Wisconsin Public Television University Place Autism: A Day With Experts  Andrew Alexander, Investigations of Brain Activity in Autism  January 28, 2012 UP674   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. [APPLAUSE]