标题： 生成神经科学中动态因果图的假设：利用观测时间序列的生成因子模型 显示英文标题

标题： Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series

摘要： 假设生成领域有望通过缩小需要研究各种现象的干预性研究范围来降低神经科学的成本。 现有的机器学习方法可以从复杂数据集中生成科学假设，但许多方法假设因果关系在时间上是静态的，这限制了它们在具有动态、状态依赖行为的系统（如大脑）中的适用性。 虽然一些技术尝试通过因子模型进行动态因果发现，但它们通常将关系限制为线性模式或施加其他简化假设。 我们提出了一种新方法，将动态图建模为静态图的条件加权叠加，其中每个静态图可以捕捉非线性关系。 这种方法能够检测变量之间超出线性限制的复杂时变交互。 在我们的某些实验中，我们的方法平均比基线提高了22-28%的预测动态因果模式的f1分数，有些改进甚至超过60%。 对真实脑数据的案例研究表明，我们的方法能够揭示与特定行为状态相关的关系，为神经动力学提供了有价值的见解。 显示英文摘要

摘要： 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.