>> Eric Horvitz: Let's go ahead and get started. It's my
pleasure to introduce Brian Caffo. He's a professor out at the
Johns Hopkins University in the biostatistics department in the
School of Public Health. In the group he runs, SMART Group, is
really fascinating, so this is statistical methods and
applications for research and technology. I think I got that
right.
>> Brian Caffo:
Yep.
>> Eric Horvitz: They do everything from wearables and
biosignals through health data and the thing he's going to talk
to us about today, quantitative neuroscience. Rather than get in
the way, I'm going to hand it over to Brian.
>> Brian Caffo: Great. Thank you. And thank you for inviting
me out here. Really lacking forward to the visit. So let's see.
What's the easiest way to do this? Okay. So I just wanted to
mention a couple of things about the places that I'm associated
with. So yeah, the group I started is called the SMART Group.
It's a backronym. We actually wanted the word SMART and worked
backwards until we got the acronym. But it's a really fun group.
We have the domain, www.smart stats.org if you want to check us
out. We do a lot of different things. It's kind of taken on a
life of its own at this point.
So I'm at Johns Hopkins in a very interesting place in the School
of Public Health, the Bloomberg School of Public Health in the
Department of Biostatistics, which is a very fun place to work.
It's actually the
we claim to be the first Department of
Biostatistics ever, and it's
it has some amount of legitimacy
to the claim of being the first statistics department, though
some of our colleagues from the UK dispute it when they come talk
to us.
And then so I also work a lot with the laboratory for neuro
cognitive and imaging research, which is at the Kennedy Krieger
Institute, which is a Children's Hospital affiliated with
Hopkins.
So about how much time do I have, about 45 minutes or something
like that?
>> Eric Horvitz:
>> Brian Caffo:
yeah.
>> Eric Horvitz:
You can go an hour, even an hour and a half.
Hour and a half, oh, great, wow.
All right,
No reason to rush.
>> Brian Caffo: No reason to rush, okay. Oh, great. So what I
like to think about is brain connectivity. So when I think about
brain connectivity, I like to think about the telephone system,
you know, old school telephone system network, right. So
and
there's been kind of a useful sort of taxonomy of brain
connectivity that, you know, some of the luminaries in the field
have come up with, and it's useful for me. I think maybe the
neuroscientists maybe don't like how crude it is, but I think for
statisticians and mathematicians and computer scientists and et
cetera, working in the field, it's sort of a very useful
organizational thought process.
And the taxonomy goes something like this. Basically, there's
there would be three sort of forms of connectivity that people
are often interested. The first is sort of the easiest to think
about is sort of anatomy, right, and that's analogous to the
wires, right? So in our brain that's white matter, right, that
connects, you know, well, at the sort of macro scale would be
white matter that connects different areas of the brain. You
know, down to the smallest scale, it would be down to sort of
synaptic connections between neurons, right.
So anatomical connectivity is maybe the easiest to think of, and
that's the sort of wiring. But again, it's that
even though
it's the conceptually simplest of the three ways to think about
things, it still is complicated because the measure
measuring
it is quite complicated and, to be honest, the brain has a lot of
connections.
So the second way that people often talk about connectivity is so
called functional connectivity. So the wiring enables the
communication, but then we might want to talk about the actual
communication that occurs, who tends to call whom, right. Which
groups of people tend to talk to one another, and there doesn't
have to be a specific, direct, physical connection between two
entities for them to talk to one another. Of course, it gets
routed, and that kind of connectivity would be sort of functional
connectivity, how two entities
how frequently two entities
sort of tend to call one another. Then the final one, which is a
little bit more sort of an aggressive style of thinking would be
effective connectivity, which would be who tends to initiate the
phone calls, right you know. Does one group tend to call another
group more often than that group calls the former? So the
effective connectivity gives us this idea of causality. Does one
brain system tend to initiate communication with another brain
system. So there's an idea of temporality and there's an idea of
causality in effective connectivity. So let me just put these up
here. So this is due to the person who wrote one of the biggest
kind of software platforms for neuroimaging data analysis, and I
think I kind of railed against it for a while, but now I quite
like it. So at any rate, the three forms of connectivity,
anatomical relating to neuro circuitry and direct neural
connections. Functional. And functional is what we're going to
talk about today. And then effective, relating to causality.
And I'll give you some examples of the ways in which we try to
study each of these things. They're all hard to study, right,
because there's this fundamental almost sort of Heisenberg
principle in studying the brain is that you're always going to
have some sort of trade off in measurement, right. So if you can
get
you know, you can get actually really nice, very small
scale study of anatomical connections, for example, in simple
systems, like you can cut open mouses and
mice and get down to
synaptic connections and that sort of thing, but you can't really
do that in humans. Or you can't do that in living humans.
And then you can go to things like neuroimaging, but then you
sort of zoom out quite a bit, or you can do
so at any rate, in
each of these different ideas, there's measurement processes, and
each of those measurement processes has their own set of benefits
and trade offs that you have to make to use them. And so we'll
go through some examples of each, maybe discussing a little bit
of their specific trade offs and benefits, yeah.
>>: The thinking is that neuro connections change over time as
individuals age or
>> Brian Caffo: So certainly, you know, in development, of
course, they develop
you develop neuronal connections. And as
you age, of course, you lose some. Then there's diseases which
I'm particularly interested, you know, I work at, you know,
Hopkins is sort of primarily
not primarily, but is very well
known for its hospital and our study of diseases. So what we're
really
what I often
what motivates me is working with a lot
of medical research and studying diseases. And some diseases,
say, for example, multiple sclerosis exactly attacks connections.
>>: And when you say attack connections, should I think about
that as destroying these connections or destroying the functional
aspects of them? The connections are still there, but they are
left unused?
>> Brian Caffo: I think you could actually
so we'll talk
about a little bit about both. But, you know, it depends on the
specific resolution you're studying and way in which you're
studying it in how you think about those
think about those
things. So I think we'll be sort of zooming out to a scale where
it's difficult to think about that with that degree of
specificity about the problem.
So functional
so functional connectivity, one thing that I
find interesting about functional connectivity is it's sort of a
biological definition where it's really defined in terms of the
estimator. It's not defined in terms of an estimand, right. You
know, the definition of functional connectivity involves
correlations, empirical correlations. So it is a bit of a weird
definition. That always bugged me. So one thing that always
bugs me about this area is what
if we could come up with a
better definition of what constitutes a functional brain network,
you know. Saying things are a brain network if there's
correlations, that's sort of analogous, like, to saying there's a
network if people tend to talk to one another.
But there's this idea of maybe there's something a little bit
more intrinsic than that. People tend to talk to one another
because they're family or they're friends or something like that.
So anyway, that's something that's always bugged me. But we're
not going to address it because it's too hard.
So let me talk a little bit about a way in which we study
anatomical connectivity. So here's a way we looked at it with
tract propagation. So there's a measurement technology called
diffusion tensor imaging. And diffusion tensor imaging and,
actually, I would say it's a new technology because it's actually
kind of old now. Diffusion tensor imaging is a way in which you
can image the directionality of water diffusion using the MR
scanner. Most of the technology I'm going to be talking about
today is using MRI. And from that, you can basically reconstruct
sort of little arrows that represent the direction that
the
principal direction that water is flowing at that particular
location in the brain. It's maybe a little bit more complicated
than that, but let's assume you reduce it down to that problem.
