COUNTERFACTUAL ANALYSIS OF BRAIN NETWORK DYNAMICS
Moo K Chung1, Luigi Maccotta2, Aaron Struck2
1University of Wisconsin-Madison, USA.
Arxiv
|May 8, 2026
Summary
We introduce a new framework for causal inference in brain networks, enabling analysis of interventions and disruptions. This approach models network changes as energy perturbations, offering insights into resilience and control.
Area of Science:
- Neuroscience
- Complex Systems
- Network Science
Background:
- Traditional causal inference models (e.g., Granger causality) in brain networks are descriptive and acyclic.
- They do not adequately address the impact of interventions or disruptions on network causality.
Purpose of the Study:
- To develop a unified framework for counterfactual causal analysis in brain networks.
- To model pathological disruptions and therapeutic interventions as energy-perturbation problems.
Main Methods:
- Utilizing Hodge theory to decompose directed communication into dissipative and persistent (harmonic) components.
- Applying an energy-perturbation framework to model network flow under hypothetical changes.
Main Results:
- The framework enables systematic analysis of causal organization reconfiguration under perturbations.
- It provides a principled foundation for quantifying network resilience, compensation, and control.
Conclusions:
- The proposed framework advances causal inference in brain networks beyond descriptive models.
- It offers new quantitative methods for understanding network dynamics in response to disruptions and interventions.
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