What the Developing Brain Could Reveal About Autism

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I always remind people, remember the why. Take
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a step back every day and remember why you're
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doing what you're doing. And usually the why
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is that you have burning questions that you want
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to answer. You have people that you want to help.
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You ultimately want to make the world a better
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place, whether it's for a small subgroup of individuals
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or for a larger population. But we are in this
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for good. And if we can remember that, I mean,
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that is worth waking up every morning to keep
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trudging this path. I'm John Fox, Director of
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the Del Monte Institute for Neuroscience here
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at the University of Rochester, and I'd like
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to welcome you to Neuroscience Perspectives.
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Please follow us, comment, and wherever you get
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your podcasts. Today, I'm completely delighted
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to welcome Dr. Shefali Jest, an internationally
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recognized child neurologist whose research has
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advanced the identification of early biomarkers
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that predict autism spectrum disorder and epilepsy
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in infants with a rare disease known as tuberous
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sclerosis complex, and we'll come back to that.
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Dr. Jess is Chair and Executive Medical Director
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of Pediatrics at the David Geffen School of Medicine
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at UCLA and Executive Director of the Children's
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Discovery and Innovation Institute. She also
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leads major national initiatives including the
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Autism Biomarkers Consortium for Clinical Trials
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and the Aligning Research to Impact Autism Initiative,
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a mouthful, both of which bring together scientists,
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clinicians and families to bridge discovery and
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treatment. Dr. Jess has been the recipient of
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numerous honors, including the Presidential Early
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Career Award for Scientists and Engineers and
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the Martha Bridge Denkle Award from the Child
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Neurology Society. Shefali, it's such a pleasure
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to have you here in Rochester. Thank you for
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having me. In our makeshift studio here at the
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Memorial Art Gallery. And we'll come back to
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why we're even here. But thank you so much for...
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coming across the coast to visit us and to bring
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your wisdom about, you know, child neurology
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and clinical trials. So let's dive in. Tell us
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what tuberous sclerosis complex is and why we
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should care. Yes, of course. So tuberous sclerosis
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complex, which we call TSC, is a genetic condition.
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And the gene mutation is in a gene called TSC1
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or TSC2. And what it eventually really leads
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to is an overactivation of a protein that's really
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important for regulating the size of our cells
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in our body and also the number of cells we have.
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And so when TSC1 or 2 are mutated, you have a
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constantly overly active process in the body
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where cells are just dividing and they're growing
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without any regulation. And so that leads to
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benign tumors in the brain, in the kidney, in
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the heart, in the lungs. But the brain is specifically
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and most affected. And what we find in children
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and infants with tuberous sclerosis is that they
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have benign tumors called tubers, hence tuberous
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sclerosis, in their brain. And also the brain
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has some abnormal connectivity as well outside
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of those tubers. Right, right. And that's because
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the connectivity is trying to get around these
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little lumps, essentially. Basically, yes. So
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what would somebody see? Would a layperson looking
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at an MRI image go, yeah, that doesn't look right?
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In fact, I think they would. If you've seen a
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couple of MRIs, you know roughly what the structure
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of the brain looks like. And these tubers really
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look like kind of bright white spots. Yeah, they
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really stick out. They really stick out. And
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what's interesting about tuberous sclerosis is
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that those tumors can show up on an ultrasound.
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Not the ones in the brain necessarily, but the
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ones in the heart, the ones in the kidneys. And
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so often babies are diagnosed with tuberous sclerosis
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before. they're born, when a mother is undergoing
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a regular prenatal ultrasound. Right, right.
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So that allows us to make a diagnosis early on.
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Before the baby even shows up, you know this
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kid's going to have this. Once you see these
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on ultrasound, is that kid definitely going to
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end up then with a regular phenotype, a clinical
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presentation as they develop? baked in then?
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They will most definitely have the TSC diagnosis.
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There's a lot of variability or a buzzword of
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heterogeneity as we talk about in neurodevelopmental
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conditions. There's a lot of variability in what
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that course might look like. So some children
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will have more significant challenges developmentally.
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They'll have early onset epilepsy or seizures,
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which can be quite a bit of a struggle to treat.
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They might have other heart issues. issues that
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need treatment, lung issues, kidney tumors that
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need treatment. But in other cases, children
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can be much more mildly affected. And in fact,
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sometimes we will diagnose a child and it turns
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out it actually is inherited from a parent who
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never knew they had TSC because they had a milder
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mutation. Tell us how rare it is. What are we
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looking at here? We think the prevalence is about
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1 in 10 ,000. 1 in 10 ,000. So not super rare.
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Not super rare, but quite rare. Yeah, quite rare.
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And TSC, the prevalence is perhaps a little bit
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higher than some other genetic conditions that
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we study because we've known about it for a very
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long time. So we're pretty efficient in getting
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the testing done and making the diagnosis. And
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that early detection component, right? It's a
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regular ultrasound. People aren't being tested
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with the ultrasound for tuberous sclerosis. It
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shows up, right? Exactly. Exactly. Yeah. So now
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somebody else to say, oh. Shefali, why are you
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worried about something that happens to 1 in
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10 ,000 kids when we've got autism and all these
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other neurodevelopmental disabilities? No, it's
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a great question. And exactly. So I take care
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of children with neurodevelopmental disorders.
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That's really the area of my research. And what
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we've been really interested in for a long time
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as a field is trying to identify really early
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signs of autism in babies who might be considered
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at higher likelihood for developing autism or...
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you know, intellectual disability. I mean, autism
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is usually diagnosed around age two. Now we're
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getting a little better diagnosing a little bit
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earlier, but really we can't make a robust diagnosis
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before. 18 months or two years of age. And what
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we know about TSC is that it turns out at least
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50 % of children with TSC will ultimately have
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an autism diagnosis. We think that rate actually
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might be even a little bit higher. And even more
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infants or children with TSC have some level
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of cognitive impairment or other neurodevelopmental
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challenges. And so, you know, the sort of obvious
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question that arose when I was actually still
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in my training was, you know, for diagnosing
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TSC, at birth or before birth. And we know that
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more than half of them will be diagnosed with
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autism when they're two or three. are there early
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signs? Can we detect some changes either in the
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brain or in their behavior in the first year
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of life? And so that's why we became really interested
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in studying infants with TSC. And I'll tell you,
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the parents also were very motivated because
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we'd see them in clinic as neurologists because
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we were following them for their seizures that
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often start very early. And the parents would
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ask, what about my child's development? I keep
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hearing about autism. Can you tell me if my child's
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going to have autism or not? What can we do about
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it now? So that's really what led to our starting
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to study infants with TSC when I was still at
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Boston Children's. So this was in, gosh, it was
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in 2007, 2008 with my mentor Chuck Nelson at
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Boston Children's at Harvard is when we started
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really studying TSC. Right, right. So in some
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ways, one never wants to say that a disease is
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good, but from a science perspective, this disease
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has a bunch of... features, characteristics that
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make it very studyable, right? Exactly. Very
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studyable. And, you know, in some ways I'd say
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good because we love to have a reason to monitor
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and follow infants before they receive a neurodevelopmental
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diagnosis that usually doesn't happen, right?
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Because if we're thinking about the general population,
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it's pretty tricky to think about following infants
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from birth across the board, right? Especially
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using some of our more involved neuroscience
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tools like EEG or eye tracking. and things like
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that. Right, right. These are not things that
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you can just deploy in every kid that's born.
