title: Discriminative analysis of Parkinson's disease based on whole-brain functional connectivity.
abstract: 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 the pathophysiology of this disease. Our results suggest that analyses of whole-brain resting-state functional connectivity patterns have the potential to improve the clinical diagnosis and treatment evaluation of PD.
authors.affiliation: Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China.
authors.forename: Yongbin
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Department of Radiology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, Guangdong, China.
authors.forename: Wanqun
authors.initials: W
authors.lastname: Yang
authors.affiliation: Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China.
authors.forename: Jinyi
authors.initials: J
authors.lastname: Long
authors.affiliation: Department of Neurology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, Guangdong, China.
authors.forename: Yuhu
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Radiology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, Guangdong, China.
authors.forename: Jieying
authors.initials: J
authors.lastname: Feng
authors.affiliation: Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China.
authors.forename: Yuanqing
authors.initials: Y
authors.lastname: Li
authors.affiliation: Department of Radiology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, Guangdong, China.
authors.forename: Biao
authors.initials: B
authors.lastname: Huang
mesh_terms: D000368:Aged; D016022:Case-Control Studies; D002531:Cerebellum / Q000503:physiopathology; D002540:Cerebral Cortex / Q000503:physiopathology; D063132:Connectome; D016002:Discriminant Analysis; D042241:Early Diagnosis; D005260:Female; D019416:Head Movements; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009415:Nerve Net / Q000503:physiopathology; D010300:Parkinson Disease / Q000175:diagnosis / Q000503:physiopathology*; D012680:Sensitivity and Specificity; D060388:Support Vector Machine