Then some very clever people said, well, what we can do is we can
we have all these little arrows. We can try a little connect the
dot exercise. And the idea is that the water diffuses more
anisotropically in a long, kind of well defined white matter
tracts. Sort of white matter tracts sort of serve as like a
straw and the water tends to diffuse along the straw, like. So
if you were to drop a droplet of ink in to a bowl of water, it
would sort of diffuse outward isotropically. But if you were to
drop it in a straw, it would diffuse anisotropically along the
gradient of the straw. So the idea is that white matter, at some
level, is acting like the straw, forcing diffusion in a
particular direction. We can measure that diffusion at some
level with gradients. We can then process it with algorithms via
these tract propagation algorithms. And from that, we can get
things like white matter tracts in the brain or estimates of
white matter tracts. And not only estimates of white matter
tracts, but estimates of how kind of
how directional, how
anisotropic the water diffusion is along those tracts. And that
process is called tractography.
So what we were interested in was sort of how to sort of quantify
and analyze tractography. So take, for example, the cortical
spine tract. What we were interested in is how do you analyze
that as a data point? And so, you know, there's lots of
instances where you're interested in things like the cortical
spinal tract, but one clear one is in the study of multiple
sclerosis. So in multiple sclerosis, you have white matter
demyelination, DTI is sort of really good measure of white matter
integrity, and so we would like to summarize this white matter
integrity with some sort of tract estimate from the tracts, and
we would like to build up ways in which to quantify it in
analytic tools.
So our approach has been kind of the following. We got really
into how you
you know, if you have some sort of non linear
structure, collection of data points, if you have some sort of
nonlinear structure, first, how do you fit a skeleton curve
through it that is sort of a minimum orthogonal distance to the
centroid of the point? And we've elected to do that with
principal curves, which is an attempt to solve the ill defined
problem of finding the curve that minimizes all of the orthogonal
differences through a set of points, noisy points in three
dimensional space.
So, for example if you wanted to fit this spiral, you know, your
principal curve would hopefully find the curve that went right
down the middle. And at any rate, that's somewhat of a hard
problem. So we think we have a reasonable algorithm for that
now. And then after that, what we do is we chop along orthogonal
planes to the curve and project the data points on to the
orthogonal planes and then sort of fit sort of ellipses, Gaussian
we fit Gaussians at each of those projection points and then we
create this tube. This tube, then, we use to kind of summarize
the fractionally isotropy, the measure of how directional the
white matter is at that point along the curve, and then we use
that in our statistical algorithms to say, for example, try to
predict white matter
I'm sorry, multiple sclerosis symptoms
from, say, current white matter integrity measured by MRI. So
who might try to
or who might successfully have an alleviation
of their symptoms based on their current imaging.
So at any rate, that's the goal. That's one example of using MR
technology to try and study
or at least that's our example of
trying to use MR technology to study anatomical connectivity.
And this exercise is sort of fraught with a lot of complexity.
We also, there's
there's one instance where
one clear, very
important instance where it fails is there's probably the biggest
single white matter structure in the brain
not probably.
Definitely the biggest white matter structure in the brain is the
so called corpus collosum. This is a
this is the white matter
that connects the left and right hemispheres of your brain. And
it looks a little like
looks a little like
like that.
Okay. So it doesn't make a lot of sense to fit a curve through
it, so now we've gotten into the business of how do you fit sort
of surfaces in a way that we feel like we can come up with
adequate quantification of the corpus collosum. Yes?
>>: Brain's full of these white matter tracts.
your analysis, isolate be one tract.
How are you, in
>> Brian Caffo: Okay. So that's interesting. So the
tractography algorithms get at little fiber bundles that are
estimated via this connect the dot algorithm. Those fiber
bundles are quite noisy, and if you were to cut into someone's
brain, it's not like you would definitely see a fiber. You
might, but you might not.
So we've kind of
we do a much more kind of low brow version of
tractography. So when I talk about the corpus collosum or the
cortical spinal tract or things like that, these are tracts that
every single person, if you were to cut open their brain, it
would be there. And you would see it. And so instead of looking
at fiber, individual fibers reconstructed by this tractography
algorithm, we look at, I guess what I would call fiber bundles or
we just call them tract. So we try to get
we try to use the
tractography algorithm and estimate the corpus collosum as a
whole entity, which we know that everyone has a corpus collosum.
It's the, probably the quantity that's best imaged by DTI.
And we do that because maybe as a function of our own uncertainty
in how high quality the individual fiber measurements are. So we
tend to focus only on these large structures. I would say
there's another way that people can get at
none of these are
let me get a corpus collosum. Yeah, here's a corpus collosum.
There's another way you can get at these structures that doesn't
involve any of this connect the dot processes.
So what someone has done, a colleague of ours, is they have taken
a white matter image and closely drawn out the major anatomical
structures. Of course, the corpus collosum is there, the
cortical spinal tract, you know, optic rays, all the big, major
white matter tracts they've drawn out. Then they'll take the
diffusion image and they'll morph it into this template space.
So they'll shrink and stretch and squeeze and do it in a local
manner so that they get it into this template space. And once
you have it in the template space, you have the labels for the
actual corpus collosum. Then you just pull it out.
So you've lost the
you've lost the anatomy, because you've
morphed it into a common space. You've lost the anatomy. But
the intensity, the photometric properties, you can retain. You
can retain how directional. You can retain how directional
things are at each location, but you've lost the anatomy. So the
tractography tries to kind of a little bit retain the anatomy,
and the shape and this template morphing procedure loses the
anatomy at some level. You can still analyze the anatomy using
it via a slightly different process. But nonetheless, we wanted
to create a procedure that would honor any of processing streams
for getting at these major tracts. But then, we've never done
anything with individual fibers. But I would say there's a
person at Wisconsin named Mu Chung who has done very analogous
analysis procedures with fibers.
So at any rate, it turns out
so the technique we used to fit
these midline structures are called principal curves and then
there's an easy extension of them called principal surfaces.
It's actually
the problem of how do you fit kind of curves in
three dimensional space is
it's one of sort of like you have
something and, let's say, if you have a 2D image and you want to
fit a curve, you kind of drag it up into a third dimension,
right? You parameterize it with a sort of a time
you can
imagine if this is something, the trajectory of something
circling the drain, circling a drain, right. If you actually
knew where it was at each time point, then fitting this would be
pretty easy, right? So if you conceptually parameterize it as a
function of time, right, and all you have to do is discover the
time points, then you've figured out a way to fit the curve.
Now, and that's, you know, the analogy breaks down when you move
to more than one latent dimension. But that's the useful kind of
analogy we use in to how we construct these algorithms to come up
with these nonlinear structures.
So at any rate, we think we have something pretty good for the
corpus collosum, and our goal is to fit these kind of central
structures in the corpus collosum, project the image intensity
data down on to it and then maybe try to give these things now,
as a summary of the corpus collosum, and in the hopes that it
maintains a lot of the important tract integrity measures without
discarding
without discarding the key information.
