18:01,647
temporal pattern of electoral
559
00:18:01,647 -->
00:18:02,481
impulses that we give.
560
00:18:02,948 -->
00:18:03,148
Exactly.
561
00:18:03,348 -->
00:18:05,517
So you know the Giddes lab has shown that
562
00:18:05,517 -->
00:18:06,418
that's possible in
563
00:18:06,418 -->
00:18:08,086
rodents and now you know
564
00:18:08,086 -->
00:18:10,155
like we're getting the first data from
565
00:18:10,155 -->
00:18:11,156
you know human clinical
566
00:18:11,156 -->
00:18:12,458
trials of these concepts
567
00:18:12,458 -->
00:18:15,994
and like yeah you know like it can happen
568
00:18:15,994 -->
00:18:16,795
and that's where
569
00:18:16,795 -->
00:18:18,330
you're tapping into not just
570
00:18:18,997 -->
00:18:21,099
you know like a VTA or the way that
571
00:18:21,099 -->
00:18:22,634
people normally think about
572
00:18:22,634 -->
00:18:24,136
neurostimulation modeling
573
00:18:24,336 -->
00:18:27,639
is like you know we're working on the you
574
00:18:27,639 -->
00:18:28,674
know one to five
575
00:18:28,674 -->
00:18:30,476
millisecond time scale of
576
00:18:30,476 -->
00:18:32,911
like knowledge right but techniques like
577
00:18:32,911 -->
00:18:33,846
you're talking about
578
00:18:33,846 -->
00:18:35,948
you're thinking about using
579
00:18:35,948 -->
00:18:39,151
the you know kind of the intrinsic
580
00:18:39,151 -->
00:18:40,752
biophysics of the neuron
581
00:18:40,752 -->
00:18:43,155
itself and its synaptic plasticity
582
00:18:43,155 -->
00:18:46,558
components to you know kind of like help
583
00:18:46,558 -->
00:18:48,460
that neuron shut down or
584
00:18:48,460 -->
00:18:49,895
turn on or whatever right
585
00:18:49,962 -->
00:18:51,964
and it's just a whole other level because
586
00:18:51,964 -->
00:18:54,299
now you're talking about manipulating the
587
00:18:54,299 -->
00:18:56,435
neuron not on the millisecond time scale
588
00:18:56,435 -->
00:18:57,836
but you're using the
589
00:18:57,836 -->
00:18:59,338
intrinsic neural properties
590
00:18:59,538 -->
00:19:03,475
to manipulate it on the you know seconds
591
00:19:03,475 -->
00:19:05,043
time scale right and
592
00:19:05,043 -->
00:19:06,478
once then you're actually
593
00:19:06,478 -->
00:19:08,313
thinking about like now you're really
594
00:19:08,313 -->
00:19:10,415
controlling things much much more
595
00:19:10,415 -->
00:19:11,984
elegantly than you know
596
00:19:11,984 -->
00:19:13,619
like I said the way we do it right now
597
00:19:13,619 -->
00:19:14,253
which is just like
598
00:19:14,253 -->
00:19:15,621
blast the whole thing apart.
599
00:19:16,021 -->
00:19:16,221
Yeah.
600
00:19:16,855 -->
00:19:18,423
The idea of regular you know I remember
601
00:19:18,423 -->
00:19:19,458
when I was doing my
602
00:19:19,458 -->
00:19:21,160
physics PhD we were looking
603
00:19:21,160 -->
00:19:22,928
at there was this idea about oscillations
604
00:19:22,928 -->
00:19:23,562
and signals we
605
00:19:23,562 -->
00:19:25,564
measure brain rhythms and my
606
00:19:25,564 -->
00:19:27,299
advisor and I were looking at data that
607
00:19:27,299 -->
00:19:28,100
we'd measured that didn't
608
00:19:28,100 -->
00:19:29,168
have a specific frequency
609
00:19:29,201 -->
00:19:31,737
range and from that we got this idea of
610
00:19:31,737 -->
00:19:32,938
broadband spectral change
611
00:19:32,938 -->
00:19:34,139
and power loss scaling and
612
00:19:34,139 -->
00:19:36,441
so forth and these brain signals but the
613
00:19:36,441 -->
00:19:38,477
thing that seems odd to
614
00:19:38,477 -->
00:19:39,878
me is and what we thought
615
00:19:39,878 -->
00:19:42,414
at the time and that's because really no
616
00:19:42,414 -->
00:19:43,916
I came as a physicist
617
00:19:43,916 -->
00:19:45,484
into into neuroscience
618
00:19:46,018 -->
00:19:47,786
much more than the other way around it
619
00:19:47,786 -->
00:19:48,687
never made sense that
620
00:19:48,687 -->
00:19:50,689
oscillations or you know that
621
00:19:50,689 -->
00:19:52,324
having a particular time scale would be
622
00:19:52,324 -->
00:19:53,692
where you would start and
623
00:19:53,692 -->
00:19:54,693
it seems that we do know
624
00:19:54,693 -->
00:19:56,428
that you have systems where you have
625
00:19:56,428 -->
00:19:57,062
tremors that have
626
00:19:57,062 -->
00:19:58,664
regular frequencies and we have
627
00:19:58,897 -->
00:20:00,599
the epileptic activity that has regular
628
00:20:00,599 -->
00:20:01,466
frequencies even if it's
629
00:20:01,466 -->
00:20:03,302
really high and the natural thing
630
00:20:03,302 -->
00:20:05,170
would be and this is you know some
631
00:20:05,170 -->
00:20:06,405
ongoing research that
632
00:20:06,405 -->
00:20:07,339
we're interested in but just
633
00:20:07,339 -->
00:20:08,907
things that don't have a time scale at
634
00:20:08,907 -->
00:20:10,576
all right where they may
635
00:20:10,576 -->
00:20:11,476
have something on average
636
00:20:11,476 -->
00:20:13,078
but that you know if you want to trick a
637
00:20:13,078 -->
00:20:14,012
neural population you
638
00:20:14,012 -->
00:20:14,646
should trick it with something
639
00:20:14,780 -->
00:20:18,383
that looks like non-pathological yeah
640
00:20:18,383 -->
00:20:21,486
inputs right yes yes and you
641
00:20:21,486 -->
00:20:23,088
know and so and you couple that
642
00:20:23,088 -->
00:20:25,290
with okay so that's you know those are
643
00:20:25,290 -->
00:20:26,658
super cool experiments those are gonna
644
00:20:26,658 -->
00:20:27,893
