Neurophysics Group, Dept. of Neurology, Charité-University Medicine Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12200 Berlin, Germany.
Neurophysics Group, Dept. of Neurology, Charité-University Medicine Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12200 Berlin, Germany.
Neuroimage. 2017 Aug 15;157:331-340. doi: 10.1016/j.neuroimage.2017.06.025. Epub 2017 Jun 13.
Neuronal oscillations synchronize processing in the brain over large spatiotemporal scales and thereby facilitate integration of individual functional modules. Up to now, the relation between the phases of neuronal oscillations and behavior or perception has mainly been analyzed in sensor space of multivariate EEG/MEG recordings. However, sensor-space analysis distorts the topographies of the underlying neuronal sources and suffers from low signal-to-noise ratio. Instead, we propose an optimized source reconstruction approach (Phase Coupling Optimization, PCO).
PCO maximizes the 'mean vector length', calculated from the phases of recovered neuronal sources and a target variable of interest (e.g., experimental performance). As pre-processing, the signal-to-noise ratio in the search-space is maximized by spatio-spectral decomposition. PCO was benchmarked against several competing algorithms and sensor-space analysis using realistic forward model simulations. As a practical example, thirteen 96-channel EEG measurements during a simple reaction time task were analyzed. After time-frequency decomposition, PCO was applied to the EEG to examine the relation between the phases of pre-stimulus EEG activity and reaction times.
In simulations, PCO outperformed other spatial optimization approaches and sensor-space analysis. Scalp topographies of the underlying source patterns and the relation between the phases of the source activity and the target variable could be reconstructed accurately even for very low SNRs (-10dB). In a simple reaction time experiment, the phases of pre-stimulus delta waves (<0.1Hz) with widely distributed fronto-parietal source topographies were found predictive of the reaction times.
From multivariate recordings, PCO can reconstruct neuronal sources that are phase-coupled to a target variable using a data-driven optimization approach. Its superiority has been shown in simulations and in the analysis of a simple reaction time experiment. From this data, we hypothesize that the phase entrainment of slow delta waves (<1Hz) facilitates sensorimotor integration in the brain and that this mechanism underlies the faster processing of anticipated stimuli. We further propose that the examined slow delta waves, observed to be phase-coupled to reaction times, correspond to the compound potentials typically observed in paradigms of stimulus anticipation and motor preparation.
神经元振荡在大时空尺度上同步大脑中的处理过程，从而促进单个功能模块的整合。到目前为止，神经元振荡的相位与行为或感知之间的关系主要在多变量 EEG/MEG 记录的传感器空间中进行分析。然而，传感器空间分析会扭曲潜在神经元源的拓扑结构，并受到低信噪比的影响。相反，我们提出了一种优化的源重建方法（相位耦合优化，PCO）。
PCO 最大化从恢复的神经元源的相位和感兴趣的目标变量（例如，实验表现）计算出的“平均向量长度”。作为预处理，通过空间-谱分解最大化搜索空间中的信噪比。使用现实的正向模型模拟，将 PCO 与几种竞争算法和传感器空间分析进行了基准测试。作为一个实际示例，分析了在简单反应时间任务期间进行的十三个 96 通道 EEG 测量。经过时频分解后，将 PCO 应用于 EEG 以检查刺激前 EEG 活动的相位与反应时间之间的关系。
在模拟中，PCO 优于其他空间优化方法和传感器空间分析。即使在非常低的 SNR（-10dB）下，也可以准确地重建潜在源模式的头皮地形图以及源活动的相位与目标变量之间的关系。在简单反应时间实验中，发现具有广泛分布的额顶源地形图的刺激前 delta 波（<0.1Hz）的相位可预测反应时间。
从多变量记录中，PCO 可以使用数据驱动的优化方法重建与目标变量相位耦合的神经元源。它的优越性已在模拟和简单反应时间实验的分析中得到证明。从这些数据中，我们假设慢 delta 波（<1Hz）的相位同步有助于大脑中的感觉运动整合，并且该机制是预期刺激更快处理的基础。我们进一步提出，所检查的慢 delta 波与反应时间相关联，与在刺激预期和运动准备范式中通常观察到的复合电位相对应。