Now, of course, we've lost whatever information is in the
orthogonal directions to this nonlinear surface we fit. But we
think that hopefully we're coming up, because the
just the
look of this is so manifold like, we're hoping that we're
retaining the important information.
So now, at this point, our next step in working with this way of
thinking about anatomical connectivity is we'd like to use these
little pieces of paper we come up with that projects this 3D
entity and use these pieces of paper as, you know, either as
diagnostic tools or to put into secondary analysis engines and
we're hoping that we can get rid of a lot of processing by this
procedure. Yeah?
>>:
These are different time points?
>> Brian Caffo: These are different subjects, yeah.
different subjects.
>>:
These are
Essentially, that is also a template, right?
>> Brian Caffo: It is a template, yeah. It is a template. So
what we're hoping is that matching the corner, say, for example,
this guy, will avoid the need for normalization, for
registration. That's our hope. But we have a while to prove
that we can do that.
Let me talk a little bit about effective connectivity. So when
we first started studying effective connectivity, we had kind of
a dumb algorithm, but I like it so I'll talk about it just for a
minute. And I also think effective connectivity is a neat area.
But I'm going to spent most of my time talking about functional
connectivity.
At any rate, so effective connectivity, remember, is this idea of
directionality. So what I mostly study is fMRI. FMRI actually
doesn't measure neurons firing, right. It measures blood
oxygenation, which is the proxy for neurons firing. And so in
that
the function that maps neurons firing and the need for
replenishment of energy via oxygen locally, that function varies
spatially in a person's brain and varies between people to
people. So it's actually very hard to get direction, causality
and temporality in fMRI, the modality that I like to work in the
most. What would be ideal is if you were to directly measure,
you know, electrical fields, little electrical fields really in
high, you know, high temporal resolution.
And there is a way to do that, right? So electroencephalograms
do that. They
that's all an EEG, if you ever go put on EEG
hat, right, the electrodes are just measuring little electrical
fields. There's a lot of problems with EEG in terms of
attenuation through the skull and these other things. And at any
rate, so another way that you can study
another way you can
study it, but maybe in a very narrow population, a very narrow
sense, is this so called electrocorticography. And
electrocorticography, a person who is undergoing brain surgery
for, you know, for something like severe epilepsy, they will put
the electro plate on a person directly on their cortex. And so
that, you know, that offers a lot of problems in terms of, you
know, how generalizable the population you're studying is, how
you know, what kind of spatial coverage you can get for the
electrodes. You can only
you know, you're not willing to,
like, shove them in there, right? You've just got to put them
where you can. And so on.
But the nice thing is you get these incredibly, very focal, high
temporally dense measurements. So we're talking about maybe a
thousand or more measurements per second, with maybe a hundred or
so electrodes. So you can study, you know, when one area
when
one area is firing, does it tend to precede another area that
sort of thing.
So we worked with a technique called Granger causality, which was
developed by the economist Granger, who won a Nobel Prize for,
among other things, his work on studying things like temporality
and time series. And there's a lot more
now I think since we
started this, there's been a lot of development in the computer
science and statistics and applied mathematics literature on how
do you fit giant directed graphs. A lot of work.
But when we did this, we weren't
you know, we weren't so clued
into that community so we said what we'll do is we'll fit these
kind of parallelized Granger causal models, which basically looks
at, you know, one node relative
you know, looks at whether an
arrow exists between two specific nodes, and you get kind of a
rat's nest here. And then we said, well, let's just now kind of
parse this rat's nest and try and find, for every triplet of
nodes, where a connection, say, between A and C could reasonably
be explained by mediating effect through B, or a connection
between B and C could be caused by a confounding effect of a
latent factor and then did all three variable Granger causal
models and just pruned it by, you know, putting B in this
relationship with A and C and seeing if the arrow goes away, as
an example.
And there's a lot of problems in how you search when you do that,
and that sort of thing. And it would be better, certainly it
would be preferable to do this as one giant thing. But it's
you know, I will say to our credit it is hard to do one giant
thing. You know, remember, each of these points is, you know,
recording a thousand measurements per second. And you have lots
of second that you've recorded.
So at any rate, when you do that just that simple step in this
case, you go from that graph to this graph where this is, and
this was during an auditory task. This is sort of the part of
the temporal lobe where you sort of hear things and this is
heading in the direction of auditory processing areas.
So that's an example of studying effective connectivity. I would
say since then, we've kind of dramatically improved our ability
to fit the kinds of models that don't require the sort of messy
trade offs we made in doing this now.
>>: [inaudible]. First few slides you talked about
[indiscernible] tensor measurements to the tracts. And now
you're talking about actually using oxygen?
>> Brian Caffo: This is actual electricity.
tiny little electrical fields, yep.
It's measuring the
>>: Okay. How are you getting precise data with electrical
fields as to [indiscernible]?
>> Brian Caffo: Well, okay. So there's a question of how do you
know the locations. So in terms of the actual experiment, they
take, you know, like a rotary saw and they cut someone's head
open, right. They cut, you know, they cut
they lift the top
of their skull off and then they, you know, get through the dura,
and then they put the electrode plate directly
>>
[indiscernible] of electrodes down?
>> Brian Caffo: Yeah, and you know the cables kind of come out
of the back of the head and they put the.
>>:
[indiscernible].
>> Brian Caffo:
Yeah.
>>: And you basically try to build this graph from that surface
region?
>> Brian Caffo: So yes. And I would say we did do one other
thing that I'm not going to present, because it failed. Which is
we were
you know, I work in a School of Public Health. We
want to compare across people. That's, you know, this is one
subject. We want to create kind of populations. And to be
honest, you know, you can't give that many people with this kind
of data, right? We can get six subjects. But we'd still like to
somehow average our six subjects or something like that.
So what you can do is you can get an MR. You can register the
template, the electrode locations via an MRI, and you can get
kind of spatial, common spatial locales for each of the points
across subjects. And then we spent a lot of time trying to, you
know
and then the registration wasn't perfect so we spent a
lot of time trying to get this registration down just right. And
then try to sort of smooth the points so that we could do
something across subjects.
But what it turns out is that these
the measurements of these
guys is so precise that even if you have another electrode in
another subject that right nearby, it's just not comparable.
It's just
you can't combine them. And with an EEG, which is
on top of the scalp, you know, the spatial extent of what an
individual electrode is measuring is large enough that you can do
a lot of smoothing. In these, you can't
>>
[inaudible].
>> Brian Caffo: So in this case, yeah, it was a stimulus. And
it was pretty simple experiments in this case, you know. They
present a, you know, a pure sign wave tone.
>>:
[inaudible].
>> Brian Caffo:
Yeah.
>>: I always find it interesting in macro stuff [indiscernible]
brain this implicit but not talked about assumption that many
neurons in the same region are doing the same thing. There's a
[indiscernible] thing they're supposed to
the alternative
world, where cells are scattered all over the place. And they're
all very different from each other.
>> Brian Caffo:
Yeah.
>>: [indiscernible] get these big giant columns and they're all
doing the same thing.
>> Brian Caffo: Yeah. But that's a principle, right? That's
the principle of
what's it called? So that principle goes
back to Bracha and Wernke and all these folks that put forward
I mean, I guess it ultimately goes back to phrenology, right,
where people posited that, you know, areas of the brain are
specialized.
