[MUSIC] Stanford University.>>Well, thanks very much, Miriam. It’s really a great pleasure,
an honor to be here. It’s interesting, in walking in and
looking around the room I know a lot of people here because
many of you have been funded by NIMH and some of you have been colleagues
over many, many years, but I don’t think I’ve ever seen all of
you in the same room at the same time.>>[LAUGH].>>Which I suppose is the,
the idea of forming this institute and how exciting that is. When the idea for this symposium
got hatched some months ago, Bill asked me if I’d participate
to give kind of a high level. I’m not sure a high level’s
the right term, but a sort of a, a view of the field from 30,000
feet to talk about where are we? Where do we need to go? And to think a little bit
about a topic which I’ll call from neurons to neighborhoods. To see if we can sort
of charter our course. And this would be quite different
from what you’ve already heard from Bruce and John. This is actually a,
a little bit more about directions and strategy maybe, than the nitty
gritty details of experiments. I want to start off in sort of a call
to action by talking to you about the really remarkable good news of
what biomedical research has been able to deliver to the public in so
many different ways. And I’m going to just focus on
biomedical research in what it’s done in a public health sense for
measures of mortality. We just don’t talk about this enough and
people tend not to realize that in the last few decades besides the increase
in longevity which has gone from 47 to 79 or so in the last century, there have been
some very specific phenomenal successes. When I was a medical student
acute lymphoblastic leukemia was the most common cancer of children,
and it was 95% of the time fatal. Today it’s cured at a 95% rate. About what is that, 6,000 deaths averted every year for
this most common cancer of childhood. Another example, which again we
don’t often recognize is that there’s been about a 63% reduction in
mortality from heart disease that if you just do the graph from the trajectory
of what we were expecting from the 60s and 70s, that means that there are over
a million deaths averted every year from heart disease based
on findings from research. AIDS ca, I think people know there’s
a 50% reduction in mortality. Last year the Department of Health and
Human Services re-labeled AIDS as a chronic disease, something which would
have been unimaginable just 15 years ago. Even within the brain. Stroke is got about a 30%
reduction in mortality, and if you can get into the emergency room within
three hours of onset, you will walk out of the emergency room without sequelae if you
receive one of the clot-busting drugs. That’s pretty phenomenal. So, these are just a few examples
of the places where we’ve had these terrific successes measured with this
very hard outcome of mortality, and that’s pretty terrific. What is maybe not so terrific is that we’re not doing so
well in many of the brain disorders. Suicide is an example
where really over the last few decades rather than seeing it decrease
as we have with these other disorders. There’s actually if anything
an increase or a slight. I mean you could say it’s gone up and down a little bit but the most
recent numbers continue to trend up. That’s 90% of the time thought to be
related to having a mental illness, most often depression,
schizophrenia, bipolar. It’s an amazing contrast to homicide, which is another, used to be an equal
source of mortality in this country. Homicide’s come down about 50%,
as have traffic fatalities, and yet suicide continues to be
a major source of mortality. I mean if you do the math
that’s kind of phenomenal. There are 39,000 suicides
in this country every year. That’s twice the number of homicides. So it’s really quite extraordinary. And it means that we lose more people
to suicide than almost any form of cancer except for colon lung and breast. Everything else has less
than 39,000 deaths a year. This is a huge, huge public health problem
which we don’t really talk much about. Of course, when we think about
brain disorders broadly, it’s not just mortality
that we care about. And when we think about public impact
we’re not just interested in mortality. We’re interested in morbidity. Morbidity is hard to measure. But we do that these days through
a measure, sort of a, one of these aggregated statistics called the DALY,
the Disability Adjusted Life Year. Com-, combining the premature mortality and actual years lost to disability, when you
do that as you can see in this graph. What’s quite surprising is that
brain disorders, mental and behavioral ones here and neurologic here,
are actually the largest source of disability, of morbidity now,
not just mortality. More than heart disease and cancer and
other major, major biomedical challenges. This wasn’t always the case. But it has become the case and in this era
of dealing with chronic non-communicable diseases these disorders are emerging
as the major public health challenge. For the mental disorders, the mental
behavioral disorders, part of the reason why they’re, they’re driving these numbers
is because they start early in life. 75% of adults with a mental illness
will describe onset by age 24. 50% by age 14. And for that reason when you actually look
at the data for morbidity for disability, you can see these are the ages
by five year increments here, that, actually, this red line which is the
mental and behavioral disorders they’re driving virtually all of the medical
disability up until about age 50. Isn’t it amazing? I mean, we don’t actually
think about this very often, but these are the chronic
disorders of young people. And the largest source
