Chu Chunguang, Zhang Zhen, Song Zhenxi, Xu Zifan, Wang Jiang, Wang Fei, Liu Wei, Lu Liying, Liu Chen, Zhu Xiaodong, Fietkiewicz Chris, Loparo Kenneth A
IEEE J Biomed Health Inform. 2023 Mar;27(3):1307-1318. doi: 10.1109/JBHI.2022.3232811. Epub 2023 Mar 7.
Variations in brain activity patterns reveal impairments of motor and cognitive functions in the human brain. Electroencephalogram (EEG) microstates embody brain activity patterns at a microscopic time scale. However, current microstate analysis method can only recognize less than 90% of EEG signals per subject, which severely limits the characterization of dynamic brain activity. As an application to early Parkinson's disease (PD), we propose an enhanced EEG microstate recognition framework based on deep neural networks, which yields recognition rates from 90% to 99%, as accompanied by a strong anti-artifact property. Additionally, gradient-weighted class activation mapping, as a visualization technique, is employed to locate the activated functional brain regions of each microstate class. We find that each microstate class corresponds to a particular activated brain region. Finally, based on the improved identification of microstate sequences, we explore the EEG microstate characteristics and their clinical associations. We show that the decreased occurrences of a particular microstate class reflect the degree of cognitive decline in early PD, and reduced transitions between certain microstates suggest injury in motor-related brain regions. The novel EEG microstate recognition framework paves the way to revealing more effective biomarkers for early PD.
大脑活动模式的变化揭示了人类大脑运动和认知功能的损伤。脑电图（EEG）微状态在微观时间尺度上体现大脑活动模式。然而，当前的微状态分析方法每个受试者只能识别不到90%的EEG信号，这严重限制了对动态大脑活动的表征。作为早期帕金森病（PD）的一种应用，我们提出了一种基于深度神经网络的增强型EEG微状态识别框架，其识别率从90%到99%，同时具有很强的抗伪迹特性。此外，作为一种可视化技术，梯度加权类激活映射被用于定位每个微状态类别的激活功能脑区。我们发现每个微状态类别都对应一个特定的激活脑区。最后，基于对微状态序列的改进识别，我们探索了EEG微状态特征及其临床关联。我们表明，特定微状态类别的出现频率降低反映了早期PD的认知衰退程度，而某些微状态之间的转换减少表明运动相关脑区受损。新颖的EEG微状态识别框架为揭示早期PD更有效的生物标志物铺平了道路。