happen right now it's
645
00:20:27,893 -->
00:20:29,761
all you know like this is this is the
646
00:20:29,761 -->
00:20:32,197
2025 but then you start
647
00:20:32,197 -->
00:20:34,299
coupling that with you know like
648
00:20:34,299 -->
00:20:36,335
the whole neural recording side of things
649
00:20:36,335 -->
00:20:38,203
and you know and so you
650
00:20:38,203 -->
00:20:40,472
couple you know local field
651
00:20:40,572 -->
00:20:43,175
potential whatever control system with
652
00:20:43,175 -->
00:20:45,677
some kind of more elegant pulsing
653
00:20:45,677 -->
00:20:47,512
paradigm man then you know
654
00:20:47,512 -->
00:20:49,147
maybe we're opening up a whole other
655
00:20:49,147 -->
00:20:51,750
level of our ability to kind of interface
656
00:20:51,750 -->
00:20:52,718
with the network which
657
00:20:52,718 -->
00:20:54,319
is you know that's always been the big
658
00:20:54,319 -->
00:20:57,522
goal so yeah and it seemed to me that
659
00:20:57,522 -->
00:20:59,424
over time you've moved
660
00:20:59,424 -->
00:21:01,693
and this may just be my outside
661
00:21:01,693 -->
00:21:02,995
perception of the things that you're
662
00:21:02,995 -->
00:21:05,163
doing and and Brian I don't want
663
00:21:05,163 -->
00:21:07,065
to steal from all the questions if you're
664
00:21:07,065 -->
00:21:08,900
about to ask this but you
665
00:21:08,900 -->
00:21:10,068
move from being somebody who
666
00:21:10,102 -->
00:21:12,504
really looks at how you shape current and
667
00:21:12,504 -->
00:21:13,305
then say well if I give
668
00:21:13,305 -->
00:21:14,439
this kind of current on average
669
00:21:14,439 -->
00:21:15,874
it should have this effect on tissue on
670
00:21:15,874 -->
00:21:17,242
average yeah to instead
671
00:21:17,242 -->
00:21:18,744
saying I'm really interested in
672
00:21:18,744 -->
00:21:21,279
understanding specific brain circuits and
673
00:21:21,279 -->
00:21:23,248
brain pathways that are
674
00:21:23,248 -->
00:21:25,083
involved with the areas that
675
00:21:25,083 -->
00:21:27,786
we think are relevant for disease and
676
00:21:27,786 -->
00:21:29,321
really characterizing sort
677
00:21:29,321 -->
00:21:31,556
of specific pathways and it
678
00:21:31,723 -->
00:21:33,792
seems like you you moved really from the
679
00:21:33,792 -->
00:21:34,726
domain of what you're
680
00:21:34,726 -->
00:21:35,827
doing to the tissue rather than
681
00:21:35,861 -->
00:21:37,596
trying to understand and sorry rather
682
00:21:37,596 -->
00:21:38,797
than trying to understand it to now
683
00:21:38,797 -->
00:21:39,798
you're much more focused
684
00:21:39,798 -->
00:21:41,600
on specific brain pathways and trying to
685
00:21:41,600 -->
00:21:44,236
understand specific circuitry yeah is
686
00:21:44,236 -->
00:21:45,671
that true yeah I think
687
00:21:45,704 -->
00:21:48,307
so I mean I still you know I always my
688
00:21:48,307 -->
00:21:50,042
first love has always been the
689
00:21:50,042 -->
00:21:51,610
fundamental biophysics of you
690
00:21:51,610 -->
00:21:54,112
know kind of electric fields and how they
691
00:21:54,112 -->
00:21:57,082
manipulate the firing of neurons but but
692
00:21:57,082 -->
00:21:58,283
those what those are
693
00:21:58,283 -->
00:22:00,552
25 30 year old questions like we're kind
694
00:22:00,552 -->
00:22:01,586
of done with yeah you know
695
00:22:01,586 -->
00:22:04,456
like so so you know the next level
696
00:22:04,623 -->
00:22:06,958
of question is really more about like
697
00:22:06,958 -->
00:22:08,260
well what are these brain
698
00:22:08,260 -->
00:22:10,162
circuits you know how can we
699
00:22:10,162 -->
00:22:14,599
characterize them at the human scale what
700
00:22:14,599 -->
00:22:15,567
are the right techniques
701
00:22:15,567 -->
00:22:17,202
to try and do that you know
702
00:22:17,202 -->
00:22:20,005
you're always a model is a proxy of you
703
00:22:20,005 -->
00:22:21,707
know the real situation we
704
00:22:21,707 -->
00:22:24,576
can't have real human you know
705
00:22:24,576 -->
00:22:26,311
neural traditional neural anatomy it's
706
00:22:26,311 -->
00:22:29,414
just like not possible right so so how
707
00:22:29,414 -->
00:22:30,549
can we characterize it
708
00:22:30,582 -->
00:22:33,218
well you know to me that was a super fun
709
00:22:33,218 -->
00:22:35,520
or that is now a super fun
710
00:22:35,520 -->
00:22:37,022
engineering challenge you know
711
00:22:37,022 -->
00:22:39,725
like how can we build you know
712
00:22:39,725 -->
00:22:41,660
anatomically realistic
713
00:22:41,660 -->
00:22:44,262
representations of you know these
714
00:22:44,262 -->
00:22:46,398
pathways of interest in the human brain
715
00:22:46,398 -->
00:22:48,133
and and can you figure out
716
00:22:48,133 -->
00:22:50,235
ways to make that happen you
717
00:22:50,235 -->
00:22:53,338
know with like super you know awesome
718
00:22:53,338 -->
00:22:56,274
science level data that you know you
719
00:22:56,274 -->
00:22:57,042
would never collect in a
720
00:22:57,042 -->
00:22:59,311
clinical environment and then once you've
721
00:22:59,311 -->
00:23:00,278
built it in that kind of
722
00:23:00,278 -->
00:23:01,313
environment can you figure out
723
00:23:01,313 -->
00:23:03,915
how to make it applicable to a de novo
724
00:23:03,915 -->
00:23:05,584
generic clinical patient
725
00:23:05,584 -->
00:23:08,019
right those are the tricks those
726
00:23:08,019 -->
00:23:10,622
are engineering hurdles you know like we
727
00:23:10,622 -->
