Abstract #3740

Identifying Foci of Brain Disorders from Effective Connectivity Networks

D Rangaprakash¹, Gopikrishna Deshpande^1,2,3, Archana Venkataraman⁴, Jeffrey S Katz^1,2,3, Thomas S Denney^1,2,3, and Michael N Dretsch^5,6

¹AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States, ²Department of Psychology, Auburn University, Auburn, AL, United States, ³Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, Birmingham, AL, United States, ⁴Department of Diagnostic Radiology, Yale University, New Haven, CT, United States, ⁵U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, United States, ⁶Human Dimension Division, HQ TRADOC, Fort Eustis, VA, United States

Brain connectivity studies report statistical differences in pairwise connection strengths. While informative, such results are difficult to interpret, since our understanding of the brain relies on region information, rather than connections. Given that large effects in natural systems are likely caused by few pivotal sources, we employed a novel framework to identify sources of disruption from directional connectivity. Using resting-state fMRI, we employed static and time-varying effective connectivities in a probabilistic framework to identify affected foci and associated affected connections. We illustrate its utility in identifying disrupted foci in Soldiers with post-traumatic stress disorder and mild traumatic brain injury.

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