So I'm Guy McKhann: as I said I'm a neurosurgeon at Columbia. I one of the things I get to
do is I specialize in awake brain surgery so I get to operate on patients while we're
looking at their brain while they're awake while they're functioning. And the more you
do that the more you realize how little we understand and actually know about the brain.
So without further ado we're really privileged to have a tremendous group of participants.
First we've got. Our first guest is professor of biomedical engineering and radiology at
Columbia University a member of the Zuckerman mind brain behavior Institute and Kavli Institute
for Brain Science.
Please welcome Elizabeth Hillman.
Our next participant is an assistant professor of psychiatry, neuroscience and physiology
at the NYU Langone Medical Center who investigates the neural circuitry of mating and aggression
in mice.
Please welcome Dayu Lin.
Also joining us is a neuroscientist and psychiatrist at Weill Cornell Medical College as a psychiatrist
specializing in the treatment of complex mood disorders. Please say hello to Conor Liston.
And our final participant is a neuroscientist,director of neuroscience at Cold Spring Harbor Laboratory
who is interested in how brain circuits give rise to behavior. Please welcome Tony Zador.
So maybe first we could talk about a little bit the history of where did optogenetics
come about from. Then you guys could talk about using a little bit.
So interestingly, actually the history of optogenetics can be traced back to an idea
that Francis Crick, who's famous for the double helical structure of DNA first came up with.
He proposed that it would be really convenient if we could activate neurons with light. He
proposed that something like 30 years ago. And it was people had tried different approaches
and were developing different strategies for trying to make it work and it was really the
fortuitous recognition that a protein from algae could be expressed in neurons and endow
them with the ability to respond immediately to to to light. And that happened I guess
about a little over ten years ago and it transformed how we do neuroscience.
When I started in neuroscience everything in terms of trying to understand behavior
was correlational - we saw neurons fire and we saw the behavior and we put them together
and we of course all recognize that correlation is not causality, but with without the ability
to sort of dissect out the circuits that were responsible for the behavior, all we could
do is keep trying to find those associations. And so that happened about I guess 2005 and
within a couple of years lots of other, lots of labs it happened. It was a paper from Karl
Deisseroth and Ed Boyden. And within a couple of years. Dozens and soon hundreds of labs
were using it. So that's really the basic history.
So Conor you spent time in Deisseroth lab before moving to Cornell so tell us about
how you've gotten with optogenetics to what you're doing with it, some of the potential
applications as you see it within your world.
Yeah. So as you guys heard I'm a psychiatrist clinically. And one of the things that really
excites me about optogenetics is this potential opportunity to understand the neurobiological
basis for mental health and mental illness and hopefully develop new treatments. And
I think a big limitation in psychiatry is that the drugs we use, I always like to emphasize
that the drugs we use they do work and they've been a great benefit for a lot of people,
but they're blunt tools, they bathe the brain and they change some aspects of brain function
in a way that that helps but they probably have lots of other unintended side effects.
And optogenetics affords us this really unique opportunity to really precisely manipulate
specific brain circuits, figure out what's going wrong and try to fix it. And that's
that's one thing that really excites me.
So give us give us an example of how you've done that, a specific circuit that you've
targeted and what you've been able to find from that.
Yeah, so during my time at Stanford, this is like a collaborative work with several
other investigators there, we were really interested in understanding how the brain
supports reward seeking behavior that's a thing that's obviously altered in the brains
of many depressed patients. And we were able to precisely manipulate the activity patterns
of specific cells in the region of the brain called the prefrontal cortex and begin to
understand how, how it how it regulates the processing of reward related cues and motivates
animals, and presumably something similar is happening in people's brains, motivates
their their their desire to work to obtain rewards. And interestingly what we found looks
a lot like what what goes wrong in the brains of depressed people there's this abnormal
pattern of increased connectivity in this brain circuit and that seems to be driving
some of the the reward processing deficits that these that these rats have and then possibly
also people
Dayu you're using a lot optogenetics so what are you using it for and what types of questions
are you able to ask with optogenetics.
Yeah. So I was lucky enough actually to be of the first person that will benefit from
the optogenetics. I remember it was still in the days when there was a postdoc. And
I had to say optogenetics, although it's a new technique but the concept wise is is not
new. In the sense that neuroscientist have tried very hard for centuries, of trying to
manipulate the brains in order to see what happened. So but the conventional manipulation
is by passing some currents into the brain so the electrical stimulation and that's where
I started when I trying to study the brain before the optogenetic era.
Electrical stimulation, there is one deficit of that which is that neurons are not just
a cell, which a ball, but they actually have a lot of processes coming out. So when we
electrically stimulate it we're not only stimulated to cell body this ball, but also all the processes
coming from it. So potentially the cells which is a far away can also be activated because
of this electric stimulation. And optogenetics is advantages because it allows you to really
target the cells interesting without affecting those cells it just sending processes to it.
That's just the specificity that makes this tool really unique. And now we're putting
optogenetics into those cells and stimulate.
So why don't we look at it if we have the aggression video first
Yeah. So this is a mouse. There was this part of the brain. It's a very deep very Ventrell.
And we turned down this area and you'll see that he start to attack this glove. And he
really you know that's not what mouse typically do, so they don't usually attack with gloves
but in this particular case when the light activated it he repeatedly sinks his teeth
into this glove. In fact when we take it out this glove is in fact deflated. So and it's
not only gloves. In fact that they would attack other conspecifics including a female mouse
that a male will typically would not attack at all. So this inanimate object probably
shows the extreme of misbehaviors that this control of this area is just a turn on this
attacking instinct immediately.