>>:
Those guys talk in terms of big areas.
>> Brian Caffo:
Yeah, big areas.
>>: Say that the really small
you have macroscopic size
[indiscernible] doesn't make a difference where you put your pin
if it's in the right area. That's called 12, right, which is
and it doesn't have to be that way, right? Very, very
structurally rich areas. Move one cell to the left and it's a
whole different story.
>> Brian Caffo:
>>:
Yeah.
As they talked about.
I think it's interesting.
>> Brian Caffo: So we, you know, I think in the degree of
focality in electro corticography gets the point where you
actually can start to concern yourself a little bit with that
problem. With fMRI, you know, with fMRI or EEG, you know, you're
averaging at an extent where, you know, the kind of
the so
called specialization, the regional specialization at a macro
scale is really all you've got.
>>: [indiscernible] what is it X by Y, how many [indiscernible]
are there?
>> Brian Caffo: In these cases, so I think there were 60 some
odd in this particular subject.
>>:
Talking about huge regions.
>> Brian Caffo:
>>:
Yeah.
Of nodes?
>> Brian Caffo:
Yeah, yeah.
>>: That might be the right way to look at things, but people
[indiscernible].
>> Brian Caffo: Yeah, you know, I think that one of the main
problems is, you know, that you can kind of pick your poison. In
this case, you get great focality, kind of a large spatial
extent, but not whole brain. Great temporal resolution, you
know, weird subject population that you're studying. So you've
made several trade offs. If you want to study living humans, the
trade offs are pretty extreme.
But I would say even if you're doing animal studies and getting
down to the cellular level, you still wind up making quite a few
trade offs in
>>
What was the process [indiscernible] to get to the graph?
>> Brian Caffo: So for developing this graph, we just used sort
of ordinary Granger causality, which takes two time series and
says, you know
does
if I look at node A, does the variation
explained by it and its history, does inclusion of node B and its
history explain enough of the variation that I can then draw an
arrow in that direction? And Granger causality doesn't
distinguish
you can have an arrow going in both directions.
So you can do that, but then you could also, of course, there's a
multi variant version of that, and you can add a third variable.
So this just does all collection of pairs and then we said, well,
instead of trying to do the massive multi variant version, which
is possible now, but at the time we hadn't really thought about
how to do it, we said instead of doing that, why don't we just
look in a smart way to see which edges we could prune. Of
course, this gets rid of problems where there's a complicated
fourth order interaction that we've just elected not to study.
Yeah.
>>:
And that led to this?
>> Brian Caffo: And that led to this, so at least it worked
pretty well for this one subject, for this one task. Yeah.
>>: Two questions. One of them is the subjects that you're
pulling their skull off and doing this.
>> Brian Caffo:
but yeah.
>>:
I mean, I'm not personally doing that,
Like there's something else wrong
>> Brian Caffo:
>>:
Yeah.
It is actual surgeons.
Is something wrong in the first place?
>> Brian Caffo: Yeah, so as far as I know, the only people who
get these kinds of surgeries have fairly severe epilepsy. So
maybe there might be some cancer applications. But in all the
data that I have, it's all epilepsy surgery, where all the
stimulation, drugs and everything have been tried and they're
moving to surgery.
>>: So the kind of stimulation that you would get this kind of
data out, is that something like you asked that surgeon, could
you piggyback this study on your surgery, or were they going to
do this kind of thing anyway?
>> Brian Caffo: Oh, okay. So there is a
yeah, so that's a
good question. There is a clinical application to the electrode
placement in that the surgeons care a lot about not cutting into,
say, for example, speech areas. And, you know, they would err on
the side of not removing speech
of, you know, they're very
conservative about certain functions that they want to try. And
it's, you know, you have to map those. And they do that with the
electrodes which have a clinical use and then they also do that
with probes. They actually probe
you know, do some sort of
electrode probing to try and get at, for example, language areas
that they would not
you know, that they would
even if, you
know, they were trying to balance the clinical utility versus the
parent's function afterwards.
So yeah, there is a clinical application. But then, you know, of
course, all the patients supply informed consent and they're all,
I think, from what they tell me, often quite happy to be involved
in the research, you know. I think, you know, in between the pre
surgical
the surgery prep and actually going to surgery, I
think there's some time, and they actually value being a part of
the science.
>>: The question I wanted to ask is about the actual timing of
the connections that you can tell the Granger causality. Like if
it's 300 milliseconds
>> Brian Caffo: Even lower than that in our example. And I
think that's a good point is how far back
yeah, how
this is
all
this is all, I guess, what I would call extremely
temporally local directionality, not
yeah, so
>> [indiscernible] the actual neural length or the speed of the
[indiscernible] or is that more like
>> Brian Caffo: No it's longer than that. It is longer than
that, but it's not, say, fMRI long where you're talking about
seconds. It is, you know, milliseconds. But I think when we put
our measurements are
let's see. So it's a thousand
measurements per second and we put, you know, a couple lag terms
in there. So, you know, at best we go back a couple
milliseconds.
>>: Okay. So then those are
those aren't going to be like
actual neurons? Those are going to be like some
>> Brian Caffo: No, no. And again, these are all measuring
electrical fields. So it's still, you know, going back to Eric's
point, it's fields of neurons, right. It's not
yeah, yeah.
Okay. So let's
so anyway, let's get to my current favorite
topic, which is talking about resting state functional
connectivity. So one of the big kind of
one of the big things
that lots of people are working on is using an fMRI scanner in a
particular way. So fMRI using a paradigm to get people in the
scanner and have them tap, tap their finger. Yeah. Have them
tap their finger, that
and showing that the motor region
lights up, that's pretty well established and that was a real
loon for sort of so called brain mapping. Then people came up
with the idea, well, you know, why don't we just study, you know,
we put a person in an fMRI scanner, which measures this sort of
proxy for neuronal activation and just calculate correlations
between disparate regions.
And I'll talk for a minute about
or talk in a minute about why
people might want to do that. But it explodes the problem a
little bit, right. So you have maybe 50,000 voxels in an fMRI
experiment collected over, say, 200 time points. And all
possible correlations then is 50,000 choose two, that's a lot.
That's a big number. And, you know, you only really, you know,
the correlation, the matrix itself only has ranked 200, right,
because it has the rank of the smaller dimension.
And so you've got to do something to make that problem tractable.
And so there aren't that many directions that people have figured
out how to do it. One is to go down decompositions and certainly
the most popular decomposition technique in this area is called
Independent Components Analysis. But the idea is not all that
different from most other kinds of decompositions. We like to do
versions of ICA where we specify a full statistical model and
we've done some computational tricks that we think are useful.
The other way you can
the other way you can study it is to
pick specific locations or regions and average over those regions
and reduce data in that way, in a kind of informed sense. So let
me talk for a minute about ICA and what people have a tendency to
do. So each one of these is a subject where they were in a
resting
resting state fMRI experiment. So over here is time,
maybe every row is two seconds or so, okay. And along this axis
is space. So every one of these is a voxel and we've just
vectorized everything and dumped into it a matrix.