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You have to have some filter to say these kids
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are kids at high risk for X or Y. And that early
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detection then also gives you a unique ability
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to do early intervention, right? So now you know
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baby hasn't even shown up. day one, and you already
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know that there's a path, a potential path here
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for intervention, how important is that window
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then? If it were the case, let me go back into
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it, if it were the case that we could do that
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for every one of the neurodevelopmental disorders,
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that somehow, you know, nice and handily, it
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all showed up on an ultrasound. Would we really
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be able to change the path for these children
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in those first two years before they actually
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would pick up a formal diagnosis, let's say,
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of autism? We hope so. And there have been some
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really solid studies of early intervention in
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babies who are considered at higher likelihood
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for autism because they have older siblings,
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for instance. And there is good evidence to show
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that early intervention is effective in improving
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developmental outcomes. So we had not asked this
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question in TS. yet. And so first, we designed
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a study that basically followed infants with
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TSC in the first two years of life just to ask
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whether there were early signs that we could
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actually measure and identify. And we did find,
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in fact, that by 12 months, we could... quite
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reliably identify behavioral differences as well
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as brain -based differences in the EEG. And so
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that then led to the first behavioral intervention
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clinical trial, where we asked the question that
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you're asking me, you know, can early intervention
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actually help? Yeah. Tell our listeners what
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EEG is, right? This is putting simple electrodes
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at the scalp. Sure. So EEG stands for electroencephalography.
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It's a pretty simple yet... I think, beautiful
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tool to really study, as I think about it, kind
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of neural synchrony or neural networks in real
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time. So we, you know, place electrodes on the
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scalp. I mean, you're, I'm telling you, you know,
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you're the expert in this area, but, and we actually
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have. modeled in my lab much of our work based
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on many of your amazing studies over time. So
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with EEG, we're placing electrodes on the scalp.
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And it turns out that when neurons fire, they
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generate a tiny electrical signal. And when thousands
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of neurons fire in synchrony, the signal's big
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enough that you can pick it up on the scalp with
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a fancy amplifier and actually measure it. And
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so the EEG is really a tool to... detect in real
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time the firing of neurons. And, you know, clinically
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in child neurology, we use EEG quite a bit to
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detect seizures or to monitor for effectiveness
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of seizure, anti -seizure medications. But it
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turns out it's also a very powerful neuroscience,
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sort of cognitive neuroscience tool, because
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it tells us what the brain is doing in real time.
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I always analogize it to kind of, you know, it's
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a video recording of the brain versus, you know,
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MRI, which is kind of a... detailed picture of
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the brain. Yeah, static in time. Static, exactly.
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And the brain's an electrical device and you're
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basically measuring the primary signal. Do you
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remember the first time you saw EEG? Somebody
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told you you could actually measure the brain
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at work with an electrode? Oh my goodness. It
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was actually in, I want to say it was in medical
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school during my neurology rotation. I, of course,
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then did reading about EEG and kind of understanding
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its history. And I mean, the history of EEG is
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amazing. It's a great story with Hans Berger,
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who actually believed you could use EEG as a
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form of almost telepathy mind reading. But yeah,
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I mean, it hasn't changed that much since its
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original use. It's very basic. We actually moved
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from... incredibly fancy tools with high density,
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expensive systems back to, in some cases in my
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lab, the headbands and other devices where we
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just need a couple of channels because we're
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looking for signals that are quite simple to
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detect and we know where to look. It's amazing.
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I have the same remembrance. It was a guy from
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British Columbia, David Spells, who showed me
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the first time EEG and just got, you're absolutely
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kidding me. We can actually watch the brain working
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in real time, doing what it does. And that started
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a long love affair with EEG. A long journey,
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yes. A key component here is with a lot of these
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neurodevelopmental disorders, and you mentioned
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this heterogeneity. Folks really have different
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trajectories. Some folks get bad. Some folks
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are in the middle. It's Goldilocks kind of stuff.
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And it presents a huge challenge then when you're
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trying to do something to intervene because it's
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very hard to know whether you're having an impact
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when you're looking at those symptoms. So now
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we're talking about EEG again, and these other
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objective brain measures provide potential ways
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to... to actually track changes, be it a pharmacological
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intervention or a training or so on. Talk to
00:13:21.200 --> 00:13:23.159
us about why that's important, like the idea
00:13:23.159 --> 00:13:25.600
of these biomarkers or neuromarkers and how that
00:13:25.600 --> 00:13:28.419
might actually impact our ability to do trials
00:13:28.419 --> 00:13:30.759
more effectively. Absolutely. It's a great question.
00:13:30.840 --> 00:13:33.730
And, you know, there's been... in the field of
00:13:33.730 --> 00:13:35.889
medicine, a tremendous amount of interest in
00:13:35.889 --> 00:13:39.190
biomarkers because they're considered more objective,
00:13:39.490 --> 00:13:42.870
easier to measure. And biomarkers work very well
00:13:42.870 --> 00:13:45.669
when you have conditions where it's obvious what
00:13:45.669 --> 00:13:47.809
you need to measure. Like in diabetes, we measure
00:13:47.809 --> 00:13:51.330
blood sugar. We measure hemoglobin A1C. In obesity,
00:13:51.490 --> 00:13:53.649
we measure weight, right? Those are really clear
00:13:53.649 --> 00:13:56.789
biomarkers. But in neurodevelopmental disorders,
00:13:57.090 --> 00:13:58.789
what are we really going to measure? It's the
00:13:58.789 --> 00:14:01.809
brain, which... Sure, we have tools like EEG,
00:14:02.029 --> 00:14:05.389
but they're complicated. And again, to your point,
00:14:05.509 --> 00:14:08.590
there's so much heterogeneity in neurodevelopmental
00:14:08.590 --> 00:14:11.250
disorders, not just in presentation, but in etiology.
00:14:11.629 --> 00:14:15.610
So TSC is one cause, but there are thousands
00:14:15.610 --> 00:14:18.129
of other genetic causes. And those genetic causes
00:14:18.129 --> 00:14:21.070
only account for about 10 % to 15 % of the whole
00:14:21.070 --> 00:14:23.409
neurodevelopmental spectrum. And so there's been
00:14:23.409 --> 00:14:25.889
interest in the field more broadly to think about,
00:14:25.929 --> 00:14:29.330
are there brain -based biomarkers that we can...
00:14:29.870 --> 00:14:32.610
measure, and EEG is a great tool to do this,
00:14:32.690 --> 00:14:35.450
where we might be able to identify maybe subgroups
00:14:35.450 --> 00:14:37.970
within the spectrum of neurodevelopmental disorders
00:14:37.970 --> 00:14:41.590
that have a certain common brain pattern or a
00:14:41.590 --> 00:14:44.850
brain feature, which then might be amenable to
00:14:44.850 --> 00:14:47.870
a certain type of treatment. And we haven't arrived
00:14:47.870 --> 00:14:50.590
there yet, but I think that's a really promising
00:14:50.590 --> 00:14:54.070
area of study. So this hundred -year -old technique
00:14:54.070 --> 00:14:56.470
that everyone uses is really potentially the
00:14:56.470 --> 00:14:59.029
key to unlock this. And the reason it's a promising
00:14:59.029 --> 00:15:01.690
technique is not simply that it's the readout
00:15:01.690 --> 00:15:03.730
of a process in the brain that we know is affected
00:15:03.730 --> 00:15:06.490
in neurodevelopmental disorders, but it's also,
00:15:06.570 --> 00:15:09.190
practically speaking, a much more... accessible
00:15:09.190 --> 00:15:14.730
and scalable tool than, you know, MRI or other
00:15:14.730 --> 00:15:17.250
types of testing. And you can take it to the
00:15:17.250 --> 00:15:19.049
rainforest. You can take it out in the field.
00:15:19.129 --> 00:15:22.769
You can't port a 20 -ton MRI machine. Exactly.
