Department of Physiological Nursing, University of California, San Francisco, CA, United States of America. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, People's Republic of China. Share the same first authorship.
J Neural Eng. 2020 Mar 19;17(2):026006. doi: 10.1088/1741-2552/ab75af.
Neurovascular coupling enables rapid adaptation of cerebral blood flow (CBF) to support neuronal activity. Modern techniques enable the simultaneous recording of neuronal activities and hemodynamic parameters. However, the causal relationship between electrical brain activity and CBF is still unclarified. In this study, we investigated the causal relationship between surface electroencephalogram (EEG) and cerebral blood flow velocity (FV) from transcranial Doppler using Granger causality (GC) analysis.
Twenty simultaneous recordings of EEG and FV from 17 acute ischemic stroke patients were studied. Each patient had simultaneous, continuous monitoring of EEG and bilateral FVs in either the middle cerebral arteries or posterior cerebral arteries. The causal interactions between FV (0.006-0.4 Hz) and EEG (delta, theta, alpha, beta and gamma bands) were investigated through GC index (GCI). In order to make the GCIs comparable, the proportion of GCI (PGCI) values where G-causality is statistically significant were calculated. Scores on the NIH Stroke Scale (NIHSS) and the modified Rankin Scale (mRS) for neurologic disability were recorded respectively at discharge. Patients were divided into a deceased (mRS = 6) and a survival group (mRS = 1 to 5), and a favorable (mRS: 1 to 2) and unfavorable outcome group (mRS: 3 ~ 6).
This study identified a causal relationship from EEG→FV, indicating EEG contained information that can be used for FV prediction. PGCI was negatively related with mRS (p < 0.05), indicating that stronger causalities between EEG and FV exist in patients with better outcome. The NIHSS was negatively related with the asymmetry of the two-side PGCI, calculated as the difference between the lesional side and non-lesional side PGCI.
A causal relationship from EEG→FV may exist in patients with ischemic stroke. The strength of G-causality may be related to stroke severity at discharge.
神经血管耦合使脑血流（CBF）能够快速适应，以支持神经元活动。现代技术使神经元活动和血液动力学参数的同时记录成为可能。然而，电脑活动与 CBF 之间的因果关系仍不清楚。在这项研究中，我们使用格兰杰因果关系（GC）分析研究了经颅多普勒超声中脑表面脑电图（EEG）和脑血流速度（FV）之间的因果关系。
对 17 例急性缺血性脑卒中患者的 20 次 EEG 和 FV 同步记录进行了研究。每位患者均同时连续监测大脑中动脉或大脑后动脉的 EEG 和双侧 FV。通过 GC 指数（GCI）研究了 FV（0.006-0.4 Hz）和 EEG（δ、θ、α、β和γ 波段）之间的因果相互作用。为了使 GCIs 具有可比性，计算了 GC 具有统计学意义的 GCI 比例（PGCI）值。记录 NIH 卒中量表（NIHSS）和改良 Rankin 量表（mRS）的评分，分别用于出院时的神经功能障碍。患者分为死亡（mRS=6）和存活组（mRS=1-5），以及预后良好（mRS：1-2）和预后不良组（mRS：3-6）。
本研究发现了从 EEG→FV 的因果关系，表明 EEG 包含可用于 FV 预测的信息。PGCI 与 mRS 呈负相关（p<0.05），表明 EEG 和 FV 之间存在更强的因果关系的患者预后更好。NIHSS 与两侧 PGCI 不对称呈负相关，计算方法为病变侧和非病变侧 PGCI 之间的差异。
缺血性脑卒中患者可能存在从 EEG→FV 的因果关系。G 因果关系的强度可能与出院时的卒中严重程度有关。
