Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:255-258. doi: 10.1109/EMBC48229.2022.9871000.
Electroencephalography (EEG) microstates analysis provides a sequence of topographies representing the scalp-related electric field over time, and each microstate is synthetically represented by a symbol. Despite recent advances on functional brain-heart interplay (BHI) assessment, to our knowledge no methodology takes EEG microstates into account to relate the causal heartbeat dynamics. Moreover, standard BHI methods are tailored to a single EEG-channel analysis, neglecting the comprehensive information associated with a multichannel cluster or a whole-brain activity. To overcome these limitations, we devised a novel methodological frame-work for the assessment of functional BHI that exploits the symbolic representation of both EEG microstates and heart rate variability (HRV) series. Directional BHI quantification is then performed through Kullback-Leibler Divergence (KLD) and Transfer Entropy. The proposed methodology is here preliminarily tested on a dataset gathered from healthy subjects undergoing a resting state and a mental arithmetic task. Except for the KLD in the from-brain-to-heart direction, all other estimates showed significant differences between the two experimental conditions. We conclude that the proposed frame-work may promisingly provide novel insights on brain-heart phenomena through a whole-brain symbolic representation.
脑电微状态分析提供了一系列随时间变化的头皮相关电场拓扑图，每个微状态都由一个符号综合表示。尽管最近在功能大脑-心脏相互作用（BHI）评估方面取得了进展，但据我们所知，尚无方法将脑电微状态考虑在内以关联因果心跳动力学。此外，标准的 BHI 方法针对单个 EEG 通道分析进行了定制，忽略了与多通道集群或全脑活动相关的综合信息。为了克服这些限制，我们设计了一种新的功能性 BHI 评估方法框架，该框架利用 EEG 微状态和心率变异性（HRV）系列的符号表示。然后通过 Kullback-Leibler 散度（KLD）和传递熵来进行定向 BHI 量化。该方法在一组健康受试者进行静息状态和心算任务时采集的数据上进行了初步测试。除了从大脑到心脏方向的 KLD 之外，所有其他估计在两种实验条件之间均显示出显著差异。我们得出结论，该方法框架可以通过全脑符号表示为脑-心现象提供有前景的新见解。
