March 6, 2024 • 24 mins
What changes happen at the cellular level in the cerebral cortex between concentration and daydreaming?
Jessica Cardin, PhD, associate professor and vice chair of the Neuroscience Department at Yale School of Medicine, joins John Foxe, PhD, director of the Del Monte Institute for Neuroscience at the University of Rochester, on NeURoscience Perspectives. They explore her interest in understanding how we learn, specifically, the flexibility of our brain, and her creative approaches in the lab that aim to improve our understanding of the cellular mechanisms behind neurodevelopmental disorders such as autism or schizophrenia. Dr. Cardin also shares how conducting experiments during childhood helped shape her curiosity.
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(00:00):
the way your brain works changes as you go from one state to another, right?
(00:05):
From being totally zoned out, right?
You know, the undergraduate in the back of the large lecture hall,
maybe doing the head bob thing, right?
Between like that state versus being really super focused and,
and like paying close attention to something that's really important, right?
You can feel that your brain is operating differently in different states.
So we spent a long time looking at that.
(00:26):
And then we thought, you know, we're missing half the signals
or more in the brain.
The human brain is the most complex structure in the known universe.
And we are in the middle of a scientific revolution to understand its inner workings.
Join us for a conversation with world renowned neuroscientists
as they visit Rochester.
I am Dr. John Foxe, director of the Del Monte Institute for Neuroscience
(00:49):
at the University of Rochester.
And you are listening to Neuroscience Perspectives.
Hi, I'm John Fox, director of the Del Monte Institute for Neuroscience
at the University of Rochester.
And I'd like to welcome you to another episode of Neuroscience Perspectives.
Today, I have a fantastic guest with us, Dr. Jessica Cardin,
who's an associate professor of neuroscience at Yale University School of Medicine.
(01:10):
Jess, welcome to Neuroscience Perspectives.
Thank you so much for having me.
Well, we really appreciate you being here.
So you really, you know, you're sort of pioneer of studying circuit dynamics.
And kind of a scavenger of techniques to get at that, which is just fantastic.
I really like that, yes.
Just, you know, if there's a technique out there that can help you get after something,
(01:31):
that's sort of just looking at the work that you've been doing.
It's like you're going to get it and make it make it work for you.
And I think that's really admirable.
But yeah, what gets you up in the morning?
What are you excited about at the moment?
Oh, my gosh, we have diversified so much in the last few years.
The real truth is that I get bored after four or five years of doing any one thing.
(01:53):
So every few years, I think it's partly that I get a little restless and we do something new,
or someone new comes to the lab and has a different set of ideas.
And then we go off in a completely different direction than I was anticipating.
So sometimes that takes us in directions that I would not have predicted.
But right now, you know, we had this long series of experiments where we were really trying
(02:20):
to deeply understand, you know, how the way your brain works changes as you go from one state
to another, right, from being totally zoned out, right, you know, the undergraduate in the back
of the large lecture hall, maybe doing the head bob thing, right, between like that,
versus being really super focused and, and like paying close attention to something that's
(02:44):
really important, right, you can feel that your brain is operating differently in different
states.
So we spent a long time looking at that.
And then we thought, you know, we're missing half the signals or more in the brain.
There are all we're only looking at neurons, the firing of neurons, we're like, we're not
looking at any of the other relevant signals.
(03:05):
And so we tell our audience what those other signals are that right.
So, you know, so there are all of these neurotransmitters floating around, right?
Sometimes, you know, in the shorthand for this is neuroscientists, sometimes that your
brain is sort of swimming in a soup of neurotransmitters and other chemicals that are that are, you
know, floating around and being released all the time.
(03:26):
And some of them are pretty familiar, I think, to most people, things like acetylcholine
or especially norepinephrine, right, those are things that you hear about often.
And they're, they're frequently targets of, you know, therapeutic drugs for psychiatric
conditions, you know, because we know that when they're released, they modify brain activity.
Right.
(03:47):
And, and we know that they're associated with things like changes in your mood or changes
in your attention levels.
Right.
And so we thought, ah, we are missing all of these signals.
And so now we've developed a whole series of approaches in the lab where we can look
at the neurons, right, the brain cells, and we also can see all those other signals at
the same time.
Right, right.
(04:08):
All right.
And then, you know, in parallel to that, we also started thinking about, well, wait, there
are other cell types, like we're only looking at one cell type, you know, maybe we should
be looking at these other cell types that are in the brain that do all these other functions,
you know, that may be related to some of these changes in brain function over time.
And so that took us to looking at these other cell types that are in the brain that do all
(04:28):
these other functions over time.
And so we started looking at non-neuronal cells.
Right.
So we just now, for the first time, have projects starting up looking at non-neuronal cells,
which is super exciting, right, because, you know, it's a, it's a whole side of, of the
brain that, you know, I've never really thought about.
Yeah.
Right.
And all to the end of sort of decision-making processes or motivated behavior.
(04:51):
Yeah.
Yeah, exactly.
Yeah.
It's, it's an interesting thing.
You're getting all of these signals.