00:15:22.769 --> 00:15:24.350
That's very, very expensive. Well, and also the
00:15:24.350 --> 00:15:26.610
MRI, the child needs to be totally still. And,
00:15:26.629 --> 00:15:28.590
you know, there's a lot of noise. And with EEG,
00:15:28.649 --> 00:15:30.269
a child can be moving around and you can still
00:15:30.269 --> 00:15:33.850
get some signal. So that's one, you know, that
00:15:33.850 --> 00:15:37.309
is one goal. To your question about the clinical
00:15:37.309 --> 00:15:39.629
trials, you know, the other, I think, really
00:15:39.629 --> 00:15:43.169
promising use for biomarkers is potentially as
00:15:43.169 --> 00:15:45.690
a measure of what we call target engagement of
00:15:45.690 --> 00:15:48.830
a treatment. So if we, a typical clinical trial
00:15:48.830 --> 00:15:50.529
is designed where you select a group of patients,
00:15:50.769 --> 00:15:54.039
you you know, deploy an intervention or a drug.
00:15:54.220 --> 00:15:56.480
And, you know, in the space of neurodevelopmental
00:15:56.480 --> 00:15:58.639
disorders, we're thinking more about treatments
00:15:58.639 --> 00:16:01.419
that target the underlying cause, like, you know,
00:16:01.419 --> 00:16:04.600
the underlying genetic cause. And then we measure
00:16:04.600 --> 00:16:06.840
an outcome, but our outcomes are all developmental
00:16:06.840 --> 00:16:09.279
behavioral. And sometimes those take a very long
00:16:09.279 --> 00:16:13.059
time to really measure change, right? If we're
00:16:13.059 --> 00:16:16.629
trying to... you know, affect cognition or language
00:16:16.629 --> 00:16:19.009
or social behavior, that may not change in a
00:16:19.009 --> 00:16:21.250
month or two. And most trials are designed to
00:16:21.250 --> 00:16:23.330
run for three months, maybe, or six months. And
00:16:23.330 --> 00:16:26.169
so if we had biomarkers that could tell us that
00:16:26.169 --> 00:16:28.490
the drug has hit the target in the brain and
00:16:28.490 --> 00:16:30.789
that, hey, this drug is probably going to work,
00:16:30.809 --> 00:16:33.090
so let's give it a little more time, it actually
00:16:33.090 --> 00:16:36.230
opens the door for us to pursue the trial. And
00:16:36.230 --> 00:16:38.250
so there's been some work. We've done work in
00:16:38.250 --> 00:16:43.460
another genetic condition where we have an EEG
00:16:43.460 --> 00:16:45.740
biomarker that we think might really be a measure
00:16:45.740 --> 00:16:48.539
of drug target engagement for certain types of
00:16:48.539 --> 00:16:50.340
treatments. And so that's another area where
00:16:50.340 --> 00:16:52.840
biomarkers, EEG biomarkers could be very useful.
00:16:52.940 --> 00:16:55.360
Very good. Yeah, yeah. I think that's, you know,
00:16:55.360 --> 00:16:57.940
it's a key thing. You know, you've got heart
00:16:57.940 --> 00:17:00.440
disease, we can... take a little bit of blood
00:17:00.440 --> 00:17:02.279
and measure your cholesterol and these sort of
00:17:02.279 --> 00:17:05.740
things. We are not in a position to stick needles
00:17:05.740 --> 00:17:07.779
into your brain and withdraw some tissue. If
00:17:07.779 --> 00:17:09.160
we could, actually, there'd be a different, we
00:17:09.160 --> 00:17:10.740
might be talking about having a different conversation.
00:17:12.059 --> 00:17:17.619
So yesterday you gave a superb talk here. Oh,
00:17:17.619 --> 00:17:19.359
thank you. Absolutely wonderful talk. And I was
00:17:19.359 --> 00:17:21.559
struck by, well, struck by a lot of the things
00:17:21.559 --> 00:17:23.819
you said, but one was the business of, we're
00:17:23.819 --> 00:17:25.900
talking about these monogenic diseases right
00:17:25.900 --> 00:17:28.529
now, where does a... a mutation in a very specific
00:17:28.529 --> 00:17:31.150
gene that leads to something like tuberous sclerosis
00:17:31.150 --> 00:17:34.490
or a lysosomal storage disorder. And a lot of
00:17:34.490 --> 00:17:36.730
those youngsters will present with an autism
00:17:36.730 --> 00:17:39.609
-like phenotype or a psychosis -type phenotype.
00:17:39.750 --> 00:17:43.289
There's a pathway then that they follow. But
00:17:43.289 --> 00:17:46.470
then there's this other huge group of folks in
00:17:46.470 --> 00:17:49.009
the neurodevelopmental disorders, what we would
00:17:49.009 --> 00:17:52.150
call idiopathic autism, where we don't know that
00:17:52.150 --> 00:17:55.390
single gene cause. And that presents a completely
00:17:55.390 --> 00:17:57.170
different challenge, right? Because they're not
00:17:57.170 --> 00:17:58.869
all the same. We can't say all of these kids
00:17:58.869 --> 00:18:01.250
have the same mutation. They're all going to
00:18:01.250 --> 00:18:03.789
kind of head in a generally similar clinical
00:18:03.789 --> 00:18:07.049
path. These kids are even more all over the place
00:18:07.049 --> 00:18:10.069
in a clinical presentation. I'm asking you, this
00:18:10.069 --> 00:18:11.910
is a terribly big question. How do we tackle
00:18:11.910 --> 00:18:13.990
that from a biomarker perspective? How do we
00:18:13.990 --> 00:18:16.349
even think about it? Great question. Well, I
00:18:16.349 --> 00:18:19.450
do think that we can learn quite a bit from the
00:18:19.450 --> 00:18:21.869
monogenic conditions from the standpoint of...
00:18:22.490 --> 00:18:24.089
you know, what we talk about as trial readiness,
00:18:24.269 --> 00:18:28.029
you know, the sort of efforts we make in engaging
00:18:28.029 --> 00:18:31.430
families in trials, again, collecting the best
00:18:31.430 --> 00:18:34.509
possible biomarker data, and we can just stick
00:18:34.509 --> 00:18:38.930
to EEG as our tool, and, you know, ensuring that
00:18:38.930 --> 00:18:41.109
families stay engaged in trials. I mean, those
00:18:41.109 --> 00:18:44.269
sorts of principles can apply across the spectrum.
00:18:44.829 --> 00:18:49.190
But to your point about how do you really parse
00:18:49.190 --> 00:18:53.309
that heterogeneity of the non -monogenic neurodevelopmental
00:18:53.309 --> 00:18:55.809
disorders, I actually think this is where biomarkers
00:18:55.809 --> 00:18:58.250
could actually be helpful. Right. Because we're
00:18:58.250 --> 00:19:01.569
not going to have a genetic biomarker that's
00:19:01.569 --> 00:19:03.150
going to parse that heterogeneity. You know,
00:19:03.150 --> 00:19:06.039
when we think about... the 80 % of individuals
00:19:06.039 --> 00:19:07.980
with neurodevelopmental disorders that don't
00:19:07.980 --> 00:19:10.180
have a monogenic cause. And maybe that number
00:19:10.180 --> 00:19:12.960
will reduce to 70 % as our technology gets better.
00:19:13.059 --> 00:19:15.440
But let's just say we're always, right? Say we'll
00:19:15.440 --> 00:19:17.779
always be somewhere on 50%, at least, right?
00:19:17.799 --> 00:19:20.920
And the geneticists might debate me on that.