So there's some specificity to the aggression.
It does. It does. In fact, one of the big questions that we are trying to ask in the
lab is when we do activate this area, is this just a biting instinct or it's actually increasing
this aggressive motivation. Of the animals and that's why we're designing different behavior
task in order to dissociate this part. But that's exactly to ask is it to just make them
bite or actually they are become has this urge to aggress.
Can you get them to attack a cat?
I haven’t tried that, attack a cat or a rat.
That's a much more complicated question.
So all of you guys are talking about a technique where you're able to select out a very small
specific part of the brain to try to isolate and look for behaviors. Now Elizabeth you've
been focusing on the one hand on using imaging techniques to look at much broader patterns
of activity and larger connectivity patterns between the brain and even whole brain activity
in various types of animals. Can you fill us in on some of the things you've been doing
along those lines?
We do a lot of technique development in the lab and I was trained as a physicist so I
did a lot of optics and so you know these technologies when they come along they don't
just come along, wonderful people conceive of them and piece them together but then using
them and figuring out how to do that is really challenging as well and that's a really important
fact to hear so that little mouse had a thing on its head and that was actually holding
an optical fiber that was allowing you to put laser light into the actual brain to turn
these cells on and off. And so in our work what we're really interested in you know how
can you how can you do that better. I mean it's not just that you can turn cells on but
you can actually turn cells off as well. And the dream is to have many many different colors
so that you can be very selectively turning one thing on and turning one thing off. And
that really lets you you know figure out what part each one of those things is playing
I was thinking of the analogy of sort of what is my role in getting my kids out of the house
in the morning. All right well how do you tell that you just throw me in there and make
me flash. No but if you take me away and then see what happens when I'm not there. You know
it's quite, quite a helpful way of figuring out what it is exactly that I contribute.
And so you know it's not that much actually
But so so so figuring all of this out I mean for me you know we do our own we do our own
experiments as well and we'll talk about that more. But but figuring out you know working
working with you guys you know what exactly experiment do you want to do. What behavior
do you want to do. Now which areas of the brain do we have to get to and how do we have
to make that all work together so that we don't have to have 10 lasers and all of this
stuff happening at the same time as is one of the things that really gets me going. And
then and then and then we combine that with imaging because you know what you've seen
mostly there is is the read out you know you change something in the brain and you look
at the way that the person's actions change or the animal's actions change. But we can
also simultaneously be looking at what effect that has then on the firing of the other neurons.
And so we do a lot of work trying to capture activity at the level of a few cells at the
level of the whole brain. We look at that activity in flies and worms and and little
fish. And then we also do it up at the scale of mice and then sort of project that up to
the scale of the human brain cells.
So this would be a good time to look at the video that you have in terms of patterns of
activity because it's very different from exciting a few neurons with one fiber. You're
looking at what the brain is doing spontaneously in its own pattern of pattern of activity.
so this is work that we've been doing. All right to take takes a little bit of explaining.
So what you're seeing here is actually that the entire top surface of a mouse's brain
and that reveals a whole ton of different areas of the brain that we can look at all
at the same time. We're using something called G camp, which is a calcium sensitive fluorescent
protein. So what we're just talking about was optogenetics where you express based on
the genetics of the cells you make the cell produce this light sensitive protein. And
one of the other major tools that has really revolutionized neuroscience in the last five
to 10 years has been these fluorescent proteins like the glowing jellyfish that that you've
probably seen or the newspapers with the glowing mice right.
So these are calcium sensitive fluorophores which means that actually when the neurons
fire that they actually flash and we can read that out. OK so it's sort of the opposite.
So anyway we've been observing this in the mice and we've found these amazing patterns
of spontaneous activity that tick around in the brain. And what I thought would be fun
to show you today is that we do a lot of quantitative analysis on this trying to understand the
cadence of these patterns and the locations of these patterns and what do they mean and
one of the things that we just were able to do. If you go to the next slide. So we. We
found that again it's hard to see with these patterns. So on the top left is just a still
of these kind of weird patterns of activity that we really see coursing through the whole
brain.
And we found that they were sort of coming on and off in different regions of the brain.
And so I'm going to play a play a movie here where what we've done is rather than just
showing you the movie of these patterns that seem to kind of roll around in the brain because
there's different regions that are lighting up one after the other. We've actually encoded
them in color and also as notes on a piano. So what you're going to hear is kind of what
it sounds like in the brain of a mouse as a mouse is just sitting there resting and
this is over pretty much the entire surface of the brain so all the regions are responsible
for seeing and hearing and motor actions. This is all done by my student Nick here who
is a biomedical engineer slash composer. So if you want to just click the. I'm not getting...
there you go. So every time a region lights up you hear a note. Give it a second
So we use this method just as a way to try to just really capture
Is that fully automated?
Sorry?
Is the relationship between the brain activity and the music fully automated or.
Yes this is actually using the NAM. Non-active matrix factorization algorithm on this stator
and then taking the factors and then putting them into musical notes
And then if you go to the next slide. So then
Just before you go there, I would argue relative to what Tony Tony was saying before
At rest, at least based on the music pattern the mice are quite calm.
Well after I started doing this analysis I remember I had a big grant on this that I
submitted right before Thanksgiving and I was sitting there in thanksgiving and I was
I just kept feeling this rhythm of my brain you know and thinking you know if I could
just sort of calm down watch some television if I just watch you know 10 Things I hate
about you one more time I thought maybe I could get myself into this state. You know
what I mean. And so this drew us onto to the next part which is we then gave the animal
caffeine.
OK. So go to the next slide and play the movie on the left so, it takes a minute.
