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Electrophysiological signals (electroencephalography, EEG, andmagnetoencephalography , MEG), as many natural processes, exhibitscale-invariance properties resulting in a power-law (1/f) spectrum.Interestingly, EEG and MEG differ in their slopes, which could be explained byseveral mechanisms, including non-resistive properties of tissues. Our goal inthe present study is to estimate the impact of space/frequency structure ofsource signals as a putative mechanism to explain spectral scaling propertiesof neuroimaging signals. We performed simulations based on the summedcontribution of cortical patches with different sizes (ranging from 0.4 to104.2 cm 2). Small patches were attributed signals of high frequencies, whereaslarge patches were associated with signals of low frequencies, on a logarithmicscale. The tested parameters included i) the space/frequency structure (rangeof patch sizes and frequencies) and ii) the amplitude factor c parametrizingthe spatial scale ratios. We found that the space/frequency structure may causedifferences between EEG and MEG scale-free spectra that are compatible withreal data findings reported in previous studies. We also found that below acertain spatial scale, there were no more differences between EEG and MEG,suggesting a limit for the resolution of both methods. Our work provides anexplanation of experimental findings. This does not rule out other mechanismsfor differences between EEG and MEG, but suggests an important role ofspatio-temporal structure of neural dynamics. This can help the analysis andinterpretation of power-law measures in EEG and MEG, and we believe our resultscan also impact computational modeling of brain dynamics, where different localconnectivity structures could be used at different frequencies.

Differences in MEG and EEG power-law scaling explained by a coupling between spatial coherence and frequency: a simulation study