Raif M. Rustamov, David Romano, Allan L. Reiss, and Leonidas J. Guibas. Compact and Informative Representation of Functional Connectivity for Predictive Modeling. Medical Image Computing and Computer Assisted Intervention Conference (MICCAI), 2014

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.

Bibtex: 

@inproceedings{rrag-cirfcpm-14,
 author = {Raif M. Rustamov and David Romano and Allan L. Reiss and Leonidas J. Guibas},
 title = {Compact and Informative Representation of Functional Connectivity for Predictive Modeling},
 booktitle = {Medical Image Computing and Computer Assisted Intervention Conference (MICCAI)},
 year = {2014},
}