Wavelet Graphs on Mutual Information Functional Connectivity for MS Patients in Resting State fMRI



In this study, we developed a novel analysis technique for the evaluation of rs-FC in fMRI data based the weighted graphs called mutual information weighted graphs (MIWG) and spectral graph wavelet transform (SGWT) to differentiate MS from healthy controls. Two analysis types were used, one for group wise comparisons, and one for machine learning classification. Classification performance using leave-one-out cross-validation (1000 iterations) yielded a sensitivity of 78.40% and specificity of 90.40% to distinguish between MS patients and controls .


Wavelet Graphs, MS, Resting State fMRI

E Eqlimi, A Eshaghi, N Riyahi Alam, A Ahmadian, M A Sahraian and H Saligheh Rad

20th Annual Meeting of the Organization for Human Brain Mapping (OHBM 2014)
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