So, so maybe a philosophical question and, and, you know, I think a lot of people in
the sciences quite would laugh at this, like, you know, if we, if we get a bit, if we, if
we could measure every single neuron in the brain and every neurotransmitter system and
arousal system and all the rest of it, and we were able to gather all that information,
(05:13):
would we, would we get to new understanding?
Is it, is it, or would we have recreated the monster?
Right.
Yeah.
I mean, do you need something even more complex than the brain in order to be able to understand
the full workings of the brain?
Right.
This is the thing that keeps teenagers up at night, you know, like staring at the ceiling
and stuff.
Like, how does my brain work?
We have, we have sort of parallel problems.
(05:35):
Right.
And this is, this was the start of the Brain Initiative actually with the NIH.
Right.
This is the, the Brain Initiative, which has funded an enormous amount of spectacular technology
development and new ways of accessing brain circuits.
Right.
Started because of this idea that if you could just measure everything, you can measure all
(05:55):
the neurons, right, you would really understand the system.
And that has given rise, I think, to this enormous upswing in big data in neuroscience,
right?
Get in there and record everything.
Right.
Yeah.
And then you, so, so we have this one problem, which is like, how do you do that?
Right.
So we need new technical approaches and that's been really productive.
Right.
We have all kinds of new approaches for this.
(06:15):
You know, you can, you can record all kinds of things, but you have a parallel problem,
which is what, what do we do with this enormous pile of data?
Right.
And how do you turn it into human understanding?
Yeah.
Something that we can.
Biological interpretability.
Right.
Like how do we, how do we distill this into something that I can actually tell you that
gives you intuition about how the brain works?
(06:35):
Right.
Exactly.
Yeah.
And that's a much bigger problem as it turns out.
And, you know, people are solving that in different ways.
And I think, you know, we've spent some time in our own group and in collaboration with
Mike Higley's lab, with whom we share a lot of projects and equipment and stuff, developing
new analytical techniques, new math, basically, like playing around with some math.
(06:59):
But also now we've kind of branched out into machine learning approaches and other, other
model based approaches that can help us take this incredibly complex series of signals
and try to distill it down into what are the important bits?
That, you know, this, I'm really interested in this idea of taking what is very, very
complex material data, doing some dimensionality reduction on it to turn it into something
(07:25):
that a human being can actually wrap their mind around.
But then the next step, of course, is one thing to have your training and to be able
to understand these concepts because of your training.
Right.
But then to communicate that to the public who pay the bills.
Yeah.
Does that keep you up at night too?
Or do you worry about that?
I asked you in the context of a time, like a time, geopolitical time where the public
(07:50):
trust in science, which used to be extraordinary, you know, 95 percent of people in the early
naughties, you know, just thought, you know, had profound trust in science.
And, you know, we come to a pandemic and we've lost our audience a little bit.
You know, there's a, people have sown a lot of doubt and so on.
Yeah.
And I think my perception of some of what's gone wrong, I think, is in how we all think
(08:17):
and communicate about what the scale of the problem and the unknowns is.
You know, we've been, you know, as a field, right, neuroscience in the sense of putting
a lecture in someone's brain has existed since maybe the 1920s, let's say, right, where the
first recordings were made of patients, right, you know, showing changes in brain activity
(08:42):
with changes in cognitive function, right.
So you ask patients to do a task, a math problem, and see a change in brain activity, right.
That's dates to the 1920s.
So basically it's been about a hundred years, right.
And you would think that in a hundred years we might have solved everything.
But the truth is that it's so complex, right.
I mean, it is fascinating and complex and it's an enormous problem, right.
(09:06):
Like, we just don't, we don't have all the answers, right.
And I think it's a mistake to suggest that we are so competent, so good.
We're just so smart that we're going to solve the brain now, right.
We're probably going to be working on this for hundreds of years, right.
We, you know, I would give it another century, right.
You know, it's an enormous challenge, right.
(09:29):
So I think some of our, some of it's our fault for not communicating what the hell we're
doing, right.
You know, and, and then some of it is also this, this idea that, you know, we should
be only focusing on disease, right.
Because I, in my mind, I think the basic research that how does the thing work should go hand
(09:51):
in hand with how do I fix it when it's broken?
You just anticipated two of my questions.
One of them, because I really wanted to, I wanted to pivot to that because I think that's
a super important point, which is, you know, just studying something for the sake of the
knowledge of how it works and not having to have the filter of utility in a disease or
something down the road.
(10:11):
But you do think about disease in your work and, and we do.
Yeah.
We used to work primarily on neurodevelopmental disorders, right.
So autism, schizophrenia, and in my lab, because most of what we do is fairly reductionist,
right.
And then they're in the circuits and manipulating things.
It's mostly animal models of genetic diseases, right.
(10:33):
So specific genetic mutations that are known to be associated with a disease.
And in our goal is really to try to understand what are the consequences of that genetic
mutation, right.
And not necessarily at the level of expression of a, of a gene, but rather it's more holistic
consequences for how does that change the way the brain works.
Right.
(10:54):
And then in recent years, we sort of kind of fell a little bit into the other end of
things, if you will.
