Counterfactual Analysis of Brain Network Dynamics
arXiv:2603.29843v1 Announce Type: new Abstract: Causal inference in brain networks has traditionally relied on regression-based models such as Granger causality, structural equation modeling, and dynamic causal modeling. While effective for identifying directed associations, these methods remain descriptive and acyclic, leaving open the fundamental question of intervention: what would the causal organization become if a pathway were disrupted or externally modulated? We introduce a unified framework for counterfactual causal analysis that models both pathological disruptions and therapeutic interventions as an energy-perturbation problem on network flows. Grounded in Hodge theory, directed communication is decomposed into dissipative and persistent (harmonic) components, enabling systemati
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Abstract:Causal inference in brain networks has traditionally relied on regression-based models such as Granger causality, structural equation modeling, and dynamic causal modeling. While effective for identifying directed associations, these methods remain descriptive and acyclic, leaving open the fundamental question of intervention: what would the causal organization become if a pathway were disrupted or externally modulated? We introduce a unified framework for counterfactual causal analysis that models both pathological disruptions and therapeutic interventions as an energy-perturbation problem on network flows. Grounded in Hodge theory, directed communication is decomposed into dissipative and persistent (harmonic) components, enabling systematic analysis of how causal organization reconfigures under hypothetical perturbations. This formulation provides a principled foundation for quantifying network resilience, compensation, and control in complex brain systems.
Comments: Published in the IEEE International Symposium on Biomedical Imaging (ISBI), 2026
Subjects:
Neurons and Cognition (q-bio.NC)
Cite as: arXiv:2603.29843 [q-bio.NC]
(or arXiv:2603.29843v1 [q-bio.NC] for this version)
https://doi.org/10.48550/arXiv.2603.29843
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Moo K. Chung [view email] [v1] Tue, 31 Mar 2026 15:01:39 UTC (1,894 KB)
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