So group ICA, it's a little bit more complicated than this, but
conceptually all you do is you stack everyone up in this
direction. And notice you have to
if you had to pick between
stacking them up in this direction or stacking them up in the
other direction, you have to do it in this direction. And the
reason is because they're just sitting in the scanner. The time
for this person has nothing to do with the time for this person.
See, it doesn't match up. You couldn't stick them hike that,
because it doesn't match up.
You might say, okay, but the space for this person doesn't match
the space for this person. But what we do is we try some very
what is a rather complicated process of morphing everyone's brain
into the same template space and then we feel justified in
matching them up.
Now, that's an anatomical warping, and we're studying function,
but whatever. That's the best we can do. So we stack everyone
up this way, and then everyone gets their own
the
decomposition kind of breaks this down into smaller parts and
every person gets their own little, I guess what I would call
weight matrix and then across everyone they have these common
spatial entities.
So we like to call these common spatial entities networks. And
then we think of these guys as how those networks are engaged for
that specific person. So you have a person specific network
measurement of network engagement and you have an across people
measurement of the networks themselves. And that's the
that's
the so called group ICA.
>>:
[indiscernible] how large is the pool for this experiment?
>> Brian Caffo: So let me show you. So here in this example, we
did 150 subjects. But we've done it to, I think, 500 subjects.
So what we've spent a lot of time doing is working on how you can
do the full likelihood calculations without kind of memory creep
and running it in parallel. So we spent a lot of time doing
that. And we feel like we have it figured out now.
The only problem is
so then now the largest repository of the
resting state data that we would apply this to, it has several
thousand subjects. You might say, well, why didn't you just do
it for several thousand subjects if you're claiming that you can?
And what we found is that the variability between sites is huge,
and that
so all the big repositories have different sites that
contribute to it. The variability between sites is so huge. And
we found, as we were adding subjects, we were actually getting
worse network estimation and so we've got to do something about
that. We haven't exactly figured out how to handle that.
>>:
[indiscernible].
>> Brian Caffo: It's going to have to be something like that,
yeah, absolutely. It's going to have to be something like that.
But right now, so this is results for one big site where we
dumped them all together. And, you know, so what we can see is
we get really good kind of network estimation. This is
>>
What's the actual stimulus here?
>> Brian Caffo: So this is no stimulus. This is just folks
sitting in the scanner. So what's interesting about resting
state is, you know, you can get, you know, the folks are resting.
They're just sitting in the scanner. And by doing these
decompositions, one of the components, one of the networks that
will come out is the motor network. They're just moving. If
their eyes are closed, you'll still get the visual network,
right? If they're sleeping, you'll still get the motor network.
If they're, you know, we can do
you know, we can do this
experiment on people in a coma, for example. You'll still get
networks even though they're not functional with respect to that
to
not currently functional with respect to that
what is
commonly used for that specific brain network. So yeah, so we
get vision networks when people's eyes are closed. We get motor
networks clearly when they're not moving and so on. So that's
why this technique has at least the promise of a lot of power.
In that it's potentially a way for quantifying a lot of networks.
And there is this idea that these kind of spontaneous,
synchronous fluctuations, measured by resting state fMRI, you
know, measure the network measurements might be very useful for
quantifying disease and recovery and that sort of thing,
especially in things like coma, where you can't have a person do
a task in a coma, right? And if you want to know
if you want
to kind of guess who's going to come out of the coma earlier,
right, it might
it would be very useful, and the idea is that
maybe some of these things will be useful biomarkers. And we'll
see later on, there's some potential troubles.
This network, in particular, this so called default mode network,
is the most famous one and easily the most controversial one. So
the original impetus for the default mode network is people
looked at areas where in a very attention demanding task, they
looked at the negative associations with the task. What went
down when the people were doing this task that required them to
attend very closely? And this network kept popping up. And then
lo and behold when you study resting state, it's easily the most
robust network that pops up.
And so this is called the so called default mode network and
people have postulated this is sort of a
it's sort of a
default state in the brain, and there's been, you know,
hypothesis about its role in introspection and all these other
things. At any rate, it's easily the most studied and
simultaneous controversial entity coming out of resting state.
But we get, we think, a pretty good map of the default mode
network by stacking up, by our particular variant of ICA. And I
should say we also
I don't want to go into it too much, but we
do our own version of ICA.
So one thing that we are very interested in, so let me give you
an example of the ways in which people might try to use resting
state data to study disease. So one thing we're very interested
in, I work a lot with a development
a lab that studies
developmental disorders. And one thing that's pretty common
across a lot of developmental disorders is motor dysfunction.
And so if you asked, for example, a kid with ADHD to hold out
their hands and move this collection of fingers, right, you know,
I think, you know, I can do it. I'm sure everyone in this room
can do it where these fingers don't move to, right? But if you
ask most kids to do that, you know, if you've got a little kid,
ask them to do it. Watch. They'll move these fingers a little
bit too.
So and what happens is that
you know, the differentiation,
this lateralization of motor function tends to happen a little
bit later for developmentally disabled kids than typically
developing kids. And so we've focused a lot on that. So that
inspires us to be interested in left
left/right brain
connections in the motor network.
And so one way in which we've looked at that, and I'll talk about
this now a lot, one thing that we've specifically looked at from
a methodological point of view is exploiting that symmetry and
quantifying that symmetry when using ICA. So just the idea that
one interesting fact is that exploiting this symmetry, this is
work of Juemin Yang, exploiting this symmetry seems to help just
it just seems to help regardless if the symmetry exists.
So if you were to take, for example, flags, some of which have
some midline symmetry, the Canadian flag, the European Union
flag, the Russian flag all have some midline symmetry, and the
Chinese and U.S. flag do not, and simulate data and do our
version of exploiting the symmetry versus the standard algorithm,
you know, here what we find is there are
they're among the
symmetric flags, they do quite a bit better and much more so as
you add noise. So what you see is in the top row in each case
being the one that exploits the symmetry and the bottom row being
the one that doesn't, you get a lot more kind of leakage between
the components if you don't exploit the symmetry.
And as a benefit of it, so let me show you
it's really kind of
simple what we're suggesting. So we take everyone's brain, we
split it down the hemisphere, the mid sagittal plane, which isn't
a plane, but whatever. And then we flip the, one of the
hemispheres over. So now we do the same decomposition style
technique. But now everyone contributes a left and a right
hemisphere. So everyone contributes two bricks instead of one
brick. But, you know, each part being a hemisphere. And you
have to flip one of them so you've got to be careful about
flipping.
There is another problem that when you register brains, the brain
that you register people to isn't exactly symmetric. So we were
in the process of creating a symmetric brain when we found out
that the Montreal Neurological Institute has released a
mathematically symmetric brain. So we used that one. Who is no
one's brain. So we register
so the comparison space that
we're doing it in is no one's brain.
that.
But we're at peace with
And so then everyone gets their own left and right hemispheric
wait matrices, and then they get hemispheric specific networks
that represent a commonality across hemispheres. And so the
result of this, the result of this is you can compare these guys
and compare using decomposition, you can compare left and right
hemisphere. So if you are interested in lateralization, which is
what we're very interested in as motor network lateralization, we
will compare the left and right weight matrices from ICA.
>>:
So ICA, as I understand, looks at temporal coherence, right?
>> Brian Caffo:
Yes.