So aging and neurodegeneration.
And there we were looking at several models of Alzheimer's disease.
And in part, because the same sort of thing I said earlier, you know, we, we kind of fell
(11:17):
into a couple of interesting ideas and then realized that there were interesting questions
that we hadn't answered yet.
And you know, we started doing these longitudinal studies where we're imaging individual animals
as they age, you know, which is really fascinating.
It generates an enormous amount of data, which we're so grappling with.
But you know, there are multiple ways to look at the disease models, right.
(11:40):
One of them is that they're a tool, right.
So it is a targeted way of breaking the system, right.
And then by perturbing it, you understand a little bit better how it worked when it
was working.
Right.
And another way to look at the disease model work is that these are all very complicated
disorders, right.
And they, they don't have autism is a great example.
(12:03):
They're like a thousand autism related genes, right.
And they do wildly different cell biological things.
And we at a fundamental level do not understand how genetic perturbation of genes that do
completely different things at the level of a single cell could give rise to something
that we perceive as being a common end point.
(12:26):
We perceive all of those consequences as having something in common.
It's all under the umbrella of autism spectrum disorder.
Right.
So that, that lack of understanding really highlights our lack of knowledge about the
basics of how the brain really works.
And this kind of idea too with diseases, like you mentioned, autism and schizophrenia, we're
(12:48):
quite a lot of those genes.
The two diseases have in common, not all of them.
Yeah, absolutely.
And yet, you know, perturbations in the genetics will send one person on a trajectory that
ends up with an autism phenotype, which is really very distinct from schizophrenia phenotype.
And so yeah, we've a lot to learn.
We have a lot to learn.
Yeah.
There is just an enormous amount of unknown there, right.
(13:09):
And that's kind of why I feel like we do a bad job of communicating the scale of the
challenge.
Right.
You know, it's a...
You said there as well, I, you know, we'll probably be doing it for another hundred years.
I don't, I don't doubt that that's true.
And a question that was in my mind is like, you know, you're in the lab like myself, you
know, and you're thinking about next year and the year after and that, but if you were
(13:32):
to project that 10 years, like where do you, where do you see us in 10 years?
Is it an impossible question?
It is.
So my part of the field, my little chunk of the field, right, is racing towards a much
more model-based, high throughput, high dimensional data set kind of approach.
(13:53):
Right.
So we are kind of converging on multimodal data with lots of different signals and needing
those, those more model-based approaches to help us make sense of things.
But so we're sort of, I think a lot of the field is moving towards the more holistic
view of how things are working, how many different signals or parts of the brain are interacting
(14:15):
at the same time.
And I, I'm, and you mentioned machine learning has a big component of this because you start
to take these extraordinary quantities of data that are from different systems, you
know, on different timescales and try to put them into one matrix and get some, derive
some understanding.
And this is the part where it's difficult for a human brain to grapple with all that.
(14:35):
So yeah, I mean, and so I see a lot of that coming, you know, I think there's going to
be more and more of that, which is, you know, great because it gives us that more holistic
understanding comes with some caveats.
Like you still need to go back again and get that biological interpretability at the other
side.
So, so big role for, for the AI revolution that we're really in the, in the middle of,
(14:57):
you think in helping to understand this or?
It's a great set of tools in combination with all the other tools.
I, I am, I'm a biologist at heart.
In fact, I'm actually a sort of old school, you know, crusty old electrophysiologist at
heart.
So in, so really, truly I want to see ground truth data at the end of the day.
(15:20):
So for me, it has to come full circle, right?
I love, I love all these tools, but at the end of the day, I want to be able to say I
gained a mechanistic level of insight into how the circuits in the brain are working.
So tell me when you, when did you catch the science bug?
Like, was that, was that baked in early?
It actually was.
I was very fortunate to have, you know, parents who love to, you know, follow their kids'
(15:46):
passions, you know, and explore things and are really supportive.
And so, you know, I got interested in looking at things in pond water and my parents, you
know, took us out to the local pond and we had water samples and, you know, put hay in
some of them and not in others and looked to see what creatures developed and, you know,
(16:07):
had a little microscope and, you know, so you're an experimentist from the, you know,
but, but with a lot of encouragement, you know, to, to keep exploring, you know, and
that was your parents where you had major influence.
Where did you grow up?
A little bit of everywhere actually.
Yeah.
Originally from Poughkeepsie, New York.
So I'm fond of Upstate, but we moved every few years for a while because my dad worked
(16:32):
for IBM actually.
And so I've lived in Poughkeepsie and I've lived in Dallas, Dallas in the late eighties,
which was fabulous.
And then in Boulder, Colorado.
Very good.
Dallas in the late eighties.
Was the TV show still running with JR?
Yeah.
And, and, and no lie, it was all big hair and, and pickup trucks.
(16:53):
So it was, it was an interesting transition.
Fantastic.
But, but you know, sort of fortuitous for me as well, because I landed in the end in
a school there that had a very strong science program and encouraged all the kids to do
science projects for the local science fairs.
Right.
And so, because it was an expectation, right?
Like everybody did this.
Right.
