OSCR

Modelling discrete states and long-term dynamics in functional brain networks.

Code ↔ Paper

36 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 36 matches
  1. [1] § Methods › Simulation analysis ↔ simulation/04_compare_simulations.py, lines 169–257 · score 0.81 · Jensen Shannon, JS distances, RV coefficient, rensen coefficient, Dice, DyNeStE
  2. [2] § Methods › Simulation analysis ↔ simulation/03_analyze_simulations.py, lines 105–151 · score 0.79 · sample Kolmogorov Smirnov, alpha threshold, lifetime distribution, state lifetime, Bonferroni correction, activation
  3. [3] § Methods › Replay data analysis › Statistical analysis ↔ replay/utils/plotting.py, lines 278–389 · score 0.77 · replay evoked network, Bonferroni corrected, state probabilities, MNE, cluster, dimension
  4. [4] § Results › Real data analysis: Replay ↔ replay/utils/plotting.py, lines 278–389 · score 0.76 · Replay evoked network, standard error, RSN state, Bonferroni corrected, state probabilities, cluster
  5. [5] § Methods › TINDA analysis › Detection of cyclical structure and cycle strength ↔ nott_meguk/utils/plotting.py, lines 830–955 · score 0.70 · unit circle, asymmetry matrix, nodes, counterclockwise, cycle, edges
  6. [6] § Methods › Model training ↔ nott_meguk/01_train_dyneste.py, lines 44–132 · score 0.69 · variational free energy, model hyperparameters, state covariance, curves, trained
  7. [7] § Methods › Power and functional connectivity ↔ nott_meguk/04_analyze_network_descriptions.py, lines 91–136 · score 0.68 · half bandwidth, 1–45 Hz, taper, computation, spectral, PSDs
  8. [8] § Methods › Power and functional connectivity ↔ replay/02_analyze_network_descriptions.py, lines 88–133 · score 0.68 · half bandwidth, 1–45 Hz, taper, computation, spectral, PSDs
  9. [9] § Methods › Datasets › Simulation ↔ simulation/04_compare_simulations.py, lines 169–257 · score 0.66 · JS distance, RV coefficients, rensen coefficient, violin, Dice, DyNeStE
  10. [10] § Methods › Model training ↔ nott_meguk/02_train_hmm.py, lines 44–120 · score 0.66 · variational free energy, model hyperparameters, state covariance, trained
  11. [11] § Methods › Dynamic network states (DyNeStE) › Inference of model parameters ↔ nott_meguk/01_train_dyneste.py, lines 44–132 · score 0.66 · variational free energy, state covariances, train DyNeStE, gradient, inference, model
  12. [12] § Methods › Replay data analysis ↔ replay/03_analyze_replay_network.py, lines 132–174 · score 0.66 · prevent high variance, replay intervals, RSN state, active
  13. [13] § Methods › Split-half reproducibility ↔ nott_meguk/06_analyze_split_half_reproducibility.py, lines 91–166 · score 0.64 · split halves, power maps, cosine, reproducible, permutation, MEGUK
  14. [14] § Results › Real data analysis: Nottingham MEGUK › DyNeStE infers plausible categorical dynamic brain networks ↔ nott_meguk/05_analyze_network_dynamics.py, lines 55–144 · score 0.64 · switching rates, fractional occupancies, network dynamics, lifetimes, DyNeStE, intervals
  15. [15] § Methods › TINDA analysis › Circle plot visualisation and timescale-specific analysis ↔ nott_meguk/09_run_tinda_analysis.py, lines 544–589 · score 0.63 · equal variance assumptions, cycle strengths, Welch, TINDA, quintiles, Bonferroni
  16. [16] § Methods › Replay data analysis › Statistical analysis ↔ replay/04_visualize_replay_network.py, lines 44–82 · score 0.63 · replay evoked, replay rates, replay intervals, Fano factor, activations, network
  17. [17] § Results › Real data analysis: Nottingham MEGUK › DyNeStE infers plausible categorical dynamic brain networks ↔ nott_meguk/05_analyze_network_dynamics.py, lines 55–144 · score 0.60 · Riemannian distances, Network dynamics, HMM state, violin, correlations, DyNeStE
