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Abstract #4182

Mutual Information Weighted Graphs for Resting State Functional Connectivity in fMRI Data

Ehsan Eqlimi 1 , Nader Riyahi Alam 1 , MA Sahraian 2 , A Eshaghi 2 , and Hamidreza Saligheh Rad 1,3

1 Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Tehran, Iran, 2 Sina MS Research Center, Sina Hospital, Tehran, Tehran, Iran, 3 Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran, Tehran, Iran

Functional magnetic resonance imaging (fMRI) can be applied to investigate resting state functional connectivity in brain without any stimulation paradigm. Resting state communication patterns between brain areas is a key to understand how brain functions. Furthermore, abnormal functional connectivity within brain networks is thought to be responsible for some pathologies. In this work, we proposed mutual information weighted graphs instead of classic correlation graphs to model brain functional networks, and extracted clustering coefficient, degree and eigenvector centrality as principal graph theoretical features for each node of graphs to demonstrate alterations in functional connectivity patterns of patients with multiple sclerosis.

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