00:19:21.000 --> 00:19:24.960
But say we're about 50%, you know, those individuals,
00:19:25.140 --> 00:19:28.539
likely the cause is what we think of as polygenic,
00:19:28.539 --> 00:19:30.700
right? So it's a bunch of common variants that
00:19:30.700 --> 00:19:34.140
exist in the population. It's sort of a... of
00:19:34.140 --> 00:19:36.480
those variants inherited from mom and dad together
00:19:36.480 --> 00:19:39.940
might be causing the autism. That's not something
00:19:39.940 --> 00:19:42.680
we're going to robustly measure. And it certainly
00:19:42.680 --> 00:19:46.539
is not going to lead to specific common phenotypes,
00:19:46.700 --> 00:19:48.799
if you will. And so that's where, though, we
00:19:48.799 --> 00:19:52.039
can ask, well, is there a marker downstream of
00:19:52.039 --> 00:19:55.200
the... multiple genes that can help us create
00:19:55.200 --> 00:19:58.359
these more homogeneous subtypes. And I would
00:19:58.359 --> 00:20:00.359
argue that's where, you know, EEG might be helpful.
00:20:00.559 --> 00:20:03.160
We might be able to stratify, as some people
00:20:03.160 --> 00:20:06.880
talk about, or subgroup children based on their
00:20:06.880 --> 00:20:10.539
brain metrics. And that that might give us some
00:20:10.539 --> 00:20:12.819
insight into common subgroups that then may be
00:20:12.819 --> 00:20:14.400
amenable to certain types of treatments. And
00:20:14.400 --> 00:20:16.720
targets to shoot at them. Exactly. And then,
00:20:16.779 --> 00:20:18.819
you know, I would say that that will take time.
00:20:18.920 --> 00:20:21.259
And in the meantime, what will practically happen
00:20:21.259 --> 00:20:24.240
is there will be clinical trials and treatments
00:20:24.240 --> 00:20:27.900
for the entire spectrum. And children or adults
00:20:27.900 --> 00:20:29.940
will be selected based on some common features
00:20:29.940 --> 00:20:32.920
like IQ or maybe some co -occurring symptoms
00:20:32.920 --> 00:20:34.700
that are the target of the treatment. Repetitive
00:20:34.700 --> 00:20:36.859
behaviors or something. Repetitive behaviors,
00:20:37.099 --> 00:20:40.299
language, anxiety. And so we'll select based
00:20:40.299 --> 00:20:43.720
on clinical features, which is fine. And then
00:20:43.720 --> 00:20:46.519
in that setting, I think still having a biomarker
00:20:46.519 --> 00:20:49.059
that might tell us, well, of those kids, are
00:20:49.059 --> 00:20:51.079
there ones that might be more responsive to that
00:20:51.079 --> 00:20:53.269
treatment? And maybe we learn that after the
00:20:53.269 --> 00:20:56.089
fact. We collect the biomarker data. We collect
00:20:56.089 --> 00:20:59.450
resting state electrophysiological data, maybe
00:20:59.450 --> 00:21:01.789
some low -level, you know, sensory processing,
00:21:01.950 --> 00:21:04.309
other types of domains we might want to test.
00:21:04.589 --> 00:21:06.670
And at the end, after the trial is done, we look
00:21:06.670 --> 00:21:08.250
back and say, hmm, it was interesting. You know,
00:21:08.269 --> 00:21:11.490
the third of the kids who responded actually
00:21:11.490 --> 00:21:14.069
had some features on their EEG. And that work
00:21:14.069 --> 00:21:16.289
is actually already being done. So there's, you
00:21:16.289 --> 00:21:18.289
know, a large R -baclofen trial that's underway
00:21:18.289 --> 00:21:26.420
in the U .S. and in Canada. you know after the
00:21:26.420 --> 00:21:28.779
fact right see if there were some things that
00:21:28.779 --> 00:21:31.500
would have predicted exactly because they're
00:21:31.500 --> 00:21:35.480
not finding holistic you know slam dunk results
00:21:35.480 --> 00:21:37.859
where the entire group is showing benefit which
00:21:37.859 --> 00:21:40.400
again is not surprising yeah but now you can
00:21:40.400 --> 00:21:42.259
go back and sort of ask the question are there
00:21:42.259 --> 00:21:44.599
subgroups that do respond Right, right, right.
00:21:44.779 --> 00:21:46.920
Once you know that, you actually have, you have,
00:21:47.019 --> 00:21:49.400
again, a pathway. We can begin to understand
00:21:49.400 --> 00:21:52.339
the molecular biology. We can make mass models.
00:21:52.420 --> 00:21:56.839
There's a whole pathway of investigation. How
00:21:56.839 --> 00:22:00.559
many of the folks that currently live, and I
00:22:00.559 --> 00:22:02.980
think you alluded to this, in the idiopathic
00:22:02.980 --> 00:22:05.480
world, i .e. we don't know the cause of mutation,
00:22:05.859 --> 00:22:09.000
would we be able to put into new boxes, if we
00:22:09.000 --> 00:22:13.400
could deploy universal... Deep genotyping. And
00:22:13.400 --> 00:22:17.039
maybe transcriptomics and everything. So just
00:22:17.039 --> 00:22:20.440
to frame that, right? Obviously, 10 years ago,
00:22:20.480 --> 00:22:22.160
we couldn't have this discussion because that
00:22:22.160 --> 00:22:27.019
would be a $20 ,000 proposition for every kid.
00:22:27.220 --> 00:22:30.019
But now we're into the couple of hundred dollars
00:22:30.019 --> 00:22:33.240
for an exome, right? Or maybe less. I forget
00:22:33.240 --> 00:22:35.200
the numbers. They go down all the time. But our
00:22:35.200 --> 00:22:37.440
technology has advanced to the point. Should
00:22:37.440 --> 00:22:41.640
we be doing deep... genotyping on every kid born
00:22:41.640 --> 00:22:44.920
in the country so that we can immediately identify
00:22:44.920 --> 00:22:48.319
if they're part of this group of monogenic disorders
00:22:48.319 --> 00:22:51.019
and to further define more monogenic disorders.
00:22:51.160 --> 00:22:55.559
Right. So the answer is yes. And I think that...
00:22:55.819 --> 00:22:57.880
you know, this issue of access to testing is
00:22:57.880 --> 00:23:00.680
a challenging one. And I think that as the cost
00:23:00.680 --> 00:23:02.720
comes down to your point, which it has, I mean,
00:23:02.720 --> 00:23:05.440
in some institutions, a whole genome costs less
00:23:05.440 --> 00:23:08.119
than $1 ,000, which, you know, less than five
00:23:08.119 --> 00:23:11.599
years ago, that was in the 10 ,000 sort of range,
00:23:11.779 --> 00:23:13.960
right? So we are absolutely, we're already finding
00:23:13.960 --> 00:23:15.660
this, you know, I mean, it used to be that the
00:23:15.660 --> 00:23:17.619
recommended genetic testing was just chromosomal
00:23:17.619 --> 00:23:19.940
microarray, because that was what was affordable
00:23:19.940 --> 00:23:22.740
and accessible. And that's detecting, you know,
00:23:22.740 --> 00:23:26.599
copy number variants that contain you know, hundreds
00:23:26.599 --> 00:23:29.059
of genes. And so it's not detecting single gene
00:23:29.059 --> 00:23:31.240
disorders. So we had a lot of normal microarrays
00:23:31.240 --> 00:23:33.500
in children who definitely had monogenic conditions.
00:23:34.079 --> 00:23:36.900
Then you move to exome, that's improving the,
00:23:37.039 --> 00:23:39.539
you know, if you will, accuracy of detection
00:23:39.539 --> 00:23:42.740
and now whole genome and beyond, to your point.
00:23:42.819 --> 00:23:44.619
I mean, this is like, just to parse this, this
00:23:44.619 --> 00:23:46.799
is like trying to look at the features of the
00:23:46.799 --> 00:23:49.319
moon with a little pair of toy binoculars as
00:23:49.319 --> 00:23:50.920
opposed to with the Hubble telescope. Exactly.
00:23:50.920 --> 00:23:52.819
And that's actually the analogy I give to parents.
00:23:52.880 --> 00:23:54.839
You know, we start, and we usually still clinically...