00:23:12,190
know how this we've got good
728
00:23:12,190 -->
00:23:13,892
ideas about how to build these
729
00:23:13,892 -->
00:23:17,162
kinds of tools in a scientific situation
730
00:23:17,162 -->
00:23:18,764
but then once again just
731
00:23:18,764 -->
00:23:20,499
like when chris and i were first
732
00:23:20,499 -->
00:23:23,201
building the you know kind of concepts of
733
00:23:23,201 -->
00:23:24,736
patient specific dbs models
734
00:23:24,736 -->
00:23:25,737
you also got to figure out
735
00:23:26,404 -->
00:23:29,241
how are you going to make it relevant to
736
00:23:29,241 -->
00:23:30,976
like an actual clinically
737
00:23:30,976 -->
00:23:33,578
relevant you know situation and
738
00:23:33,578 -->
00:23:36,481
that's a that's that's the real challenge
739
00:23:36,481 -->
00:23:38,683
um and you know and like i
740
00:23:38,683 -->
00:23:39,951
said it's right now it's a
741
00:23:39,951 -->
00:23:42,487
methods you know kind of evaluation the
742
00:23:42,487 -->
00:23:43,588
concept the concepts have
743
00:23:43,588 -->
00:23:45,023
been around for forever you know
744
00:23:45,023 -->
00:23:46,691
there's nothing novel about that sure
745
00:23:46,691 -->
00:23:50,762
it's how do you make it work um you know
746
00:23:50,762 -->
00:23:52,364
in a skill on a scale
747
00:23:52,664 -->
00:23:54,399
that would be both clinically predictive
748
00:23:54,399 -->
00:23:56,601
but also anatomically
749
00:23:56,601 -->
00:23:58,637
realistic and we're not there yet in
750
00:23:58,637 -->
00:24:02,040
my opinion so what did you major in in
751
00:24:02,040 -->
00:24:04,376
undergrad uh biomedical
752
00:24:04,376 -->
00:24:06,711
engineering okay brian majoring
753
00:24:06,711 -->
00:24:09,648
computer science i was physics yep now we
754
00:24:09,648 -->
00:24:11,616
teach people sort of neural
755
00:24:11,616 -->
00:24:12,851
engineering from the start
756
00:24:12,884 -->
00:24:18,657
yeah right and it seems that and and you
757
00:24:18,657 -->
00:24:19,691
you do a lot of things that
758
00:24:19,691 -->
00:24:20,826
are really important education
759
00:24:21,059 -->
00:24:23,028
actually educating neurosurgeons and
760
00:24:23,028 -->
00:24:24,663
neurologists and lots of other people
761
00:24:24,663 -->
00:24:25,597
about anatomy with the
762
00:24:25,997 -->
00:24:27,899
with the virtual reality tools you're
763
00:24:27,899 -->
00:24:29,301
creating and so forth yeah but just in
764
00:24:29,301 -->
00:24:30,202
terms of you trained a
765
00:24:30,202 -->
00:24:32,037
lot of people that subsequently went on
766
00:24:32,037 -->
00:24:32,671
to do very important
767
00:24:32,671 -->
00:24:34,639
things in our field do you think
768
00:24:34,639 -->
00:24:36,274
that the approach of sort of starting
769
00:24:36,274 -->
00:24:39,277
with the nervous system in
770
00:24:39,277 -->
00:24:40,312
the beginning of your education
771
00:24:40,312 -->
00:24:41,413
because that's what people are interested
772
00:24:41,413 -->
00:24:44,216
in makes sense right or do
773
00:24:44,216 -->
00:24:45,684
you need to learn the the tool
774
00:24:46,084 -->
00:24:48,553
controlled environment um like
775
00:24:48,553 -->
00:24:50,655
engineering or science or physics and
776
00:24:50,655 -->
00:24:52,224
only then start to learn
777
00:24:52,224 -->
00:24:55,060
the biology and apply it there yeah i
778
00:24:55,060 -->
00:24:56,795
don't know so i you know i
779
00:24:56,795 -->
00:24:58,997
was a super lucky person for at
780
00:24:58,997 -->
00:25:00,932
least for this career path that i went in
781
00:25:00,932 -->
00:25:03,068
because like i was a neural
782
00:25:03,068 -->
00:25:04,703
engineer or you know being
783
00:25:04,703 -->
00:25:06,238
trained as a neural engineer from my
784
00:25:06,238 -->
00:25:08,173
sophomore year in college on
785
00:25:08,173 -->
00:25:09,274
like it was just that was the
786
00:25:09,274 -->
00:25:12,177
environment that i was in was full of
787
00:25:12,177 -->
00:25:13,912
people that like i thought that was
788
00:25:13,912 -->
00:25:15,247
normal like i didn't
789
00:25:15,247 -->
00:25:17,616
understand that and the you know when i
790
00:25:17,616 -->
00:25:19,584
was you know an undergrad at
791
00:25:19,584 -->
00:25:22,153
case um you know i just was
792
00:25:22,153 -->
00:25:24,222
like oh wow this is this must be the way
793
00:25:24,222 -->
00:25:25,423
everyone in this field works
794
00:25:25,423 -->
00:25:27,092
right and then you go out and
795
00:25:27,092 -->
00:25:29,528
you realize that like oh wow most of the
796
00:25:29,528 -->
00:25:31,296
people that are you know
797
00:25:31,296 -->
00:25:33,398
using these commercial grade
798
00:25:33,431 -->
00:25:35,400
dbs devices like they don't have any
799
00:25:35,400 -->
00:25:37,002
training in this kind of
800
00:25:37,002 -->
00:25:38,870
stuff like the physics of you know
801
00:25:38,970 -->
00:25:41,539
electric fields or the neural you know
802
00:25:41,539 -->
00:25:43,475
kind of like biophysics
803
00:25:43,475 -->
00:25:44,976
or whatever right and so
804
00:25:45,443 -->
00:25:46,811
so that's kind of an eye-opening
805
00:25:46,811 -->
00:25:49,915
experience and you know i guess it was
806
00:25:49,915 -->
00:25:51,082
good for me because then
807
00:25:51,082 -->
00:25:53,685
it was like you know there was a need for
808
00:25:53,685 -->
00:25:55,820
that kind of like someone who could
809
00:25:55,820 -->
00:25:57,622
provide some training
810
00:25:57,756 -->