>>: So when you're separating the hemispheres, you're
essentially assuming that both hemispheres are temporally
[indiscernible].
>> Brian Caffo: So not necessarily. So it's very confusing, but
what we've done is
so let me rephrase your question. Your
question is could this algorithm estimate a lateralized network.
That's a rephrasement of your question, right? And it could.
It's just that this weight matrix, if it was lateralized in a
left hemisphere, this weight matrix would be zero. So take
language network, for example, which is a lateralized network.
But the point is you would never do this
not that you would
never do this.
The point is to what extent do lateralized networks gum up the
works for doing better estimation in non
in symmetric
networks? And what we found is that the symmetric networks do
just as well, if not better, in the presence of lateralized
networks. If you want to study the lateralized networks, then
there's no point in doing what we're suggesting. We're just
making it more complicated.
That's why when this flag example included flags that were
lateralized and flags that were symmetric and we find that
you
know so here I'm only plotting the symmetric ones because you
only do this for the symmetric things that you're interested in
estimating. But we find that, you know, the symmetric ones get
estimated better even in the presence of lateralized networks.
>>: So did this function in, let's say, the motor cortex, shows
up as separate spatially coherent regions? Like different shape
if you're doing this method
>> Brian Caffo:
>>:
It's the same shape, yeah.
So then you're losing your functional boundaries, right?
>> Brian Caffo: So that is
yes. So we have a different
technique for dealing with that, but it's not this, yeah. So I
see what you're
if
so it's not clear what happens if things
are symmetric but, say, subsumed. One is entirely contained in
the left hemisphere, yeah. So fair enough.
But what we've found, one thing I would say, is we get these
great
I mean, this is only 20 subjects. These phenomenal
network estimates out of this process if the network is
symmetric. And it looks pretty, because I think our eyes
visually like symmetric things.
So want to just briefly touch on a couple other things. So
decompositions were the kind of the big thing going for a while,
and decomposition is this idea you get these nice blobs, right.
But now if you go to human brain mapping graphs of the new blobs
and it graphs of the new blobs, then sort of how you pick the
nodes that you use to create these graphs, that's the house of
cards that the whole enterprise is built upon. And just want to
point out one way in which we create
to answer your question,
one way in which we create nodes. So we'll take, say, the motor
network, and we'll do a clustering algorithm, and then we'll take
these
the clusters, estimated from the clustering algorithm,
and use those to create the graphs. So in that case, there is no
spatial constraint between the left and right hemisphere, for
example.
>>: This starts with atlas, right, which is very different
from
>> Brian Caffo:
>>
Oh, you would create a functional?
>> Brian Caffo:
>>:
No, no, so we would create a functional
Yeah.
So it would be functional clustering.
On fMRI data?
>> Brian Caffo: On fMRI data.
groups, not on subjects.
>>:
But functional clustering on
I see.
>> Brian Caffo: Yeah. So, in fact, we have a study that Mary
Beth looked at the motor network and showed that the functional
clustering for autistic kids and typically developing kids, the
functional clusterings look about the same. But then as the
typically developing kids get just a little bit older, they look
identical to adults. Their clustering looks identical to the
clustering of adults, whereas as the autistic kids get a little
bit older, they still have functional clusterings that look
similar to the typically developing kids. So this was
confused you. This was ADHD.
So I want to
>>
[indiscernible] actually make graphs from that data?
>> Brian Caffo:
>>:
Yes.
Where are they?
Do you have examples of some?
>> Brian Caffo: So let me talk about the different ways
so
unfortunately, there's like a million different ways to create
graphs and there's a million different ways to create nodes and
then there's a million different ways to create graphs. So one
way you can create graphs is you can take these independent
component analysis, style weights, and, you know, taking the
outer product, and that, you know, each row or column of these
corresponds to a spatial network and that creates a graph, right.
That creates a covariance matrix then you can use to create a
graph.
Our preferred way to create graphs now is to take the time series
that exists in some node definition and use Gaussian graphical
models, which means to estimate a conditional independence graph
based on the inverse covariance matrix. So but either way
and
so one point I would like to make is regardless of how you
construct the graph, because of the incredible amount of
uncertainty in this process and all the different choices you can
make, a very important aspect of it is to become
is to get
reproducibility. So if you put a subject in the scanner and you
make all these choices to create a graph and then you take them
and scan them again an hour later, you would hope to get the same
graph, right?
So one thing we have done is come up with a measure of graph
reproducibility. So for a single number reproducibility, a very
famous measure of reproducibility is the ICC, the inter class
correlation. And so we came up with
with a basically a graph
ICC. So that measures the reproducibility of graphs.
Can't do very big graphs yet, but
>> [indiscernible] calibrate across different sites.
other problem you have one subject
In the
>> Brian Caffo: Yeah, so people do that. Sort of traveling test
subject type studies, yeah. I am involved in a study where
they're doing exactly that to try and get calibration.
Calibration's a very hard problem, though. It's a very hard
problem. Getting the same
so this is calibration within the
same scanner, within the same processing team. Everything is
held fixed. It's just scanning the subject once versus the
other. And even that, it's surprising, you know, it's a little
hard to get reproducibility, you know, good reproducibility. You
have to control those processes pretty tightly to get good
reproducibility.
Just because I'm, you know
so I would say these are the kinds
of things that we work toward, toward getting, and we do have
some semi interesting results at this point where we're
where
we're finding that kind of the variability in the graph is a lot
higher at the younger ages. So we get these less, less dense
networks among kids, right. So but in visionary, they're dense
and nice, so there appears to be a lot of commonality among kids
in visual areas where, you know, that that develops very early.
And then as we age, we get these much denser networks that are
more common across subjects, across subjects. So at any rate,
just in the interest of time, I wanted to talk about the last
thing, which was
just because it was fun.
So the group of people that put out the data or a huge chunk of
the resting state
public resting state data that you can just
download ran a competition, and it was called the ADHD 200
competition. So the ADHD 200 competition is a competition where
they released
let me see. So they
a bunch of sites
contributed. Here's all the sites that contributed, and they
you have three subclasses of ADHD, control, combined and
hyperactive impulsive. And they released this information as
well as the scans on a large collection of subjects and then they
withheld it on a bunch of subjects and then your goal was to
predict withheld ones.
And there's some
just looking at the site demographics is
fairly informative. One is they withheld one entire site, right.
That was introducing Brown 100 percent was withheld.
Another one was, you know, things like Pittsburgh that was 91
controls and no ADHD subjects and then nine withheld. You know,
I'm going to pick control for those nine.
>>:
[indiscernible].
>> Brian Caffo: So these three are all different ADHD sub types.
So ADHD, most of the developmental disorders are so called
spectrum disorders, where they have some amount of commonality of
symptoms, but there's a certain amount of distinction between
there's a high variety
I mean, take autism, for example. The
distinction between, you know, high functioning autism and low
functioning autism is enormous. And so ADHD is similar.
The one, this one, the hyperactive impulsive one is
there were
so few subjects they just said forget about that. Don't predict
anyone for that.
So we have the imaging data and we had the some amount, a small
amount of covariants. So we had gender, we had quality control,
you know, so there's a lot of different ways in which you could
the imaging could fail. You could have a lot of motion, that
sort of thing. We had a nice, fa fairly nice age range. It was
all kids, though. I think the largest age was 21 or something
like that. Yeah.