00:23:54.920 --> 00:23:57.039
start with the microarray. I say that's kind
00:23:57.039 --> 00:23:58.619
of like a magnifying glass, and then you move
00:23:58.619 --> 00:24:00.839
to the microscope. But to answer your question,
00:24:01.099 --> 00:24:03.420
I think that based on actually the literature
00:24:03.420 --> 00:24:05.339
that already exists, I would say that at least
00:24:05.339 --> 00:24:09.240
10 % to 20 % of children with negative testing
00:24:09.240 --> 00:24:12.700
will ultimately be found to have some causative
00:24:12.700 --> 00:24:17.069
genetic variant. Incredibly meaningful from a
00:24:17.069 --> 00:24:19.569
clinical perspective, from a parent's perspective.
00:24:19.690 --> 00:24:22.250
It plugs them in potentially to a network of
00:24:22.250 --> 00:24:24.609
other parents who are dealing with it. So these
00:24:24.609 --> 00:24:26.650
are rare diseases. That means, you know, the
00:24:26.650 --> 00:24:28.069
kid down the road is not going to have the same
00:24:28.069 --> 00:24:31.029
disease by the happenstance. But there's a network
00:24:31.029 --> 00:24:33.559
of parents out there in the country. 60, 70,
00:24:33.720 --> 00:24:36.880
100 parents who know about this. And you get
00:24:36.880 --> 00:24:38.559
plugged into that community. And they've dealt
00:24:38.559 --> 00:24:40.220
with many of the things that you have. And they
00:24:40.220 --> 00:24:42.079
know what works and what doesn't work. Exactly.
00:24:42.079 --> 00:24:45.839
And that's the answer. I often have parents ask
00:24:45.839 --> 00:24:48.859
me, why do I need genetic testing? It's just
00:24:48.859 --> 00:24:50.799
academic. You just want to have our data. And
00:24:50.799 --> 00:24:52.599
there's some mistrust in the system, actually.
00:24:52.740 --> 00:24:55.180
And people are worried about the implications
00:24:55.180 --> 00:24:58.859
of a genetic diagnosis on health insurance, on
00:24:58.859 --> 00:25:02.720
stigma for their child over time. And the way
00:25:02.720 --> 00:25:06.240
I explain it to parents is that, you know, we
00:25:06.240 --> 00:25:09.339
may not yet have a treatment for the genetic
00:25:09.339 --> 00:25:13.319
diagnosis that we make if we make one. I may
00:25:13.319 --> 00:25:15.779
not even be able to give you a really accurate
00:25:15.779 --> 00:25:18.039
clinical prognosis yet because you may have –
00:25:18.039 --> 00:25:20.819
your child may have a mutation or a variant that
00:25:20.819 --> 00:25:24.759
– We don't know a whole lot about. But what it
00:25:24.759 --> 00:25:30.519
will do is – you know, is narrow your community
00:25:30.519 --> 00:25:33.940
and focus it, you know, with a group of families
00:25:33.940 --> 00:25:37.359
that have children with your child's exact diagnosis.
00:25:37.640 --> 00:25:40.680
And that is incredibly powerful because if you
00:25:40.680 --> 00:25:44.319
Google autism and autism patient advocacy groups
00:25:44.319 --> 00:25:46.839
or autism parent communities or go on social
00:25:46.839 --> 00:25:49.819
media and, you know, explore the same. the same
00:25:49.819 --> 00:25:51.680
type of community, I mean, it is overwhelming.
00:25:52.059 --> 00:25:54.680
And unfortunately, most of it is not relevant
00:25:54.680 --> 00:25:57.980
to these families. And so I think that those
00:25:57.980 --> 00:26:00.339
patient advocacy groups are very powerful. And
00:26:00.339 --> 00:26:02.539
I will say that not only do they provide support,
00:26:02.660 --> 00:26:08.200
but you bring together a powerful group of parents
00:26:08.200 --> 00:26:10.220
who are incredibly motivated to help their children.
00:26:10.619 --> 00:26:15.460
They will go to all ends of the earth. to find
00:26:15.460 --> 00:26:18.339
answers. They will find the scientists who are
00:26:18.339 --> 00:26:21.460
studying mechanisms of disease that are relevant
00:26:21.460 --> 00:26:25.180
to the genetic mutation. They'll find a clinic
00:26:25.180 --> 00:26:27.460
to start seeing the kids to develop a registry
00:26:27.460 --> 00:26:29.960
and a natural history study. I mean, I've watched
00:26:29.960 --> 00:26:33.400
it firsthand. And in fact, there's stories. Angelman
00:26:33.400 --> 00:26:36.220
syndrome is a great example where one parent,
00:26:36.319 --> 00:26:39.460
one motivated parent, essentially spearheaded
00:26:39.460 --> 00:26:43.059
the development of a biotech company that is
00:26:43.059 --> 00:26:46.240
now in phase three of an ASO trial for Angelman,
00:26:46.279 --> 00:26:48.720
ASO being antisense, oligonucleotide. I mean,
00:26:48.740 --> 00:26:52.039
she... found families and fundraised and started
00:26:52.039 --> 00:26:53.880
a foundation called FAST, you know, Foundation
00:26:53.880 --> 00:26:56.279
for Angelman Syndrome Therapeutics. They then
00:26:56.279 --> 00:26:59.359
funded Ultragenyx, which is a biotech that then
00:26:59.359 --> 00:27:01.400
partnered with a, you know, neuroscientist who
00:27:01.400 --> 00:27:03.700
was developing it. So, I mean, it's just, it's
00:27:03.700 --> 00:27:06.279
extraordinary. Oh, there's nothing more powerful
00:27:06.279 --> 00:27:08.859
than a motivated parent with concern for their
00:27:08.859 --> 00:27:11.500
son or their daughter. And that's where, yeah.
00:27:11.700 --> 00:27:14.160
You gave some beautiful examples yesterday. I
00:27:14.160 --> 00:27:17.650
know that... from my own perspective, working
00:27:17.650 --> 00:27:20.369
on Batten's disease and cystinosis and Rett syndrome.
00:27:21.119 --> 00:27:22.720
being with the parents at one of those national
00:27:22.720 --> 00:27:25.859
meetings is the most profound motivator. You
00:27:25.859 --> 00:27:28.759
go back. Absolutely. Work is just a different,
00:27:28.900 --> 00:27:30.839
it has a different flavor. And it's almost like
00:27:30.839 --> 00:27:33.319
a shot to the arm, a shot of adrenaline. And
00:27:33.319 --> 00:27:35.859
they're so grateful to meet you. Oh, it's so
00:27:35.859 --> 00:27:38.900
uplifting. You could be choosing to study anything,
00:27:39.000 --> 00:27:40.779
right? And you choose to study their kids. And
00:27:40.779 --> 00:27:42.759
that's incredibly powerful for them too. Well,
00:27:42.799 --> 00:27:45.000
you mentioned this yesterday too, because of
00:27:45.000 --> 00:27:47.259
course there are many people in our field who
00:27:47.259 --> 00:27:50.039
are working in the molecular stew or the soup.
00:27:50.730 --> 00:27:53.170
or working in a mouse model, and I've never seen
00:27:53.170 --> 00:27:56.769
a patient, and how key it is. So important. All
00:27:56.769 --> 00:27:59.309
right, talk to us about that. Yeah, I mean, I
00:27:59.309 --> 00:28:01.710
can tell a story about it, and actually I'm not
00:28:01.710 --> 00:28:04.329
now the only one who is convinced of this, and
00:28:04.329 --> 00:28:08.779
the Simons Foundation now actually funds... graduate
00:28:08.779 --> 00:28:10.460
students and postdocs who are working in the
00:28:10.460 --> 00:28:12.900
basic science realm to go attend family meetings.
