Abstract #0672

Multi-Modal Structural Networks: Mapping of Connectivity Through Diffusion, Functional, and Structural Assessment of Intervening Pathways

John A. Bogovic¹, Min Chen¹, Aaron Carass¹, Pierre-Louis Bazin², Dzung Pham², Susan M. Resnick³, Jerry L. Prince^1,4, 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.