Information transfer of an Ising model on a brain network

TL;DR

This study models the brain's structural network using an Ising model to analyze information transfer, revealing maximal transfer at criticality and identifying bottleneck nodes affecting information flow.

Contribution

It extends previous oscillator models by incorporating lagged, directional influences and analyzing information bottlenecks in brain networks using an Ising model.

Findings

Maximum information transfer occurs at the critical temperature.

Nodes with more incoming links tend to have higher outgoing/incoming information ratios.

The model reveals saturation effects consistent with diminishing returns in information redistribution.

Abstract

We implement the Ising model on a structural connectivity matrix describing the brain at a coarse scale. Tuning the model temperature to its critical value, i.e. at the susceptibility peak, we find a maximal amount of total information transfer between the spin variables. At this point the amount of information that can be redistributed by some nodes reaches a limit and the net dynamics exhibits signature of the law of diminishing marginal returns, a fundamental principle connected to saturated levels of production. Our results extend the recent analysis of dynamical oscillators models on the connectome structure, taking into account lagged and directional influences, focusing only on the nodes that are more prone to became bottlenecks of information. The ratio between the outgoing and the incoming information at each node is related to the number of incoming links.