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. © 2014 Springer International Publishing.
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CITATION STYLE
Rustamov, R. M., Romano, D., Reiss, A. L., & Guibas, L. J. (2014). Compact and informative representation of functional connectivity for predictive modeling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8675 LNCS, pp. 153–160). Springer Verlag. https://doi.org/10.1007/978-3-319-10443-0_20
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