of medical challenge, biomedical challenge, compared to, you can actually at age 40 you could add
all of these other sources together and you still don’t get up to where we
are with mental and behavioral disorders. It’s not just mortality and
morbidity that we care about. It’s also, we have to talk about
cost at some point because that is such a driver at least in
the American health care system. A few years back, the World Economic
Forum, that’s the group that meets in Davos every January and now in China every
September, got together and said health care, especially chronic noncommunicable
diseases are going to become a really big challenge, not only in the developed
world, but in the developing world. How big of a challenge will that be? And they decided to look at diabetes,
and chronic respiratory diseases, cancer, and heart disease. And just for kicks,
because they needed a control group, they threw in mental illness. And much to their amazement,
as you can see here, actually mental illness was
turned out to be the trump card. It was actually the largest
cost all together. You can see the numbers. In 2010, it was estimated to be
$2.5 trillion annually in US. Dollars going up to something like 6
trillion by their projection by 2030. Which is really phenomenal. That’s how you get these enormous numbers. Just in the US alone, the number just for
serious mental illness, even a few years back, were exceeding
$300 billion dollars a year. So, these are enormous costs. And what makes them even more extraordinary is that unlike a lot of the
other costs that we’re seeing in medicine, these are all on the public dollar side. So serious mental illness is virtually
all paid for by Medicaid, Medicare. 85% of people with
schizophrenia are unemployed so these enormous costs for SSI and welfare
and for what we would call indirect costs. In young people. There’s a huge social problem. Absolutely, not just huge, but it’s going to become one of
the largest problems that will prevent developing countries from fully
developing, and it will prevent developed countries from being able to reach even
the economic standards that we expect. The flip side of all this is what’s
happening demographically and you see that when you talk
about Alzheimer’s disease. So here’s the other side of brain
disorders not now the mental illnesses that are largely disorders that begin
early in life, but here we’re talking about diseases that are neurodegenerative
and we think about as late-life. And you can see from this graph that as we
project out what we’re talking about, even though currently there are about 5 million
people with with Alzheimer’s disease. It’s kind of amazing, actually. One in eight over 65, but
it’s one in three over 85. This is really common. But what’s happening is that because of
change in demographics this is becoming also a huge driver so that even though
now the figures are around 200 million, $200 billion a year that’s expected to
surpass a trillion dollars by 2050. What’s interesting about
this graph is people have calculated what would happen if, and this is the dark bars, if you could
just forestall the onset by five years. And you can see that even if you
don’t cure it and don’t prevent it, just by slowing the progression you
can have an enormous economic impact. But to understand this you
really have to look at, what’s happening to our nation
in terms of demography. And it is really kind of
an extraordinary story. 1965, 9% of people over age 65,
and you can see that now it’s up to 14% and will soon be at,
at 20 and, and even above 21%. It’s maybe even easier to see here, when you look at what parts of
the population are growing fastest. And you can see it’s the over
65 in this decade and the next that are going
to be the real drivers. And memo to any Stanford administrators
who are thinking about how to keep this the university healthy. This is your market. This is the, this is the new. These are the students of
the future are people over 65. This is the largest by far. Fastest growing part of our population. But that’s also the challenge when
you think about Alzheimer’s and when you think about
neurodegenerative diseases. Because obviously we’re on
the wrong side of history here unless we can do something about this. So, here’s the message. These kinds of diseases are going to
be of the 21st century what infectious diseases were
in the 20th century. Brain disorders are the most disabling and the most costly of these chronic
non-communicable diseases. And yet,
we do not know enough about the brain to address the challenge
that’s in front of us. Or to put this another way,
quoting one of my favorite scientists, Jerry Garcia,
somebody has to do something and it is just incredibly pathetic
that it has to be us.>>[LAUGH]>>I showed this slide to my wife recently
and she said, you can’t show that.>>[LAUGH]
>>That sounds.>>[INAUDIBLE]
>>[LAUGH] She said what about that hope the elders slide,
we are the ones we’ve been waiting for? I said, everybody’s heard that. So this is actually. Jerry got it, had it just right, I mean, there isn’t
anybody else who’s going to do this. This is, it’s up to us and the world is
waiting and this is really a big problem. So here’s the, here’s the challenge. We’ve got to figure out how to do this,
and it is rather pathetic that it’s going to
be left to us because we don’t, even though we maybe smart
we don’t have great tools. And that’s what I want to talk about. Because I think where the challenge here,
for the next few years, is gotta be is coming up with a way to be able to
do this better than what we’ve been doing. You’ve heard great science
already this afternoon. But I think even our previous speakers
