What kind of noise is brain noise: anomalous scaling behavior of the resting brain activity fluctuations

TL;DR

This paper uncovers scale-invariant properties of resting brain activity fluctuations, revealing how large-scale brain networks maintain optimal information sharing, which may serve as markers of healthy brain dynamics.

Contribution

It identifies novel scale-invariant properties of resting state networks, including divergence of correlation length and mutual information, and constant variance across cluster sizes.

Findings

Correlation length diverges with cluster size.

Mutual information measures also diverge.

Variance of mean fMRI signal remains constant across scales.

Abstract

The continuous interaction between brain regions "at rest" defines the so-called resting state networks (RSN) which can be reconstructed from the analysis of functional magnetic resonance imaging (fMRI) data. What dynamical mechanism allows for a flexible large-scale organization of the RSN still remains an important challenge. Here, three key novel properties of the RSN are uncovered. First, the correlation length (i.e., the length at which correlation between two regions vanishes) diverges with the cluster's size considered. Second, this divergence it is observed also for measures of mutual information. Third, the variance of the fMRI mean signal remains constant across the entire range of observed clusters sizes, in contrast with naive expectations. The unveiled scale invariance exposes the RSN optimal information-sharing properties across very diverse networks sizes, architectures and functions, which can be an important marker of healthy brain dynamics.