深度学习与全脑网络在生物标志物发现中的应用：建模静息态与认知任务中脑波动的动力学 (Deep learning and whole-brain networks for biomarker discovery: modeling the dynamics of brain fluctuations in resting-state and cognitive tasks)

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.

翻译：背景：脑网络模型为理解脑动力学提供了洞见，但模型衍生的分岔参数作为生物标志物的效用仍未得到充分探索。目的：本研究评估了全脑网络模型中的分岔参数作为生物标志物，用于区分与静息态和基于任务的认知条件相关的脑状态。方法：使用超临界Hopf脑网络模型生成合成BOLD信号，以训练深度学习模型进行分岔参数预测。在人类连接组计划数据上进行推断，包括静息态和基于任务的条件。通过统计分析评估基于分岔参数分布的脑状态可分离性。结果：分岔参数分布在任务和静息态条件之间存在显著差异（除一项比较外，所有比较的$p < 0.0001$）。与静息态相比，基于任务的脑状态表现出更高的分岔值。结论：分岔参数能有效区分认知状态和静息状态，值得作为脑状态表征和神经系统疾病评估的生物标志物进行进一步研究。