Abdalbari Helmi, Durrani Mohammad, Pancholi Shivam, Patel Nikhil, Nasuto Slawomir J, Nicolaou Nicoletta
Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia, Cyprus.
Department of Biomedical Engineering, School of Biological Sciences, University of Reading, Reading, United Kingdom.
Front Neurosci. 2022 Sep 15;16:927111. doi: 10.3389/fnins.2022.927111. eCollection 2022.
In this exploratory study we apply Granger Causality (GC) to investigate the brain-brain and brain-heart interactions during wakefulness and sleep. Our analysis includes electroencephalogram (EEG) and electrocardiogram (ECG) data during all-night polysomnographic recordings from volunteers with apnea, available from the Massachusetts General Hospital's Computational Clinical Neurophysiology Laboratory and the Clinical Data Animation Laboratory. The data is manually annotated by clinical staff at the MGH in 30 second contiguous intervals (wakefulness and sleep stages 1, 2, 3, and rapid eye movement (REM). We applied GC to 4-s non-overlapping segments of available EEG and ECG across all-night recordings of 50 randomly chosen patients. To identify differences in GC between the different sleep stages, the GC for each sleep stage was subtracted from the GC during wakefulness. Positive (negative) differences indicated that GC was greater (lower) during wakefulness compared to the specific sleep stage. The application of GC to study brain-brain and brain-heart bidirectional connections during wakefulness and sleep confirmed the importance of fronto-posterior connectivity during these two states, but has also revealed differences in ipsilateral and contralateral mechanisms of these connections. It has also confirmed the existence of bidirectional brain-heart connections that are more prominent in the direction from brain to heart. Our exploratory study has shown that GC can be successfully applied to sleep data analysis and captures the varying physiological mechanisms that are related to wakefulness and different sleep stages.
在这项探索性研究中,我们应用格兰杰因果关系(GC)来研究清醒和睡眠期间的脑-脑和脑-心相互作用。我们的分析包括来自患有呼吸暂停的志愿者的全夜多导睡眠图记录中的脑电图(EEG)和心电图(ECG)数据,这些数据可从马萨诸塞州总医院的计算临床神经生理学实验室和临床数据动画实验室获得。数据由MGH的临床工作人员以30秒连续间隔(清醒和睡眠阶段1、2、3以及快速眼动(REM))进行手动注释。我们将GC应用于50名随机选择患者的全夜记录中可用的EEG和ECG的4秒非重叠片段。为了识别不同睡眠阶段之间GC的差异,将每个睡眠阶段的GC从清醒期间的GC中减去。正(负)差异表明与特定睡眠阶段相比,清醒期间的GC更大(更低)。应用GC研究清醒和睡眠期间的脑-脑和脑-心双向连接证实了这两种状态下前后连接的重要性,但也揭示了这些连接的同侧和对侧机制的差异。它还证实了脑-心双向连接的存在,且这种连接在从脑到心的方向上更为突出。我们的探索性研究表明,GC可以成功应用于睡眠数据分析,并捕捉到与清醒和不同睡眠阶段相关的不同生理机制。