The prevailing view on the dynamics of large-scale electrical activity in the human cortex is that it constitutes a functional network of discrete and localized circuits. Within this view, a natural way to analyse magnetoencephalographic (MEG) and electroencephalographic (EEG) data is by adopting methods from network theory. Invasive recordings, however, demonstrate that cortical activity is spatially continuous, rather than discrete, and exhibits propagation behavior. Furthermore, human cortical activity is known to propagate under a variety of conditions such as non-REM sleep, general anesthesia, and coma. Although several MEG/EEG studies have investigated propagating cortical activity, not much is known about the conditions under which such activity can be successfully reconstructed from MEG/EEG sensor-data. This study provides a methodological framework for inverse-modeling of propagating cortical activity. Within this framework, cortical activity is represented in the spatial frequency domain, which is more natural than the dipole domain when dealing with spatially continuous activity. We define angular power spectra, which show how the power of cortical activity is distributed across spatial frequencies, angular gain/phase spectra, which characterize the spatial filtering properties of linear inverse operators, and angular resolution matrices, which summarize how linear inverse operators leak signal within and across spatial frequencies. We adopt the framework to provide insight into the performance of several linear inverse operators in reconstructing propagating cortical activity from MEG/EEG sensor-data. We also describe how prior spatial frequency information can be incorporated into the inverse-modeling to obtain better reconstructions.

中文翻译：

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使用 MEG/EEG 对传播皮层活动进行逆向建模的方法学框架

关于人类皮层中大规模电活动动力学的流行观点是，它构成了离散和局部电路的功能网络。在这种观点下，分析脑磁图 (MEG) 和脑电图 (EEG) 数据的一种自然方法是采用网络理论的方法。然而，侵入性录音表明皮层活动在空间上是连续的，而不是离散的，并且表现出传播行为。此外，已知人类皮质活动会在各种条件下传播，例如非快速眼动睡眠、全身麻醉和昏迷。尽管几项 MEG/EEG 研究调查了传播皮层活动，但对于从 MEG/EEG 传感器数据成功重建这种活动的条件知之甚少。这项研究为传播皮层活动的逆向建模提供了方法学框架。在这个框架内，皮质活动在空间频率域中表示，在处理空间连续活动时，这比偶极子域更自然。我们定义了角功率谱，它显示了皮层活动的功率如何在空间频率、角增益/相位谱中分布，它表征了线性逆算子的空间滤波特性，以及角分辨率矩阵，它总结了线性逆算子如何泄漏信号在空间频率内和跨空间频率。我们采用该框架来深入了解几个线性逆算子在从 MEG/EEG 传感器数据重建传播皮层活动方面的性能。