  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 2). 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.
