cc >> ...Symptoms of autism
and points toward possible, at least diagnostic,
implications. So, Andy, why don't you take it
from here. >> Thank you, Marsha.
It's really a privilege to be here and describe to you some of
our research that we're doing to try and understand the brain in
people with autism. And I'm going to give you a
fairly broad picture and a few snap shots of both what MRI is
as well as the different types of studies that we're doing that
focus specifically on autism. So just a little bit about
myself. I got my PhD in optical sciences
in 1994. I've been doing research in MRI
for about 20 years. And after getting my PhD, I went
to the University of Utah, started working in brain imaging
there and then joined the faculty here at the University
of Wisconsin in 2001. I'm the director of MR Physics
Research at the Waisman Brain Imaging Laboratory here, and
that's really a dream job for me because I get the opportunity to
work with people from all over campus in a broad range of
projects. My focus is on advanced
neuroimaging with MRI, so we do functional MRI, diffusion tensor
imaging, other methods to try and characterize white matter,
study brain metabolism and so forth.
But one of my main areas of interest is actually applying
these to autism. So I've worked with both groups
here at the University of Wisconsin as well as the
University of Utah. And I'll present some results
from both of those studies. One of the areas that we've
gotten into recently is to try and use MRI to study brain
networks and brain connectivity, and I'll explain that in a
little bit. So, obviously, the brain is an
important organ. It's responsible for sensory
integration, thoughts, decision making, really anything that is,
most of our daily functions are caused by the brain.
And we can think of this simplistically as two types of
tissue, sort of like a computerama.
So the gray matter is like the processing units for the brain,
and that's basically where, when you see a light, you have an
area of your visual cortex that's basically being excited,
but then we also have what's called white matter and that's
the connections or the wiring of our brain computer.
And so that's taking the signals from that visual cortex area and
projecting that to other parts of the brain, and then depending
on if the light is picture then we're processing it one way.
If it's just a pulse of light, it might be processed a
different way. Most of my studies are done
using magnetic resonance imaging, and that's done in an
MRI scanner which looks like a giant doughnut which consists of
a coil of superconducting metal that essentially induces the
magnetic field. And the field strength that we
operate at is three Tesla and that's about 10,000 times
stronger than the Earth's magnetic field.
So it's a very strong magnetic field.
To do the superconducting, keep the superconduction going, we
need to keep that magnet cooled with liquid helium and different
than some other imaging technologies, it uses radio
waves instead of x-rays, and so it's a much safer technology
than some of the other imaging modalities, and it has a lot of
flexibility in terms of the types of images that we can get.
The image on the upper left is a cartoon showing a person inside
of an MRI scanner. Around the person's, the blob
that is their head here, is what's called the RF coil.
And that's what is transmitting these RF radio frequency waves
into the brain. So that excites the protons and
the water molecules in our tissue, and then that absorption
becomes reemitted and we detect that also with the coil.
And based on the number of these protons and the water molecules
are basically the density of the water and also the local
magnetic tissue environment, we get an image of the brain that
has contrast between different types of tissue.
And here's a nice example of the kinds of images that we can get
with MRI. This is what's called a coronal
image. It's basically a cross-section
that goes through the brain like this.
The top of the brain is up here, and this is the right side and
the left side of the brain. This is the brain stem down
here. The black signal in the middle
here are the ventricles. That's where the cerebral spinal
fluid is. We also have spinal fluid on the
outside of the brain. And then the gray tissue here is
the gray matter. So this is the cortex.
And then we have white matter which is the connections between
the gray matter areas. So you can see how the brain is
essentially integrated. Clinically, how MRI is used is
often, what they're looking for is conspicuous lesions or gross
abnormalities. So here's an example of a series
of clinical scans in a patient with Multiple Sclerosis where
they've done different types of imaging, so T2-weighted imaging,
a FLAIR image here, T1-weighted imaging, and you can see you can
get different types of image contrast with MRI, and that's
one of the really exciting things about this technology is
that you can basically apply different kinds of filters to
these images to look at different features of the
tissue. And people who don't have gross
abnormalities, this is what the structural MRI images look like.
On the left we have a T1-weighted image, and then on
the right is a T2-weighted image.
And you can see the contrast is essentially reversed between the
cerebral spinal fluid, the white matter or the gray matter.
And with this sort of technology, if you compare
people with autism versus people without autism, the images tend
to look very similar. So there's no specific hallmark
signature of autism if you look at structural images that's
obvious. There are some observations that
have been made. Early in life there tends to be
an overgrowth of the brain called macroencephaly, and
that's fairly common. Also another common attribute
appears to be that the corpus callosum appears to be slightly
smaller in people with autism. This is an over generalization.
There are a lot of people with autism that don't demonstrate
these features. But there are no consistent
macroscopic abnormalities in people with autism.
