Frequency-Dependent Covariance Reveals Critical Spatiotemporal Patterns of Synchronized Activity in the Human Brain

Recent analyses, leveraging advanced theoretical techniques and high-quality data from thousands of simultaneously recorded neurons across regions in the brain, compellingly support the hypothesis that neural dynamics operate near the edge of instability. However, these and related analyses often fail to capture the intricate temporal structure of brain activity, as they primarily rely on time-integrated measurements across neurons. Here, we present a novel framework designed to explore signatures of criticality across diverse frequency bands and construct a much more comprehensive description of brain activity. Furthermore, we introduce a method for projecting brain activity onto a basis of spatiotemporal patterns, facilitating time-dependent dimensionality reduction. Applying this framework to a magnetoencephalography dataset, we observe significant differences in criticality signatures, effective dimensionality, and spatiotemporal activity patterns between healthy subjects and individuals with Parkinson's disease, highlighting its potential impact.