would agree with me that we don’t want in 2020 and 2030 to be dealing
with only the toolbox we’ve got now. And in fact that’s true at every level. And, and this is going to
be a multi level challenge. So, building on the Jerry Garcia
concept what I’d, like to share with you is another
favorite quote which is actually from. A real scientist, an astrophysicist
from Harvard Freeman Dyson who coming from astrophysics has a very good sense
of the importance of having better and better telescopes. New directions in science are launched
by new tools much more often than by new concepts. The effect of a concept-driven revolution
is to explain old things in new ways. The effect of a tool-driven
revolution is to discover new things that
have to be explained. I think that’s actually very apt for
where we are in this field, and the useful thing to recognize is that
even in the last five years we have come pretty far in developing, at every level,
some really interesting new opportunities. The toolbox is getting better
virtually on a, I would say, couple, every two to three months
there’s something new coming along. I’m not going to be able to summarize
all of those different areas though, that would take the rest of the afternoon. But I want to focus just on systems
because this is going to be for the neurosciences,
neurosciences institute, I think probably the key
place you want to live. And there just to give you
a taste of the sorts of things, some of which you’ve already heard. But, that are worth thinking about, certainly we’ve got some
pretty cool tools right now. We’re doing brain structure in
a way that we couldn’t before. Brainbow is a nice way of disentangling
complex areas using multiple fluorophores to be able to identify individual
neurons and their projections in a way that we could not do a decade ago,
and that is an enormous advance. The contribution from
Carl’s lab of clarity, and being able to think about 3D
anatomy is going to be, I think, transformative, not just for the brain,
but for other organ systems as well. And you’ve already heard about,
in terms of structural changes, looking at the wiring diagram as Bruce described
it from the Human Connectome Project to be able to look with much,
much higher resolution, including at those very complicated
areas where you’re crossing fibers, to begin to look at what
the human brain looks like. It may be even more stunning now to be
able to think about what we can do in terms of function. And the recent evidence using light
sheet microscopy from Janelia. From Aaron’s and their colleagues. To be able to look at virtually
every neuron in real time as it fires in a zebra fish larva. Awake behaving. And to begin to try to ask questions
like what do these patterns mean? What is this? You get these blasts of
activity every now and then. What is that about? What is the language that this little
fish is using to make sense of the world? And to get around in the world? I’ll let it go a little bit longer so
you can see this blast point. Right. It’s coming, it’s coming, it’s coming. Somewhere in there
the whole thing lights up. There it is. So, so that’s, you know,
it’s, I think we’re at a point where we now have the tools to
begin to look at those kinds of issues. I don’t know if Mark Schnitzer and
his folks from his lab are here, but there’s a really a very
analogous example of Mark’s work using in this
case using calcium imaging to look at plate cells with one
of his micro endoscopes. To be able to go right into
the hippocampus and look at a mouse. In an eight-arm radio arm
base as he runs around and to try to see what how is space and
place being encoded. This is a good week to
be talking about this. Got a nobel prize, for
this kind of work, this week. But what John O’Keefe and, and what the
Mosers have done, which is spectacular, is to lay out what the grid looks like,
and where the cells are. But what I think Mark is giving us and his colleagues is this dynamic picture for
the first time to see. What’s the code? Could we take this pattern,
and reverse engineering, and use that to describe where the mouse is. Well, almost. We’re making real progress there. And the same kind of an approach,
when we talk about function, to think about in human imaging. And this is the work, again,
from the Human Connectome Project from Camille Ugerbil and
colleagues to look at these, these trenchant networks that
are co-activated during the resting state. Is there a code embedded there,
is there meaning? And how could we make sense of that. So given those kinds of advances
over the last very short period, maybe three to five years
not surprising that people felt this was the time to
really make a major commitment. And the President announced
in April of 2013 this idea of the Human Brain Project which,
the BRAIN Initiative Project I should say, the next great American project. What he meant by next, was he was,
I think, thinking about, the Human Genome Project as
the last great American project. But he didn’t actually specify that. Assuming that it was something scientific
but we don’t actually know that. But look at the quote that’s
kind of that’s very inspiring. Because what President Obama did was to
sort of take a page from Freeman Dyson and others by saying what he wants is to give
scientists the tools they need to get a dynamic picture of the brain in action. So, sort of, reading the language of the brain at
the speed of thought, that’s the concept. What will we need to do that? Where is the information encoded? And from, from my perspective,
that’s a really tough problem. And I want to just give you sort of
three angles on this which make it particularly tough. One is that we don’t really
know what the right scale is. So, a lot of our advances
