Wan Wang, Gu Zhongze, Peng Chung-Kang, Cui Xingran
State Key Laboratory of Digital Medical Engineering, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China.
Center for Nonlinear Dynamics in Medicine, Southeast University, Nanjing 210096, China.
Brain Sci. 2024 May 11;14(5):487. doi: 10.3390/brainsci14050487.
Exploring the spatiotemporal dynamic patterns of multi-channel electroencephalography (EEG) is crucial for interpreting dementia and related cognitive decline. Spatiotemporal patterns of EEG can be described through microstate analysis, which provides a discrete approximation of the continuous electric field patterns generated by the brain cortex. Here, we propose a novel microstate spatiotemporal dynamic indicator, termed the microstate sequence non-randomness index (MSNRI). The essence of the method lies in initially generating a sequence of microstate transition patterns through state space compression of EEG data using microstate analysis. Following this, we assess the non-randomness of these microstate patterns using information-based similarity analysis. The results suggest that this MSNRI metric is a potential marker for distinguishing between health control (HC) and frontotemporal dementia (FTD) (HC vs. FTD: 6.958 vs. 5.756, < 0.01), as well as between HC and populations with Alzheimer's disease (AD) (HC vs. AD: 6.958 vs. 5.462, < 0.001). Healthy individuals exhibit more complex macroscopic structures and non-random spatiotemporal patterns of microstates, whereas dementia disorders lead to more random spatiotemporal patterns. Additionally, we extend the proposed method by integrating the Complementary Ensemble Empirical Mode Decomposition (CEEMD) method to explore spatiotemporal dynamic patterns of microstates at specific frequency scales. Moreover, we assessed the effectiveness of this innovative method in predicting cognitive scores. The results demonstrate that the incorporation of CEEMD-enhanced microstate dynamic indicators significantly improved the prediction accuracy of Mini-Mental State Examination (MMSE) scores (R = 0.940). The CEEMD-enhanced MSNRI method not only aids in the exploration of large-scale neural changes in populations with dementia but also offers a robust tool for characterizing the dynamics of EEG microstate transitions and their impact on cognitive function.
探索多通道脑电图（EEG）的时空动态模式对于解释痴呆症及相关认知衰退至关重要。EEG的时空模式可通过微状态分析来描述，该分析提供了大脑皮层产生的连续电场模式的离散近似。在此，我们提出了一种新颖的微状态时空动态指标，称为微状态序列非随机性指数（MSNRI）。该方法的本质在于，首先通过使用微状态分析对EEG数据进行状态空间压缩来生成微状态转换模式序列。在此之后，我们使用基于信息的相似性分析来评估这些微状态模式的非随机性。结果表明，该MSNRI指标是区分健康对照组（HC）和额颞叶痴呆（FTD）（HC与FTD：6.958对5.756，<0.01）以及HC与阿尔茨海默病（AD）人群（HC与AD：6.958对5.462，<0.001）的潜在标志物。健康个体表现出更复杂的宏观结构和微状态的非随机时空模式，而痴呆症会导致更随机的时空模式。此外，我们通过整合互补总体经验模态分解（CEEMD）方法来扩展所提出的方法，以探索特定频率尺度下微状态的时空动态模式。此外，我们评估了这种创新方法在预测认知分数方面的有效性。结果表明，纳入CEEMD增强的微状态动态指标显著提高了简易精神状态检查表（MMSE）分数的预测准确性（R = 0.940）。CEEMD增强的MSNRI方法不仅有助于探索痴呆症人群的大规模神经变化，还为表征EEG微状态转换的动态及其对认知功能的影响提供了一个强大的工具。
