Neuroimaging Schizophrenia and Related Disorders Clinical Neuroscience Division Laboratory of Neuroscience Department of Psychiatry, and Surgical Planning Laboratory, MRI Division, Department of Radiology Brigham & Women’s Hospital, Harvard Medical School My name is Marek Kubicki, I am an assistant professor in the Department of Psychiatry Harvard Medical School, where I collaborate with Drs Shenton. Drs. Shenton and Saykin are PI and site PI on Core 3.1 of this U54 grant. Our lab, at Harvard/Brockton VA has extensive expertise in schizophrenia research. AND schizophrenia is the driving biological project of this grant for many reasons. First, schizophrenia is a brain disease, with many functional circuits affected. Multiple regions of the brain show abnormalities in both structural, as well as functional imaging. Also connectivity, as measured by DTI and fMRI, seems to be affected in schizophrenia. Yet, these abnormalities are so subtle, that not all studies detect them. The sensitivity of the methods that we use so far, does not allow also to diagnose schizophrenia based on any single imaging parameter, and that is why we need more sensitive and more specific tools for image analysis. Core 1 of this U54, NAMIC, will be crucial for the development of such tools and their application to schizophrenia. Here I will review some of the needs for tool development based on the driving biological problem, schizophrenia. Schizophrenia background??

Volumes There are many aspects of image analysis where we hope NAMIC will be able to help us. For example, one of the main foci of schizophrenia imaging research is to measure volume deficits observed in schizophrenia.

Here we see the image of the brain, showing very complex structures with multiple gyri and sulci. For the last 15 years our lab has measured the volumes of several of these anatomical structures, with particular attention to temporal lobe structures, where we have reported volume deficits in the superior temporal gyrus, a brain region thought to be important for language processing. Such abnormalities have also been correlated with frontal lobe volume measures. Of note, however, with large subject populations, the measurement of single brain structures can take several months of labor intensive work, and thus automated methods to define regions of interest would be especially helpful in our research, so that we could evaluate several ROIs at once, in a timely fashion. Methods to enhance automated registration and segmentation of brain regions would thus be most helpful.

Coronal SPGR Image of Normal and SZ Here we see two coronal Images selected from a normal control on the left and a patient diagnosed with chronic schizophrenia on the right. Note that the image on the right shows disproportionaly enlarged lateral ventricles, smaller STG and atrophy of the amygdala-hippocampal complex. This is of course for illustrative purposes only, as not all schizophrenics show such radiologic symptoms. Again, however, this emphasizes why we need larger samples and a combination of methods to increase both the sensitivity and specificity of our investigations.

Caudate Nucleus ROI B C A D E F (Koo et al., In Preparation) Subcortical structures (caudate nucleus, but also other subcortical structures, such as thalamus) are very important in schizophrenia, and are likely involved in neurotransmitter (dopaminergic) circuitry that is affected in this disease. This image is an example of a manually drawn ROI. Such projects are very time consuming, and boundary definitions are sometimes difficult as well as arbitrary. More automated tools are thus needed. (Koo et al., In Preparation)

Caudate Nucleus (Koo et al., In Preparation) Here is a 3D representation of the structure drawn on the previous slide. With models like this, we can look at not only volumes, but also shapes of these structures in 3D. Which brings us to another aspect of image analysis- shape. (Koo et al., In Preparation)

Shape

(Levitt et al., in Preparation) Shape Analysis of Caudate Nucleus Regions of inflation (blue color) and deflation (red color) in SZ with p>0.05 Here, we see a 3D model of caudate nucleus drawn by one of our colleagues. Then these 3D models are subjected to shape analysis, which show local differences in shape between schizophrenics and controls. We want to be able to apply this sort of analysis to all our regions of interest, and use it routinely. Of course some of the questions that have to be answered first, include those of how to quantify local shape differences, versus just global shape differences, etc. Core 1 will be crucial for developing such tools. (Levitt et al., in Preparation)

DTI and Other Complex Data How to Quantify? How to integrate with other measures? Third, and perhaps the most challenging imaging technique used by us and others in schizophrenia research is diffusion tensor imaging. Several studies have been done with respect to white matter fiber tracts connecting frontal and temporal lobes, as we think these are the most affected in schizophrenia, but because of the lack of appropriate tools, we have been able to analyze only short portions of these structures, defined manually with the only quantitative measure being fractional anisotropy. Since tensor data is characterized by higher complexity than scalar intensity data, to combine this information with other imaging techniques, such as structural and functional MRI, we need precise tools that will allow us to co-register these images.

Axonal Fibers using Diffusion Tensor Imaging of 3T MRI This is what we already can do in terms of fiber tract visualization. But what we need, is to be able to separate these fiber bundles based on their anatomy and/or function, follow these fiber tracts from region A to region B (defined either by structural or functional data), and quantify these connections, as well as relate them to functional imaging data and other data such as cognition, behavior, and genetics. Hae-Jeong Park, Ph.D.

Manual Delineation of Fiber Tracts Yellow = Cortico-Spinal Tract Blue = Corpus Callosum Red = Cingulate Fasciculus Green = Arcuate Fasciculus Orange = Uncinate Fasciculus Again, our group has been involved in labor intensive manual work, coloring fiber tracts, with no automatic tool for ROI definitions. Core 1 will be most helpful in assisting us in delineating specific fiber tracts. (Mamata et al., AJNR 2002)

Fiber Tractography Based on Spectral Graph Analyses Sagittal and axial view of the white matter fiber tracts, which demonstrate anatomical connectivity by fibers of similar colors. We are hoping to be able to apply this technique to our studies- it is an experimental method of clustering fiber bundles based on their coherence, or similarity, with the colors reflecting this coherence/similarity. Core 1 will be most helpful with developing such tools for their application to schizophrenia. Laplacian Eigenmaps (Brun et al., In Preparation)

Anterior Limb of the Internal Capsule ROI was drawn on 6 consecutive slices. The first 3 include the anterior limb of the internal capsule (left-purple and right-green), and the next three include the posterior limb of the internal capsule (left-blue and right-yellow). Problems: How to measure the whole bundle that goes through the ROI? How to better separate anterior and posterior limbs of the internal capsule? Some problems that I already mentioned- we can define ROIs that include short portions of the fiber bundles. What we need, however, is to be able to measure the whole bundle. Separation, as shown on the previous slide, will also be helpful.

Conclusions How can we automatically measure brain structures with robustness and precision? How can we quantify white matter fiber tracts? How can we associate multiple medical images with non-imaging data? In conclusions, as the driving biological problem, we will provide data and ask questions. The computer scientists and engineers will focus on a clearly defined set of questions and develop new tools that will assist us in evaluating brain abnormalities in schizophrenia. The methods that will be developed will be applicable not only to schizophrenia research but will have a wider application for other neuropsychiatric and neurological disorders.