00:28:13.440 --> 00:28:17.960
But I, in 2018, I was at a family meeting for
00:28:17.960 --> 00:28:20.599
another rare disorder that I studied called Duke
00:28:20.599 --> 00:28:23.660
15Q syndrome. Again, another syndromic form of
00:28:23.660 --> 00:28:26.859
autism, intellectual disability, epilepsy. And
00:28:26.859 --> 00:28:29.859
they, like many of these family meetings, they
00:28:29.859 --> 00:28:33.039
often will couple the science meeting with the
00:28:33.039 --> 00:28:35.740
family meeting. So the scientists, you know,
00:28:35.740 --> 00:28:37.960
converge the day before the family meeting. family
00:28:37.960 --> 00:28:40.420
meeting to talk about science. And then the family
00:28:40.420 --> 00:28:43.339
meeting starts usually the next day. And I was
00:28:43.339 --> 00:28:45.660
at the science meeting giving a talk. I was supposed
00:28:45.660 --> 00:28:47.339
to give a talk on the clinical manifestations
00:28:47.339 --> 00:28:50.180
of this syndrome. And I had all sorts of videos
00:28:50.180 --> 00:28:56.480
and examples and data to show on our natural
00:28:56.480 --> 00:28:59.339
history study that we had just started. And the
00:28:59.339 --> 00:29:02.400
audience was mostly basic scientists, mostly
00:29:02.400 --> 00:29:05.839
people studying the gene of interest, UB3A. And
00:29:05.839 --> 00:29:08.599
many young people, you know, students, graduate
00:29:08.599 --> 00:29:11.140
students, postdocs were just entering the field.
00:29:12.009 --> 00:29:14.710
And I'm about to give this talk and I was actually
00:29:14.710 --> 00:29:17.170
informed that the family meeting had started
00:29:17.170 --> 00:29:19.470
and the launch of the family meeting involves
00:29:19.470 --> 00:29:22.089
a family parade. Right. Where they literally
00:29:22.089 --> 00:29:24.930
have a parade where each child or adult with
00:29:24.930 --> 00:29:28.710
the syndrome is either wheeled or walks down,
00:29:28.809 --> 00:29:33.670
you know, a runway, if you will, in a ballroom.
00:29:34.440 --> 00:29:36.480
And they celebrate the child. They have a big
00:29:36.480 --> 00:29:38.599
slide up with the child's name and their age
00:29:38.599 --> 00:29:40.960
and their diagnosis. And it's a really beautiful
00:29:40.960 --> 00:29:44.680
just celebration of everyone who is there. And
00:29:44.680 --> 00:29:46.740
so I'm about to give this talk. And I'm thinking,
00:29:46.839 --> 00:29:48.839
all these kids are downstairs. Like, why would
00:29:48.839 --> 00:29:49.700
I give a lecture about this? I'm going to show
00:29:49.700 --> 00:29:52.700
videos. Exactly. So I said, you know what? We're
00:29:52.700 --> 00:29:54.099
going to take a little field trip. And I brought
00:29:54.099 --> 00:29:56.440
the entire meeting downstairs. And we watched
00:29:56.440 --> 00:29:59.619
the parade instead. And it was incredibly powerful.
00:29:59.900 --> 00:30:03.980
I mean, there were so many tears of just... I
00:30:03.980 --> 00:30:07.539
think a mix of gratitude that they were able
00:30:07.539 --> 00:30:10.440
to meet these families, but I think also tears
00:30:10.440 --> 00:30:14.940
of true concern about how clinically affected
00:30:14.940 --> 00:30:20.299
some of these children and adults are. So I,
00:30:20.380 --> 00:30:22.180
after the parade ended, you know, I introduced
00:30:22.180 --> 00:30:24.220
some of the families to these scientists and
00:30:24.220 --> 00:30:25.759
they were so grateful. And, you know, parents
00:30:25.759 --> 00:30:27.940
were saying things like, you know, please find
00:30:27.940 --> 00:30:30.140
a treatment for us or thank you so much for doing
00:30:30.140 --> 00:30:33.039
this work. Like, keep at it. And then we, you
00:30:33.039 --> 00:30:39.250
know, we... returned back to our conference room
00:30:39.250 --> 00:30:41.930
to finish the meeting. And I really believe that
00:30:41.930 --> 00:30:44.450
the meeting was infused with, as you said, a
00:30:44.450 --> 00:30:46.269
different energy, a different sense of purpose.
00:30:46.470 --> 00:30:48.670
And I will say very even specific scientific
00:30:48.670 --> 00:30:52.369
ideas were generated from the observations. So
00:30:52.369 --> 00:30:57.089
one of the postdocs was actually studying gait
00:30:57.089 --> 00:31:00.930
in a preclinical model of a mouse model of the
00:31:00.930 --> 00:31:03.549
condition because the motor system is very affected.
00:31:04.269 --> 00:31:06.269
After he had watched a bunch of these children
00:31:06.269 --> 00:31:10.269
walk, he had a new idea for how to measure gait
00:31:10.269 --> 00:31:12.390
in these mice. And actually that paper, he actually
00:31:12.390 --> 00:31:14.849
did that work and published a paper a couple
00:31:14.849 --> 00:31:17.789
of years later quantifying gait in these mice.
00:31:18.960 --> 00:31:21.539
There's so much value, you know, I think on both
00:31:21.539 --> 00:31:24.019
sides. But it was a really powerful experience
00:31:24.019 --> 00:31:27.700
for me. And now, again, I think many of these
00:31:27.700 --> 00:31:29.980
patient advocacy groups are encouraging and inviting,
00:31:30.079 --> 00:31:34.599
you know, the basic researchers to come and spend
00:31:34.599 --> 00:31:37.059
time with them. It's such a beautiful story.
00:31:37.180 --> 00:31:40.059
And you told it yesterday. And, you know, one
00:31:40.059 --> 00:31:42.869
of the things is that this. Issue of connecting
00:31:42.869 --> 00:31:45.869
to patients oozes right out of you. I mean, this
00:31:45.869 --> 00:31:49.109
is a big component of your motivation. I think
00:31:49.109 --> 00:31:52.390
you transmit that to people very, very effectively.
00:31:52.430 --> 00:31:56.490
Oh, thank you. So I'll use that as a pivot point
00:31:56.490 --> 00:31:59.490
because where does the passion for science...
00:31:59.490 --> 00:32:02.269
Who was Shefali just when she was a youngster?
00:32:02.309 --> 00:32:05.730
When did this science... Oh, it's a great question.
00:32:05.890 --> 00:32:08.029
Well, we're about to get to Halloween. And I
00:32:08.029 --> 00:32:10.349
will say the best way to answer that question
00:32:10.349 --> 00:32:13.410
is that I think I was five my first Halloween
00:32:13.410 --> 00:32:17.869
where I dressed up as a doctor. So I, yes. I
00:32:17.869 --> 00:32:19.849
don't know that I knew I wanted to be a scientist,
00:32:19.890 --> 00:32:22.769
but I always was curious and always wanted to
00:32:22.769 --> 00:32:25.369
find ways to answer questions. And my mind was
00:32:25.369 --> 00:32:27.349
always full of questions. And I actually studied
00:32:27.349 --> 00:32:31.609
philosophy in college. to be able to find more
00:32:31.609 --> 00:32:35.450
effective ways to understand how other people,
00:32:35.490 --> 00:32:37.970
you know, tackled hard questions of the world
00:32:37.970 --> 00:32:40.759
and answered them. You know, I think that just
00:32:40.759 --> 00:32:42.880
taking, going way back, I mean, my parents are
00:32:42.880 --> 00:32:45.720
first generation immigrants from India. They,
00:32:45.759 --> 00:32:48.119
you know, literally came with the American dream
00:32:48.119 --> 00:32:51.240
in mind. Both are physicians. So they came right
00:32:51.240 --> 00:32:53.779
after medical school. Both are psychiatrists.
