背景:脑磁图(MEG)是一种广泛使用的非侵入性工具,用于以高时间分辨率估计大脑活动。然而,由于MEG源成像(MSI)问题的病态性质,MSI准确识别皮质表面潜在脑源的能力仍不确定,需要验证.
方法:我们通过与颅内脑电图(iEEG)图集(https://mni-open-ieegatlas)进行比较,验证了MSI估计45名健康参与者的背景静息状态活动的能力。
方法:mcgill。ca/)。首先,我们应用基于小波的均值最大熵(wMEM)作为MSI技术。接下来,我们通过将正向模型应用于MEG重建的源图,将MEG源图转换为颅内空间,并估计每个iEEG通道位置上的虚拟iEEG(ViEEG)电位;我们最终将这些电位与来自典型频带中38个感兴趣区域的图集的实际iEEG信号进行了定量比较。
结果:与内侧区域相比,外侧区域的MEG光谱更准确地估计。更准确地恢复了ViEEG中比iEEG中振幅更高的区域。在深层地区,MEG估计的振幅在很大程度上被低估了,光谱恢复不良。总的来说,我们的wMEM结果与通过最小范数或波束形成器源定位获得的结果相似。此外,MEG大大高估了α波段的振荡峰,尤其是在前部和深部。这可能是由于α振荡在扩展区域上的相位同步更高,超过iEEG的空间灵敏度,但被MEG检测到。重要的是,我们发现,在去除非周期性成分后,MEG估计的光谱与iEEG图谱中的光谱更具可比性.
结论:这项研究确定了MEG源分析可能可靠的大脑区域和频率,这是解决从非侵入性MEG研究中恢复脑内活动的不确定性的有希望的一步。
Magnetoencephalography (MEG) is a widely used non-invasive tool to estimate brain activity with high temporal resolution. However, due to the ill-posed nature of the MEG source imaging (MSI) problem, the ability of MSI to identify accurately underlying brain sources along the cortical surface is still uncertain and requires validation.
We validated the ability of MSI to estimate the background resting state activity of 45 healthy participants by comparing it to the intracranial EEG (iEEG) atlas (https://mni-open-ieegatlas.
mcgill.ca/). First, we applied wavelet-based Maximum Entropy on the Mean (wMEM) as an MSI technique. Next, we converted MEG source maps into intracranial space by applying a forward model to the MEG-reconstructed source maps, and estimated virtual iEEG (ViEEG) potentials on each iEEG channel location; we finally quantitatively compared those with actual iEEG signals from the atlas for 38 regions of interest in the canonical frequency bands.
The MEG spectra were more accurately estimated in the lateral regions compared to the medial regions. The regions with higher amplitude in the ViEEG than in the iEEG were more accurately recovered. In the deep regions, MEG-estimated amplitudes were largely underestimated and the spectra were poorly recovered. Overall, our wMEM results were similar to those obtained with minimum norm or beamformer source localization. Moreover, the MEG largely overestimated oscillatory peaks in the alpha band, especially in the anterior and deep regions. This is possibly due to higher phase synchronization of alpha oscillations over extended regions, exceeding the spatial sensitivity of iEEG but detected by MEG. Importantly, we found that MEG-estimated spectra were more comparable to spectra from the iEEG atlas after the aperiodic components were removed.
This study identifies brain regions and frequencies for which MEG source analysis is likely to be reliable, a promising step towards resolving the uncertainty in recovering intracerebral activity from non-invasive MEG studies.