(17:13):
You know, we got into doing science fair projects and, you know, and so I think it, it sort
of carried that thread through a little bit, you know.
Tell me about mentorship then, like along the, along the road, right.
You pick up people and there are people who have a major impact on, on the trajectory.
Is there one or two people that you'd point to specifically and times in your life where,
(17:36):
where, you know, this really made a big impact on you?
Yeah, absolutely.
I mean, a lot of it came from, you know, the sort of early mentorship, you know, in college
in, you know, not even, not even in a continuous sense, but in like key moments where, you
know, people take you into their lab and give you a, you know, a spot in the lab and a project
(18:00):
to work on, you know, and then encourage you to keep going.
And for me, grad school was amazing, you know, because you end up with not just one mentor,
but a whole team of people in the end that you interact with.
And they may not all agree with each other in the end or have different mentoring strategies,
but you can kind of pick and choose a little bit, you know, the things that work for you.
(18:22):
Right, right.
And through those experiences, have you, have you picked up a specific mentoring style yourself?
Is there something that you bring or that you'd say this is, this is a key component?
I think, you know, thus far in my experience, you know, so I've been at Yale for 14 years
or so.
Every student is different, right?
(18:43):
You know, and I don't, I think if I have a mentoring style, it might be mostly centered
around being very patient, you know, very, trying to be very, very tolerant of the differences
between other people, how their styles might, might interact with others in the lab group
or with me.
You know, I, I try not to impose too many of my own, the expectations I had for myself,
(19:09):
I try not to impose them on my students because their style and their, their learning style
might be different from mine or their goals in life might be different from mine.
So yeah, I love that answer in, I often get asked the same thing and I say like, you know,
people in the lab are just human beings, like the human beings in your world, in your life,
and they come in all shapes and sizes and you have to figure out a way to fit around
(19:33):
that.
Yeah, everybody has a real life too, you know, it isn't, it isn't science 100% of the time,
although sometimes it is like, I have trouble turning off the science in my head.
But you know, people come to the lab and they have had a bad day or they have, you know,
a family crisis or, you know, or something else is more important that day than, you
(19:57):
know, this one experiment.
And you know, that's, that's a real thing.
It's not just science, it's also everybody's real life too.
Now we can't close this interview.
We have a few more minutes, I think, without talking about your laundry room and the three
blind mice and the nine mice, you have to tell that story.
What went on there and how did that happen and how did your mother tolerate this?
(20:22):
It was partly their idea, my parents right now.
This is again back to high school science fairs, you know, and I had, we actually done
some science fairs using pet guinea pigs in the years prior to this.
So I had guinea pigs and we, we had done learning and memory experiments, you know, with like,
and they learned to associate a cue with a food reward and that kind of thing.
(20:45):
And so what age are you now?
This, so sort of in like eighth and ninth grade.
So preteen, you know, pretty, you know, somewhere 12, 12 to 13.
Yeah, young, you know, and, and I, I had wanted, you know, to do another science fair project
(21:06):
like this.
So my parents helped me find a researcher to local university.
This was Southern Methodist, you know, in Dallas and a very kind researcher who gave
me a set of mice, lab mice to work with, which we would never do now, right?
Like, you know, sending lab mice home to some kid's house.
So we took the mice home, had a group of males and a group of females, and I was interested
(21:32):
in sex differences and learning and memory.
And so we had a group of males and a group of females.
We kept them in the laundry room for a while and I would run them on this T maze based
task where there was a cue.
And I also learned a bit about programming because this is the, we, we had to collect
all the behavioral data and then do statistics on it and everything.
So, and, and then even in Texas, it gets cold at night sometimes and the laundry room got
(21:58):
very cold one night and we had a whole bunch of mice with hypothermia.
And so my mom would tell you that she spent hours resuscitating hypothermic mice.
Was the hairdryer involved?
I think just towels.
And you know, but in the end it was, it was actually a really cool data set.
You know, we did great statistics and I learned all about Chi-square tests and like, you know,
(22:23):
and we had this sort of longitudinal learning data for males and females.
It turns out the males were better and better at learning the task.
And you know, and it was, you know, you could look at that and say that has all the parts
of a, the same thing that I do with my grad students now.
Let me ask you one last thing before we, before we stop.
(22:44):
You know, we, a lot of people who tune into neuroscience perspectives are youngsters and
young grad students or, you know, college students thinking about getting into a career
in science.
Would you have, do you have some gems, surprise of wisdom, some guidance?
I love my job.
This is a, this is so much fun, right?
It's, it's, it's a, it's a profession or a career for people who love to play, right?
(23:11):
Love to think about ideas and be creative and play with the science.
It has room for people who come from different backgrounds with different perspectives.
There's plenty of room for that in science these days, which is wonderful.
Right.
And you know, and there are kind of endless opportunities right now.
(23:34):
Right.
I mean, there are an enormous number of opportunities to do basic science, translational science,
right?
All the different flavors, right?
And you know, there are certainly these days more fellowships and summer opportunities
and internships and postdoc spots and everything than there were 20 or 30 years ago, right?