00:25:59,557
or some you know or i could write papers
811
00:25:59,557 -->
00:26:00,458
about that and people
812
00:26:00,458 -->
00:26:01,793
would read them kind of thing
813
00:26:01,793 -->
00:26:04,195
right so nowadays you know like there
814
00:26:04,195 -->
00:26:05,096
there are many more
815
00:26:05,096 -->
00:26:07,098
opportunities i think for undergraduates
816
00:26:07,232 -->
00:26:10,101
to kind of go right into being what i
817
00:26:10,101 -->
00:26:12,537
would call a neural engineer most you
818
00:26:12,537 -->
00:26:13,605
know 18 year olds when
819
00:26:13,605 -->
00:26:14,806
they're deciding where they want to go to
820
00:26:14,806 -->
00:26:15,840
college haven't really
821
00:26:15,840 -->
00:26:17,242
figured out that they want to be
822
00:26:17,242 -->
00:26:19,911
that specific so is there a trade-off
823
00:26:19,911 -->
00:26:21,746
like yeah you know maybe
824
00:26:21,746 -->
00:26:24,583
maybe if you have such a myopic
825
00:26:25,116 -->
00:26:27,819
focus so young you're going to miss out
826
00:26:27,819 -->
00:26:30,255
on like the broadness that
827
00:26:30,255 -->
00:26:31,323
if you just did a physics
828
00:26:31,523 -->
00:26:34,092
undergrad that you would get right and so
829
00:26:34,092 -->
00:26:35,660
i see it both ways like
830
00:26:35,660 -->
00:26:36,962
positive and negative i guess
831
00:26:37,295 -->
00:26:38,830
maybe you could tell me about that race
832
00:26:38,830 -->
00:26:41,633
car okay yeah so this is a
833
00:26:41,633 -->
00:26:44,135
picture of your kids and my
834
00:26:44,169 -->
00:26:48,573
son leo in my race garage which is under
835
00:26:48,573 -->
00:26:50,775
my house in riley i guess maybe i
836
00:26:50,775 -->
00:26:51,443
shouldn't tell people
837
00:26:51,443 -->
00:26:53,878
that so they don't come steal it but but
838
00:26:53,878 -->
00:26:55,981
uh hold on you have a race garage and
839
00:26:55,981 -->
00:26:56,781
then a normal garage
840
00:26:57,082 -->
00:27:00,418
yeah well i mean like you know my wife's
841
00:27:00,418 -->
00:27:01,753
car you know like she needs
842
00:27:01,753 -->
00:27:03,254
her own space and stuff and
843
00:27:03,622 -->
00:27:06,057
pretty serious yeah yeah yeah so i mean
844
00:27:06,057 -->
00:27:08,460
i've been you know racing um
845
00:27:08,460 -->
00:27:10,528
you know as you know like my
846
00:27:10,528 -->
00:27:12,831
main hobby for basically my whole life
847
00:27:12,831 -->
00:27:15,967
and and so you know like you know once
848
00:27:15,967 -->
00:27:16,901
you get into it deep
849
00:27:16,901 -->
00:27:18,503
enough like you know like some people
850
00:27:18,503 -->
00:27:19,604
have a horse stable and
851
00:27:19,604 -->
00:27:20,839
they have horses right you know
852
00:27:20,839 -->
00:27:26,611
it's the same thing so yeah so um so i
853
00:27:26,611 -->
00:27:28,713
have you know like one of the great
854
00:27:28,713 -->
00:27:29,914
things about you know
855
00:27:30,215 -->
00:27:32,484
moving to north carolina was well one
856
00:27:32,484 -->
00:27:34,786
it's such a great racing culture um you
857
00:27:34,786 -->
00:27:36,021
know it's it's a big
858
00:27:36,021 -->
00:27:38,023
thing down there and then on top of it
859
00:27:38,023 -->
00:27:39,157
the only way i could get my
860
00:27:39,157 -->
00:27:41,226
wife to agree to move was that
861
00:27:41,226 -->
00:27:42,927
we were going to build a new house and
862
00:27:42,927 -->
00:27:44,062
that she would design it and
863
00:27:44,062 -->
00:27:44,963
i would have no say over the
864
00:27:44,963 -->
00:27:47,799
budget but the one thing that i did get
865
00:27:47,799 -->
00:27:51,469
to do was uh you know integrate the race
866
00:27:51,469 -->
00:27:52,704
garage into our house
867
00:27:53,271 -->
00:27:56,141
and so for you know my most of my life i
868
00:27:56,141 -->
00:27:57,675
had you know had a race
869
00:27:57,675 -->
00:27:59,177
garage that was you know some in
870
00:27:59,210 -->
00:28:01,413
some industrial park basically you know
871
00:28:01,413 -->
00:28:02,681
out in the middle of nowhere
872
00:28:02,681 -->
00:28:05,283
and you know and so now being
873
00:28:05,283 -->
00:28:08,186
able to have you know my my fun you know
874
00:28:08,186 -->
00:28:10,155
like literally you know
875
00:28:10,155 -->
00:28:11,790
right below me at all times
876
00:28:11,790 -->
00:28:14,359
made makes for a much more enjoyable you
877
00:28:14,359 -->
00:28:15,527
know hobby kind of things
878
00:28:15,527 -->
00:28:17,962
yeah yeah um and then the
879
00:28:17,962 -->
00:28:19,731
trailer you know is kind of parked out
880
00:28:19,731 -->
00:28:23,001
front and technically my hoa is uh not
881
00:28:23,001 -->
00:28:24,402
it's not allowed but
882
00:28:24,436 -->
00:28:26,971
no you know no one ever complains so yeah
883
00:28:26,971 -->
00:28:29,174
film yeah i know well you know i'll do
884
00:28:31,142 -->
00:28:32,644
so how often do you actually take it out
885
00:28:32,644 -->
00:28:35,013
then so well you know like
886
00:28:35,013 -->
00:28:36,815
in a normal race season i'm
887
00:28:36,815 -->
00:28:40,018
probably you know try to race you know
888
00:28:40,018 -->
00:28:42,420
one you know race weekend a
889
00:28:42,420 -->
00:28:43,855
month kind of thing like that's
890
00:28:43,855 -->
00:28:46,091
kind of historically how i've done things
891
00:28:46,091 -->
00:28:47,959
you know the the peak of
892
00:28:47,959 -->
00:28:49,494
the season is in the you know
893
