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

TL;DR

This study demonstrates that bifurcation parameters derived from whole-brain network models can reliably distinguish between resting-state and task-based brain conditions, highlighting their potential as biomarkers for brain state analysis.

Contribution

The paper introduces the use of bifurcation parameters from a supercritical Hopf brain network model as novel biomarkers for differentiating brain states in resting and cognitive tasks.

Findings

Bifurcation parameters significantly differ between resting and task states.

Task states show higher bifurcation values than resting states.

Bifurcation parameters can effectively classify brain states.

Abstract

Background: Brain network models offer insights into brain dynamics, but the utility of model-derived bifurcation parameters as biomarkers remains underexplored. Objective: 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. Methods: 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. Results: Bifurcation parameter distributions differed significantly across task and resting-state conditions ($p < 0.0001$ for all but one comparison). Task-based brain states exhibited higher bifurcation values compared to rest. Conclusion: Bifurcation parameters effectively differentiate cognitive and resting states, warranting further investigation as biomarkers for brain state characterization and neurological disorder assessment.