So we could we could play this music to a mouse listening to its own brain.
We talked about that just recently actually if you can use that as a way to actually.
So you want to quickly pay the caffeine one It's not so different. It's a little faster.
Has some gaps. Feels a little different.
OK and then so we gave it ketamine. So ketamine is a drug Some people use it recreationally
and they shouldn't. And it's an anesthetic which really makes you basically unconscious
makes you high has various effects. Of making you forget and so on. So that's the one on
the right here and you'll see it has a really profound effect.
What's what's really interesting about this is that this is a very odd modality that we
came up with. we actually just take the data using LEDs and camera.. It's very low key
compared to the really high tech stuff that we also do in the lab. But the reason why
I'm really excited about this is I think neuroscience for a long time has zoomed really far in on
just just the cells you know looking at single cells and how does cells interact with each
other. And when we discovered this activity that's really everywhere. It really changes
the way that I'm now trying to think about you know. The brain is just sort of talking
to it. It's like the Internet. Right. It's not just you and you sitting there it's you
connected to all your Facebook friends and all of those things kind of interacting together
and so we're just dumb.
We're having fun with this of course but we're really using this now to try to understand
how that sort of whole brain contributes to behavior. And particularly here we think about
state so a state of being anxious a state of being calm. And really what does that look
like and then. How would each one of these react differently if you presented it with
an aggressive glove.
So you're showing these amazing patterns of how the brain connects and what connects to
what. You know Tony you've taken a totally different approach to try to study major areas
and how what actually connects to what and how we unravel that maybe can you can you
explain that for us.
Yeah so to sort of put it in perspective. When the when the optogenetics revolution
hit, my lab jumped in whole hog we were already training mice, or at that time rats, to perform
what we thought were cognitive tasks and we could talk more about that. But we were training
them to listen to sounds and make decisions based on the sounds they heard to get rewards.
And we were trying to understand the circuits that were involved in making those decisions.
And we had identified certain subsets of neurons, using optogenetics later we had gone beyond
simply recording the activity of these neurons and actually shown that one set of neurons,
turns out that project to the auditory striatum, were actually really important in carrying
information about a sound toward driving movement. But, what I realized from that was that you
know we can make guesses about the circuits involved and using optogenetics we can actually
test whether our guesses are right for the first time like we could test at the circuit
level which subset of neurons within a particular area are driving this behavior versus that
behavior and what roles they play.
But the problem was that, we just have to make educated guesses. And one of my students
was really fantastic and made a really educated guess and guessed right. And chose this this
particular pathway. But other students made less successful guesses. And the cost of guessing
wrong is a couple years of work. You you do a set of experiments and if you guessed wrong,
the answer was nope. That wasn't the right guess. And you learn very little
Time for a new career.
Not my career. As I told you I used to tell people I still tell them who enter my lab
I quote from Shrek always there's a there's a for those of you my son when he was younger
watched Shrek over and over again and there's a scene where Lord Farquhar sends his knights
out to find the princess. And he says this mission is dangerous, many of you may die
but that's a risk I'm prepared to take. So, If we guessed wrong there still ones who come
up empty handed. So anyway.
So how did you make it better for them?
So the so the idea was that we needed a way to to screen through the guesses. We needed
to know what the circuit was in order to know what was a reasonable guess. Knowing the circuit
would not tell us how it actually works but it would rule out all sorts of possibilities.
And the problem was that the tools that were available were either not high enough resolution,
didn't have a single neuron resolution or they were incredibly slow. So that what we
needed to know was whether there are neurons, let's say in my case and the auditory cortex
that send their axons to this region and that region and whether there are other neurons
that send their neurons here and there. And the problem was there was no way of knowing
in sort of a high throughput way what the millions and billions of neurons, well millions
and a mouse billions in a human where they send their axons. And so the traditional methods
for asking those questions were all based on microscopy. The typical high-resolution
method is to take one neuron in one animal fill it with with a fluorescent tracer or
some other kind of tracer and then track that trace all throughout the brain. The problem
is…one neuron per mouse lots of neurons. That's very slow. If you try to do more than
one neuron the problem is that the processes start overlapping and you can't resolve where
the individual processes from these neurons go.
And so the idea that I had eight years ago now, but it's finally working was to replace
the usual way of visualizing the neurons, which is just to look at a fluorescent dye,
with what has turned out to be a fantastic marker
which is DNA or in fact RNA. So what we do is we, we cause each neuron to express a unique
random sequence of RNA which then gets transported all throughout the cell.
So but how do you do that why does a neuron want, not want real neural RNA.
Yeah, in real life most neurons do not express random, in general, you don't have a lot of
random labels floating around your body. In fact the only case in our body that we know
of where we have random labels is your immune system where the genetic material gets scrambled
each time to make antibodies. So we thought about actually trying, and we're still kind
of working on trying to trick neurons into scrambling their DNA, but an easier way to
do that is we can make viruses that are, that each express a unique random thirty nucleotide,
thirty letter string of DNA and then we just squirt that into the brain and each neuron
takes up one viral particle. Sometimes they take up two and we could talk about technically
whether it's not really a problem. And then those, that it gets amplified within the neuron
and gets transported out to the axon.
So now we no longer have to carefully trace each little process, we can just use DNA sequencing
technology, which has transformed other fields of biology. We can just just piggyback off
of these tremendous advances in DNA sequencing technology which have driven the price of
sequencing our genome down to below a thousand bucks. We can we can use literally that same
technology to figure out where all these neurons project. So we...
Every neuron has its owns its own individual..
Every neuron has its own individual label, and it actually I should have shown this picture.
