Frequency-Specific Changes of Resting Brain Activity in Parkinson’s Disease: A Machine Learning Approach

Publication date: Available online 12 February 2020Source: NeuroscienceAuthor(s): 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, Jianbo ShaoAbstractThe application of Resting State functional MRI (RS-fMRI) in Parkinson’s disease was widely performed using standard statistical tests, however, the machine learning 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 IPL and SMG, was detected as the discriminative features exclusively in Slow-4. Our findings, at the first time, indicated that the machine learning approach is a promising choic...
Source: Neuroscience - Category: Neuroscience Source Type: research