00:28:49,561 -->
00:28:53,298
kind of spring and the fall summer you
894
00:28:53,298 -->
00:28:54,499
know kind of it's usually too
895
00:28:54,499 -->
00:28:56,301
hot i mean especially in the
896
00:28:56,301 -->
00:28:58,870
south it's it's really tough and then the
897
00:28:58,870 -->
00:29:00,205
winter you know once again
898
00:29:00,205 -->
00:29:01,372
it's a little you know a little
899
00:29:01,372 -->
00:29:04,242
too cold and you know race tires they
900
00:29:04,242 -->
00:29:06,978
need to work in a very you know specific
901
00:29:06,978 -->
00:29:08,146
operating temperature
902
00:29:08,246 -->
00:29:10,982
and and so you know you kind of want it's
903
00:29:10,982 -->
00:29:12,283
for personal comfort like
904
00:29:12,283 -->
00:29:14,119
you know the inside of a
905
00:29:14,119 -->
00:29:16,020
race car you know in the middle of a race
906
00:29:16,020 -->
00:29:17,722
is like 120 degrees and
907
00:29:17,722 -->
00:29:19,357
you're wearing you know like
908
00:29:19,758 -->
00:29:22,794
you know a giant thick blanket you know
909
00:29:22,794 -->
00:29:24,028
jumpsuit or you know race
910
00:29:24,028 -->
00:29:26,164
suit with a helmet on and like
911
00:29:26,264 -->
00:29:31,002
it's hot right so so so it's uh you know
912
00:29:31,002 -->
00:29:32,704
you kind of have to as i've
913
00:29:32,704 -->
00:29:33,972
gotten older i've definitely
914
00:29:33,972 -->
00:29:36,241
noticed like i can't take it like i used
915
00:29:36,241 -->
00:29:39,477
to yeah so yeah fair enough
916
00:29:39,477 -->
00:29:41,679
sure enough uh there was a
917
00:29:41,679 -->
00:29:44,449
whole lot of trophies yeah in there well
918
00:29:44,449 -->
00:29:46,618
a lot a lot of years so you
919
00:29:46,618 -->
00:29:47,519
know like you only have to
920
00:29:47,552 -->
00:29:49,220
win once a year and then it looks like
921
00:29:49,220 -->
00:29:50,421
i'm you know really good
922
00:29:50,421 -->
00:29:53,658
but yeah so okay that's pretty
923
00:29:53,658 -->
00:29:56,561
good yeah it wasn't like like it's like
924
00:29:56,561 -->
00:29:58,763
30 or 40 trophies so it's
925
00:29:58,763 -->
00:30:01,399
like more than once a year yeah
926
00:30:02,634 -->
00:30:04,736
impressive well it's super fun and but
927
00:30:04,736 -->
00:30:06,471
like it's it's like a skill
928
00:30:06,471 -->
00:30:07,472
and you know and when you're
929
00:30:07,539 -->
00:30:09,440
maybe been doing it for a long long time
930
00:30:09,440 -->
00:30:11,442
you know you you get better
931
00:30:11,442 -->
00:30:13,044
the more you practice and so
932
00:30:13,378 -->
00:30:15,947
but i have noticed like so i i turned 50
933
00:30:15,947 -->
00:30:18,850
this year and from 30 to 40 like no
934
00:30:18,850 -->
00:30:19,951
detriment whatsoever
935
00:30:20,084 -->
00:30:25,623
in skill 40 to 45 like plateau 45 to 50
936
00:30:25,623 -->
00:30:27,892
like i can tell i'm getting slower okay
937
00:30:27,892 -->
00:30:28,726
so there's this movie
938
00:30:28,726 -->
00:30:32,130
coming out right and uh it's called f1
939
00:30:32,130 -->
00:30:33,331
yeah i'm sure you know about
940
00:30:33,331 -->
00:30:34,732
it yes brad pitts like in his
941
00:30:34,732 -->
00:30:37,135
mid 60s right well in the movie i think
942
00:30:37,135 -->
00:30:37,902
he's only playing someone
943
00:30:37,902 -->
00:30:40,238
who's 50 but yes yeah yeah yeah
944
00:30:40,672 -->
00:30:43,675
but yeah highly highly unrealistic yeah
945
00:30:43,675 -->
00:30:44,776
that would never have never
946
00:30:44,776 -->
00:30:45,977
happened no no no the movie
947
00:30:45,977 -->
00:30:47,912
i hear the movie's great and i can't wait
948
00:30:47,912 -->
00:30:50,281
to see it um but highly highly
949
00:30:50,281 -->
00:30:52,016
unrealistic that anyone
950
00:30:52,083 -->
00:30:54,853
over the age of 30 would be brought into
951
00:30:54,853 -->
00:30:55,687
the formula one
952
00:30:55,687 -->
00:30:57,622
environment like that's that is a
953
00:30:57,822 -->
00:31:01,192
complete non-stop retirement age 30 okay
954
00:31:01,192 -->
00:31:03,194
yeah yeah like fernando
955
00:31:03,194 -->
00:31:04,829
alonzo is the only person
956
00:31:04,963 -->
00:31:07,732
who is legitimately you know still one of
957
00:31:07,732 -->
00:31:08,833
the greatest drivers on
958
00:31:08,833 -->
00:31:10,401
earth that's older than that
959
00:31:10,802 -->
00:31:13,872
there's not like roger federer or i mean
960
00:31:13,872 -->
00:31:15,773
so you can still be a
961
00:31:15,773 -->
00:31:17,909
great driver like true pros can
962
00:31:17,942 -->
00:31:21,412
still be great from the age of 30 to 50
963
00:31:21,412 -->
00:31:22,747
and but what they do is
964
00:31:22,747 -->
00:31:24,482
they transition out of formula
965
00:31:24,582 -->
00:31:26,217
one and then they go and race what's
966
00:31:26,217 -->
00:31:27,885
called prototypes or
967
00:31:27,885 -->
00:31:31,389
sports cars so uh 24 hour lemond
968
00:31:32,156 -->
00:31:34,058
cars you know that kind of thing so they
969
00:31:34,058 -->
00:31:35,760
they migrate into what
970
00:31:35,760 -->
00:31:36,794
people will call the sports
971
00:31:36,894 -->
00:31:39,264
car world that's the world i race in is
972
00:31:39,264 -->
00:31:41,799
sports cars okay so okay so
973
00:31:41,799 -->
00:31:43,034
yeah and you know over the
974
00:31:43,034 -->
00:31:44,535
years i've gotten to know a bunch of
975
00:31:44,535 -->
00:31:46,137
these you know famous
976
00:31:46,137 -->
00:31:48,873