So IQ was
one thing we were very concerned about was how
different the sites were. So the exception of the neuro image
site, IQ was about the same across sites. That was one of the
ways in which we were kind of testing the demographic change,
because the sites
you know, the studies were all collected for
other reasons so we were concerned, you know, some study was
yeah, so we were concerned about the distinction between the
populations. So the IQ was not that different by site.
So at any rate, so then we started
you know, we spent months,
and this was the kind of competition where, you know, there were
maybe 50 teams that entered. I think 20 people actually got
around to submitting, 20 groups actually got around to submitting
entries. And you really couldn't
you wouldn't bother to get
into this business unless you really kind of worked in the area.
Because the processing was so annoying, right. I mean, there was
a huge amount of processing. You had to know how to process a
thousand fMRI or structural images. And so we started digging
in, and one thing
one thing we tried very hard to do was to
use motion to predict disease type, because we figured the ADHD
kids would move more in the scanner.
So that has nothing to do, by the way, with anything about the
brain, right? So but they were clever enough to think
to try
and thwart that. But we found this kind of decomposition
technique called the CUR decomposition, and we just sort of
blindly threw it at the data and found these weird results. And
found that the CUR decomposition was kind of predictive of
disease status. And what we think is the CUR detection was just
kind of a little bit more of a
a little bit better of a motion
detector. And so we think we were getting some nice motion
information.
What was interesting is after this competition, there was these
papers that came out that showed residual motion even among
groups that were very attentive to the study of motion in fMRI.
I mean, these are the top, top people. They really understand
how motion can impact fMRI. And they're saying even in our
studies, even in our biggest studies, motion is creeping in
somehow, we think, right.
So motion is a really hard problem.
that
And it's not unrealistic
>> Would you be happy with discriminating hyperactivity if you
[indiscernible] motion completely with it was just motor activity
[indiscernible] hyperactive kids.
>> Brian Caffo: That would
so we would be fine with that. So
in this case, we were just trying to win the competition. So in
this case, we were just trying to win the competition. And so it
gets weirder. It gets weirder. There's always these
competitions are like this, right?
So at any rate, here's why
we took Mary Beth's atlas of the
motor network atlas, and that was the one of kind of scientific
thing, that was the one kind of
let's see. That was the one
kind of scientific thing that kind of came out was that this
dorso medial, dorso lateral connectivity from Mary Beth's atlas
seemed to have some kind of really weak predictability for ahead
status. And we've since looked at it in autism. And it's not
terribly predictive, right? Here's your control subjects.
Here's your ADHD combined and here's your ADHD inattentive.
I mean, you know, few look at the mean and the conference
interval around the mean, it's not like this is an extremely
predictive entity. But that was the one kind of sciencey thing
we found. Everything else was gaming the system like this, like
trying to find motion. We directly put in motion as well.
>>:
[indiscernible].
>> Brian Caffo: So this, to the best of our knowledge, is
legitimately significant. To the best of our knowledge is
legitimately significant. Though, of course, we did
you know,
we paid a lot of attention to overfitting.
>>:
And what do your behavioral people say about this result?
>> Brian Caffo: They were pretty happy that this is what came
out, to be honest. This does go back to
so if you look at the
kind of homuncular organization of the motor network, this is
consistent with kind of symptomatology of ADHD. It's, you know,
again, it is a just so story, right? We found this and then we
searched for an explanation. But there is a reasonable just so
story, scientifically motivated just so story that went
goes along with it.
that
So ultimately, our final algorithm included the connectivity
between these regions from this clustering algorithm that we
used. It included this weird kind of motion detector we
developed, and it included age, IQ, I think we put the quality
control in there as a predictor and sight in there as a
predictor. Sight being mostly a predictor for demographics,
right.
And at any rate, we won, but there's a story. But there's a
story. So how did we win? So what we
how we won
and it
was weird. The scoring system was very weird. So the scoring
system gave you a full point if you got a control correct. And
then it gave you a half a point if you declared someone ADHD and
they were actually ADHD. And then a full extra half a point
or they gave you an extra half a point, so to get a full point
you have to get their sub type right, okay?
Then it was just total number of points was what you went for.
Okay. But there were clearly a lot more controls than there were
ADHD in the site
or in the study. So at that point, you got
to be really stingy with declaring someone ADHD to do well in the
competition. You have to be really stingy. And, in fact, I'm
pretty confident
we haven't done this full analysis. They've
since released the withheld data. But back when we were spending
a lot, a lot of time on this, I kind of calculated that I think
you would have gotten at least third or fourth just by declaring
everyone a control. And maybe higher. Maybe higher. Depends on
how the withheld shake out. But I think I could calculate that
you would have gotten, you know, top five or better by declaring
everyone a control for sure.
So but we did almost that. So we got, you know, super high
accuracy on typically developing. And then very low accuracy
when we declared someone ADHD. We missed a lot of ADHD kids,
right. But then when we declared someone ADHD, we got their sub
type right more often than everyone else. So what we did is we
so that was our strategy. And that was the putatively winning
strategy.
However, what you'll notice if you look at point, there's one
team with a higher point value. From Alberta. And what happened
is they got disqualified because they didn't use any imaging data
at all.
>>:
Scoring system?
>> Brian Caffo: They used the scoring system, IQ, gender, age,
sight and quality control. Just the database and no imaging.
And what's interesting about this Alberta
>>
[indiscernible].
>> Brian Caffo: So I'm of two minds about this. One is that
it's clever and I'm actually good friends with the Alberta guy.
So the Alberta guy is a really interesting character, first of
all. He's a computer science masters student at Alberta, working
entirely on his own. That's one thing. So he's an interesting
guy. And since then, we talk a lot, and we submitted a paper
where he wrote an article about baseball predictions. So he's
super interesting, smart guy. And he legitimately looked at the
imaging data and said it doesn't
it isn't very predictive.
Now, all of us looked at the imaging data and thought it wasn't
predictive, and all of us almost did exactly the same thing he
did. Everyone who did well. And, in fact, we spent a lot of
time seriously entertaining the possibility of putting in all
controls as our answer.
So from the competition potent of view, you know, they wanted
people to sift through the imaging data, right? I mean, a person
that sifts through the imaging data and uses the imaging for
prediction has lots and lots of meaningless numbers that they're
adding to their prediction equation that make it a lot harder.
And, of course, there's a good chance you're going to wind up
with worse prediction if you do that.
If you add, you know, all the different ways in which you can
combine the structural and functional imaging data, you know, the
chance that you're going to you know, that that's going to make
your prediction worse is actually pretty high.
So from the competition organizer's point of view, it's fair,
but, then, you know, what constitutes a legitimate use of the
imaging data? I mean, we've practically called everyone a
control. I mean, were we really using the imaging data? I mean,
we did. But we practically called everyone a control. So at any
rate, it was interesting and it was a lot of fun. All of us that
worked on it kind of now talk a lot more. Everyone
it got
everyone to sift through a huge amount of data, you know. I'm
good friends with the guy from Alberta now. So
>> Is there any intuition that this is a
like if you had
three orders of magnitude more data, do you think the imaging
would clearly dominate? Like in other words, do you think this
is
>> Brian Caffo: Three orders of magnitude more data with a tiny
signal embedded in there? Or three orders of magnitude more data
you're saying
oh, I'm sorry, more subjects.