(23:56):
So I think I would view it as a, as an opportunity, you know, if I were a student right now.
Well, that's, it's fantastic.
It's completely clear from the time we got spent together that you love your job.
And I think that's a great message to go out on.
Jess, thanks so much for being here in Rochester.
Thanks.
It was really a pleasure.
Great to see you.
Thanks for having me.
the way your brain works changes as you go from one state to another, right?
(00:05):
From being totally zoned out, right?
You know, the undergraduate in the back of the large lecture hall,
maybe doing the head bob thing, right?
Between like that state versus being really super focused and,
and like paying close attention to something that's really important, right?
You can feel that your brain is operating differently in different states.
So we spent a long time looking at that.
(00:26):
And then we thought, you know, we're missing half the signals
or more in the brain.
The human brain is the most complex structure in the known universe.
And we are in the middle of a scientific revolution to understand its inner workings.
Join us for a conversation with world renowned neuroscientists
as they visit Rochester.
I am Dr. John Foxe, director of the Del Monte Institute for Neuroscience
(00:49):
at the University of Rochester.
And you are listening to Neuroscience Perspectives.
Hi, I'm John Fox, director of the Del Monte Institute for Neuroscience
at the University of Rochester.
And I'd like to welcome you to another episode of Neuroscience Perspectives.
Today, I have a fantastic guest with us, Dr. Jessica Cardin,
who's an associate professor of neuroscience at Yale University School of Medicine.
(01:10):
Jess, welcome to Neuroscience Perspectives.
Thank you so much for having me.
Well, we really appreciate you being here.
So you really, you know, you're sort of pioneer of studying circuit dynamics.
And kind of a scavenger of techniques to get at that, which is just fantastic.
I really like that, yes.
Just, you know, if there's a technique out there that can help you get after something,
(01:31):
that's sort of just looking at the work that you've been doing.
It's like you're going to get it and make it make it work for you.
And I think that's really admirable.
But yeah, what gets you up in the morning?
What are you excited about at the moment?
Oh, my gosh, we have diversified so much in the last few years.
The real truth is that I get bored after four or five years of doing any one thing.
(01:53):
So every few years, I think it's partly that I get a little restless and we do something new,
or someone new comes to the lab and has a different set of ideas.
And then we go off in a completely different direction than I was anticipating.
So sometimes that takes us in directions that I would not have predicted.
But right now, you know, we had this long series of experiments where we were really trying
(02:20):
to deeply understand, you know, how the way your brain works changes as you go from one state
to another, right, from being totally zoned out, right, you know, the undergraduate in the back
of the large lecture hall, maybe doing the head bob thing, right, between like that,
versus being really super focused and, and like paying close attention to something that's
(02:44):
really important, right, you can feel that your brain is operating differently in different
states.
So we spent a long time looking at that.
And then we thought, you know, we're missing half the signals or more in the brain.
There are all we're only looking at neurons, the firing of neurons, we're like, we're not
looking at any of the other relevant signals.
(03:05):
And so we tell our audience what those other signals are that right.
So, you know, so there are all of these neurotransmitters floating around, right?
Sometimes, you know, in the shorthand for this is neuroscientists, sometimes that your
brain is sort of swimming in a soup of neurotransmitters and other chemicals that are that are, you
know, floating around and being released all the time.
(03:26):
And some of them are pretty familiar, I think, to most people, things like acetylcholine
or especially norepinephrine, right, those are things that you hear about often.
And they're, they're frequently targets of, you know, therapeutic drugs for psychiatric
conditions, you know, because we know that when they're released, they modify brain activity.
Right.
(03:47):
And, and we know that they're associated with things like changes in your mood or changes
in your attention levels.
Right.
And so we thought, ah, we are missing all of these signals.
And so now we've developed a whole series of approaches in the lab where we can look
at the neurons, right, the brain cells, and we also can see all those other signals at
the same time.
Right, right.
(04:08):
All right.
And then, you know, in parallel to that, we also started thinking about, well, wait, there
are other cell types, like we're only looking at one cell type, you know, maybe we should
be looking at these other cell types that are in the brain that do all these other functions,
you know, that may be related to some of these changes in brain function over time.
And so that took us to looking at these other cell types that are in the brain that do all
(04:28):
these other functions over time.
And so we started looking at non-neuronal cells.
Right.
So we just now, for the first time, have projects starting up looking at non-neuronal cells,
which is super exciting, right, because, you know, it's a, it's a whole side of, of the
brain that, you know, I've never really thought about.
Yeah.
Right.
And all to the end of sort of decision-making processes or motivated behavior.
(04:51):
Yeah.
Yeah, exactly.
Yeah.
It's, it's an interesting thing.
You're getting all of these signals.
So, so maybe a philosophical question and, and, you know, I think a lot of people in
the sciences quite would laugh at this, like, you know, if we, if we get a bit, if we, if
we could measure every single neuron in the brain and every neurotransmitter system and
arousal system and all the rest of it, and we were able to gather all that information,
(05:13):
would we, would we get to new understanding?
Is it, is it, or would we have recreated the monster?
Right.
