用动态图模型发现脑科学中随时间变化的因果关系。
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series
- 将动态因果图建模为静态图的条件加权组合，支持非线性关系。
- 在真实脑数据上提升预测准确率22%-60%以上。
- 适合研究行为状态相关的神经动态机制的研究者。
假设生成有望降低神经科学中的干预研究成本。现有机器学习方法虽能从复杂数据生成假设，但多假设因果关系为静态，难以适用于脑等具有动态、状态依赖特性的系统。部分动态因果发现方法虽基于因子模型，却常受限于线性关系或简化假设。本文提出新方法：将动态图表示为静态图的条件加权叠加，每个静态图可捕捉非线性关系，从而揭示超越线性的复杂时变交互。实验显示，该方法在部分测试中使动态因果模式预测的F1分数平均提升22%-28%，个别情况超60%。对真实脑数据的案例研究验证了其发现与特定行为状态相关联的因果关系的能力，为理解神经动态提供了新洞见。
原文摘要 · Abstract (English)
The field of hypothesis generation promises to reduce costs in neuroscience by narrowing the range of interventional studies needed to study various phenomena. Existing machine learning methods can generate scientific hypotheses from complex datasets, but many approaches assume causal relationships are static over time, limiting their applicability to systems with dynamic, state-dependent behavior, such as the brain. While some techniques attempt dynamic causal discovery through factor models, they often restrict relationships to linear patterns or impose other simplifying assumptions. We propose a novel method that models dynamic graphs as a conditionally weighted superposition of static graphs, where each static graph can capture nonlinear relationships. This approach enables the detection of complex, time-varying interactions between variables beyond linear limitations. Our method improves f1-scores of predicted dynamic causal patterns by roughly 22-28% on average over baselines in some of our experiments, with some improvements reaching well over 60%. A case study on real brain data demonstrates our method's ability to uncover relationships linked to specific behavioral states, offering valuable insights into neural dynamics.
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