Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, Palermo, 90128, Italy.
Epilepsy Monitoring Unit, Department of Pediatrics, Full Member of EpiCARE, Medical University Vienna, Währinger Gürtel 18-20, Vienna, 1090, Austria.
J Neural Eng. 2022 Jul 25;19(4). doi: 10.1088/1741-2552/ac7fba.
While it is well-known that epilepsy has a clear impact on the activity of both the central nervous system (CNS) and the autonomic nervous system (ANS), its role on the complex interplay between CNS and ANS has not been fully elucidated yet. In this work, pairwise and higher-order predictability measures based on the concepts of Granger Causality (GC) and partial information decomposition (PID) were applied on time series of electroencephalographic (EEG) brain wave amplitude and heart rate variability (HRV) in order to investigate directed brain-heart interactions associated with the occurrence of focal epilepsy.HRV and the envelopes ofandEEG activity recorded from ipsilateral (ipsi-EEG) and contralateral (contra-EEG) scalp regions were analyzed in 18 children suffering from temporal lobe epilepsy monitored during pre-ictal, ictal and post-ictal periods. After linear parametric model identification, we compared pairwise GC measures computed between HRV and a single EEG component with PID measures quantifying the unique, redundant and synergistic information transferred from ipsi-EEG and contra-EEG to HRV.The analysis of GC revealed a dominance of the information transfer from EEG to HRV and negligible transfer from HRV to EEG, suggesting that CNS activities drive the ANS modulation of the heart rhythm, but did not evidence clear differences betweenandrhythms, ipsi-EEG and contra-EEG, or pre- and post-ictal periods. On the contrary, PID revealed that epileptic seizures induce a reorganization of the interactions from brain to heart, as the unique predictability of HRV originated from the ipsi-EEG for thewaves and from the contra-EEG for thewaves in the pre-ictal phase, while these patterns were reversed after the seizure.These results highlight the importance of considering higher-order interactions elicited by PID for the study of the neuro-autonomic effects of focal epilepsy, and may have neurophysiological and clinical implications.
虽然众所周知,癫痫对中枢神经系统 (CNS) 和自主神经系统 (ANS) 的活动都有明显的影响,但它在 CNS 和 ANS 之间复杂相互作用中的作用尚未完全阐明。在这项工作中,基于格兰杰因果关系 (GC) 和部分信息分解 (PID) 概念的成对和更高阶可预测性度量被应用于脑电图 (EEG) 脑波幅度和心率变异性 (HRV) 的时间序列,以研究与局灶性癫痫发作相关的定向脑-心相互作用。在 18 名患有颞叶癫痫的儿童中,分析了记录自同侧 (ipsi-EEG) 和对侧 (contra-EEG) 头皮区域的 HRV 和 EEG 活动的包络,这些儿童在发作前、发作中和发作后期间进行了监测。在进行线性参数模型识别后,我们比较了 HRV 和单个 EEG 分量之间计算的 GC 成对度量与 PID 度量,PID 度量量化了从 ipsi-EEG 和 contra-EEG 传递到 HRV 的独特、冗余和协同信息。GC 的分析表明,从 EEG 到 HRV 的信息传递占主导地位,而从 HRV 到 EEG 的信息传递可以忽略不计,这表明 CNS 活动驱动了心脏节律的 ANS 调制,但未在和节律、ipsi-EEG 和 contra-EEG 或发作前和发作后期间之间观察到明显差异。相反,PID 表明癫痫发作会引起从大脑到心脏的相互作用的重新组织,因为在发作前阶段,HRV 的独特可预测性源自 ipsi-EEG 的波,源自 contra-EEG 的波,而这些模式在发作后则相反。这些结果强调了考虑 PID 引起的高阶相互作用对于研究局灶性癫痫的神经自主影响的重要性,并且可能具有神经生理学和临床意义。