Yeah.
I mean, do you need something even more complex than the brain in order to be able to understand
the full workings of the brain?
Right.
This is the thing that keeps teenagers up at night, you know, like staring at the ceiling
and stuff.
Like, how does my brain work?
We have, we have sort of parallel problems.
(05:35):
Right.
And this is, this was the start of the Brain Initiative actually with the NIH.
Right.
This is the, the Brain Initiative, which has funded an enormous amount of spectacular technology
development and new ways of accessing brain circuits.
Right.
Started because of this idea that if you could just measure everything, you can measure all
(05:55):
the neurons, right, you would really understand the system.
And that has given rise, I think, to this enormous upswing in big data in neuroscience,
right?
Get in there and record everything.
Right.
Yeah.
And then you, so, so we have this one problem, which is like, how do you do that?
Right.
So we need new technical approaches and that's been really productive.
Right.
We have all kinds of new approaches for this.
(06:15):
You know, you can, you can record all kinds of things, but you have a parallel problem,
which is what, what do we do with this enormous pile of data?
Right.
And how do you turn it into human understanding?
Yeah.
Something that we can.
Biological interpretability.
Right.
Like how do we, how do we distill this into something that I can actually tell you that
gives you intuition about how the brain works?
(06:35):
Right.
Exactly.
Yeah.
And that's a much bigger problem as it turns out.
And, you know, people are solving that in different ways.
And I think, you know, we've spent some time in our own group and in collaboration with
Mike Higley's lab, with whom we share a lot of projects and equipment and stuff, developing
new analytical techniques, new math, basically, like playing around with some math.
(06:59):
But also now we've kind of branched out into machine learning approaches and other, other
model based approaches that can help us take this incredibly complex series of signals
and try to distill it down into what are the important bits?
That, you know, this, I'm really interested in this idea of taking what is very, very
complex material data, doing some dimensionality reduction on it to turn it into something
(07:25):
that a human being can actually wrap their mind around.
But then the next step, of course, is one thing to have your training and to be able
to understand these concepts because of your training.
Right.
But then to communicate that to the public who pay the bills.
Yeah.
Does that keep you up at night too?
Or do you worry about that?
I asked you in the context of a time, like a time, geopolitical time where the public
(07:50):
trust in science, which used to be extraordinary, you know, 95 percent of people in the early
naughties, you know, just thought, you know, had profound trust in science.
And, you know, we come to a pandemic and we've lost our audience a little bit.
You know, there's a, people have sown a lot of doubt and so on.
Yeah.
And I think my perception of some of what's gone wrong, I think, is in how we all think
(08:17):
and communicate about what the scale of the problem and the unknowns is.
You know, we've been, you know, as a field, right, neuroscience in the sense of putting
a lecture in someone's brain has existed since maybe the 1920s, let's say, right, where the
first recordings were made of patients, right, you know, showing changes in brain activity
(08:42):
with changes in cognitive function, right.
So you ask patients to do a task, a math problem, and see a change in brain activity, right.
That's dates to the 1920s.
So basically it's been about a hundred years, right.
And you would think that in a hundred years we might have solved everything.
But the truth is that it's so complex, right.
I mean, it is fascinating and complex and it's an enormous problem, right.
(09:06):
Like, we just don't, we don't have all the answers, right.
And I think it's a mistake to suggest that we are so competent, so good.
We're just so smart that we're going to solve the brain now, right.
We're probably going to be working on this for hundreds of years, right.
We, you know, I would give it another century, right.
You know, it's an enormous challenge, right.
(09:29):
So I think some of our, some of it's our fault for not communicating what the hell we're
doing, right.
You know, and, and then some of it is also this, this idea that, you know, we should
be only focusing on disease, right.
Because I, in my mind, I think the basic research that how does the thing work should go hand
(09:51):
in hand with how do I fix it when it's broken?
You just anticipated two of my questions.
One of them, because I really wanted to, I wanted to pivot to that because I think that's
a super important point, which is, you know, just studying something for the sake of the
knowledge of how it works and not having to have the filter of utility in a disease or
something down the road.
(10:11):
But you do think about disease in your work and, and we do.
Yeah.
We used to work primarily on neurodevelopmental disorders, right.
So autism, schizophrenia, and in my lab, because most of what we do is fairly reductionist,
right.
And then they're in the circuits and manipulating things.
It's mostly animal models of genetic diseases, right.
(10:33):
So specific genetic mutations that are known to be associated with a disease.
And in our goal is really to try to understand what are the consequences of that genetic
mutation, right.
And not necessarily at the level of expression of a, of a gene, but rather it's more holistic
consequences for how does that change the way the brain works.
Right.
(10:54):
And then in recent years, we sort of kind of fell a little bit into the other end of
things, if you will.
So aging and neurodegeneration.
And there we were looking at several models of Alzheimer's disease.
And in part, because the same sort of thing I said earlier, you know, we, we kind of fell
(11:17):
into a couple of interesting ideas and then realized that there were interesting questions
that we hadn't answered yet.
And you know, we started doing these longitudinal studies where we're imaging individual animals
as they age, you know, which is really fascinating.
