Frequency-Dependent Microstate Characteristics for Mild Cognitive Impairment in Parkinson’s Disease

作者：Chen Liu, Zhiqi Jiang, Shang Liu, Chunguang Chu, Jiang Wang, Wei Liu, Yanan Sun, Mengmeng Dong, Qingqing Shi, Pengcheng Huang, Xiaodong Zhu · 发表于：IEEE Transactions on Neural Systems and Rehabilitation Engineering · 年份：2023 · DOI：10.1109/tnsre.2023.3324343 · 被引用次数：18 · 研究领域：EEG and Brain-Computer Interfaces、Parkinson's Disease Mechanisms and Treatments、Functional Brain Connectivity Studies

Cognitive impairment is typically reflected in the time and frequency variations of electroencephalography (EEG). Integrating time-domain and frequency-domain analysis methods is essential to better understand and assess cognitive ability. Timely identification of cognitive levels in early Parkinson's disease (ePD) patients can help mitigate the risk of future dementia. For the investigation of the brain activity and states related to cognitive levels, this study recruited forty ePD patients for EEG microstate analysis, including 13 with mild cognitive impairment (MCI) and 27 without MCI (control group). To determine the specific frequency band on which the microstate analysis relies, a deep learning framework was employed to discern the frequency dependence of the cognitive level in ePD patients. The input to the convolutional neural network consisted of the power spectral density of multi-channel multi-point EEG signals. The visualization technique of gradient-weighted class activation mapping was utilized to extract the optimal frequency band for identifying MCI samples. Within this frequency band, microstate analysis was conducted and correlated with the Montreal Cognitive Assessment (MoCA) Scale. The deep neural network revealed significant differences in the 1-11.5Hz spectrum of the ePD-MCI group compared to the control group. In this characteristic frequency band, ePD-MCI patients exhibited a pattern of global microstate disorder. The coverage rate and occurrence frequ...