>>: More subjects, more [indiscernible].
more.
I don't know what, but
>> Brian Caffo: I think even if you have more subjects, a lot
more subjects, it would still, the signal in the fMRI data is
pretty weak, and you wind up with a lot of numbers and you don't
exactly know where to look either, right. You are a little bit
flying blind. And asking algorithms to sift between all the
possible ways in which you can take this 50,000 by 200 matrix per
subject and
and that's after a fair amount of processing too,
I might add, and distill down into 200 predictions is a lot to
ask. Yeah.
So at any rate, that's the last thing I wanted to say.
>>:
So where is this going?
>> Brian Caffo:
>>:
What's next in your
In fMRI?
Yeah.
>> Brian Caffo: So I, you know, I think there's a community of
us that are kind of dedicated to sort of keeping on sort of
hammering away at resting state and to improve it as a
methodology. So that
and it has to get attacked on several
fronts to get it to work. One is we have to improve the
processing. The processing's very hard. And the community is
working very hard on getting the processing right. There's some
physics that might help too. There's biomedical physicists that
are working very hard on trying to change the measurement process
to account for some of the ways in which the processing's hard.
Then I think there's still a lot of work to be done in terms of
analysis and figuring out how, you know, what
what kinds of
problems this is going to be relevant for and what kinds of uses
it's going to be relevant for.
We're kind of highly committed. We're a little bit opinionated
about the kind of specific kind of graph that we think people
should be creating, and basically, what I would say is 100
percent of the computer scientists, statisticians, and applied
mathematicians that work in the area all agree on a particular
way in which these graphs should be created, but the field hasn't
the field, the broader field hasn't agreed on it. We
everyone
thinks that it should be kind of inverse covariance matrices and
Gaussian graphical models and that sort of thing.
And the field is still thresholding raw marginal correlations.
>>: I can see how the graphs are
could be [indiscernible] how
things work and are connected. But they also could be a source
of rich features to make these discriminations that wouldn't be
in the [indiscernible] data, potentially, as a high level
representation. Has that been studied directly, showing that
graph gives you more discrimination, the power to diagnose
cohorts of patients than going to this broad lower level
[indiscernible]?
>> Brian Caffo:
>>
So people have
The actual graphs they're generating and the properties.
>> Brian Caffo: So yes, there is, actually, that is a big
movement right now, actually, in that there are a group of people
that are looking at kind of traditional graph matrix of, say, you
know, they'll take a node and they'll see the
the things about
edge distributions coming out of the node, for example, and
measures of small worldness as an example. And then show
and
then try and look at are these small worldness measures better
discriminators of the
between
and so, yeah, actually,
there's a big movement along that lines.
I think there's a huge problem right now, and so I don't have any
bearing on what's the correct solution to this because I think
the graph problem is quite hard. But there seems to be what a
lot of people are trying to do is to use graphs to makes
statements about neurological organization, right. If you say
that
if you say that, you know, the graph represents, say,
small world organization of the brain, that's a statement about
neurological organization. And if you say it exists at multiple
levels of hierarchy, that's a statement about neurological
organization.
But that is a tremendously hard thing to test, and the only way
to get at it is that these indirect measures, like graph
distributional properties of the graph, right. But to me, what
you really need is something akin to a likely ratio where you're
saying among the class of graphs that represent small world
small worldness, here's the one that's best supported by the
data. And among the class of graphs that would represent the
alternative, here's the one that's best supported by the data.
And here's, you know, factoring in the impact, the randomness in
the process, here's how well differentiated these two hypothesis
of network organization are.
And to me, that problem is very hard but is one of the more
interesting ones. And I think what would be nice about cracking
that nut is then I think a lot of the how you would then use it
to study disease would follow. But I think, you know, people
have been working on, you know, how do you test hypothesis about
graphs living in spaces, that's been going on for a long time,
and it's apparently a pretty hard problem.
So it's not my area. So it's apparently a pretty hard problem.
So we tend to try to borrow a lot of the methods that have been
built up in there. But it's apparently very hard. And so I
think until then, I think actually doing things like just coming
up with graph distributional properties and testing them, versus
diseased or non diseased is the best thing going.
>>: [indiscernible] neuropathology like severe FTD or
Alzheimer's versus normal, you'd think that there would be big
signal there's.
>> Brian Caffo: So that has been done. That has been done. And
Alzheimer' disease, in Alzheimer's disease [indiscernible]
Alzheimer's disease, the big question in Alzheimer's disease is
you want
once the disease becomes very severe, the brains of
Alzheimer's patients are very different from that of control
patients. They've had a lot of atrophy. It's very apparent.
>>:
[indiscernible].
>> Brian Caffo: I think at all levels. At all levels. I mean,
you have memory impairments and so all the action in Alzheimer's
disease is at what level can we find precursors before the
disease becomes severe. Maybe at the stage of mild cognitive
impairment or even preferably before mild cognitive impairment.
And so then you're looking at a much subtler difference, but it's
where kind of 100 percent of the action is because if you can
solve that problem, then there's a chance you can pretreat the
disease. And there are potential medications to pretreat the
disease. And I think Alzheimer disease is clearly the most
visible of these kinds of problems. But a lot of the ways in
which people want to use this technology have similar
characteristics.
So in the
when I work with people who study traumatic brain
injury and coma, what they really want to determine is who has
who's going to have good function or bad functional outcomes
based on their current imaging.
>>:
Detecting whether the concussion goes to [indiscernible].
>> Brian Caffo: Yeah, exactly, exactly. Now, in the
developmental disorders, I think it's a little bit different.
There, they're interested in what is the
in refining the
phenotype, right. So what is it
but that is itself a hard
problem for a different reason. And there, I think the fog is a
lot, you know
there's no gold standard to build things off of.
>>: So my question is about the contest, the last part. Should
I think about these sort of results as, you know, when you have
kids with ADHD, they can [indiscernible] having them do an MRI is
not very useful? Or is that conclusion too simplistic, because
this seems to sort of suggest that.
>>: Remember, this is a specific kind of MRI. This is a
functional MRI. But to be honest, you know, people don't
the
doctors don't prescribe MRIs idly, so more often than not, my
guess that is for diagnosing a developmental disability, they
would not prescribe an MRI. The definitive diagnoses for all of
these disabilities is still a physician's assessment.
And I think if you were to ask most psychiatrists, for example,
they would say we're comfortable that we can diagnose autism, but
it's really, you know, the biological underpinnings of the
disease, the real legitimate kind of characterization of the sub
phenotypes is where imaging maybe would play a better role. It's
not clear to me that the end goal
and these
the people who
organized this competition are like, you know, top people in the
area of developmental disorders. So they're all very aware of
this.
I think more than anything, what they wanted people to do was
sift through a mountain of fMRI data and get at some nice ideas
as to the biological underpinnings of what's a kind of really
complicated disorder. But I don't think they ever had any
illusions that people would use fMRI as a diagnosis machine for
this particular disease.
>> Eric Horvitz:
Thank you very much.