Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA.
Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA; Department of Psychiatry, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA.
J Neurosci Methods. 2019 Dec 1;328:108401. doi: 10.1016/j.jneumeth.2019.108401. Epub 2019 Aug 21.
Simultaneous functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) measurements may represent activity from partially divergent neural sources, but this factor is seldom modeled in fMRI-EEG data integration.
This paper proposes an approach to estimate the spatial overlap between sources of activity measured simultaneously with fMRI and EEG. Following the extraction of task-related activity, the key steps include, 1) distributed source reconstruction of the task-related ERP activity (ERP source model), 2) transformation of fMRI activity to the ERP spatial scale by forward modelling of the scalp potential field distribution and backward source reconstruction (fMRI source simulation), and 3) optimization of fMRI and ERP thresholds to maximize spatial overlap without a priori constraints of coupling (overlap calculation).
FMRI and ERP responses were recorded simultaneously in 15 subjects performing an auditory oddball task. A high degree of spatial overlap between sources of fMRI and ERP responses (in 9 or more of 15 subjects) was found specifically within temporoparietal areas associated with the task. Areas of non-overlap in fMRI and ERP sources were relatively small and inconsistent across subjects.
The ERP and fMRI sources estimated with solely jICA overlapped in just 4 of 15 subjects, and strictly in the parietal cortex.
The study demonstrates that the new fMRI-ERP spatial overlap estimation method provides greater spatiotemporal detail of the cortical dynamics than solely jICA. As such, we propose that it is a superior method for the integration of fMRI and EEG to study brain function.
同时进行功能磁共振成像（fMRI）和脑电图（EEG）测量可能代表来自部分发散的神经源的活动，但在 fMRI-EEG 数据集成中很少对该因素进行建模。
本文提出了一种方法来估计同时用 fMRI 和 EEG 测量的活动源之间的空间重叠。在提取与任务相关的活动后，关键步骤包括：1）对与任务相关的事件相关电位（ERP）活动进行分布式源重建（ERP 源模型），2）通过头皮电位场分布的正向建模和反向源重建（fMRI 源模拟）将 fMRI 活动转换到 ERP 空间尺度，3）优化 fMRI 和 ERP 阈值，以在没有耦合先验约束的情况下最大化空间重叠（重叠计算）。
在 15 名执行听觉Oddball 任务的受试者中同时记录了 fMRI 和 ERP 响应。在与任务相关的颞顶区域内，发现了 fMRI 和 ERP 源之间高度的空间重叠（在 15 名受试者中的 9 名或更多名中）。在 fMRI 和 ERP 源之间的非重叠区域相对较小且在受试者之间不一致。
仅使用 jICA 估计的 ERP 和 fMRI 源仅在 15 名受试者中的 4 名中重叠，并且严格在顶叶皮层中重叠。
该研究表明，新的 fMRI-ERP 空间重叠估计方法提供了比仅 jICA 更详细的皮质动力学时空细节。因此，我们提出它是用于研究大脑功能的 fMRI 和 EEG 集成的更优方法。
