Schizophrenia classification using functional network features

Irina Rish; Guillermo A. Cecchi; Kyle Heuton

doi:10.1117/12.911773

14 April 2012 Schizophrenia classification using functional network features

Irina Rish, Guillermo A. Cecchi, Kyle Heuton

Proceedings Volume 8317, Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging; 83170W (2012) https://doi.org/10.1117/12.911773
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

This paper focuses on discovering statistical biomarkers (features) that are predictive of schizophrenia, with a particular focus on topological properties of fMRI functional networks. We consider several network properties, such as node (voxel) strength, clustering coefficients, local efficiency, as well as just a subset of pairwise correlations. While all types of features demonstrate highly significant statistical differences in several brain areas, and close to 80% classification accuracy, the most remarkable results of 93% accuracy are achieved by using a small subset of only a dozen of most-informative (lowest p-value) correlation features. Our results suggest that voxel-level correlations and functional network features derived from them are highly informative about schizophrenia and can be used as statistical biomarkers for the disease.

Citation Download Citation

Irina Rish, Guillermo A. Cecchi, and Kyle Heuton "Schizophrenia classification using functional network features", Proc. SPIE 8317, Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging, 83170W (14 April 2012); https://doi.org/10.1117/12.911773

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