It's inspired actually by an idea that scientists at Harvard, Josh Staines and Jeff Lichtman
came up with about 10 years ago, called ‘brainbow’ where they cause each neuron to express not
one color but a rainbow of colors. And so basically the idea is to replace a rainbow
of colors, it's hard to read out more than a couple of colors so there wasn't such a
big win but it makes beautiful pictures, to replace those with these sequences. And so
now what we do is we inject in to one sight in the brain or in fact we can now tile the
entire brain with this virus and figure out where all the neurons in the cortex project
to.
So in fact I was looking at your pictures and one of them, we were just now starting
to analyze this whole brain connectivity map and it turns out that there are sort of communities
of neurons that talk preferentially to each other. And in fact the the communities that
we find based on these connectivity actually are very reminiscent of the the sort of different
chords especially in the ketamine brain where it looks like there's there's sort of within
their community there's a lot of conversation and then it passes the message to the next
community.
So it's possible that you may be seeing with nerve axonal labeling some of the same things
I think actually the substrate for what you're seeing is precisely…
We should hang out.
Yeah.
So how do you say how do you take that to the next or a different level and say Now
we know how things all communicate. How do you start studying what's normal and what's
abnormal?
Right. So so the idea here is that we can now for a couple thousand bucks figure out
the whole connectivity of our brain at, there are some issues about resolution. It's a single
neuron resolution but the spatial resolution is limited. We have some ideas and actually
we're working on methods that give you higher resolution. But the real interesting idea
is that we can now compare the wiring diagram of a normal mouse to a mouse that has a genetic
deficit that has been associated with the human disease. And there are now dozens or
hundreds of these mice these so-called animal models of autism and schizophrenia and depression.
So we know that that there are genes that cause that are associated with human conditions.
We know that when you put those when you disrupt those genes in mice they disrupt behavior
in ways that are sometimes similar or sometimes not. But in many cases we think that what's
going on is that disruption of those genes causes some kind of change in the neural circuit.
But once again the traditional approach to figuring out what that change is to take an
educated guess. Burn up a couple of grad students and if you guess right then... well but that
is how these things go it's incredibly frustrating. And so the idea is now we can we can actually
take one of these mice and say look here is the disruption we're not sure which of these
disruptions are causal in the behavior. But this totally sort of constrains the set of
hypotheses that now physiologists like me and the other people on this stage have to
consider.
So you're taking a lot of guesswork for the grad students out of picking your project?
That's right. That's right that's really my goal is to sacrifice fewer grad students on
the altar of bad guesses,
Which is very admirable. We would all agree
Connor. Connor you've taken a different approach to try to look at populations of neurons by
putting little prisms in the brain and combining it with optogenetics. Can you talk about that
and what it allows you to do that's different from traditional optogenetics.
Yes so this work is really similar to what Elizabeth was just describing but it's kind
of on a more microscopic scale. So we're using the same kind of calcium sensors that fluoresce
in proportion to their activity. Like you saw on those beautiful videos that Elizabeth
showed us a few minutes ago. But we are looking with single cell precision on a much smaller
area of the brain. One of the functions that we're really interested in is working memory.
This basic kind of fundamental form of short term memory that we all use all the time like
if we decided we wanted to take take a break now and order some pizza. Working memory is
like what would enable us to kind of look up our favorite pizza place on your phone
and store the memory of the phone number long enough to dial it. But it goes away rapidly
It's already gone away with cell phones.
Yeah exactly.
Teamwork. And it's disrupted in depression and in schizophrenia and in many other psychiatric
conditions in different and interesting ways. So we think it's kind of a fundamental process
we can show that,
Can we see this Video
that video. So so what you're seeing here are cells in the prefrontal cortex those black,
those black kind of bands are blood vessels. But these are individual neurons, brain cells
that are expressing this this fluorescent calcium sensor and that Starry Night effect
is basically a a readout of how active each one of these cells is over time. And what
you what you don't see is that this mouse is awake and it's behaving it's performing
a working memory task while we're getting this readout. And so what we hope to be able
to do is test hypotheses about how particular cell types in this image that you're seeing
here how they interact to encode and maintain a trace of the working memory during a delayed
period during the time it would take us to dial the phone number in that analogy and
how these different cell types interact to imbue working memory with interesting properties.
And then once we understand that like like we were just discussing a minute ago we can
we can formulate hypotheses, guesses and actually test them using optogenetics to manipulate
the activity of specific cell types and and test whether our predictions are actually
true or not.
So everybody here is using different animal models to study these things to try to come
up with information that can help us fundamentally understand human behavior. So maybe as a group,
whoever wants to can talk a little bit about about how do we best do that. How do we best
do that in a way that we are minimizing the use of animals, being compassionate in that
you know, but yet understanding what we need to understand. So what are your thoughts on
that?
We recently started using fruit flies and I don't feel bad about that at all.
So tell us what can you learn out of a fruit fly?
The beauty of the fruit fly model is that you can breed them really really quick. And
so these genetic techniques now that are really at the core of neuroscience that let us label
the cells different colors and change them, introduce genetic diseases, make them express
optogenetic options. You can do that in the space of a week. You can just cross two flies
and then you have another fly and it's got rainbows in its brain and it does all this
stuff. For me what's really nice because I when I build microscopes is what you showed
what you saw there was about a ten millimeter field of view which is what we need, but we
couldn't see single cells.
But if you if you have a fly's brain we can fit that whole thing within our microscopes
field of view and so we can see every single cell in the entire brain. And so we do these
completely crazy things where we get the fly and we actually have it walking on a ball
and we're imaging it's whole brain while it's on a ball and then you can actually puff odors
and odors of lady flies and then you can see what it does. And you can see where it crawls
and then you're seeing all of this activity in the brain. You know as it's doing that
and so, are flies the same as humans ? Probably not. They they can fly which we can't. So
I don't know.