00:32:53.819 --> 00:32:56.380
My father's a geriatric psychiatrist who is officially
00:32:56.380 --> 00:33:01.160
retired but will never retire. He is a true academic,
00:33:01.359 --> 00:33:05.500
a true thinker, and now studies healthy aging
00:33:05.500 --> 00:33:09.359
and wisdom. And and was really, I think, an inspiration
00:33:09.359 --> 00:33:12.579
from the standpoint of just like loving medicine
00:33:12.579 --> 00:33:14.480
and science, you know, from an early age. And
00:33:14.480 --> 00:33:16.440
my mother is a child psychiatrist and purely
00:33:16.440 --> 00:33:18.819
clinical, worked at Kaiser her whole life. And
00:33:18.819 --> 00:33:21.740
so I actually grew up sort of. um, observing
00:33:21.740 --> 00:33:23.940
this, like, I'd say sometimes interplay, sometimes
00:33:23.940 --> 00:33:26.240
conflict between like a pure clinician and a
00:33:26.240 --> 00:33:29.000
pure researcher around like whose work was more
00:33:29.000 --> 00:33:31.940
valuable. So you're literally the perfect commission.
00:33:32.380 --> 00:33:35.539
Exactly. So I, yes. And I think that's actually
00:33:35.539 --> 00:33:37.420
too personality wise, probably a little bit too,
00:33:37.500 --> 00:33:40.480
but yes, I, I sort of, I did. I grew up, uh,
00:33:40.640 --> 00:33:43.180
I grew up, you know, appreciating the value of
00:33:43.180 --> 00:33:46.279
both. And so when I went to college and studied
00:33:46.279 --> 00:33:48.779
philosophy, I was, I, I just always had these
00:33:48.779 --> 00:33:52.230
like you on it. burning questions around how
00:33:52.230 --> 00:33:55.049
does the mind develop? How do these behaviors
00:33:55.049 --> 00:33:59.529
that we see in human beings emerge? Why do they
00:33:59.529 --> 00:34:02.269
go awry sometimes? Or why are people different?
00:34:02.490 --> 00:34:07.069
And really simple questions. And so as I sort
00:34:07.069 --> 00:34:09.909
of studied these questions in college, I realized
00:34:09.909 --> 00:34:14.699
that there was a way to... sort of pursue my
00:34:14.699 --> 00:34:17.460
love for medicine and ultimately really helping
00:34:17.460 --> 00:34:20.980
people and answering those kind of burning questions.
00:34:21.199 --> 00:34:23.239
And so when I went to medical school, I knew
00:34:23.239 --> 00:34:25.820
I wanted to study child development. And there's
00:34:25.820 --> 00:34:28.039
different paths you can take, developmental behavioral
00:34:28.039 --> 00:34:31.719
pediatrics, general pediatrics, psychiatry. And
00:34:31.719 --> 00:34:33.260
I fell in love with neurology really because
00:34:33.260 --> 00:34:35.119
I fell in love with the brain. I knew that that
00:34:35.119 --> 00:34:37.809
was... And I'm telling, you know, I'm preaching
00:34:37.809 --> 00:34:40.289
to the choir here. But, you know, the brain is
00:34:40.289 --> 00:34:43.210
where all of the answers lie, right? It's a really
00:34:43.210 --> 00:34:47.110
tough organ to measure and to study. But there's
00:34:47.110 --> 00:34:49.269
some fun challenges there. And so that's why
00:34:49.269 --> 00:34:51.230
I chose neurology and child neurology. And I've
00:34:51.230 --> 00:34:54.289
never, never questioned it since. I love it.
00:34:54.309 --> 00:34:56.530
I love being a child neurologist. Tell me, what
00:34:56.530 --> 00:34:58.630
do mom and dad think when they see you as the,
00:34:58.670 --> 00:35:01.789
you know, physician -in -chief? Oh, they're proud
00:35:01.789 --> 00:35:05.170
parents for sure. You know, my mother still calls
00:35:05.170 --> 00:35:09.329
me to ask me if I remember to eat breakfast and
00:35:09.329 --> 00:35:12.130
whether I got enough sleep and to not get stressed.
00:35:12.429 --> 00:35:14.309
That's usually her general. Pretty good, yeah.
00:35:14.550 --> 00:35:17.110
Yeah, my dad will call me about a new paper he's
00:35:17.110 --> 00:35:18.550
written because he's still churning out, I think,
00:35:18.550 --> 00:35:21.329
a paper a day. Oh, my God. And asking me for
00:35:21.329 --> 00:35:24.210
advice on it. I actually got to do a podcast
00:35:24.210 --> 00:35:27.610
with him where he wrote a book called Wiser about
00:35:27.610 --> 00:35:32.340
eight years ago. interview him about his book
00:35:32.340 --> 00:35:34.880
which was super fun and so that was kind of coming
00:35:34.880 --> 00:35:37.119
full circle um so you know in the end they're
00:35:37.119 --> 00:35:38.920
still my parents and they yeah there might be
00:35:38.920 --> 00:35:41.739
a little uptick now in purchases of wiser yeah
00:35:41.739 --> 00:35:45.539
i know maybe exactly good plug unintended so
00:35:45.539 --> 00:35:49.349
i know just We also share a different passion
00:35:49.349 --> 00:35:52.670
for running. Yes. You're a real runner, though.
00:35:52.730 --> 00:35:55.309
That's 100 years ago. You still do it. I'm a
00:35:55.309 --> 00:35:58.869
runner wannabe, but you actually ran competitively.
00:35:59.309 --> 00:36:03.449
I ride a bike now, but you still run a lot. Is
00:36:03.449 --> 00:36:06.690
that an important part of psychological health?
00:36:06.710 --> 00:36:11.289
Yes. Yeah, so I was not the same level of athlete
00:36:11.289 --> 00:36:13.030
as my children, but I was a tennis player growing
00:36:13.030 --> 00:36:15.670
up. And then when I got to college, I wasn't
00:36:15.670 --> 00:36:18.750
at the level to play Division I tennis. And I
00:36:18.750 --> 00:36:21.489
just missed – I love being active. I think it's
00:36:21.489 --> 00:36:24.159
really important. to take care of our bodies.
00:36:24.280 --> 00:36:26.119
But I also actually would say to your point,
00:36:26.239 --> 00:36:28.840
I mean, to me, any kind of exercise is a catharsis.
00:36:29.039 --> 00:36:30.920
It's a way for me to kind of quiet my mind for
00:36:30.920 --> 00:36:33.079
a little bit. And so I had a friend in college
00:36:33.079 --> 00:36:36.139
who was a runner and she invited me to start
00:36:36.139 --> 00:36:37.559
running with her. I initially thought it was
00:36:37.559 --> 00:36:39.900
a complete waste of time. And then once I started
00:36:39.900 --> 00:36:42.260
running and I was in New Haven for undergraduate
00:36:42.260 --> 00:36:44.579
and there are some, just like it is outside right
00:36:44.579 --> 00:36:46.820
now, just these beautiful, especially on the
00:36:46.820 --> 00:36:48.980
fall days. I mean, we would just explore the
00:36:48.980 --> 00:36:51.960
city on our feet. And so I fell in love with
00:36:51.960 --> 00:36:54.650
it. And then, of course, the competitor in me
00:36:54.650 --> 00:36:57.530
decided that if I'm going to run, I may as well
00:36:57.530 --> 00:37:00.289
start running some races. And that led to, you
00:37:00.289 --> 00:37:02.309
know, I think the sweet spot is the half marathon.
00:37:02.510 --> 00:37:04.349
I hope to keep doing those. But then, of course,
00:37:04.429 --> 00:37:07.329
I decided when I was in residency to try to run
00:37:07.329 --> 00:37:11.360
Boston, the Boston Marathon. So I ran that. When
00:37:11.360 --> 00:37:14.500
I moved to LA, I ran the LA Marathon in four
00:37:14.500 --> 00:37:16.840
hours of torrential downpour. That was an experience.
