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Discriminative analysis of Parkinson's disease based on whole-brain functional connectivity
- Source :
- PLoS ONE, Vol 10, Iss 4, p e0124153 (2015), PLoS ONE
- Publication Year :
- 2015
- Publisher :
- Public Library of Science (PLoS), 2015.
-
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.
- Subjects :
- Male
Parkinson's disease
Support Vector Machine
lcsh:Medicine
Disease
Bioinformatics
Sensitivity and Specificity
Neuroimaging
Discriminative model
Cerebellum
Connectome
Medicine
Humans
lcsh:Science
Default mode network
Aged
Cerebral Cortex
Multidisciplinary
medicine.diagnostic_test
business.industry
lcsh:R
Discriminant Analysis
Parkinson Disease
Middle Aged
medicine.disease
Magnetic Resonance Imaging
Support vector machine
Early Diagnosis
Case-Control Studies
Head Movements
Pattern recognition (psychology)
Female
lcsh:Q
Nerve Net
business
Functional magnetic resonance imaging
Neuroscience
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 10
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....6e8148d72623bf752e7c7a381e8f877e