Scale-free brain functional networks

TL;DR

This paper demonstrates that human brain functional networks derived from fMRI data exhibit scale-free and small-world properties, revealing important insights into brain connectivity and functional states.

Contribution

It provides the first evidence that brain functional networks are scale-free and small-world, with implications for understanding brain organization.

Findings

Functional connections follow a scale-free distribution.

The probability of links decreases with distance in a scale-free manner.

Brain networks have small characteristic path lengths and high clustering coefficients.

Abstract

Functional magnetic resonance imaging (fMRI) is used to extract {\em functional networks} connecting correlated human brain sites. Analysis of the resulting networks in different tasks shows that: (a) the distribution of functional connections, and the probability of finding a link vs. distance are both scale-free, (b) the characteristic path length is small and comparable with those of equivalent random networks, and (c) the clustering coefficient is orders of magnitude larger than those of equivalent random networks. All these properties, typical of scale-free small world networks, reflect important functional information about brain states.