It generates an enormous amount of data, which we're so grappling with.
But you know, there are multiple ways to look at the disease models, right.
(11:40):
One of them is that they're a tool, right.
So it is a targeted way of breaking the system, right.
And then by perturbing it, you understand a little bit better how it worked when it
was working.
Right.
And another way to look at the disease model work is that these are all very complicated
disorders, right.
And they, they don't have autism is a great example.
(12:03):
They're like a thousand autism related genes, right.
And they do wildly different cell biological things.
And we at a fundamental level do not understand how genetic perturbation of genes that do
completely different things at the level of a single cell could give rise to something
that we perceive as being a common end point.
(12:26):
We perceive all of those consequences as having something in common.
It's all under the umbrella of autism spectrum disorder.
Right.
So that, that lack of understanding really highlights our lack of knowledge about the
basics of how the brain really works.
And this kind of idea too with diseases, like you mentioned, autism and schizophrenia, we're
(12:48):
quite a lot of those genes.
The two diseases have in common, not all of them.
Yeah, absolutely.
And yet, you know, perturbations in the genetics will send one person on a trajectory that
ends up with an autism phenotype, which is really very distinct from schizophrenia phenotype.
And so yeah, we've a lot to learn.
We have a lot to learn.
Yeah.
There is just an enormous amount of unknown there, right.
(13:09):
And that's kind of why I feel like we do a bad job of communicating the scale of the
challenge.
Right.
You know, it's a...
You said there as well, I, you know, we'll probably be doing it for another hundred years.
I don't, I don't doubt that that's true.
And a question that was in my mind is like, you know, you're in the lab like myself, you
know, and you're thinking about next year and the year after and that, but if you were
(13:32):
to project that 10 years, like where do you, where do you see us in 10 years?
Is it an impossible question?
It is.
So my part of the field, my little chunk of the field, right, is racing towards a much
more model-based, high throughput, high dimensional data set kind of approach.
(13:53):
Right.
So we are kind of converging on multimodal data with lots of different signals and needing
those, those more model-based approaches to help us make sense of things.
But so we're sort of, I think a lot of the field is moving towards the more holistic
view of how things are working, how many different signals or parts of the brain are interacting
(14:15):
at the same time.
And I, I'm, and you mentioned machine learning has a big component of this because you start
to take these extraordinary quantities of data that are from different systems, you
know, on different timescales and try to put them into one matrix and get some, derive
some understanding.
And this is the part where it's difficult for a human brain to grapple with all that.
(14:35):
So yeah, I mean, and so I see a lot of that coming, you know, I think there's going to
be more and more of that, which is, you know, great because it gives us that more holistic
understanding comes with some caveats.
Like you still need to go back again and get that biological interpretability at the other
side.
So, so big role for, for the AI revolution that we're really in the, in the middle of,
(14:57):
you think in helping to understand this or?
It's a great set of tools in combination with all the other tools.
I, I am, I'm a biologist at heart.
In fact, I'm actually a sort of old school, you know, crusty old electrophysiologist at
heart.
So in, so really, truly I want to see ground truth data at the end of the day.
(15:20):
So for me, it has to come full circle, right?
I love, I love all these tools, but at the end of the day, I want to be able to say I
gained a mechanistic level of insight into how the circuits in the brain are working.
So tell me when you, when did you catch the science bug?
Like, was that, was that baked in early?
It actually was.
I was very fortunate to have, you know, parents who love to, you know, follow their kids'
(15:46):
passions, you know, and explore things and are really supportive.
And so, you know, I got interested in looking at things in pond water and my parents, you
know, took us out to the local pond and we had water samples and, you know, put hay in
some of them and not in others and looked to see what creatures developed and, you know,
(16:07):
had a little microscope and, you know, so you're an experimentist from the, you know,
but, but with a lot of encouragement, you know, to, to keep exploring, you know, and
that was your parents where you had major influence.
Where did you grow up?
A little bit of everywhere actually.
Yeah.
Originally from Poughkeepsie, New York.
So I'm fond of Upstate, but we moved every few years for a while because my dad worked
(16:32):
for IBM actually.
And so I've lived in Poughkeepsie and I've lived in Dallas, Dallas in the late eighties,
which was fabulous.
And then in Boulder, Colorado.
Very good.
Dallas in the late eighties.
Was the TV show still running with JR?
Yeah.
And, and, and no lie, it was all big hair and, and pickup trucks.
(16:53):
So it was, it was an interesting transition.
Fantastic.
But, but you know, sort of fortuitous for me as well, because I landed in the end in
a school there that had a very strong science program and encouraged all the kids to do
science projects for the local science fairs.
Right.
And so, because it was an expectation, right?
Like everybody did this.
Right.
(17:13):
You know, we got into doing science fair projects and, you know, and so I think it, it sort
of carried that thread through a little bit, you know.
Tell me about mentorship then, like along the, along the road, right.
You pick up people and there are people who have a major impact on, on the trajectory.
Is there one or two people that you'd point to specifically and times in your life where,
(17:36):
where, you know, this really made a big impact on you?
