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Discriminative network models of Schizophrenia

Abstract : Schizophrenia is a complex psychiatric disorder that has eluded a characterization in terms of local abnormalities of brain activity, and is hypothesized to affect the collective, "emergent" working of the brain. We propose a novel data-driven approach to capture emergent features using functional brain networks [4] extracted from fMRI data, and demonstrate its advantage over traditional region-of-interest (ROI) and local, task-specific linear activation analyzes. Our results suggest that schizophrenia is indeed associated with disruption of global brain properties related to its functioning as a network, which cannot be explained by alteration of local activation patterns. Moreover, further exploitation of interactions by sparse Markov Random Field classifiers shows clear gain over linear methods, such as Gaussian Naive Bayes and SVM, allowing to reach 86% accuracy (over 50% baseline- random guess), which is quite remarkable given that it is based on a single fMRI experiment using a simple auditory task.
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Contributor : Bertrand Thirion Connect in order to contact the contributor
Submitted on : Tuesday, January 15, 2013 - 10:20:22 PM
Last modification on : Friday, November 18, 2022 - 9:28:05 AM


  • HAL Id : hal-00776664, version 1



Guillermo A. Cecchi, Irina Rish, Benjamin Thyreau, Bertrand Thirion, Marion Plaze, et al.. Discriminative network models of Schizophrenia. NIPS 2009 - 23rd Annual Conference on Neural Information Processing Systems, Dec 2009, Vancouver, Canada. pp.252-260. ⟨hal-00776664⟩



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