Compact and Informative Representation of Functional Connectivity for Predictive Modeling 17th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) Rustamov, R. M., Romano, D., Reiss, A. L., Guibas, L. J. SPRINGER-VERLAG BERLIN. 2014: 153–60

Abstract

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.

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