Adaptive reconfiguration of fractal small-world human brain functional networks

TLDR

Brain function depends on adaptive self‑organization of large‑scale neural assemblies, yet quantitative network parameters governing these processes in humans remain poorly understood. The study aims to describe the topology and synchronizability of frequency‑specific brain functional networks by applying wavelet decomposition to magnetoencephalographic time series and constructing undirected graphs. The authors used wavelet‑decomposed MEG recordings from 22 subjects (half performing a finger‑tapping task, half at rest) to build undirected graphs and analyze their topological and synchronizability properties. The networks displayed small‑world, scale‑invariant characteristics across six wavelet scales, hovered near the order/disorder transition, and revealed that the γ band had higher synchronizability and clustering, while motor task performance induced long‑range β and γ connections without altering global topology.

Abstract

Brain function depends on adaptive self-organization of large-scale neural assemblies, but little is known about quantitative network parameters governing these processes in humans. Here, we describe the topology and synchronizability of frequency-specific brain functional networks using wavelet decomposition of magnetoencephalographic time series, followed by construction and analysis of undirected graphs. Magnetoencephalographic data were acquired from 22 subjects, half of whom performed a finger-tapping task, whereas the other half were studied at rest. We found that brain functional networks were characterized by small-world properties at all six wavelet scales considered, corresponding approximately to classical δ (low and high), θ, α, β, and γ frequency bands. Global topological parameters (path length, clustering) were conserved across scales, most consistently in the frequency range 2–37 Hz, implying a scale-invariant or fractal small-world organization. Dynamical analysis showed that networks were located close to the threshold of order/disorder transition in all frequency bands. The highest-frequency γ network had greater synchronizability, greater clustering of connections, and shorter path length than networks in the scaling regime of (lower) frequencies. Behavioral state did not strongly influence global topology or synchronizability; however, motor task performance was associated with emergence of long-range connections in both β and γ networks. Long-range connectivity, e.g., between frontal and parietal cortex, at high frequencies during a motor task may facilitate sensorimotor binding. Human brain functional networks demonstrate a fractal small-world architecture that supports critical dynamics and task-related spatial reconfiguration while preserving global topological parameters.