Abreu Rodolfo, Simões Marco, Castelo-Branco Miguel
Faculty of Medicine, Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal.
Center for Informatics and Systems (CISUC), University of Coimbra, Coimbra, Portugal.
Front Neurosci. 2020 Apr 16;14:323. doi: 10.3389/fnins.2020.00323. eCollection 2020.
Functional magnetic resonance imaging (fMRI) is the technique of choice for detecting large-scale functional brain networks and to investigate their dynamics. Because fMRI measures brain activity indirectly, electroencephalography (EEG) has been recently considered a feasible tool for detecting such networks, particularly the resting-state networks (RSNs). However, a truly unbiased validation of such claims is still missing, which can only be accomplished by using simultaneously acquired EEG and fMRI data, due to the spontaneous nature of the activity underlying the RSNs. Additionally, EEG is still poorly explored for the purpose of mapping task-specific networks, and no studies so far have been focused on investigating networks' dynamic functional connectivity (dFC) with EEG. Here, we started by validating RSNs derived from the continuous reconstruction of EEG sources by directly comparing them with those derived from simultaneous fMRI data of 10 healthy participants, and obtaining an average overlap (quantified by the Dice coefficient) of 0.4. We also showed the ability of EEG to map the facial expressions processing network (FEPN), highlighting regions near the posterior superior temporal sulcus, where the FEPN is anchored. Then, we measured the dFC using EEG for the first time in this context, estimated dFC brain states using dictionary learning, and compared such states with those obtained from the fMRI. We found a statistically significant match between fMRI and EEG dFC states, and determined the existence of two matched dFC states which contribution over time was associated with the brain activity at the FEPN, showing that the dynamics of FEPN can be captured by both fMRI and EEG. Our results push the limits of EEG toward being used as a brain imaging tool, while supporting the growing literature on EEG correlates of (dynamic) functional connectivity measured with fMRI, and providing novel insights into the coupling mechanisms underlying the two imaging techniques.
功能磁共振成像（fMRI）是检测大规模功能性脑网络并研究其动态变化的首选技术。由于fMRI间接测量脑活动，近年来脑电图（EEG）被认为是检测此类网络的可行工具，尤其是静息态网络（RSN）。然而，对于此类说法仍缺乏真正无偏的验证，由于RSN活动的自发性质，这只能通过同时采集EEG和fMRI数据来完成。此外，在绘制特定任务网络方面，EEG的探索仍很有限，到目前为止还没有研究专注于用EEG研究网络的动态功能连接性（dFC）。在此，我们首先通过将从EEG源的连续重建中得出的RSN与10名健康参与者的同步fMRI数据得出的RSN直接比较，来验证RSN，得出平均重叠率（由Dice系数量化）为0.4。我们还展示了EEG绘制面部表情处理网络（FEPN）的能力，突出了FEPN所锚定的后颞上沟附近的区域。然后，我们在此背景下首次使用EEG测量dFC，使用字典学习估计dFC脑状态，并将这些状态与从fMRI获得的状态进行比较。我们发现fMRI和EEG的dFC状态之间存在统计学上的显著匹配，并确定存在两种匹配的dFC状态，其随时间的贡献与FEPN处的脑活动相关，表明FEPN的动态变化可以通过fMRI和EEG来捕捉。我们的结果拓展了EEG作为脑成像工具的应用范围，同时支持了越来越多关于用fMRI测量的（动态）功能连接性的EEG相关性的文献，并为这两种成像技术背后的耦合机制提供了新的见解。
