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- Zhi-Yao Tian, Long Qian, Lei Fang, Xue-Hua Peng, Xiao-Hu Zhu, Min Wu, Wen-Zhi Wang, Wen-Han Zhang, Bai-Qi Zhu, Miao Wan, Xin Hu, and Jianbo Shao.
- Medical Imaging Center of Wuhan Children's Hospital, Wuhan Maternal and Child Healthcare Hospital, Tongji Medical College, Huazhong University of Science & Technology, 430000 Wuhan, Hubei, China.
- Neuroscience. 2020 Jun 1; 436: 170-183.
AbstractThe application of resting state functional MRI (RS-fMRI) in Parkinson's disease (PD) was widely performed using standard statistical tests, however, the machine learning (ML) approach has not yet been investigated in PD using RS-fMRI. In current study, we utilized the mean regional amplitude values as the features in patients with PD (n = 72) and in healthy controls (HC, n = 89). The t-test and linear support vector machine were employed to select the features and make prediction, respectively. Three frequency bins (Slow-5: 0.0107-0.0286 Hz; Slow-4: 0.0286-0.0821 Hz; conventional: 0.01-0.08 Hz) were analyzed. Our results showed that the Slow-4 may provide important information than Slow-5 in PD, and it had almost identical classification performance compared with the Combined (Slow-5 and Slow-4) and conventional frequency bands. Similar with previous neuroimaging studies in PD, the discriminative regions were mainly included the disrupted motor system, aberrant visual cortex, dysfunction of paralimbic/limbic and basal ganglia networks. The lateral parietal lobe, such as right inferior parietal lobe (IPL) and supramarginal gyrus (SMG), was detected as the discriminative features exclusively in Slow-4. Our findings, at the first time, indicated that the ML approach is a promising choice for detecting abnormal regions in PD, and a multi-frequency scheme would provide us more specific information.Copyright © 2020 IBRO. Published by Elsevier Ltd. All rights reserved.
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