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Abstract #4155

Dynamics of functional and effective brain connectivity better predicts disease state compared to traditional static connectivity

Gopikrishna Deshpande 1,2 , Hao Jia 1 , Xiaoping Hu 3 , Changfeng Jin 4 , Lingjiang Li 4 , and Tianming Liu 5

1 MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama, United States, 2 Department of Psychology, Auburn University, Auburn, Alabama, United States, 3 Biomedical Imaging Technology Center, Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States, 4 The Mental Health Institute, The Second Xiangya Hospital, Central South University, Changsha, China, 5 Department of Computer Science, University of Georgia, Athens, Georgia, United States

It is acknowledged that functional connectivity (FC) in the brain obtained from resting state fMRI dynamically changes with time. Further, it has been shown that dynamic changes in FC and effective connectivity (EC) are relevant to disease processes. However, an outstanding question that remains is whether dynamic information from FC and EC provide increased sensitivity for identifying brain pathologies in addition to that obtained by static connectivity metrics? Here, we provide answers to these questions by demonstrating that information from temporal variations in FC and EC provides better accuracy for classifying subjects with PTSD (post-traumatic stress disorder) from healthy controls.

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