What we're trying to do on so many different levels is understand building blocks understand
basic principles understand you know how does behavior emerge from all of these little cells
flashing on and off and so doing it in that system is a very efficient very good nice
way of understanding something at that level and then scaling it up so. So there's these
people in neuroscience working at every single level. What we really need to do is all talk
to each other and sort of piece together all of those clues. And we also do the maggot's
as well. I mean it's gross.
Dayu. Dayu, you showed us that you work on aggression but you also work on maternal instinct
so another thing that's across the animal kingdom but that we all think of as an incredible
human thing but actually it's much across the animal kingdom so maybe you could talk
to us about how you study that sort of thing with something like optogenetics.
You know they are in my views a lot of social behaviors instinctive behaviors that they're
all controlled by those ancient structures that the mechanisms is very conserved. And
the maternal and paternal behaviors and in fact parental behaviors in general in fact
is also highly sexual dimorphism. I'm all for them and I'm one of the regions we study
is controlling the maternal behaviors. So I to show you a video here just to say that
we are not only looking at the years which is fighting but also this is like just a heartwarming
videos and look at it.
So this was this is a virgin female that date she doesn't really care about the pups so
much and she doesn't have much experience with the pups. So this is called a sham sham
stimulation. In those imitation there were just the endless areas that we put a puppy
in the corner and she basically ignored the pups. The pups is a whipping around probably
making other boys sound. Tried to get her attention. So. So this part will be sixty
seconds which I guess was just trying to convince you that there's really nothing happened.
So now pay attention. So now we start to stimulate this part of the brain Right here. And then.
Walked right out. And to bring the PAP .Back home. So they are part of the brain regions
which are really just controlling those maternal instincts when it turns out they should be
instamom mom.
So supermom do this all day long.
Yeah. So before we move because a lot of you guys are doing. Not just mouse modeling but
also doing human work too. Can you all comment on because, because I don't know if anyone
here is actually working with it in addition to optogenetics out of the Deisseroth lab,
another great development has been this technique CLARITY, the ability to take the brain which
is in oblique structure and essentially turned it into a window pane. So can you all talk
about how you see that as a tool, the strength of it, and can you potentially merge it with
some of these other techniques?
Well I think that. So for those of you who haven't heard there is a technique that allows,
so we'd like to see where all the neurons are we'd like to see how they're all connected,
and what limits it is the fact that the neurons are embedded in all sorts of fat and extracellular
stuff. And when you do microscopy it's very difficult to see clearly what's going on.
And so for several decades people have been trying various methods for clearing tissue
for making it transparent. And there have been a couple of major advances in the last
couple of years including the CLARITY technique from Deisseroth lab and some other techniques
that have made it much easier to see a lot of the brain clearly, to be able to image
down one to three millimeters in fact as the limits now have less to do with the actual
tissue and more to do with microscopy.
I think that really is making it a lot easier to quickly see what the circuitry is how different
parts of the brain are connected, We’re using sort of related techniques. In fact
we're using a technique developed by a guy named Ed Boyden at MIT which in some sense
goes one step further it allows you to not only clear the tissue but simultaneously to
expand it. So some of the structures we're interested in, it's called expansion microscopy
surprisingly enough. So some of the structures we're interested in are below the resolution
of light and the usual approach to resolving a structure that's below the resolution of
visible light is to use electron microscopy, which is a lot of work.
A lot of grad students.
It's more it's a, it's a it's a million dollar machine and it's it's it's a nightmare at
least on this scale first for for applying electron microscopy to sort of macroscopic
structures. And so what he had, I mean Ed is actually the co-developer of channel and
along with Karl Deisseroth, what Ed realized was that instead of getting, making microscopy
better so you could see smaller things. It would be just as good if you could make the
smaller things bigger. And so he developed a technique that was really that was inspired
by and actually uses literally the stuff that they make diapers out of. And you know how
you, for those of you who are familiar with diapers you had liquid will say water to a
diaper and it expands. And so he imbeds this this this diaper stuff in between the bits
of neuron and then binds every little protein to a bit of it and then expands every protein
isotropically in all directions. And so you can get expansion factors that are two and
four and now even twenty times the original. So you can take a synapse which is below the
resolution of light point below point two microns and make it a three micron, five micron
structure which we can easily resolve and I think that that technique is really on the
verge of transforming a lot of what we can do.
Can think, you merge that?
So we are we are actively working with lab to merge our approach. So all of this just
to be clear is in fixed tissue and tissue from the brain and after it's out of the skulls.
Optogenetics is fantastic for working with a live animal but then to figure out the circuitry
of that animal that what you have to do is take it out and then use sort of these methods
so we are barcoding neurons in all the animals you know regenerate to perform a task and
then we expand the tissue and do the sequencing in situ so we can actually sequence the barcodes
while the bar codes are still inside the brain and it gives us the spatial resolution to
actually see which barcodes are at a synapse and actually figure out which neurons are
connected to each other neurons. So there are some engineering issues, throughputs,
throughput issues but conceptually we can expand an entire brain and figure out the
wiring diagram of an entire brain at synoptic resolution. And while we're at it we can also
figure out which genes are being expressed. So this is, you know, one of one of the people
in Ed’s lab calls it the Rosetta project because basically we can figure out everything
about a brain all the way from activity while the animal is performing a task, the behavior.
And then the circuitry and gene expression that cause those things so that that's where
we're hoping to go with them.
