Do intrinsic functional networks reflect interacting brain rhythms? Assessing the power bias in MEG amplitude connectivity at rest

The oscillatory nature of intrinsic brain networks is largely taken for granted in the systems neuroscience community. However, the hypothesis that brain rhythms--and by extension transient bursting oscillations--underlie functional networks has not been demonstrated per se. Electrophysiological measures of functional connectivity are indeed affected by the power bias, which may lead to artefactual observations of spectrally specific network couplings not genuinely driven by neural oscillations, bursting or not. We investigate this crucial question by introducing a unique combination of a rigorous mathematical analysis of the power bias in frequency-dependent amplitude connectivity with a neurobiologically informed model of cerebral background noise based on hidden Markov modeling of resting-state magnetoencephalography (MEG). We demonstrate that the power bias may be corrected by a suitable renormalization depending nonlinearly on the signal-to-noise ratio, with noise identified as non-bursting oscillations. Applying this correction preserves the spectral content of amplitude connectivity, definitely proving the importance of brain rhythms in intrinsic functional networks. Our demonstration highlights a dichotomy between spontaneous oscillatory bursts underlying network couplings and non-bursting oscillations acting as background noise but whose function remains unsettled. Significance statementBrain rhythms are paramount electrophysiological correlates of human cerebral activity as they coordinate neurons across distinct brain areas and establish neural synchronization. Spontaneous cortical oscillations, and particularly transient "bursts" of oscillations, also appear as the main electrophysiological subtrate of intrinsic functional brain networks. However, we argue that this oscillatory theory of brain networks, despite being widely accepted, should be requestioned due to a critical bias in electrophysiological measures of network connectivity. Here, we combined mathematical and neurobiological modeling techniques with magnetoencephalography recordings to set this theory on firm and rigorous grounds. Key to our analysis are scarcely studied non-bursting cortical oscillations. Although their precise function remains elusive, our results reveal that dissociating bursting and non-bursting oscillations is fundamental to the rigorous interpretation of electrophysiological network connectivity.