pros you know and it's it's
977
00:31:48,873 -->
00:31:50,575
interesting i bet they're interesting
978
00:31:50,575 -->
00:31:52,410
life exactly they have a
979
00:31:52,410 -->
00:31:55,580
life that we cannot understand
980
00:31:56,180 -->
00:31:59,684
it's very different yeah yeah sure i mean
981
00:31:59,684 -->
00:32:01,419
not necessarily all good
982
00:32:01,419 -->
00:32:02,654
like i don't envy their
983
00:32:02,720 -->
00:32:05,556
lives frankly like especially like if
984
00:32:05,556 -->
00:32:07,225
you're super super great
985
00:32:07,225 -->
00:32:09,027
and you're famous you're set
986
00:32:09,027 -->
00:32:11,162
financially there's no worries you're
987
00:32:11,162 -->
00:32:12,764
there to do your job and you
988
00:32:12,764 -->
00:32:14,265
do your job well and there's no
989
00:32:14,265 -->
00:32:16,601
worries right but it's the guys that are
990
00:32:16,601 -->
00:32:18,469
on the fringe every day
991
00:32:18,469 -->
00:32:21,205
they are racing for their food
992
00:32:21,372 -->
00:32:23,541
you know or their opportunity the next
993
00:32:23,541 -->
00:32:25,009
time and the next time and
994
00:32:25,009 -->
00:32:26,311
there's no savings and there's
995
00:32:26,344 -->
00:32:30,381
no safety net and like those guys like i
996
00:32:30,381 -->
00:32:31,816
have just crazy respect
997
00:32:31,816 -->
00:32:33,318
for but they're also crazy
998
00:32:33,318 -->
00:32:34,886
because they're like this isn't really a
999
00:32:34,886 -->
00:32:37,956
very good life choice well
1000
00:32:37,956 -->
00:32:40,358
like tennis if you're above
1001
00:32:40,391 -->
00:32:43,428
about 100 in the world or maybe 200 in
1002
00:32:43,428 -->
00:32:45,330
the world those guys are and
1003
00:32:45,330 -->
00:32:46,998
and women are just like just
1004
00:32:46,998 -->
00:32:48,967
scraping by to get the plane ticket to go
1005
00:32:48,967 -->
00:32:51,135
to the place but if you're
1006
00:32:51,135 -->
00:32:52,804
if you're the let's say you're
1007
00:32:52,804 -->
00:32:54,672
the the three thousand number three
1008
00:32:54,672 -->
00:32:55,840
thousand soccer player in
1009
00:32:55,840 -->
00:32:56,641
the world i bet you're doing
1010
00:32:56,641 -->
00:32:58,843
pretty well i bet you're raw yes yes so
1011
00:32:58,843 -->
00:33:00,278
you got your kids playing
1012
00:33:00,278 -->
00:33:01,980
soccer i think yeah yeah maybe
1013
00:33:02,013 -->
00:33:03,648
maybe that's the maybe that's the plan
1014
00:33:03,648 -->
00:33:07,452
yeah yeah um good stuff i
1015
00:33:07,452 -->
00:33:09,354
have one other question i i want
1016
00:33:09,354 -->
00:33:12,190
to ask you that and the more lurid the
1017
00:33:12,190 -->
00:33:13,291
details you have here the
1018
00:33:13,291 -->
00:33:16,361
better okay this guy your boss
1019
00:33:16,961 -->
00:33:20,064
oh it's yeah it's hard to see so jerry
1020
00:33:20,064 -->
00:33:21,399
grant oh jerry grant
1021
00:33:21,399 -->
00:33:23,935
yeah he's uh he's one of my
1022
00:33:24,168 -->
00:33:26,471
very close mentors and friends and means
1023
00:33:26,471 -->
00:33:30,875
everything to me yes so the more lurid
1024
00:33:30,875 -->
00:33:31,743
stories that you have
1025
00:33:31,743 -->
00:33:33,244
like if you have something i can hold
1026
00:33:33,244 -->
00:33:34,912
over him at a neurosurgery meeting i
1027
00:33:34,912 -->
00:33:35,380
would love this the guy
1028
00:33:35,380 -->
00:33:37,915
the guy is a saint like i mean he i've
1029
00:33:37,915 -->
00:33:40,118
never seen him do anything you know
1030
00:33:40,118 -->
00:33:41,519
sketchy or questionable
1031
00:33:41,686 -->
00:33:43,521
and he's like the most helpful person in
1032
00:33:43,521 -->
00:33:44,589
the world so that's why i'm saying i
1033
00:33:44,589 -->
00:33:45,456
think i have the exact
1034
00:33:46,257 -->
00:33:48,092
but but if you've got anything i know
1035
00:33:48,092 -->
00:33:49,927
that's why i don't know you
1036
00:33:49,927 -->
00:33:51,729
know like i'm sure everyone's got
1037
00:33:51,729 -->
00:33:53,831
a dark side right like i just haven't
1038
00:33:53,831 -->
00:33:57,402
i've never seen his so yeah yeah yeah i
1039
00:33:57,402 -->
00:33:59,737
don't know yeah but i
1040
00:33:59,737 -->
00:34:02,039
mean he you know so i got a soft spot for
1041
00:34:02,039 -->
00:34:03,141
pediatric neurosurgery yeah
1042
00:34:03,141 -->
00:34:04,509
absolutely yeah yeah i mean
1043
00:34:04,642 -->
00:34:06,477
they're you know you got to question
1044
00:34:06,477 -->
00:34:08,246
their life choices in general anyway
1045
00:34:08,246 -->
00:34:09,680
right but well i don't
1046
00:34:09,714 -->
00:34:12,750
know my life you know there's that but
1047
00:34:12,750 -->
00:34:15,820
exactly yeah but no you know like jerry
1048
00:34:15,820 -->
00:34:17,755
and i have had a great
1049
00:34:18,823 -->
00:34:21,359
synergistic relationship and you know
1050
00:34:21,359 -->
00:34:22,293
over the last couple years
1051
00:34:22,293 -->
00:34:24,495
and basically you know we both
1052
00:34:24,495 -->
00:34:26,464
came into duke at approximately the same
1053
00:34:26,464 -->
00:34:28,933
time and we really you know
1054
00:34:28,933 -->
00:34:30,802
kind of like you know saw eye to
1055
00:34:30,802 -->
00:34:33,738
eye immediately and and he really does
1056
00:34:33,738 -->
00:34:36,040
believe in the value of
1057
00:34:36,040 -->
00:34:38,543
engineers working you know like in
1058
00:34:38,643 -->
00:34:40,812
nursery you know like he sees it he