That's pretty amazing to think about. So but as you say that's finding out everything about
a mouse brain.
Yeah.
So we're talking a lot about you know mouse mice have human behaviors and things. You
know, Elizabeth you talk a little bit that you have done a very strong proponent of that
we've already got great human imaging techniques that you're using. But also in humans in trying
to trying to study the differences and similarities and what we can gain more out of our MRI techniques
we already have and how you're going about that and helping translate ideas.
Sure. So, so I touched on this a little bit before but you know anybody here had an MRI
scan? Have you ever volunteered for a functional MRI scan where they showed you how to do tasks
and things like this? OK. So I've done lots of them, I fall asleep I'm terrible subject.
So we, so you go into an MRI scanner it makes this beautiful image of your brain. And what
they discovered about 30 years ago now is if you actually took a sequence of images
over time that you could see small changes in the signal in different regions of the
brain while you were doing things so like if you actually moved your hand you could
find a region of the brain that would have some sort of correlated change in the MRI
signal while you were doing that task and we call that BOLD, signal the blood oxygen
level dependent signal. And it's what generates those beautiful movies images that you see
on the front of the New York Times saying this is the area of the human brain that reacts
when you see a picture of a puppy or something.
The puppy area
And so you know I did my I did my postdoc up in Boston in one of the centers that actually
developed this method. And so right now you know this is the best method that we have
for looking at the human brain. It's noninvasive, any one of you can go sit and do one of these
studies and pretend to be a mouse, you know tapping your fingers or being shown pictures.
And, and it does a pretty good job it points to this area here, this something happened
here. Right? The problem is that that signal actually comes about because there was a change
in blood flow in that area. So there's not really a change in the MRI signal when a neuron
fires but there's a change in the magnetic properties of blood when it becomes oxygenated
or deoxygenated. And so that little signal change that you see is actually coming from
a change in blood flow to the area. And that's why it gets a little bit confusing because
it's a little slower than the neural activity because it takes a while for blood flow to
change after neurons fire.
And so I've spent, in addition to the other work I've done, I spent a long time studying
this the relationship between neural activity in the brain and the blood flow activity that
that kind of accompanies it. Partly initially to help with understanding those fMRI images
better, functional MRI because when you're seeing blood. We wanted to understand what
that meant in terms of neurons I could talk about this all day but it CT cause us to have
to develop a lot of imaging methods to go in and use microscopes to simultaneously look
at neurons firing and blood vessels next to them.
And I got very interested in it from the standpoint of it's actually quite surprising that every
time or region of your brain has some neural activity that there's actually a big increase
in blood flow in that area and that has to happen for a reason and that has to be an
important part of brain health. And so now my work has sort of moved towards not just
trying to understand the fMRI signal better but also recognizing that there might be situations
in which that relationship between neural activity and blood flow gets disrupted. And
if every time you're hungry you try to eat a sandwich but you miss something's going
to happen you're going to lose weight you're not going to perform as well. And so, what,
all that stuff I showed you with the coffee and the ketamine I mean the really the core
of that study in the beginning was looking at how that neural activity actually was coupled
to blood flow changes.
And we've got ways now we actually can give drugs that disrupt the relationship between
neural activity and blood flow and we can then see what effects do they actually have
in real time on neural activity, what effects they have on behavior. So we're training the
animals to sit and actually push a lever, this is one of Nick's other projects right
,there were we're try and get them to push a lever disrupt this blood flow relationship
and see whether they actually become slower at pushing the lever simply because this is
just another aspect of the brain that we really don't understand.
Stepping back if we can get a better handle on this relationship between blood flow in
your neural activity, it gives us a way to really better use functional MRI and it has,
this method has kind of an image problem, right? We all agree. Because it's not so clear
what it means. And in fact when you do it in someone who just had a stroke you might
see huge activity in an area where they're paralyzed or you might see no activity at
all and they go on to be, to have a great recovery. And so sort of my mission is to,
to use these methods more in the mice where we can do this very detailed imaging but to
try to disambiguate all of this. Try to find is there an early signature of Alzheimer's.
Is there something that happens in stroke that we can actually understand in terms of
what it's going to look like in MRI and then can we use fMRI in a much more guided way
to get to those conditions in human brain. So sounds a little circuitous but it keeps
me busy.
I think we all agree it's something there's a huge need for, to understand what is it
we're seeing and how do we learn from it. You know you've done some of the same thing
in trying to tease apart subtypes of depression using, using functional imaging. Can you tell
us kind of how you approach that and what you've found and what you think the potential
for that kind of analysis is.
Yeah, yeah. We're really excited about, this is another area we're really excited about
in my lab the opportunity to potentially use brain imaging to rethink the way we diagnose
psychiatry, if you make diagnoses in psychiatry. If you'll bear with me for like two minutes
I'll tell you an interesting historical like little background story. So our DSM, this
book that we use for diagnosis is kind of seeped into the public lexicon right, you
read about in the Times a lot of people are familiar with it. And the whole way we diagnose
mental illness is really not very old, it was completely changed in 1980. I was born
in 1980. You can't say that about very many other areas of medicine. And yet it's the
truth. Everything we do in psychiatry is quite different from before 1980, due in part to
this kind of controversial study that was published in Science seven years before that
just showed essentially that are our diagnostic system at that time was not very reproducible
and very prone to bias. So social psychologist at Stanford very cleverly designed a set of
experiments where he sent his investigators into hospitals around the country presenting
with a fake symptom. They said I'm experiencing voices saying the words ‘empty’, ‘hollow’
or ‘thud’ and otherwise they were supposed to answer all questions honestly truthfully
and and the results they just followed them and the results were really shocking to everybody.
