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Discriminative Analysis of Parkinson’s Disease Based on Whole-Brain Functional Connectivity by Yongbin Chen; Wanqun Yang; Jinyi Long; Yuhu Zhang; Jieying Feng; Yuanqing Li; Biao Huang is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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Recently, there has been an increasing emphasis on applications of pattern recognition and neuroimaging techniques in the effective and accurate diagnosis of psychiatric or neurological disorders. In the present study, we investigated the whole-brain resting-state functional connectivity patterns of Parkinson's disease (PD), which are expected to provide additional information for the clinical diagnosis and treatment of this disease. First, we computed the functional connectivity between each pair of 116 regions of interest derived from a prior atlas. The most discriminative features based on Kendall tau correlation coefficient were then selected. A support vector machine classifier was employed to classify 21 PD patients with 26 demographically matched healthy controls. This method achieved a classification accuracy of 93.62% using leave-one-out cross-validation, with a sensitivity of 90.47% and a specificity of 96.15%. The majority of the most discriminative functional connections were located within or across the default mode, cingulo-opercular and frontal-parietal networks and the cerebellum. These disease-related resting-state network alterations might play important roles in

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Author

Yongbin Chen; Wanqun Yang; Jinyi Long; Yuhu Zhang; Jieying Feng; Yuanqing Li; Biao Huang

Publisher

Public Library of Science (PLoS)

Published

2015

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)