title: Compact and informative representation of functional connectivity for predictive modeling.
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.
authors.forename: Raif M
authors.initials: RM
authors.lastname: Rustamov
authors.forename: David
authors.initials: D
authors.lastname: Romano
authors.forename: Allan L
authors.initials: AL
authors.lastname: Reiss
authors.forename: Leonidas J
authors.initials: LJ
authors.lastname: Guibas
mesh_terms: D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D063132:Connectome / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014796:Visual Perception / Q000502:physiology*