One of the more exciting technologies that's been
developed for MRI is called functional magnetic resonance
imaging, or fMRI. And the way that this works is
we essentially apply some sort of task, and this task could be
showing pictures to the individual, it could be pressing
buttons, playing a game. Really the options are endless
in terms of what kind of task you could do.
But what that does is in the areas of the brain that are
involved with that task, we get neuronal activation which leads
to increased blood flow to those brain areas.
And one of the important parts of this blood flow is that we
get through the hemoglobin that's within our red blood
cells, basically they bring oxygen to the parts of the brain
that are being activated, we get an increase in what's called
oxyhemoglobin. And that's important because
oxyhemoglobin has a more similar magnetic property to the
surrounding tissue than deoxyhemoglobin which doesn't
have the oxygen. And that induces a signal change
in that brain area. And so, let's see, so here.
This is an axial slice to the brain now.
This is for a task, and the areas that are colored are the
areas that are activated during this task.
The signal in the area where we have activation is increasing
during the task period and then decreasing during what we call
the baseline period, and then areas where there aren't
involved with a specific task, don't show that same level of
activation. So this is a schematic showing
how the fMRI experiments work. Basically, we do our what we
call our paradigm which is a task of activity and rest that
is alternating, and during that time we get a series of MRI
images, and from these MRI images, in areas where we have
activation, we see the signal increase, and then areas where
we don't have any activation, it doesn't increase.
And then by modeling the signal, we can detect what areas are
activated. One of the primary hypotheses
about the brain and autism right now is that there is abnormal
connectivity. So in people without autism,
it's believed that the connections are a composite of
sort of short range local connections in the brain
combined with long range connections to try and integrate
distant brain areas. This is an oversimplification,
of course, but in autism it's believed that these long range
connections are diminished and there's an over-connectivity of
the short range connections. What I don't like about this
diagram is that there still are long range connections in people
with autism, so it's a little bit of an oversimplification.
So, for example, this is the surface of a brain, and with
brain connectivity mapping what we're interested in is how is
this yellow region connected to this red region, for instance,
or maybe this purple region connected to this green region.
And so that's what we're trying to assess with MRI.
So just going back to this slide I showed earlier.
The gray matter is the area of the processing, so that includes
the cortex, and then the white matter are the connections
between the brain areas. So the imaging tools to study
brain connectivity include functional MRI, and we can look
at the similarities of the signals within different brain
regions, diffusion tensor imaging which is a technology we
can use to assess what's called microstructure of the white
matter, and that's important because white matter is what's
conducting the signals between different areas, and then white
matter tractography where we can try and estimate and map
specific connection patterns within the brain.
So what is functional connectivity.
Like the experiment where I showed before where you have a
specific task, here the task is looking at how similar the
signal responses are in two different portions of the brain.
And you could do that either while someone's doing an active
task, like looking at pictures or listening to sounds, or it
could be just during a resting period and looking at the
temporal signal correlations while someone's supposedly not
doing anything in the scanner. Of course, your brain is always
active, and so this idea that there's a true state of rest
isn't true. So the area on the right here
and the area on the left have very similar signal patterns
here, and so there's a high level of functional
connectivity. So one of my collaborators at
the University of Utah has been starting to apply this to people
with autism as well as people without autism to study how
functional connectivity is affected.
And one of the first things that he looked at was how the
interhemispheric connectivity, and we've shown also in other
studies and I'll come to them in a little bit, that the corpus
callosum, which is one of the main structures that connects
the two hemispheres, appears to be abnormal.
So that might imply impairments in the interhemispheric
connectivity. And so the two dots here in the
posterior part of the brain in basically what we call
homologous areas of the brain showing very similar signal
patterns, as a function of time now, have a very high degree of
correlation. So those areas are functionally
connected. And so then what he did was
taking the homologous areas on each side of the brain and then
comparing them with each other, he compared the functional
connectivity in people with autism versus people without
autism and found that there were certain parts of this
interhemispheric connectivity that were stronger in the people
without autism. And these tend to be in areas of
insula and also superior parietal lobe here, and that's
important, as we'll come to in a minute.
So there does seem to be a slight impairment in
interhemispheric connectivity. So taking this one step further,
Jeff Anderson at Utah also has been looking at whether you can
use this to classify individuals with autism.
So is there potentially an image-based measure that could
figure out which brain has more autistic properties than
another. So here is the results of this
classifier where basically individuals without autism have
a higher classification score, and people with autism have a
lower classification score, and he was able to achieve almost an
80% accuracy rate which is pretty good based on brain
imaging measures. However, for individuals under
20 years old, the classification actually goes up almost to 90%.
So this might imply that there is some sort of developmental
changes that are going on that are unique to autism.
