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Compact and Informative Representation of Functional Connectivity for Predictive Modeling

  • Raif M. Rustamov
  • David Romano
  • Allan L. Reiss
  • Leonidas J. Guibas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

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.

Keywords

Support Vector Machine Functional Connectivity Linear Discriminant Analysis Blood Oxygenation Level Dependent Blood Oxygenation Level Dependent Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Raif M. Rustamov
    • 1
  • David Romano
    • 2
  • Allan L. Reiss
    • 2
  • Leonidas J. Guibas
    • 1
  1. 1.Computer Science DepartmentStanford UniversityStanfordUSA
  2. 2.Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral SciencesStanford University School of MedicineStanfordUSA

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