用脑网络模型参数区分静息与认知状态，为神经疾病诊断提供新指标。
Deep learning and whole-brain networks for biomarker discovery: modeling the dynamics of brain fluctuations in resting-state and cognitive tasks
- 基于超临界霍普夫脑网络生成仿真信号，训练深度学习预测分岔参数。
- 任务态脑区分岔参数显著高于静息态（所有对比均p<0.0001）。
- 适合研究脑状态识别、神经疾病生物标志物的科研人员参考。
背景：脑网络模型有助于理解脑动态，但模型衍生的分岔参数作为生物标志物的潜力尚未充分探索。目标：评估全脑网络模型中的分岔参数，以区分与静息态及任务态认知活动相关的脑状态。方法：利用超临界霍普夫脑网络模型生成合成BOLD信号，训练深度学习模型预测分岔参数；在人类连接组计划（Human Connectome Project）数据上进行推理，涵盖静息态和任务态条件。统计分析评估了基于分岔参数分布的脑状态可分离性。结果：分岔参数分布在线任务态与静息态间存在显著差异（除一项外，所有比较p<0.0001）；任务态脑状态的分岔值更高。结论：分岔参数能有效区分认知状态与静息状态，值得进一步研究作为脑状态表征及神经系统疾病评估的生物标志物。
原文摘要 · Abstract (English)
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.
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