1059
00:34:40,812 -->
00:34:42,180
understands it and he's put
1060
00:34:42,180 -->
00:34:43,681
his you know the money where his
1061
00:34:43,681 -->
00:34:46,117
mouth is kind of thing where you know he
1062
00:34:46,117 -->
00:34:47,251
and i have worked
1063
00:34:47,251 -->
00:34:49,520
together to you know make several
1064
00:34:49,620 -->
00:34:51,622
key hires at duke that i think are
1065
00:34:51,622 -->
00:34:54,659
transformative right and and you know and
1066
00:34:54,659 -->
00:34:56,461
and he trusts that i
1067
00:34:56,461 -->
00:34:58,563
am a good evaluator of talent and he
1068
00:34:58,563 -->
00:35:01,265
knows how to you know grease
1069
00:35:01,265 -->
00:35:03,234
the grease the wheels with the
1070
00:35:03,301 -->
00:35:05,803
you know the deans and presidents and
1071
00:35:05,803 -->
00:35:06,971
whatever to say like this
1072
00:35:06,971 -->
00:35:08,973
is what we need and like it's
1073
00:35:09,006 -->
00:35:10,975
we've just done a great job i think i'm
1074
00:35:10,975 -->
00:35:12,777
really proud so having
1075
00:35:12,777 -->
00:35:14,645
having trained under him and spent
1076
00:35:14,645 -->
00:35:15,880
a lot of time with him and he took me
1077
00:35:15,880 -->
00:35:17,415
under his wing he still takes me under
1078
00:35:17,415 -->
00:35:18,049
his wing even though
1079
00:35:18,049 -->
00:35:20,318
i'm almost fished to myself yeah you know
1080
00:35:20,318 -->
00:35:22,487
he uh he's the kind of
1081
00:35:22,487 -->
00:35:23,654
person that inspires you to
1082
00:35:23,988 -->
00:35:26,324
try to do better and to be a better like
1083
00:35:26,324 -->
00:35:27,892
human being and do good
1084
00:35:27,892 -->
00:35:29,026
things for the world and i think
1085
00:35:29,060 -->
00:35:31,796
that uh you know we're lucky enough to
1086
00:35:31,796 -->
00:35:32,563
have nerry nche who
1087
00:35:32,563 -->
00:35:33,798
joined us he's he's a scientist
1088
00:35:33,798 -->
00:35:36,567
that works with brian and i um and and
1089
00:35:36,567 -->
00:35:39,504
having solid engineers who
1090
00:35:39,504 -->
00:35:41,839
get the importance of what
1091
00:35:41,839 -->
00:35:44,041
they do for helping patients i think is
1092
00:35:44,041 -->
00:35:46,244
key yes and uh you know
1093
00:35:46,244 -->
00:35:48,513
we're lucky to have you in our
1094
00:35:48,513 -->
00:35:51,115
community oh i mean i love it the the
1095
00:35:51,115 -->
00:35:52,683
luckiest thing ever for me
1096
00:35:52,683 -->
00:35:54,318
was you know kind of like
1097
00:35:55,119 -->
00:35:58,456
going to emory and seeing what you know
1098
00:35:58,456 -->
00:36:01,192
clinical neuromodulation was
1099
00:36:01,192 -->
00:36:03,127
in practice and then realizing
1100
00:36:03,294 -->
00:36:05,363
that like that was a niche and i could
1101
00:36:05,363 -->
00:36:07,231
stay in that and you know
1102
00:36:07,231 -->
00:36:08,766
build a career around it and then
1103
00:36:09,200 -->
00:36:11,369
you know like over the years you know
1104
00:36:11,369 -->
00:36:12,937
like maybe just because i just kept
1105
00:36:12,937 -->
00:36:14,138
hanging around so much
1106
00:36:14,172 -->
00:36:16,474
like you know feeling like i had become
1107
00:36:16,474 -->
00:36:18,476
part of the functional neurosurgery
1108
00:36:18,476 -->
00:36:19,610
community yeah you are
1109
00:36:20,645 -->
00:36:23,314
it's awesome i love it and and you know
1110
00:36:23,314 -->
00:36:24,549
and so i wouldn't i
1111
00:36:24,549 -->
00:36:26,017
wouldn't change anything was it
1112
00:36:26,083 -->
00:36:27,518
were you working with bob down there bob
1113
00:36:27,518 -->
00:36:29,754
gross yeah so bob was uh
1114
00:36:29,754 -->
00:36:31,889
bob was there um aviva was
1115
00:36:31,889 -->
00:36:34,792
still there uh right when i started and
1116
00:36:34,792 -->
00:36:36,060
actually you know one of the
1117
00:36:36,060 -->
00:36:37,862
very like the first dbs case
1118
00:36:37,862 -->
00:36:39,797
that i saw in emory and kind of like had
1119
00:36:39,797 -->
00:36:40,998
someone like you know
1120
00:36:40,998 -->
00:36:42,533
directly explained to me like step
1121
00:36:42,533 -->
00:36:44,435
by step this is what's going on kind of
1122
00:36:44,435 -->
00:36:45,503
thing that was aviva yeah
1123
00:36:45,503 -->
00:36:46,437
she's awesome we have we have
1124
00:36:46,504 -->
00:36:48,739
some work we do together related to brain
1125
00:36:48,739 -->
00:36:49,373
computer interfaces
1126
00:36:49,373 -->
00:36:51,275
and it's more editorial
1127
00:36:51,275 -->
00:36:53,144
kind of work and talking about different
1128
00:36:53,144 -->
00:36:56,414
things she's been uh for me
1129
00:36:56,414 -->
00:36:57,615
a really wonderful influence
1130
00:36:57,615 -->
00:37:02,186
but yeah she's uh actually nuri inche all
1131
00:37:02,186 -->
00:37:03,054
the science that he did
1132
00:37:03,054 -->
00:37:04,055
when he started was with
1133
00:37:04,055 -->
00:37:05,389
her yeah that's right that's right yeah
1134
00:37:05,389 -->
00:37:07,258
yeah yeah yeah and and bob
1135
00:37:07,258 -->
00:37:09,226
unfortunately left emory and uh
1136
00:37:09,560 -->
00:37:10,995
you know went to new jersey yeah he's
1137
00:37:10,995 -->
00:37:13,231
doing well but he uh is it ruckus he's
1138
00:37:13,231 -->
00:37:13,831
chair there yeah yeah
1139
00:37:14,098 -->
00:37:16,300
but uh yeah i mean he like i've called
1140
00:37:16,300 -->
00:37:19,270
him you know sort of in in
1141
00:37:19,270 -->
