Blockmodels for connectome analysis

Daniel Moyer; Boris Gutman; Gautam Prasad; Joshua Faskowitz; Greg Ver Steeg; Paul Thompson

doi:10.1117/12.2211519

22 December 2015 Blockmodels for connectome analysis

Daniel Moyer, Boris Gutman, Gautam Prasad, Joshua Faskowitz, Greg Ver Steeg, Paul Thompson

Proceedings Volume 9681, 11th International Symposium on Medical Information Processing and Analysis; 96810A (2015) https://doi.org/10.1117/12.2211519
Event: 11th International Symposium on Medical Information Processing and Analysis (SIPAIM 2015), 2015, Cuenca, Ecuador

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.

Citation Download Citation

Daniel Moyer, Boris Gutman, Gautam Prasad, Joshua Faskowitz, Greg Ver Steeg, and Paul Thompson "Blockmodels for connectome analysis", Proc. SPIE 9681, 11th International Symposium on Medical Information Processing and Analysis, 96810A (22 December 2015); https://doi.org/10.1117/12.2211519

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