They got different diagnoses in different places and later work showed that your socioeconomic
background and your ethnic background were could bias the way people assigned diagnoses
to you. And, and it was really hard to detect that these were fake patients basically that's,
that's what he showed you could bias people's judgment in that way.
And then in the second part of this study, and this is kind of the clincher, in the second
part of the study the lead of this study had met the director of a famous hospital at the
time and he said you know this is this is an anomaly what you found is not representative
of the way we do business. Come to my hospital see if you can see if you'll get different
results. And so they agreed he would send an unknown number of people into his hospital
over the next few months and during that time their job was to figure out who is a fake
patient who is a real patient. Twenty percent of people were identified as definite impostures
and other twenty percent were identified as probable impostures. So fully two out of every
five people who came to the hospital had, were thought to be possible impostures. And
the truth was that he hadn't sent any impostors and that they were all real patients. So you
could bias people's judgments in both ways.
And so getting to that point here, the DSM3 which was released in 1980 and very similar
to the one we use today was really designed to lump lots of people together with very
different problems in big diagnostic categories that we could all agree on. And it did a really
good job of that. The diagnoses are much more reproducible than they used to be, less prone
to bias. But the problem is they don't correspond to biology very well.
So, depression is a great example of that. You have this like choose five or more of
nine symptoms. So there's at least two hundred fifty six ways you can be depressed. And some
of them are almost opposites of one another like sleeping nineteen hours a day, can't
get out of bed. Not enjoying any activities, very slowed, gained a lot of weight. That
person is depressed. So is a person who's sleeping three hours a day anxious, weight
loss, almost the opposite. And they get the same diagnoses but they probably don't have
the same biological problem and it's a miracle really that treatments work as well as they
do in these in these very different people. So what we're trying to do with fMRI is figure
out whether we can identify clusters of patients that have similar biological problems as indexed
by fMRI and which can be used to map functional connections in the brain. And if so then maybe
we can then maybe we can target specific treatments to individual people who are most likely to
benefit from that.
And can you find different patterns of patients based on fMRI?
Yeah. So we've found, I always emphasize I don't think this is the best or final solution
to this problem but it's it's a it's one solution that works well we've found, that we can identify
four very different patterns of abnormal functional connectivity in the brains of different depressed
people. We can assign individual people to those categories based just on their brain
scan and then those, those categories predict different kinds of symptoms and a different
likelihood of responding to transcranial magnetic stimulation which is a brain stimulation based
treatment that you could imagine, I mean it it's on a much coarser scale, but the goal
is the same as with optogenetics where you're trying to stimulate a particular circuit to
to modify the activity of that circuits. TCS works in much the same way and it's and it's
much more effective in one of these subtypes than in than in the others.
And do you think if you look for those patterns in mice you could find out for Tony if mice
are depressed.
Yeah where we're working on that. One thing I think that's really nice about the mice
is that you can, you know, Elizabeth talked about this. The the one of the limits of MRI
is it's fundamentally kind of correlational in nature. You observe this change in the
brain scan and it seems to correlate with a particular behavior or a symptom. But there
are many changes in these brain scans and it's really hard to pinpoint which one if
any is causally related to particular behaviors and mice afford us that that opportunity.
So that's that's one thing we're trying to do in the brains of our, of our mice.
So, we want to leave a little bit of time for questions, but so, one final thought for
anyone here, so looking down the road, five years down the road what's your wish list
or what's your vision of where you think we're going to be or where do you think is going
to be the next? What are we going to do to unlock this and take it the next step and
what are what are we going to be talking about a decade from now?
Well I can tell you what we're trying to do. I laid it out. It's the Rosetta brain. I think
being able to understand behavior all the way through from the behavior to the neural
activity to the actual wiring diagram for me is sort of the Holy Grail. To to reduce,
it's not even to reduce, it's to provide the foundation. I went into neuroscience because
I wanted to understand how three pounds of stuff gives everything that we see, hear and
feel and it just seemed incredibly mysterious to me. And after being in a neuroscientist
for several decades it remains just as mysterious. I don't think we're any closer to…
A little bit?
No sadly.
But I mean the core question, why does it how does it…. why are we conscious? How
do we even how does it feel. How is it that we feel the way we do? I don't think we're
any closer to understanding. And I sat there thinking about it for a long time and decided
that thinking about it wasn't going to get us anywhere further. But I'm hoping that if
we actually see an example where we can go all the way from the neural activity to the,
through the behavior to the circuitry, it'll at least provide us a way of maybe formulating
the right next question which I currently don't have.
My Ph.D. adviser was Christof Koch who has spent most of his career trying to understand
consciousness. And I think now he he's the scientific director at the Allen Institute
where their goals are very similar to the goals of the people here which is to tease
apart the circuitry of a mouse. And so my my feeling is that although the, the circuitry
of a mouse is clearly different from the circuitry of a human, basically the building blocks
are already there by the time you get to the cortex. If you look at a piece of mouse cortex
and you change the scale bar and you put it next to a piece of human cortex, they are
indistinguishable.
So the basic building blocks we think, that you're smiling because you're a neurosurgeon
or you will not be able to tell the difference if I change the scale bar - even the neuroanatomists
at the Allen Institute were fooled by this this pair of pictures. So I think that ,you
know, we as as humans evolved in the last couple of hundred thousand, maybe million
years as primates over the last couple of million. I mean most of the work was done
I think by the time we evolved as mammals. We split off from mice seventy, eighty million
years ago. The basic building blocks I think are there for us to understand. And once we
get those, than I think we're off to the races to figure out what makes us human. But I would
be, I would be pretty happy if we could get an understanding of what makes us mammal.
