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Synapse
September 28, 2025Open Access

Modelling Discrete States and Long-Term Dynamics in Functional Brain Networks

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Authors

SCSungJun ChoRHR. HuangCGChetan Gohil

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Overview

Observational analysis shows improved interpretability and temporal structure in functional brain networks, suggesting Dynamic Network States enhances understanding of cognition.

Key Points

  • Dynamic Network States effectively model long-range temporal dynamics in brain networks.
  • In simulations, DyNeStE outperformed Hidden Markov Models in capturing temporal dependencies.
  • The model uses recurrent neural networks with amortised Bayesian inference for greater interpretability.
  • Dynamic networks generated were consistent across independent data splits, reinforcing their reliability.

Cite This Study

Cho et al. (2025) studied this question.

synapsesocial.com/papers/68d9052541e1c178a14f56b2https://doi.org/10.1101/2025.09.25.678554
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Finer-Grained Dynamic Functional Graph Structure Learning for EEG Sequence Modeling2025
  2. 2Evidence for transient, uncoupled power and functional connectivity dynamics2024
  3. 3Switching Models of Oscillatory Networks Greatly Improve Inference of Dynamic Functional Connectivity2024
  4. 4Advanced complex networks methods for brain structure-function analysis2025
  5. 5Deep learning models reveal the link between dynamic brain connectivity patterns and states of consciousness2024