Abstract：
Neurovascular coupling (NVC) provides important insights into the intricate activity of brain functioning and may aid in the early diagnosis of brain diseases. Emerging evidences have shown that NVC could be assessed by the coupling between electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). However, this endeavor presents significant challenges due to the absence of standardized methodologies and reliable techniques for coupling analysis of these two modalities. In this study, we introduced a novel method, i.e., the collaborative multi-output variational Gaussian process convergent cross-mapping (CMVGP-CCM) approach to advance coupling analysis of EEG and fNIRS. To validate the robustness and reliability of the CMVGP-CCM method, we conducted extensive experiments using chaotic time series models with varying noise levels, sequence lengths, and causal driving strengths. In addition, we employed the CMVGP-CCM method to explore the NVC between EEG and fNIRS signals collected from 26 healthy participants using a working memory (WM) task. Results revealed a significant causal effect of EEG signals, particularly the delta, theta, and alpha frequency bands, on the fNIRS signals during WM. This influence was notably observed in the frontal lobe, and its strength exhibited a decline as cognitive demands increased. This study illuminates the complex connections between brain electrical activity and cerebral blood flow, offering new insights into the underlying NVC mechanisms of WM.
摘要：
神经血管耦合（NVC）为大脑功能的复杂活动提供了重要的见解，并可能有助于早期诊断脑部疾病。新出现的证据表明，可以通过脑电图（EEG）和功能近红外光谱（fNIRS）之间的耦合来评估NVC。然而,这一努力提出了重大挑战，由于缺乏标准化的方法和可靠的技术耦合分析这两种模式。在这项研究中,我们介绍了一种新的方法，即，协作多输出变分高斯过程收敛交叉映射（CMVGP-CCM）方法来推进EEG和fNIRS的耦合分析。为了验证CMVGP-CCM方法的鲁棒性和可靠性,我们使用具有不同噪声水平的混沌时间序列模型进行了广泛的实验，序列长度,和因果驱动优势。此外,我们采用CMVGP-CCM方法探索了使用工作记忆(WM)任务从26名健康参与者收集的EEG和fNIRS信号之间的NVC.结果揭示了脑电信号的显著因果效应，尤其是三角洲，theta,和阿尔法频带，在WM期间的fNIRS信号上。这种影响尤其在额叶观察到，随着认知需求的增加，其强度表现出下降。这项研究阐明了脑电活动和脑血流量之间的复杂联系，提供对WM潜在NVC机制的新见解。
