John A. Bogovic1, Min Chen1, Aaron Carass1, Pierre-Louis Bazin2, Dzung Pham2, Susan M. Resnick3, Jerry L. Prince1,4, Bennett Allan Landman, 4,5
1Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States; 2Radiology, Johns Hopkins University, Baltimore, MD, United States; 3Laboratory of Personality and Cognition, National Institute on Aging, Baltimore, MD, United States; 4Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States; 5Electrical Engineering, Vanderbilt University, Nashville, TN, United States
Understanding anatomical connectivity and multivariate relationships in neuroimaging data may be essential to elucidate multiple small changes across the brain that combine to manifest in observable phenotypes. While there are powerful tools to assess connectivity through graphs using diffusion weighted MRI (DW-MRI), association of DW-MRI metrics with connectivity necessitates ad hoc choices. Herein, we show how connectivity can be interpreted by multimodal characterization of the tissues through which estimated tracts pass (in addition to metrics on the DW-MRI tracts). We define and compute multi-modal structural networks, which are multivariate graphs representing connectivity among structural regions.