00:37:20,538
those times when you need
1142
00:37:20,871 -->
00:37:23,140
somebody who's frank about what to do in
1143
00:37:23,140 -->
00:37:24,175
that clinical situation
1144
00:37:24,175 -->
00:37:28,045
and he he is uh he's done a
1145
00:37:28,045 -->
00:37:29,914
lot for this oh yeah that's awesome oh my
1146
00:37:29,914 -->
00:37:30,848
gosh yeah totally
1147
00:37:30,848 -->
00:37:32,650
awesome guy yeah absolutely
1148
00:37:33,684 -->
00:37:34,719
any anything else you want
1149
00:37:34,719 -->
00:37:36,921
to ask uh dr mackentyre camera
1150
00:37:37,455 -->
00:37:41,225
um we got our signs here this is awesome
1151
00:37:41,225 -->
00:37:43,294
and with with water yeah yes
1152
00:37:46,998 -->
00:37:48,299
what do you think is going to be the next
1153
00:37:48,299 -->
00:37:51,302
um i guess frontier here
1154
00:37:51,302 -->
00:37:52,770
that's going to push us forward
1155
00:37:52,770 -->
00:37:54,939
i know you talked about the the the kind
1156
00:37:54,939 -->
00:37:55,573
of different simulation
1157
00:37:55,573 -->
00:37:56,641
parameters and tracking
1158
00:37:56,641 -->
00:37:59,043
how that affects things is that kind of
1159
00:37:59,043 -->
00:37:59,844
what you think or is there
1160
00:37:59,844 -->
00:38:02,313
going to be um well i think from
1161
00:38:02,346 -->
00:38:04,815
the science perspective i think you know
1162
00:38:04,815 -->
00:38:07,018
kind of like this next generation of
1163
00:38:07,018 -->
00:38:08,386
understanding of the
1164
00:38:08,386 -->
00:38:10,388
you know the the intimate neural
1165
00:38:10,388 -->
00:38:12,590
circuitry that we're like getting that
1166
00:38:12,590 -->
00:38:14,325
right and so but we need
1167
00:38:14,325 -->
00:38:16,127
to build the neuroanatomy foundation
1168
00:38:16,127 -->
00:38:17,628
first that doesn't really
1169
00:38:17,628 -->
00:38:20,498
exist and then we'll be able to do
1170
00:38:20,564 -->
00:38:22,633
you know kind of the the clinical
1171
00:38:22,633 -->
00:38:25,403
connections or the you know kind of uh
1172
00:38:25,403 -->
00:38:27,004
therapy connections to
1173
00:38:27,038 -->
00:38:29,073
this network or that network right i
1174
00:38:29,073 -->
00:38:30,408
think all that stuff is very
1175
00:38:30,408 -->
00:38:31,909
premature right now but we're
1176
00:38:31,942 -->
00:38:34,378
going to get there i think that yeah on
1177
00:38:34,378 -->
00:38:35,346
the engineering front
1178
00:38:35,346 -->
00:38:36,781
you know like we will learn
1179
00:38:36,781 -->
00:38:39,283
how to explore this parameter space in
1180
00:38:39,283 -->
00:38:40,484
much much more elegant ways
1181
00:38:40,484 -->
00:38:41,819
and that is really in my eyes
1182
00:38:41,819 -->
00:38:43,621
this is one of the main powers of the
1183
00:38:43,621 -->
00:38:45,322
whole ai revolution and how
1184
00:38:45,322 -->
00:38:47,124
it will help the field of deep
1185
00:38:47,124 -->
00:38:48,759
brain simulation you know there are
1186
00:38:48,759 -->
00:38:50,294
literally it's an infinite
1187
00:38:50,294 -->
00:38:52,329
parameter space and you know
1188
00:38:52,329 -->
00:38:55,032
there's no way that we we don't have the
1189
00:38:55,032 -->
00:38:56,233
we don't know the language
1190
00:38:56,233 -->
00:38:57,401
and we don't know the logic
1191
00:38:57,802 -->
00:39:00,304
of how to search that space right now in
1192
00:39:00,304 -->
00:39:02,073
my opinion right we have
1193
00:39:02,073 -->
00:39:03,374
we have good ways that we
1194
00:39:03,374 -->
00:39:05,342
can kind of ping the system if you will
1195
00:39:05,342 -->
00:39:06,844
with you know single pulse or
1196
00:39:06,844 -->
00:39:08,813
whatever but but the concept
1197
00:39:08,879 -->
00:39:11,015
of you know really being able to
1198
00:39:11,015 -->
00:39:13,918
understand the the interaction between
1199
00:39:13,918 -->
00:39:15,419
you know this infinite
1200
00:39:15,419 -->
00:39:18,055
parameter space of stimulation and and
1201
00:39:18,055 -->
00:39:18,723
its subsequent
1202
00:39:18,723 -->
00:39:21,292
recording you know feedback like
1203
00:39:21,726 -->
00:39:23,861
ai is going to be you know instrumental
1204
00:39:23,861 -->
00:39:26,163
in in making you know
1205
00:39:26,163 -->
00:39:28,199
giving us direction on where to
1206
00:39:28,199 -->
00:39:30,901
go with that because like i said we don't
1207
00:39:30,901 -->
00:39:33,104
know how to you know we don't know the
1208
00:39:33,104 -->
00:39:34,238
language and we don't
1209
00:39:34,238 -->
00:39:36,707
know how to efficiently explore that
1210
00:39:36,707 -->
00:39:38,042
space and so that's where
1211
00:39:38,042 -->
00:39:39,810
we'll we'll get there though yeah
1212
00:39:40,978 -->
00:39:43,347
good well it's great to have you with us
1213
00:39:43,347 -->
00:39:44,448
i'm really happy that
1214
00:39:44,448 -->
00:39:45,950
you're able to join us and
1215
00:39:45,950 -->
00:39:47,518
um and we enjoyed having you at the
1216
00:39:47,518 -->
00:39:49,253
meeting as well so thanks for coming such
1217
00:39:49,253 -->
00:39:49,987
a distance yeah we're
1218
00:39:50,020 -->
00:39:51,355
looking forward to having you back soon
1219
00:39:51,355 -->
00:39:53,424
to the uh STEIN headquarters time to
1220
00:39:53,424 -->
00:39:54,658
disclose location yes
1221
00:39:54,658 -->
00:39:56,994
Rochester Minnesota yes yeah absolutely
1222
00:39:56,994 -->
00:39:58,596
awesome well thanks great
1223
00:39:58,596 -->
00:40:01,132
thank you yeah yeah yeah yep
