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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