Dinh Christoph, Samuelsson John G, Hunold Alexander, Hämäläinen Matti S, Khan Sheraz
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States.
Department of Radiology, Massachusetts General Hospital (MGH), Charlestown, MA, United States.
Front Neurosci. 2021 Mar 9;15:552666. doi: 10.3389/fnins.2021.552666. eCollection 2021.
Most magneto- and electroencephalography (M/EEG) based source estimation techniques derive their estimates sample wise, independently across time. However, neuronal assemblies are intricately interconnected, constraining the temporal evolution of neural activity that is detected by MEG and EEG; the observed neural currents must thus be highly context dependent. Here, we use a network of Long Short-Term Memory (LSTM) cells where the input is a sequence of past source estimates and the output is a prediction of the following estimate. This prediction is then used to correct the estimate. In this study, we applied this technique on noise-normalized minimum norm estimates (MNE). Because the correction is found by using past activity (context), we call this implementation Contextual MNE (CMNE), although this technique can be used in conjunction with any source estimation method. We test CMNE on simulated epileptiform activity and recorded auditory steady state response (ASSR) data, showing that the CMNE estimates exhibit a higher degree of spatial fidelity than the unfiltered estimates in the tested cases.
大多数基于脑磁图和脑电图（M/EEG）的源估计技术是逐样本地、在时间上独立地得出其估计值。然而，神经元组件之间存在着复杂的相互连接，这限制了通过脑磁图（MEG）和脑电图（EEG）检测到的神经活动的时间演变；因此，观察到的神经电流必然高度依赖上下文。在这里，我们使用了一个长短期记忆（LSTM）细胞网络，其中输入是过去源估计的序列，输出是对下一个估计值的预测。然后，这个预测值被用于校正估计值。在本研究中，我们将此技术应用于噪声归一化最小范数估计（MNE）。由于校正是通过使用过去的活动（上下文）来找到的，我们将这种实现方式称为上下文MNE（CMNE），尽管该技术可与任何源估计方法结合使用。我们在模拟癫痫样活动和记录的听觉稳态反应（ASSR）数据上测试了CMNE，结果表明，在测试案例中，CMNE估计值比未滤波的估计值表现出更高程度的空间保真度。