are kind of at this end of the spectrum where we can like
in nematode with 304 neurons. Begin to look at every one of them, the same sort of thing that
I showed you with zebrafish. You know, you could actually begin to
get some information about the coding system there but how well that will
translate to these other kinds of systems when you’re going across nine orders of
magnitude isn’t really entirely clear yet. It’s also not clear whether the
information that we care about is being encoded to the level of synapses or
at the level of individual or clusters of neurons, or really in a kind of macro way
through oscillations in very large changes over longer periods of time in,
in the cortex and subcortical areas. And we, actually it’s kind of amazing. I mean I, I always have to explain
this when I try to argue for our budget in Congress. I have to do that and I do that
very unsuccessfully I should say. But I do it anyway. And I always try to explain that w-, we know so much less about the brain than
we know about the heart or the kidney. I mean, it’s kind of astonishing. We’ve known that this is an information
processing organ like the hearts are pump, and the kidneys are filter. But we don’t have a clue, even about what
level that information is processed, and how to, how to do it. Now, we are I have to say, with the tools
we’ve got, we’ve got lots of advances. But still,
much of what we do well is kind of static. And clearly the code is dynamic. It’s all about time. And we don’t yet even known what
the right temporal domain is for that. On the structural side, and
this kind of question number two, we’ve thought a lot about getting the map,
the, the connectome. And, of course, there are great arguments
that take place within the community between whether we need this, this micro,
the macro-connectome, which you’ve heard about from Bruce, or the micro-connectome
and which one is more important. And I think that most of us now
think that we need both and, and everything in between. I’ll say more about that in a moment. And then trying to get the right,
at least with human imaging, trying to get the right level of both spatial and
temporal resolution is really a challenge. And some of the questions to
Bruce at the end about, you know, how far can you push this? What we’re struggling with generically. We actually don’t know how far we
can push the current methods for both spatial and temporal resolution. We think we can get a lot of
mileage by combining them. And doing multimodal imaging where you
put lots of different things together. But beware, because we do so
much around bold for instance. Which is way out here. This is the bold signal which evolves
because it’s all blood flow over many, many seconds. But the actual ERP or the actual electrical signal is going
over, you know, a few milliseconds. So we’re, we’re a couple orders of
magnitude at least off when we try to tie those two together. We got a problem. We’re still not where we need to be
in terms of the tools that we have. Now, the BRAIN Initiative to get
really launched in the right way we decided we’d bring in a lot of very smart
people, many of whom are in the room. The group was led by by Bill and
by Corey Bardman and, and they said yeah, you know, this macro connectum and
micro connectum are kind of neat but we really need is this meso connectum,
something in the middle. They put together this terrific report,
BRAIN 2025, A Scientific Vision. I, I do a lot of reading. I think this is one of the best
things I’ve read in 2014. I would highly recommend it
to everybody in the audience, especially if you ever
plan to get funded by NIH.>>[LAUGH].>>This,
this is a really good thing to read. All of the NIH BRAIN directors have
read this and talked about it and think about it, and every time we reread
it we something we didn’t know was there. So it’s a,
it’s a really marvelous document. It’s a little long and hopefully we’ll
have a shorter version at some point. Note to Bill, yes? Okay but, but this is, this is for the take home which is that the goal
here is to map the circuits. Measure the fluctuating patterns of
activity within those circuits and understand how they create these unique
cognitive and behavioral capabilities. This is so easy to say and so hard to do. We really don’t even know how,
but what a fantastic mission. What a fantastic goal. And hopefully that will take place by
bringing what we know at this level macro, this level micro, and
this sort of meso level in between. One of the hopes for this,
if we could ever do it correctly, would be something like what you’ll
see I think in this video as it shows. And this is work from the Braingate
Group Lee Hochberg and John Donahue. In which, by understanding that language, the way in which the brain
is encoding information, they could take someone who in this
case is this women who’s been paralyzed. She quadriplegic. She can move her head but
neither her arms or legs. She has about 100 neurons being
recorded in her premotor cortex which have trained up a computer
algorithm to then drive this robotic arm, and for the first time she’s using her
thoughts about movement, about reach and grasp to be able to drive the arm to
be able to get a drink of coffee. And again,
by thinking about the movement and this has required a significant
amount of training. She can drive the robotic arm back. And voila. Now the goal is not this. The goal, of course,
would be to drive her own arm at some point because her
arm is perfectly functional. It’s just not connected. But figuring out how to do
that is a lot of the, where we think the ultimate hope of being able
to decode that language really resides. So, I’m going to finish up in just a
