NA-MIC National Alliance for Medical Image Computing Velocardiofacial Syndrome as a Genetic Model for Schizophrenia Marek Kubicki DBP2, Brigham and Women’s Hospital, Harvard Medical School

National Alliance for Medical Image Computing Team HARVARD Marek Kubicki, MD, PhD Sylvain Bouix, PhD Katharina Quintus, MA Tri Ngo, BA Doug Markant, BA Usman Khan, BA NAMIC Polina Gollard MIT Brad Davis Kitware Others

National Alliance for Medical Image Computing NAMIC Roadmap Project: Stochastic Tractography Accomplished: 1. We have successfully used Stochastic Tractography with old schizophrenia data available to NAMIC (1.5T GE data) to trace internal capsule. This study is finished, and data analyzed. Results were presented at the ACNP symposium, December 2007 (Shenton et al., 2007). 2. Algorithm has been tested on smaller structures (cingulum, uncinate, arcuate, fornix), and demonstrated good performance. Data was presented at Santa Fe NAMIC meeting this past October.

National Alliance for Medical Image Computing NAMIC Roadmap Project: Stochastic Tractography Accomplished: 3. Algorithm has been tested, and optimized to work with newly acquired, high resolution 3T DTI data (November, December 2007). Data is now also available to NAMIC. 4. Algorithm has been also tested and further debugged on phantom data (Programming week). 5. Several new tools have been added to the module per our request: cutting the tracts at the ROIs, streamline tractography option to test continuity of the tract.

National Alliance for Medical Image Computing NAMIC Roadmap Project: Stochastic Tractography Accomplished: 6. Slicer3 module has been built and tested (programming week).

National Alliance for Medical Image Computing NAMIC Roadmap Project: Stochastic Tractography Planned: We plan to use Stochastic Tractography to delineate connections within the language network. –We are currently working on drawing ROIs that would include Brocka and Wernicke areas and on generating white matter masks that will be used for tracking purposes. –We plan to use slicer3 module of stochastic tractography to extract fiber connections between ROIs, and analyze them. –This analysis will be performed first on already collected new schizophrenia dataset (20 controls, 20 schizophrenics). –We plan to finish collecting VCFS data within the next year, and then start applying the method to these data as well.

National Alliance for Medical Image Computing Other Contributions to the NAMIC Kit DLPFC project We have been testing and improving slicer 2 DLPFC segmentation module, and optimizing segmentation for 3T structural data. We have been testing different bias field correction programs, in order to further fine-tune 3T slicer segmentation. We are in touch with Jim Fallon, who developed the anatomical landmarks for the module, and will work with him to improve the method reliability. Module will be adapted for slicer3 within a year.

National Alliance for Medical Image Computing Other Contributions to the NAMIC Kit Resting state fMRI analysis project We have collected resting-state fMRI data, and started analyzing it. Preliminary results will be presented at Biological Psychiatry Symposium. We are in the process of co-registering fMRI and DTI data in order to use fMRI activation sites for tracking (anatomical connectivity measures). We are still working on distortion correction problem, that would improve registration between fMRI and DTI modalities.

National Alliance for Medical Image Computing Other Contributions to the NAMIC Kit DTI atlas registration project We have obtained DTI atlas of white matter labels from MIND (Susumu Mori) We are trying to register these labels to our 3T DTI data through anatomical SPGR images. Preliminary data will be presented at Biological Psychiatry Symposium. We are using slicer 3, and working on improving co-registration procedures (estimating and applying deformations to different images, reverse deformations, etc).

National Alliance for Medical Image Computing Collaborations within NAMIC UNC Corpus Callosum Subdivision using tractography-based model (Kubicki et al., paper in preparation). MIT Whole Brain Group Spectral Clustering (new data analysis ongoing) GaTech Cingulum Bundle analysis using Finsler method (ongoing) Utah DTI data analysis using atlas building (ongoing)