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bioRxiv · 10.1101/2024.10.09.617395

A Graph Neural Network Approach to Investigate Brain Critical States Over Neurodevelopment

Abstract

Recent studies show that functional resting-state dynamics may be modelled by lattice models near criticality, such as the 2D Ising model. The Ising temperature, which is the control parameter dictating the phase transitions of the model, can provide insight into the large-scale dynamics and is being used to better understand different brain states and neurodevelopment. This period is categorized by intricated changes in the microcircuits to consolidate networks. These changes influence the macroscopic brain dynamics and also its functional relations, which can be observed in functional Magnetic Resonance Imaging (fMRI). Therefore, this work investigates neurodevelopment through a novel method to estimate the Ising temperature of the brain from fMRI data using functional connectivity and Graph Neural Networks (GNNs) trained on Ising model networks. The main finding indicates a statistically significant negative correlation between age and temperature for typically developing children ( =- 0. 48, < 0. 0001) and also children with Attention deficit/hyperactivity disorder (ADHD) ( =- 0. 49, < 0. 0001). This study suggests that the brain gets distant from criticality as age increases, leading to a more ordered state. Author SummaryIn this study, we employed Graph Neural Networks to investigate functional connectivity patterns of resting-state fMRI data using the 2D Ising model as a theoretical framework to access critical dynamics. Our key finding is the significant decrease in Ising temperature with age in both typically developing children and those with ADHD, indicating a developmental transition from a high-entropy, disordered state to a more ordered, decreasing the availability of dynamical states that the system can occupy and transit in different conditions. Moreover, this decrease in temperature is crucial as it implies that the brain is moving away from criticality, leading the system to a more sub-critical regime. The correlation coefficients across the groups showed no significant differences, indicating the same developmental trajectory.

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BibTeXRIS

Cabral-Carvalho, R. M., Pinaya, W. H. L., Sato, J. R.. 2024-10-12. A Graph Neural Network Approach to Investigate Brain Critical States Over Neurodevelopment. https://doi.org/10.1101/2024.10.09.617395

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