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

2025-05-27

AI Summary

Modeling dynamic, state-dependent causal relationships in neuroscience remains challenging, as existing methods typically assume static and linear causal structures—limitations that hinder accurate characterization of time-varying interactions in complex systems like the brain. Method: We propose the Conditional Weighted Static Graph Overlay (CW-SGO) framework, the first to jointly integrate generative factor models, dynamic graph neural networks, and nonlinear causal inference for end-to-end learning of nonlinear, time-varying causal graphs. CW-SGO explicitly models how latent brain states modulate causal connectivity, thereby relaxing restrictive assumptions of stationarity and linearity. Results: Evaluated on synthetic benchmarks and real human fMRI data, CW-SGO achieves average F1-score improvements of 22–28% over baselines, with gains exceeding 60% in specific tasks. It successfully uncovers task- and rest-state-specific functional connectivity patterns, demonstrating strong interpretability and neuroscientific validity.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Dynamic causal graphs in neuroscience lack accurate modeling methods
• Existing approaches assume static or linear causal relationships
• Current techniques fail to capture nonlinear, time-varying neural interactions

Innovation

• Dynamic graphs as weighted static graphs superposition
• Nonlinear relationships in static graphs
• Improved dynamic causal pattern detection