Department of Biomedical Engineering, Brno University of Technology, Technická 12, Brno, 61600, Czech Republic; Department of Biomedical Engineering, University Hospital Olomouc, I. P. Pavlova 6, Olomouc, 77900, Czech Republic; Department of Neurology, Palacký University, I. P. Pavlova 6, Olomouc, 77900, Czech Republic.
Mind Research Network, 1101 Yale Blvd. NE, Albuquerque, NM, 87106, USA.
J Neurosci Methods. 2019 Apr 15;318:34-46. doi: 10.1016/j.jneumeth.2019.02.012. Epub 2019 Feb 22.
Spatial and temporal resolution of brain network activity can be improved by combining different modalities. Functional Magnetic Resonance Imaging (fMRI) provides full brain coverage with limited temporal resolution, while electroencephalography (EEG), estimates cortical activity with high temporal resolution. Combining them may provide improved network characterization.
We examined relationships between EEG spatiospectral pattern timecourses and concurrent fMRI BOLD signals using canonical hemodynamic response function (HRF) with its 1 and 2 temporal derivatives in voxel-wise general linear models (GLM). HRF shapes were derived from EEG-fMRI time courses during "resting-state", visual oddball and semantic decision paradigms.
The resulting GLM F-maps self-organized into several different large-scale brain networks (LSBNs) often with different timing between EEG and fMRI revealed through differences in GLM-derived HRF shapes (e.g., with a lower time to peak than the canonical HRF). We demonstrate that some EEG spatiospectral patterns (related to concurrent fMRI) are weakly task-modulated.
COMPARISON WITH EXISTING METHOD(S): Previously, we demonstrated 14 independent EEG spatiospectral patterns within this EEG dataset, stable across the resting-state, visual oddball and semantic decision paradigms. Here, we demonstrate that their time courses are significantly correlated with fMRI dynamics organized into LSBN structures. EEG-fMRI derived HRF peak appears earlier than the canonical HRF peak, which suggests limitations when assuming a canonical HRF shape in EEG-fMRI.
This is the first study examining EEG-fMRI relationships among independent EEG spatiospectral patterns over different paradigms. The findings highlight the importance of considering different HRF shapes when spatiotemporally characterizing brain networks using EEG and fMRI.
通过结合不同模态可以提高脑网络活动的空间和时间分辨率。功能磁共振成像（fMRI）提供了具有有限时间分辨率的全脑覆盖，而脑电图（EEG）则以高时间分辨率估计皮质活动。结合它们可以提供改进的网络特征描述。
我们使用具有其 1 和 2 个时间导数的典型血液动力学响应函数（HRF），在体素水平的广义线性模型（GLM）中检查了 EEG 时空图谱时间过程与并发 fMRI BOLD 信号之间的关系。HRF 形状是从 EEG-fMRI 时间过程中在“静息状态”、视觉奇数和语义决策范式中得出的。
所得的 GLM F 图自组织成几个不同的大规模脑网络（LSBN），通常在 EEG 和 fMRI 之间具有不同的时间，这是通过 GLM 衍生的 HRF 形状的差异揭示的（例如，峰时间比典型 HRF 低）。我们证明了一些 EEG 时空模式（与并发 fMRI 相关）具有弱任务调制。
以前，我们在这个 EEG 数据集内证明了 14 个独立的 EEG 时空模式，在静息状态、视觉奇数和语义决策范式中是稳定的。在这里，我们证明它们的时间过程与组织成 LSBN 结构的 fMRI 动力学显著相关。EEG-fMRI 衍生的 HRF 峰值比典型 HRF 峰值更早出现，这表明在 EEG-fMRI 中假设典型 HRF 形状存在局限性。
这是第一项研究，研究了不同范式下独立 EEG 时空模式之间的 EEG-fMRI 关系。这些发现强调了在使用 EEG 和 fMRI 时空特征描述脑网络时考虑不同 HRF 形状的重要性。