00:37:17.500 --> 00:37:20.519
And then just two years ago, actually, 10 years
00:37:20.519 --> 00:37:23.800
after my last marathon, I actually was able to
00:37:23.800 --> 00:37:26.679
join the team for Duke 15Q Syndrome, the syndrome
00:37:26.679 --> 00:37:29.300
that I study. They had a marathon team. And I
00:37:29.300 --> 00:37:31.360
ran the New York Marathon, which was a huge bucket
00:37:31.360 --> 00:37:34.679
list item, an experience I will never forget.
00:37:35.550 --> 00:37:37.969
And I have one more in me in three weeks, and
00:37:37.969 --> 00:37:39.909
then I'm hanging up those shoes. I can't do it
00:37:39.909 --> 00:37:41.329
anymore. I don't believe it. I fundamentally
00:37:41.329 --> 00:37:45.630
don't believe it. It's too much. My legs and
00:37:45.630 --> 00:37:48.550
my feet are screaming at me right now. Why are
00:37:48.550 --> 00:37:50.250
you training for another marathon? Well, it's
00:37:50.250 --> 00:37:53.380
the... Passion for excellence and the part where
00:37:53.380 --> 00:37:55.719
you pick something up like running. And I was
00:37:55.719 --> 00:37:58.219
struck by that, you know, that, you know, you're
00:37:58.219 --> 00:37:59.980
going to you're going to do it till you've got
00:37:59.980 --> 00:38:03.099
it done properly. You're not a dabbler in these
00:38:03.099 --> 00:38:05.500
things. And that's what drives your excellence
00:38:05.500 --> 00:38:08.199
in science that we have been privileged to witness
00:38:08.199 --> 00:38:10.559
over the years. I have one last question for
00:38:10.559 --> 00:38:13.800
you. I always ask this question because, you
00:38:13.800 --> 00:38:15.760
know, we live in. Difficult times for science.
00:38:15.780 --> 00:38:17.579
There's no other way to put it. And, you know,
00:38:17.579 --> 00:38:20.280
the body politic, you know, the public trust.
00:38:20.400 --> 00:38:22.380
And you mentioned it, you know, around genetics.
00:38:22.719 --> 00:38:24.880
When you're training, and I know you train lots
00:38:24.880 --> 00:38:27.780
of young scientists now, what do you say to them?
00:38:27.860 --> 00:38:30.880
And are you hopeful? Tell me about mentorship
00:38:30.880 --> 00:38:33.980
and what the message is so that we... continue
00:38:33.980 --> 00:38:36.739
to have the next generation of Shefali Jess to
00:38:36.739 --> 00:38:39.639
step into the space? It's a great question. And
00:38:39.639 --> 00:38:41.719
in fact, I think one of the motivators for me
00:38:41.719 --> 00:38:44.780
to move back to UCLA as chair of pediatrics was
00:38:44.780 --> 00:38:47.820
not to add to my administrative burden, but was
00:38:47.820 --> 00:38:54.260
actually to be able to mentor and to try to really,
00:38:54.480 --> 00:38:58.000
you know. inspire, if you will, or at least,
00:38:58.000 --> 00:39:00.380
you know, encourage this next generation, because
00:39:00.380 --> 00:39:04.039
I worry greatly about the future of healthcare
00:39:04.039 --> 00:39:08.260
and science. And I think it will rely on this
00:39:08.260 --> 00:39:13.239
new generation of budding scientists and clinicians.
00:39:13.719 --> 00:39:16.280
And I'll answer your question, actually, with
00:39:16.280 --> 00:39:18.860
the exact words that I used yesterday. I had
00:39:18.860 --> 00:39:22.260
to film a quick video for our pediatric residents
00:39:22.260 --> 00:39:24.519
who are on a retreat today, and I wasn't able
00:39:24.519 --> 00:39:26.860
to join because I'm here. And so the program
00:39:26.860 --> 00:39:28.460
directors asked me just to film a quick video
00:39:28.460 --> 00:39:30.940
for them. So I did. And at the end of it, I said,
00:39:31.059 --> 00:39:34.059
I want all of you to always remember the why.
00:39:35.000 --> 00:39:37.940
remember why you went into medicine. You could
00:39:37.940 --> 00:39:40.900
have chosen any other path. And frankly, many
00:39:40.900 --> 00:39:43.280
of them in residency have friends who right out
00:39:43.280 --> 00:39:45.920
of college chose different paths or are already
00:39:45.920 --> 00:39:48.260
probably close to retirement because they've
00:39:48.260 --> 00:39:50.260
made enough money to be comfortable the rest
00:39:50.260 --> 00:39:51.920
of their lives. I mean, medicine's a tough path.
00:39:52.159 --> 00:39:54.960
Neuroscience is a tough path. It's a long road
00:39:54.960 --> 00:39:58.360
that often feels thankless. But there's a reason.
00:39:58.639 --> 00:40:01.599
And so I always remind people, remember the why.
00:40:01.800 --> 00:40:04.800
Take a step back every day and remember why you're
00:40:04.800 --> 00:40:07.079
doing what you're doing. And usually the why
00:40:07.079 --> 00:40:08.840
is that you have burning questions that you want
00:40:08.840 --> 00:40:11.300
to answer. You have people that you want to help.
00:40:11.460 --> 00:40:14.320
You ultimately want to make the world a better
00:40:14.320 --> 00:40:16.840
place, whether it's for a small subgroup of individuals
00:40:16.840 --> 00:40:20.000
or for a larger population. But we are in this
00:40:20.000 --> 00:40:23.599
for good. And if we can remember that, I mean,
00:40:23.599 --> 00:40:25.679
that is worth waking up every morning to keep
00:40:25.679 --> 00:40:27.639
trudging this path. And I really, really believe
00:40:27.639 --> 00:40:29.800
that. I mean, I feel that way. You know, you
00:40:29.800 --> 00:40:31.840
and I could step away from this right now probably
00:40:31.840 --> 00:40:34.380
and probably have a much easier life doing something
00:40:34.380 --> 00:40:36.639
else. And we don't because we are so motivated
00:40:36.639 --> 00:40:41.039
by the reason, you know, that we continue to.
00:40:41.849 --> 00:40:44.610
you know, strive for excellence. And so that's
00:40:44.610 --> 00:40:46.630
what I remind people. And I think that mentorship
00:40:46.630 --> 00:40:48.909
for us is really important right now. We have
00:40:48.909 --> 00:40:52.750
to keep encouraging and supporting this next
00:40:52.750 --> 00:40:55.329
generation so that we don't lose the momentum
00:40:55.329 --> 00:40:58.619
we've built. Because we have made... absolutely
00:40:58.619 --> 00:41:01.380
transformative discoveries in the last 10 years.
00:41:01.500 --> 00:41:04.500
I mean, even in my career path, when I started
00:41:04.500 --> 00:41:06.519
child neurology, it was an academic field that
00:41:06.519 --> 00:41:09.239
largely was proficient in diagnosis. We did not
00:41:09.239 --> 00:41:11.559
offer treatments. Now we offer hope and treatments
00:41:11.559 --> 00:41:14.500
to a large proportion of our patients. And that
00:41:14.500 --> 00:41:17.480
is extraordinary. Real sea change. So beautifully
00:41:17.480 --> 00:41:19.880
put. Shafali Jess, thank you so much for taking
00:41:19.880 --> 00:41:21.960
time out for Neuroscience Perspectives. Such
00:41:21.960 --> 00:41:24.380
a pleasure. Thank you so much. It was lots of
00:41:24.380 --> 00:41:28.710
fun. Thank you.