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Abstract #3930

Approaches for Modeling Spatially Varying Associations Between Multi-Modal Images

Alessandra Michelle Valcarcel1, Simon N Vandekar2, Tinashe Tapera3, Azeez Adebimpe3, David Roalf3, Armin Raznahan4, Theodore Satterthwaite3, Russell T Shinohara1, and Kristin A Linn1
1Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, United States, 2Department of Biostatistics, Vanderbilt University, Nashville, TN, United States, 3Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States, 4Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD, United States

Multi-modal MRI modalities quantify different, yet complimentary, properties of the brain and its activity. When studied jointly, multi-modal imaging data may improve our understanding of the brain. We aim to study the complex relationships between multiple imaging modalities and map how these relationships vary spatially across different anatomical brain regions. Given a particular location in the brain, we regress an outcome image modality on one or more other modalities using all voxels in a local neighborhood of a target voxel. We apply our method to study how the relationship between local functional connectivity and cerebral blood flow varies spatially.

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