Where the most important areas that are related to this
classifier include regions of superior parietal lobe as well
as insula which are areas we saw in the interhemispheric
connectivity, and then also areas of what we call the
default mode network which is important for things like
self-reflection and that sort of thing.
So this is starting to give us a map of areas that seem to be
more implicated in the autistic brain.
Another important attribute is that these studies also showed
that long range connections appeared to be decreased and
then there was increased connectivity of short range
connections. So that was consistent with the
hypothesis of abnormal brain connectivity.
Another technique that my lab has been very active in
developing is called diffusion tensor imaging, and it's a
noninvasive method for assessing the microstructural properties
of the tissue, especially white matter, and what's important
about this and how it relates to brain connectivity is that
attributes like myelin and the axonal density, those are
basically white matter consists of myelinated axons, changes or
alterations in that also influence the diffusion
properties. But with diffusion tensor
imaging, we can try and characterize these
microstructure through a variety of different types of images.
So here we have the main diffusion image which is bright
in areas of cerebral spinal fluid, the direction of the
white matter using this orientation map, the anisotropy
which is high in areas of white matter and is very sensitive to
microstructural changes, a map of what's call the axonal
density diffusion, and then a map of the myelin diffusion.
So what we're looking at with diffusion MRI is the diffusion
of water molecules and how they interact with the cellular
membranes of the tissue. And so in a tightly packed
tissue here, the water molecules are, it's essentially like a
bumper car course, they're going to bump into the surface of
these cells very frequently. Whereas, in a less dense tissue,
the water can go much further distances without running into
these membranes. And so the diffusion coefficient
for this case is much higher than it is for this case.
Another important attribute here is that diffusion has angular
dependence, so if we think of white matter tracts as being
like parallel fibers, the diffusion in that case is going
to have, it's going to run into less stuff when it's going
parallel to these tracks, the diffusion distribution is going
to be longer in this case, and when it's moving perpendicular,
it's going to run into these barriers more frequently.
The distribution rotates with the fiber structures as well.
And so we can actually map out the orientation of these fibers
with this technology. So using what's called the
diffusion tensor, we can estimate the diffusion
properties using a matrix. This is just to illustrate that
both the, essentially the complexity of this sort of
measurement both gives us an opportunity to more fully
characterize the diffusion but also presents challenges for the
analysis. But with this sort of framework,
we can characterize the diffusion as being more
isotropic, which means that the diffusion is the same in all
directions, or more anisotropic, which means it has a more
directional dependence, and we can also measure the orientation
of this anisotropy. So what do we measure with this?
So this sort of ellipsoidal picture here in the shortest
dimension and also in the medium direction which is in and out of
the plane here, those measures seem to be related to the myelin
of the tissue or the axonal density.
In the direction parallel, it's more sensitive to the axons.
If we take the average of all these, it's related to the
tissue density, and then if we look at how much these vary with
respect to the three directions here, it's a measure of the
diffusion anisotropy. So going back to the example
images here, we have our anisotropy map, our total
diffusion map, the orientation here.
Red is indicating fibers are going right to left, green is
front to back, and then blue is from the bottom to the top of
the brain. Our axonal density diffusion and
our myelin diffusion. So about 10 years ago, Janet
Lainhart at the University of Utah approached me and said I'd
like to really apply diffusion tensor imaging in people with
autism, can you help me? And I said, wow, that would be
fabulous. So we've been collaborating now
for about 10 years, and we have a longitudinal imaging study.
And I'm just going to present some of the results from that
study. Essentially, most of this comes
from a small study of 43 individuals with autism and then
34 controls that are matched for age, IQ and handedness as well
as head circumference. One of the first structures that
we studied was the corpus callosum.
So this interhemispheric connectivity, this is the main
white matter structure that connects the two brain
hemispheres, and that's shown here in these colored areas.
The different colors are representing different parts of
the corpus callosum that we also looked at.
Essentially, this is the results for the overall corpus callosum.
And what we found is that there was a group of individuals with
autism that appear to have increased total diffusion and
decreased anisotropy, and that would also be consistent with
impaired connectivity through this white matter tract.
But there is also a lot of individuals with autism that
have a high degree of overlap. And this fits in with the
heterogeneity of the disorder. So it might be that one of the
reasons why certain people have more autistic features is that
their white matter is abnormal, but in other individuals, maybe
there's another biological mechanism.
We've also applied this diffusion tensor imaging to see
how well we can discriminate the brains of people with autism.
So here we have a study of 30 individuals with autism and 30
controls using measures of temporal lobe diffusion tensor
properties, and with these measurements in this sample, we
were able to achieve a 92% accuracy of classification which
is very high. And then in the small
replication sample, we get similar levels of accuracy.
