Deep learning and whole-brain networks for biomarker discovery: modeling the dynamics of brain fluctuations in resting-state and cognitive tasks

Abstract

Abstract Brain network models offer insights into brain dynamics, but the utility of model-derived bifurcation parameters as biomarkers remains underexplored. This study evaluates bifurcation parameters from a whole-brain network model as biomarkers for distinguishing brain states associated with resting-state and task-based cognitive conditions. Synthetic BOLD signals were generated using a supercritical Hopf brain network model to train deep learning models for bifurcation parameter prediction. Inference was performed on Human Connectome Project data, including both resting-state and task-based conditions. Statistical analyses assessed the separability of brain states based on bifurcation parameter distributions. Bifurcation parameter distributions differed significantly across task and resting-state conditions ( $$p < 0.0001$$ for all but two comparisons). Task-based brain states exhibited higher bifurcation values compared to rest. At the individual level, a machine learning model was able to classify the predicted bifurcation values into eight cohorts with 62.63% accuracy (well above the 12.50% chance level). Bifurcation parameters effectively differentiate cognitive and resting states, warranting further investigation as biomarkers for brain state characterization and neurological disorder assessment.