Bénar C G, Grova C, Jirsa V K, Lina J M
Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.
PERFORM Centre and Physics Department, Concordia University, Montreal, QC, Canada.
J Comput Neurosci. 2019 Aug;47(1):31-41. doi: 10.1007/s10827-019-00721-9. Epub 2019 Jul 11.
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 cm). 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.
电生理信号（脑电图，EEG，和脑磁图，MEG），与许多自然过程一样，呈现出尺度不变性，从而产生幂律（1/f）频谱。有趣的是，EEG和MEG在斜率上存在差异，这可以通过多种机制来解释，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，作为一种可能的机制来解释神经成像信号的频谱缩放特性。我们基于不同大小（范围从0.4到104.2厘米）的皮质斑块的总和贡献进行了模拟。在对数尺度上，小斑块被赋予高频信号，而大斑块与低频信号相关。测试的参数包括：i）空间/频率结构（斑块大小和频率范围）和ii）参数化空间尺度比率的幅度因子c。我们发现，空间/频率结构可能导致EEG和MEG无标度频谱之间的差异，这与先前研究中报告的实际数据结果相符。我们还发现，在一定空间尺度以下，EEG和MEG之间不再存在差异，这表明两种方法的分辨率存在极限。我们的工作为实验结果提供了解释。这并不排除EEG和MEG之间差异的其他机制，但表明神经动力学时空结构的重要作用。这有助于EEG和MEG中幂律测量的分析和解释，并且我们相信我们的结果也会影响脑动力学的计算建模，其中可以在不同频率使用不同的局部连接结构。
