Exploring Neurofunctional Phase Transition Patterns in Autism Spectrum Disorder via Thermodynamics Parameters

Area of Science:

• Complex Systems Science
• Network Science
• Computational Neuroscience

Background:

• Characterizing structural changes in dynamic complex networks is crucial for understanding evolving systems.
• Existing methods may lack compact representations for temporal graph organization.
• Thermodynamics offers a potential analogy for describing system reconfiguration.

Purpose of the Study:

• To introduce a thermodynamics-inspired framework for dynamic graph analysis.
• To apply this framework to resting-state fMRI data from individuals with autism spectrum disorder (ASD) and controls.
• To evaluate the framework's sensitivity on synthetic dynamic graphs.

Main Methods:

• Development of a framework based on Spectral Core Entropy (SCE), node energy, internal energy, and a temperature-like reconfiguration index.
• Application to resting-state fMRI data from ASD and control subjects.
• Testing on controlled synthetic dynamic graphs with varying reconfiguration mechanisms (rewiring, gain modulation).

Main Results:

• The temperature index weakly but reproducibly marks rapid network changes in fMRI data.
• Framework sensitivity is regime-dependent on synthetic data: spectral-core change for rewiring, temperature index for gain modulation.
• Node energy analysis revealed regional differences in dynamic brain connectivity between ASD and control groups.

Conclusions:

• The proposed framework offers a computationally tractable approach for characterizing reconfiguration in dynamic brain networks.
• Node energy provides interpretable neuroscientific insights into group differences in dynamic connectivity.
• The framework shows promise for analyzing evolving complex systems beyond neuroimaging.