Metastable brain waves

TLDR

Brain waves have been observed across many recordings and species, yet their emergence in humans remains poorly understood. The study analyzes complex nonlinear dynamics arising from modeling large‑scale spontaneous neural activity on a whole‑brain network derived from human tractography. Large‑scale spontaneous neural activity was modeled on a whole‑brain network derived from human tractography. The model reveals a rich array of metastable three‑dimensional wave patterns—including traveling waves, spiral waves, sources, and sinks—that sequentially visit multiple spatiotemporal states, with transitions driven by nonlinear instabilities, and these dynamics align with empirical data from electrical, MEG, and electrocorticography recordings, unifying diverse neuroimaging phenomena and offering predictions for future experiments.

Abstract

Abstract Traveling patterns of neuronal activity—brain waves—have been observed across a breadth of neuronal recordings, states of awareness, and species, but their emergence in the human brain lacks a firm understanding. Here we analyze the complex nonlinear dynamics that emerge from modeling large-scale spontaneous neural activity on a whole-brain network derived from human tractography. We find a rich array of three-dimensional wave patterns, including traveling waves, spiral waves, sources, and sinks. These patterns are metastable, such that multiple spatiotemporal wave patterns are visited in sequence. Transitions between states correspond to reconfigurations of underlying phase flows, characterized by nonlinear instabilities. These metastable dynamics accord with empirical data from multiple imaging modalities, including electrical waves in cortical tissue, sequential spatiotemporal patterns in resting-state MEG data, and large-scale waves in human electrocorticography. By moving the study of functional networks from a spatially static to an inherently dynamic (wave-like) frame, our work unifies apparently diverse phenomena across functional neuroimaging modalities and makes specific predictions for further experimentation.