Interdependent scaling exponents in the human brain

TL;DR

This study uses renormalization group techniques on fMRI data to uncover interdependent scaling laws in brain activity, revealing intrinsic organizational principles linked to brain structure and function.

Contribution

It introduces a novel application of renormalization group analysis to brain data, demonstrating linear interdependencies among scaling exponents and their relation to brain anatomy and cognition.

Findings

Scaling exponents exhibit linear interdependencies.

Exponent values correlate with gray matter volume.

Scaling relations resemble thermodynamic critical points.

Abstract

We apply the phenomenological renormalization group to resting-state fMRI time series of brain activity in a large population. By recursively coarse-graining the data, we compute scaling exponents for the series variance, log probability of silence, and largest covariance eigenvalue. The exponents clearly exhibit linear interdependencies, which we derive analytically in a mean-field approach. We find a significant correlation of exponent values with the gray matter volume and cognitive performance. Akin to scaling relations near critical points in thermodynamics, our findings suggest scaling interdependencies are intrinsic to brain organization and may also exist in other complex systems.