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Proceedings Paper

Blockmodels for connectome analysis
Author(s): Daniel Moyer; Boris Gutman; Gautam Prasad; Joshua Faskowitz; Greg Ver Steeg; Paul Thompson
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Paper Abstract

In the present work we study a family of generative network model and its applications for modeling the human connectome. We introduce a minor but novel variant of the Mixed Membership Stochastic Blockmodel and apply it and two other related model to two human connectome datasets (ADNI and a Bipolar Disorder dataset) with both control and diseased subjects. We further provide a simple generative classifier that, alongside more discriminating methods, provides evidence that blockmodels accurately summarize tractography count networks with respect to a disease classification task.

Paper Details

Date Published: 22 December 2015
PDF: 9 pages
Proc. SPIE 9681, 11th International Symposium on Medical Information Processing and Analysis, 96810A (22 December 2015); doi: 10.1117/12.2211519
Show Author Affiliations
Daniel Moyer, Univ. of Southern California (United States)
Boris Gutman, Univ. of Southern California (United States)
Gautam Prasad, Univ. of Southern California (United States)
Joshua Faskowitz, Univ. of Southern California (United States)
Greg Ver Steeg, Univ. of Southern California (United States)
Paul Thompson, Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 9681:
11th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Juan D. García-Arteaga; Jorge Brieva, Editor(s)

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