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Related Experiment Video

Updated: May 9, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
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COUNTERFACTUAL ANALYSIS OF BRAIN NETWORK DYNAMICS.

Moo K Chung1, Luigi Maccotta2, Aaron Struck2

  • 1University of Wisconsin-Madison, USA.

Arxiv
|May 8, 2026
PubMed
Summary
This summary is machine-generated.

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.

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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.