Scaling and tuning to criticality in resting-state human magnetoencephalography

TL;DR

This study applies renormalization group-inspired analysis to human MEG data, revealing scale-invariant neural activity patterns indicative of near-critical dynamics and providing insights into excitation-inhibition balance in the brain.

Contribution

It introduces a novel RG-inspired coarse-graining method to detect criticality signatures in large-scale human brain activity recordings.

Findings

Scale-invariant behavior observed in MEG activity measures

Neuronal avalanche statistics remain stable across scales

Scaling exponents relate to excitation-inhibition balance

Abstract

From 1/f noise to neuronal avalanches, evidence of scaling in brain activity has been increasingly linked to tuning to or near criticality. The concept of scaling is intimately related to the renormalization group (RG), in essence providing coarse-grained, simplified descriptions that generalize to classes of diverse physical systems. Following the RG idea, scaling laws have been reported in populations of spiking neurons at microscopic scales. Whether similar scaling principles govern large-scale neural activity in the human brain and how they relate to underlying neural physiology remains unresolved. Here, we analyze large-scale electrophysiological recordings (MEG) of human resting-state brain activity and apply a RG-inspired coarse-graining approach to track collective neural dynamics across spatial scales. We find that multiple observables exhibit robust scale-invariant behavior under coarse-graining: activity variance and correlations grow according to power laws, covariance eigenspectra follow a characteristic scaling relation, and neuronal avalanche statistics remain invariant. Using an analytically tractable neural network model, we show that the observed scaling signatures arise when the system operates slightly below criticality, and that the scaling exponents depend on the excitation-inhibition balance. These findings demonstrate that RG-inspired scaling analysis can uncover signatures of critical dynamics in non-invasive human electrophysiology and suggest a principled route toward estimating excitation-inhibition balance from large-scale brain recordings.