Electrophysiological signals (electroencephalography, EEG, and magnetoencephalography, MEG), as many natural processes, exhibit scale-invariance properties resulting in a power-law (1/f) spectrum. Interestingly, EEG and MEG differ in their slopes, which could be explained by several mechanisms, including non-resistive properties of tissues. Our goal in the present study is to estimate the impact of space/frequency structure of source signals as a putative mechanism to explain spectral scaling properties of neuroimaging signals. We performed simulations based on the summed contribution of cortical patches with different sizes (ranging from 0.4 to 104.2 cm²). Small patches were attributed signals of high frequencies, whereas large patches were associated with signals of low frequencies, on a logarithmic scale. The tested parameters included i) the space/frequency structure (range of patch sizes and frequencies) and ii) the amplitude factor c parametrizing the spatial scale ratios. We found that the space/frequency structure may cause differences between EEG and MEG scale-free spectra that are compatible with real data findings reported in previous studies. We also found that below a certain spatial scale, there were no more differences between EEG and MEG, suggesting a limit for the resolution of both methods.Our work provides an explanation of experimental findings. This does not rule out other mechanisms for differences between EEG and MEG, but suggests an important role of spatio-temporal structure of neural dynamics. This can help the analysis and interpretation of power-law measures in EEG and MEG, and we believe our results can also impact computational modeling of brain dynamics, where different local connectivity structures could be used at different frequencies.

中文翻译：

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通过空间相干性和频率之间的耦合解释了MEG和EEG幂律定标的差异：模拟研究。

与许多自然过程一样，电生理信号（脑电图，脑电图和磁脑电图，MEG）表现出尺度不变性，从而形成幂律（1 / f）谱。有趣的是，EEG和MEG的斜率不同，这可以通过几种机制来解释，包括组织的非电阻特性。我们在本研究中的目标是估计源信号的空间/频率结构的影响，作为解释神经影像信号的频谱缩放特性的推定机制。我们基于不同大小（范围从0.4到104.2 cm ^2）的皮质贴片的总贡献进行了模拟）。小补丁被归因于高频信号，而大补丁被归因于对数尺度的低频信号。测试的参数包括：i）空间/频率结构（贴片大小和频率范围）和ii）振幅因子c参数化空间比例。我们发现，空间/频率结构可能会导致EEG和MEG无标度频谱之间的差异，这与先前研究中报道的真实数据发现兼容。我们还发现，在一定空间范围内，EEG和MEG之间没有更多差异，这表明这两种方法的分辨率都存在局限性。我们的工作为实验结果提供了解释。这并不排除其他机制导致脑电图和脑电图之间存在差异，但表明神经动力学的时空结构具有重要作用。这可以帮助分析和解释EEG和MEG中的幂律度量，并且我们相信我们的结果还可以影响大脑动力学的计算模型，其中可以在不同频率下使用不同的局部连接结构。