Integrated Program in Neuroscience, Montreal Neurological Institute, McGill University, Montréal, QC, H3A 2B4, Canada.
Graduate Program in Biological and Biomedical Engineering, McGill University, Montréal, QC, H3A 2B4, Canada.
Brain Topogr. 2022 May;35(3):302-321. doi: 10.1007/s10548-022-00898-w. Epub 2022 Apr 30.
Being able to accurately quantify the hemodynamic response function (HRF) that links the blood oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI) signal to the underlying neural activity is important both for elucidating neurovascular coupling mechanisms and improving the accuracy of fMRI-based functional connectivity analyses. In particular, HRF estimation using BOLD-fMRI is challenging particularly in the case of resting-state data, due to the absence of information about the underlying neuronal dynamics. To this end, using simultaneously recorded electroencephalography (EEG) and fMRI data is a promising approach, as EEG provides a more direct measure of neural activations. In the present work, we employ simultaneous EEG-fMRI to investigate the regional characteristics of the HRF using measurements acquired during resting conditions. We propose a novel methodological approach based on combining distributed EEG source space reconstruction, which improves the spatial resolution of HRF estimation and using block-structured linear and nonlinear models, which enables us to simultaneously obtain HRF estimates and the contribution of different EEG frequency bands. Our results suggest that the dynamics of the resting-state BOLD signal can be sufficiently described using linear models and that the contribution of each band is region specific. Specifically, it was found that sensory-motor cortices exhibit positive HRF shapes, whereas the lateral occipital cortex and areas in the parietal cortex, such as the inferior and superior parietal lobule exhibit negative HRF shapes. To validate the proposed method, we repeated the analysis using simultaneous EEG-fMRI measurements acquired during execution of a unimanual hand-grip task. Our results reveal significant associations between BOLD signal variations and electrophysiological power fluctuations in the ipsilateral primary motor cortex, particularly for the EEG beta band, in agreement with previous studies in the literature.
能够准确量化将血氧水平依赖功能磁共振成像（BOLD-fMRI）信号与基础神经活动联系起来的血流动力学响应函数（HRF），对于阐明神经血管耦合机制和提高基于 fMRI 的功能连接分析的准确性都非常重要。特别是，由于缺乏关于基础神经元动力学的信息，使用 BOLD-fMRI 进行 HRF 估计在静息状态数据的情况下特别具有挑战性。为此，使用同时记录的脑电图（EEG）和 fMRI 数据是一种很有前途的方法，因为 EEG 提供了更直接的神经激活测量。在本工作中，我们使用同时的 EEG-fMRI 来研究静息状态下采集的测量数据的 HRF 的区域特征。我们提出了一种新的方法，基于结合分布式 EEG 源空间重建，这提高了 HRF 估计的空间分辨率，并使用基于块结构的线性和非线性模型，使我们能够同时获得 HRF 估计和不同 EEG 频带的贡献。我们的结果表明，静息状态 BOLD 信号的动力学可以使用线性模型来充分描述，并且每个频带的贡献是特定于区域的。具体而言，发现感觉运动皮层呈现正 HRF 形状，而外侧枕叶和顶叶区域，如下顶叶和上顶叶呈现负 HRF 形状。为了验证所提出的方法，我们使用在执行单手握力任务期间采集的同时 EEG-fMRI 测量值重复了分析。我们的结果揭示了在同侧初级运动皮层中，BOLD 信号变化与电生理功率波动之间存在显著关联，特别是对于 EEG 贝塔频带，这与文献中的先前研究一致。