We're in the process of trying to replicate this across a
larger population. The last part of my talk is
going to be about what's called diffusion tensor white matter
tractography where we take the diffusion tensor information and
try to estimate the connection patterns in the brain.
My lab's been doing this for about the same length of time,
about 10 years. So from the diffusion tensor, we
have this orientational information.
So here this anisotropy tensor is pointing in the Z direction.
Here it's pointing in some other arbitrary oblique direction,
maybe a combination of the X and Z direction.
And that's what's causing the differences in the color map
here. So the red, again in the corpus
callosum appears red. The tracts that go front to back
are green, and then tracts like these cortical spinal tract that
connect the brain stem to the cortex appear blue.
And there's different representations for how we can
map this direction. We can look at the tensor glyphs
themselves. We can look at a vector
representation of these directions.
But using the vector representation we can then try
and reconstruct the pathways through the brain using this
technology. And we call this white matter
tractography. And so here is an example of
white matter tract reconstructions that we can get.
So for instance, here is the corpus callosum, the inferior
fronto occipital fasciculus, the uncinate fasciculus that
connects the prefrontal cortex to the temporal lobe, the fornix
which is part of the limbic system, and also the cingulum
bundles which are also part of the limbic system.
And these are just a few of the major white matter fiber tracts
that we see in the human brain. Another application of this that
we have developed is to apply it to patients that are going to
undergo brain surgery so we can see, for instance, where are the
white matter tracts of the cortical spinal tract relative
to a large brain tumor. And so this is before the
surgery and then after the surgery, and what's nice about
this is that we show the tracts are, indeed, preserved, and this
patient had preserved motor function after the resection of
this brain tumor. So how do we use this to study
brain connectivity? Well, first thing we do is we
basically try and reconstruct all the fibers within the brain,
and then we take the regions of gray matter, so basically the
processing nodes of our brain network, and then define which
tracts are connecting, say, this red region with this blue region
back here and then map that into a giant matrix that looks like
this. You can binarize it and then
basically map where these connections are within the brain
as a network. Moo Chung, one of my
collaborators here in the biostatistics department has
essentially taken this technology a little bit further
and developed a technique where, at different scales of
connectivity, he can define how these tracts are connected
within different brain areas. And applying this technology,
he's looked at comparing people with autism versus people
without autism, and so here is a map of the brain connection
nodes, so basically the gray matter areas, that are connected
to other nodes. And there are certain areas, and
this is an area of what's called the precuneus which is one of
the most highly connected brain areas in the brain, showing very
high connectivity with other brain areas in people without
autism. And then in the autism group,
the distribution of these nodes is much more distributed.
There is a precuneus cluster here on the right side, but on
the left side it's not quite as strong and there seems to be
another node structure that's more anterior.
So the patterns of these connections appear to be
different. So, in summary, with MRI we can
study the properties of brain connections.
We found that the functional connectivity is affected in
autism. So we have decreased long range
connections and slightly increased short range
connections. We also have observed that white
matter microstructure is affected in autism.
And what seems to be mostly affected are the diffusion
anisotropy and the myelin diffusion measures.
What we don't know yet is what this is indicating biologically,
are there less dense axons or is it less myelin.
And then also we find that the tractography connectivity maps
appear to be more variable and affected.
So where do we go from here? Well, I think we need to try and
understand how these connectivity measures relate to
the behavioral attributes of autism.
We're currently collecting longitudinal imaging data.
We're in the process of analyzing that data.
We have, now, imaging data that spans over eight years, and so
it should be really exciting to try and understand how these
connectivity measures are modulated as a function of age
in people with autism. And then finally, how do we
integrate these different modalities together.
We have functional MRI, diffusion tensor imaging and
tractography. Are these measures
complementary, do they tell us something different or are they
just relaying the same information.
And I couldn't have done this without a really great team of
collaborators. Here's my lab here in front of
the new MRI scanner that was installed last year.
In particular, -- Lee did a lot of the autism DTI work.
Nagesh Adluru worked with Moo Chung on a lot of the
tractography work. Brittany Travers and Chad --
have done a lot of the more recent DTI analyses in the lab.
And then Richie Davidson and Kim Dalton, I didn't show any of
their work today but they've also done some very nice work
with functional MRI to study the brain in autism.
And then also my collaborators at other institutions, Janet
Lainhart who introduced me to research of the brain in people
with autism. Erin Bigler who is at Brigham
Young University and is a psychologist and neuroanatomist
and really just a great mentor in terms of helping me
understand what behavioral measures mean, and then Nick
Lang who is a biostatistician at Harvard who's been working on a
lot of the classification analyses.
And I'd like to really thank the participants in these studies.
Obviously, we can't do it without them.
And hopefully this work will help benefit them in the
long-term. And also the National Institutes
of Health for funding. Thank you.
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