Exploring Neurofunctional Phase Transition Patterns in Autism Spectrum Disorder: A Thermodynamics Parameters Analysis Approach

TL;DR

This paper introduces a thermodynamic framework to analyze dynamic complex networks, especially brain activity in autism, using parameters like spectral core entropy, node energy, and temperature to identify structural and state transitions.

Contribution

It presents a novel thermodynamic approach for analyzing brain network changes in autism, providing quantitative measures for structural and state transitions.

Findings

Temperature correlates with brain state transitions

Node energy captures activity levels and differences in autism

Framework effectively identifies structural changes in brain networks

Abstract

Designing network parameters that can effectively represent complex networks is of significant importance for the analysis of time-varying complex networks. This paper introduces a novel thermodynamic framework for analyzing complex networks, focusing on Spectral Core Entropy (SCE), Node Energy, internal energy and temperature to measure structural changes in dynamic complex network. This framework provides a quantitative representation of network characteristics, capturing time-varying structural changes. We apply this framework to study brain activity in autism versus control subjects, illustrating its potential to identify significant structural changes and brain state transitions. By treating brain networks as thermodynamic systems, important parameters such as node energy and temperature are derived to depict brain activities. Our research has found that in our designed framework the thermodynamic parameter-temperature, is significantly correlated with the transitions of brain states. Statistical tests confirm the effectiveness of our approach. Moreover, our study demonstrates that node energy effectively captures the activity levels of brain regions and reveals biologically proven differences between autism patients and controls, offering a powerful tool for exploring the characteristics of complex networks in various applications.