Ren Bin, Yang Kun, Zhu Li, Hu Lang, Qiu Tao, Kong Wanzeng, Zhang Jianhai
College of Computer Science, Hangzhou Dianzi University, Hangzhou, China.
Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China.
Front Comput Neurosci. 2022 Mar 31;16:785397. doi: 10.3389/fncom.2022.785397. eCollection 2022.
Evaluating the impact of stroke on the human brain based on electroencephalogram (EEG) remains a challenging problem. Previous studies are mainly analyzed within frequency bands. This article proposes a multi-granularity analysis framework, which uses multiple brain networks assembled with intra-frequency and cross-frequency phase-phase coupling to evaluate the stroke impact in temporal and spatial granularity. Through our experiments on the EEG data of 11 patients with left ischemic stroke and 11 healthy controls during the mental rotation task, we find that the brain information interaction is highly affected after stroke, especially in delta-related cross-frequency bands, such as delta-alpha, delta-low beta, and delta-high beta. Besides, the average phase synchronization index (PSI) of the right hemisphere between patients with stroke and controls has a significant difference, especially in delta-alpha ( = 0.0186 in the left-hand mental rotation task, = 0.0166 in the right-hand mental rotation task), which shows that the non-lesion hemisphere of patients with stroke is also affected while it cannot be observed in intra-frequency bands. The graph theory analysis of the entire task stage reveals that the brain network of patients with stroke has a longer feature path length and smaller clustering coefficient. Besides, in the graph theory analysis of three sub-stags, the more stable significant difference between the two groups is emerging in the mental rotation sub-stage (500-800 ms). These findings demonstrate that the coupling between different frequency bands brings a new perspective to understanding the brain's cognitive process after stroke.
基于脑电图（EEG）评估中风对人类大脑的影响仍然是一个具有挑战性的问题。以往的研究主要在频段内进行分析。本文提出了一种多粒度分析框架，该框架使用由频率内和跨频率相位-相位耦合组装而成的多个脑网络，从时间和空间粒度上评估中风的影响。通过对11例左侧缺血性中风患者和11名健康对照者在心理旋转任务期间的EEG数据进行实验，我们发现中风后脑信息交互受到高度影响，特别是在与δ波相关的跨频段，如δ- α波、δ-低β波和δ-高β波。此外，中风患者与对照组之间右半球的平均相位同步指数（PSI）存在显著差异，尤其是在δ- α波频段（左手心理旋转任务中p = 0.0186，右手心理旋转任务中p = 0.0166），这表明中风患者的非病变半球也受到了影响，而在频率内频段中未观察到这种情况。对整个任务阶段的图论分析表明，中风患者的脑网络具有更长的特征路径长度和更小的聚类系数。此外，在三个子阶段的图论分析中，两组之间更稳定的显著差异出现在心理旋转子阶段（500 - 800毫秒）。这些发现表明，不同频段之间的耦合为理解中风后脑的认知过程带来了新的视角。