Yeah, absolutely.
I mean, a lot of it came from, you know, the sort of early mentorship, you know, in college
in, you know, not even, not even in a continuous sense, but in like key moments where, you
know, people take you into their lab and give you a, you know, a spot in the lab and a project
(18:00):
to work on, you know, and then encourage you to keep going.
And for me, grad school was amazing, you know, because you end up with not just one mentor,
but a whole team of people in the end that you interact with.
And they may not all agree with each other in the end or have different mentoring strategies,
but you can kind of pick and choose a little bit, you know, the things that work for you.
(18:22):
Right, right.
And through those experiences, have you, have you picked up a specific mentoring style yourself?
Is there something that you bring or that you'd say this is, this is a key component?
I think, you know, thus far in my experience, you know, so I've been at Yale for 14 years
or so.
Every student is different, right?
(18:43):
You know, and I don't, I think if I have a mentoring style, it might be mostly centered
around being very patient, you know, very, trying to be very, very tolerant of the differences
between other people, how their styles might, might interact with others in the lab group
or with me.
You know, I, I try not to impose too many of my own, the expectations I had for myself,
(19:09):
I try not to impose them on my students because their style and their, their learning style
might be different from mine or their goals in life might be different from mine.
So yeah, I love that answer in, I often get asked the same thing and I say like, you know,
people in the lab are just human beings, like the human beings in your world, in your life,
and they come in all shapes and sizes and you have to figure out a way to fit around
(19:33):
that.
Yeah, everybody has a real life too, you know, it isn't, it isn't science 100% of the time,
although sometimes it is like, I have trouble turning off the science in my head.
But you know, people come to the lab and they have had a bad day or they have, you know,
a family crisis or, you know, or something else is more important that day than, you
(19:57):
know, this one experiment.
And you know, that's, that's a real thing.
It's not just science, it's also everybody's real life too.
Now we can't close this interview.
We have a few more minutes, I think, without talking about your laundry room and the three
blind mice and the nine mice, you have to tell that story.
What went on there and how did that happen and how did your mother tolerate this?
(20:22):
It was partly their idea, my parents right now.
This is again back to high school science fairs, you know, and I had, we actually done
some science fairs using pet guinea pigs in the years prior to this.
So I had guinea pigs and we, we had done learning and memory experiments, you know, with like,
and they learned to associate a cue with a food reward and that kind of thing.
(20:45):
And so what age are you now?
This, so sort of in like eighth and ninth grade.
So preteen, you know, pretty, you know, somewhere 12, 12 to 13.
Yeah, young, you know, and, and I, I had wanted, you know, to do another science fair project
(21:06):
like this.
So my parents helped me find a researcher to local university.
This was Southern Methodist, you know, in Dallas and a very kind researcher who gave
me a set of mice, lab mice to work with, which we would never do now, right?
Like, you know, sending lab mice home to some kid's house.
So we took the mice home, had a group of males and a group of females, and I was interested
(21:32):
in sex differences and learning and memory.
And so we had a group of males and a group of females.
We kept them in the laundry room for a while and I would run them on this T maze based
task where there was a cue.
And I also learned a bit about programming because this is the, we, we had to collect
all the behavioral data and then do statistics on it and everything.
So, and, and then even in Texas, it gets cold at night sometimes and the laundry room got
(21:58):
very cold one night and we had a whole bunch of mice with hypothermia.
And so my mom would tell you that she spent hours resuscitating hypothermic mice.
Was the hairdryer involved?
I think just towels.
And you know, but in the end it was, it was actually a really cool data set.
You know, we did great statistics and I learned all about Chi-square tests and like, you know,
(22:23):
and we had this sort of longitudinal learning data for males and females.
It turns out the males were better and better at learning the task.
And you know, and it was, you know, you could look at that and say that has all the parts
of a, the same thing that I do with my grad students now.
Let me ask you one last thing before we, before we stop.
(22:44):
You know, we, a lot of people who tune into neuroscience perspectives are youngsters and
young grad students or, you know, college students thinking about getting into a career
in science.
Would you have, do you have some gems, surprise of wisdom, some guidance?
I love my job.
This is a, this is so much fun, right?
It's, it's, it's a, it's a profession or a career for people who love to play, right?
(23:11):
Love to think about ideas and be creative and play with the science.
It has room for people who come from different backgrounds with different perspectives.
There's plenty of room for that in science these days, which is wonderful.
Right.
And you know, and there are kind of endless opportunities right now.
(23:34):
Right.
I mean, there are an enormous number of opportunities to do basic science, translational science,
right?
All the different flavors, right?
And you know, there are certainly these days more fellowships and summer opportunities
and internships and postdoc spots and everything than there were 20 or 30 years ago, right?
(23:56):
So I think I would view it as a, as an opportunity, you know, if I were a student right now.
Well, that's, it's fantastic.
It's completely clear from the time we got spent together that you love your job.
And I think that's a great message to go out on.
Jess, thanks so much for being here in Rochester.
Thanks.
It was really a pleasure.
Great to see you.
Thanks for having me.
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