couple of minutes by saying that there is sort of an embedded
inconvenient truth here, which is in spite of all that we’ve done
recently and all that we’re hoping to do. We’re still not really
bending the curve I think for people with serious mental illness. These are you know,
got great stuff and great papers but it doesn’t yet seem to make a difference
for people with autism or schizophrenia. And I’m going to argue that if we
want to do that we’re going to have to completely transform the way
that we take neuroscience and put it into clinical practice. So one option is that
actually the field that today is called psychiatry could actually
be reframed as clinical neuroscience. And the way this could work is
that you begin to think about diagnosis very differently. In this case diagnosis is
really more about biology and cognition as well as behavior. So this is a place where we
need just the very best of cognitive science as well as the very
best in biological sciences. If we think about therapeutics we want
to be able to move from the idea that these are sort of you know,
quart low in serotonin when you’re depressed to thinking much more
about this being a circuit problem. And that treatments, then,
are more about how do we tune circuits and make a difference. And finally,
the culture of neuroscience, or the culture of science,
will need to change as well. And I want to say a bit
about open science, and why we think that’s so important. On the, on the, in the case of diagnosis,
really across all of these issues from neurons to
neighborhoods, actually you may say, maybe we should say molecules to
neighborhoods, we’ve got, I think, lots of ways now of pulling out new
data beyond just observable behavior. That will give us, I think, a much more
precise way of defining what’s wrong with people who used to be called depressed,
or used to be called schizophrenic. There may be 25 forms of schizophrenia. And, and
who knows how many forms of depression. And, the may require very
different treatments. But we can’t do that until we begin
to collect these kinds of data. We know this from cancer, we know this
from many other parts of medicine. The same with as we look at therapeutics. The, the,
going across every one of these levels and realizing there isn’t
going to be a magic bullet. There’s not a pill that’s
going to be the right answer for any of the disorders we care about. What it’s going to require is a kind
of network approach that brings all of these together in a way that actually
makes the most difference and there’s been a lot written
about this recently. Whether we call it beyond magic bullets or
network solutions. Trying to find ways of taking the kinds
of tools that we now have and applying them in the clinic to begin to tune
circuits, and to get people to recover. Finally, about the culture itself,
which I think is really a challenge, it may be the hardest of the three
things that need to transform. There is an enormous frustration in
the public that we haven’t done better. I hear this literally every day,
whether it’s parents with autism, with a kid with autism or people with
a relative with severe depression or someone who’s lost a child to suicide. They’re just enormously frustrated
with how long it takes and how little we’ve been
able to translate how all this very cool science into changes
that they can they actually realize. And the argument here is that
the way this has to happen is that the whole ecosystem for how we do science
has to change, that And it is changing, that everybody has a role in this
that it’s a whole series now of these collaborative efforts that bring
these various components together. But the really fundamental change is that,
and it’s going to make a difference for people in this institute I think, is that we have to think
about what we do with data. I mean, in a, in, in the information age
data are really the coin of the realm, and the extent to which we can
standardize, integrate, and share. Is one of the ways in which we can be
somewhat confident that things could go along much more quickly and also solves a
number of other problems by creating more transparency in the science we do. So open science is, I think very much a mantra that you’ll be
hearing more of from NIH and that I hope. Become something that folks here
can adopt and, and promote. So, to summarize what I’ve told you,
brain disorders are going to be the major public health challenge
of the next century, this century. To address this we’re going to need
new tools tools that will help us to decode the language of the brain
from neurons to neighborhoods. And we need to be able to translate
those into public health benefit. And that really comes
at those three levels. Diagnosis, therapeutics, and changing this
culture of discovery through open science. I’ll finish with one last quote
which I think is probably timely for all of you because much of
what we’re talking about is really how things will go in the future. And I wanted to share this one,
which I use a lot from Bill Gates. Kind, kind of long way here from
Jerry Garcia to Bill Gates. [LAUGH] We always overestimate the change
that will occur in the next two years, and underestimate the change that
will occur in the next ten. I think that’s a good guide that. There are going to be enormous
shifts partly driven by the toolbox that will be created here and
elsewhere. We need to adapt that, adopt that,
and to really promote that. So that it’s not only
giving us cool papers, but actually giving us something that will
also make a difference for the public. Thanks very much for your attention. [APPLAUSE]>>For more,
please visit us at www.stanford.edu.

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