So we want to learn a little bit of time of questions for questions We've got two microphones
going around so if you have a question please wait till somebody brings you a microphone.
Thank you very much to the panel. You've given a really great overview of some of the genetic
and biochemical techniques that have advanced the field in the last decade or so. But can
you talk more to some of the informatic techniques that might be advancing the field you mentioned
NMF for dimensionality reduction. There have been issues with, you know far more specifically
with sort of the kernels and statistics that are going on there. But how has you know Big
Data, data science, machine learning also influenced the field Thank you.
Well I’ll just say that I started off in machine learning neural networks as a grad
student and I think that those that field has affected it in two ways. First of all
it inspires us how to think about how the brain works. Trying to build something is
one of the best ways of figuring out how something works. Beyond that, in the last couple of
years machine learning techniques have transformed big data in neuroscience just like they've,
they're transforming it in all other, in many other fields. So that our ability, I mean
we're now collecting huge data sets and, you know, either calcium imaging or connectivity,
and, you know, you can't you can't just stare at it anymore it's the same question of, machine
learning approaches provide ways of generating hypotheses again, just just like knowing the
connectivity. And then it takes a real brain at least so far, we're not quite out of business
yet, but these these big data techniques transform, have are in the process of transforming how
we sort of generate scientific hypotheses that then we can go out and test. At least
for me that’s one of the big ways
I would say coming back again to the other point is, we need computer scientists and
people who understand code and big data to understand the brain. Right. Because it is
a computer right? And again the insights you get from people who who who know that are
really critical. What's really fascinating we've got Amazon and Google and all of those
big companies now really interested in brain research. They're hiring our graduate students,
right? This deep learning, these algorithms. I mean the whole thing is coming together.
So so I think both fields are now really stimulating each other and I think you know that's going
to be a path towards getting where we need to go.
Part of the difficulty is to understand, actually the behaviors. And the behaviors, it turned
that a machine can do a better job than humans in order to determine how many forms of a
behaviors they are. Actually Bob Datta in Harvard, they are creating a syllable of the
behaviors and they're trying to find a neural correlate to that, which turned out, you actually
finding exact in neural correlate to the syllables of the behaviors, which we as a human observer
you could have completely missing it. So I think that's kind of how these two things
are neuroscience and machine learning aspects are really assisting each other.
Another question?
Depressed lab animals produce, developed isolated, do you alter the genetics?
How do you produce a depressed animal?
Yeah I can take that one. The real challenge you mentioned like th, like animal models
of mouse models of schizophrenia, autism, we have a much better handle on the genetics
of some psychiatric disorders like schizophrenia and autism than we do for depression. What
we know about depression is it's it's it's heritable. If your mom is depressed you're
much more likely to grow up to be depressed yourself. And that's not just about environment.
But it's probably the result of the shared impact of many many genes each having a very
small influence on your likelihood of becoming depression and that interacting with your
experience. So that makes it a lot harder to have like a genetically-based model of
depression that they just don't exist yet. They may one day
I'll push back on that a little. There's a, there's a model of learned…one model of
depression as a model of cold learned helplessness. And there was a guy who bred generation after
generation rats that showed this this trait of learned helplessness and over the last,
I think it took him a good fifteen, twenty years to breed these. Unfortunately he started
doing this when rats seemed like they were going to be the right model system not mice.
But he bred these rats that were genetically distinct and showed this trait of learned
helplessness and they, he and others have gone on to dissect some of the circuitry that
is disrupted in these and identified the lateral habenula, the particularly important circuit
in this. Now that's one particular type of depression. But I think it just shows how
the kinds of behaviors that we have can actually often be modeled in a rodent..
And I'll just also add to that to say that you can even study things like confidence
in a rat or a mouse. It's not obvious that something like confidence, which feels like
it's an internal state would have a neural correlate. But one of my colleagues at Cold
Spring Harbor is identified a brain region and particular types of neurons that seem
to encode an animal's confidence and confidence is for example one of the things that can
be easily disrupted in depression. So I think that when you actually really think hard about
how to probe that behavior in a way that, traditionally, people who work on rats and
mice have not but are now starting to, you can actually sort of get more traction on
these things than you might have thought. The sort of ridiculous notion of you know
a depressed mouse or rat becomes less ridiculous.
Another question.
Thank you for the presentation. You talked a little bit about the advance how these advances
impact the diagnosis of mental disorders. How do you see on the practical level, what
do you see these advances such as optogenetics or anything else that you talked about here,
how do you see them advancing the treatment? Like what can we expect in the next couple
of years with things like ECT or anything that I might not even be able to understand
from your answer to this. Thank you.
I think getting back to the transcranial magnetic stimulation thing as one example of a treatment
that's based on stimulating a brain region is, electroconvulsive therapy is is is our
most effective by far treatment for depression. And it's basically a big jolt that is to the
brain that's very stimulating to everywhere in the brain. Right? And and yet it also has
a lot of undesirable side effects. And so we reserve it for people who are most who
most need it, who don't respond well to other treatments. I think one potentially exciting
avenue in future would be figuring out whether there are particular more focal brain areas
that require a particular kind of stimulation. You could even imagine maybe a little further
down the road, cell type specific stimulation getting with a lot of Tony's work was describing.
The overall goal being more precision in the way where we're stimulating brain regions
might lead to more effective antidepressant responses and fewer side effects. And I think
that's a vision that everyone kind of shares.
So unfortunately we are running out of time. We really need to thank these amazing scientists
for being here.