A graph neural network approach to investigate brain critical states over neurodevelopment

Recent studies show that functional resting-state dynamics may be modeled 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 intricate 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 trained on Ising model networks. The main finding indicates a statistically significant negative correlation between age and temperature for typically developing children (r = -0.48, p < 0.0001) and also children with attention-deficit/hyperactivity disorder (r = -0.49, p < 0.0001). This study suggests that the brain gets distant from criticality as age increases, leading to a more ordered state.