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Resting state functional connectivity holds great potential for diagnostic prediction of neurological and psychiatric illness. This paper introduces a compact and information-rich representation of connectivity that is geared directly towards predictive modeling. Our representation does not require a priori identification of localized regions of interest, yet provides a mechanism for interpretation of classifier weights. Experiments confirm increased accuracy associated with our representation and yield interpretations consistent with known physiology.
Pubmed ID: 25320794 RIS Download
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Matlab based cross platform software package for computation, display, and analysis of functional connectivity in fMRI (fcMRI). Used for resting state data (rsfMRI) as well as task related designs. Covers pipeline from raw fMRI data to hypothesis testing.
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