1. Frequency-Specific Changes of Resting Brain Activity in Parkinson’s Disease: A Machine Learning Approach
- Author
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Xin Hu, Miao Wan, Bai-qi Zhu, Min Wu, Wen-zhi Wang, Xue-hua Peng, Xiao-hu Zhu, Long Qian, Jianbo Shao, Lei Fang, Wenhan Zhang, and Zhi-yao Tian
- Subjects
0301 basic medicine ,Parkinson's disease ,Brain activity and meditation ,Rest ,Machine learning ,computer.software_genre ,03 medical and health sciences ,0302 clinical medicine ,Supramarginal gyrus ,Neuroimaging ,Basal ganglia ,Humans ,Medicine ,Visual Cortex ,Resting state fMRI ,business.industry ,General Neuroscience ,Parietal lobe ,Brain ,Parkinson Disease ,medicine.disease ,Magnetic Resonance Imaging ,030104 developmental biology ,Visual cortex ,medicine.anatomical_structure ,Artificial intelligence ,business ,computer ,030217 neurology & neurosurgery - Abstract
The 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.
- Published
- 2020
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