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Abstract #5312

Eigenvector Centrality Mapping in Detecting Parkinson’s Disease

Zhengshi Yang1, Ryan Walsh1, Virendra Mishra1, Karthik Sreenivasan1, Xiaowei Zhuang1, Sarah Banks1, and Dietmar Cordes1,2

1Cleveland Clinic Lou Ruvo Center for Brain Health, LAS VEGAS, NV, United States, 2University of Colorado Boulder, CO, United States

Eigenvector centrality (EC) is a parameter-free method to measure the centrality of complex brain network structures without a priori assumption. It is here applied to resting state fMRI data acquired from normal controls (NC) and Parkinson’s disease (PD) subjects for the purpose of detecting centrality abnormality in PD, a disease known to impact neural networks diffusely. The features extracted from EC were able to accurately classify subjects when used with linear discriminant analysis and support vector machine.

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