  18. [18] § Results › Simulation analysis › DyNeStE captures long-range temporal dependencies in simulated data ↔ simulation/03_analyze_simulations.py, lines 47–103 · score 0.59 · ground truth, rensen coefficients, covariance matrices, Dice, simulation, matched
  19. [19] § Methods › Datasets › Simulation ↔ simulation/03_analyze_simulations.py, lines 105–151 · score 0.59 · state lifetime distributions, covariance matrices, KS, gamma, distances, simulated
  20. [20] § Methods › Replay data analysis ↔ replay/03_analyze_replay_network.py, lines 87–130 · score 0.59 · baseline corrected, Replay evoked, epoched, activations, network
  21. [21] § Methods › Measures of long-range temporal dependencies › Statistical analysis ↔ nott_meguk/08_visualize_long_term_dependency.py, lines 97–153 · score 0.59 · Bonferroni correction, MNE, Fano factor, cluster, MI, lags
  22. [22] § Results › Simulation analysis › DyNeStE learns categorical latent states in simulated data ↔ simulation/03_analyze_simulations.py, lines 47–103 · score 0.58 · ground truth, rensen coefficient, covariance matrices, Dice, simulated, DyNeStE
  23. [23] § Methods › Measures of long-range temporal dependencies › Time-lagged mutual information ↔ nott_meguk/utils/analysis.py, lines 322–416 · score 0.58 · mutual information, geometric, entropy, nats, lags, MI
  24. [24] § Methods › Power and functional connectivity ↔ nott_meguk/04_analyze_network_descriptions.py, lines 180–257 · score 0.57 · power spectral densities, power maps, connectivity, PSDs, networks, HMM
  25. [25] § Results › Real data analysis: Nottingham MEGUK › DyNeStE learns long-range temporal dependencies ↔ nott_meguk/08_visualize_long_term_dependency.py, lines 97–153 · score 0.57 · mutual information, Bonferroni corrected, window lengths, Fano factors, cluster, lags
  26. [26] § Results › Simulation analysis › DyNeStE captures long-range temporal dependencies in simulated data ↔ simulation/01_train_dyneste.py, lines 67–108 · score 0.57 · transition probability matrix, ground truth, trained DyNeStE, simulated, models
  27. [27] § Methods › Power and functional connectivity ↔ replay/02_analyze_network_descriptions.py, lines 177–244 · score 0.57 · power spectral densities, power maps, connectivity, PSDs, networks
  28. [28] § Methods › TINDA analysis › Detection of cyclical structure and cycle strength ↔ nott_meguk/09_run_tinda_analysis.py, lines 544–589 · score 0.57 · equal variance assumptions, cycle strength, alpha, matrix, HMM, DyNeStE
  29. [29] § Methods › TINDA analysis › Circle plot visualisation and timescale-specific analysis ↔ nott_meguk/utils/statistics.py, lines 76–138 · score 0.56 · equal variance assumptions, Bonferroni correction, Welch, threshold, alpha
  30. [30] § Results › Real data analysis: Replay ↔ replay/04_visualize_replay_network.py, lines 44–82 · score 0.56 · replay evoked, replay rates, replay interval, activations, network, HMM
  31. [31] § Methods › TINDA analysis › Fractional occupancy asymmetry matrix ↔ nott_meguk/utils/analysis.py, lines 671–692 · score 0.56 · fractional occupancy, asymmetry matrices, cyclic, TINDA
  32. [32] § Methods › Dynamic network states (DyNeStE) › Generative model ↔ simulation/01_train_dyneste.py, lines 110–183 · score 0.55 · variational free energy, model RNN, predict, DyNeStE, inference, covariance
  33. [33] § Methods › Dynamic network states (DyNeStE) › Inference of model parameters ↔ nott_meguk/02_train_hmm.py, lines 44–120 · score 0.54 · variational free energy, state covariances, inference, train, model
  34. [34] § Methods › Split-half reproducibility ↔ nott_meguk/utils/statistics.py, lines 141–191 · score 0.52 · power maps, cosine, reproducible, split, permutation, MEGUK
  35. [35] § Results › Real data analysis: Nottingham MEGUK › DyNeStE infers plausible categorical dynamic brain networks ↔ nott_meguk/utils/analysis.py, lines 12–45 · score 0.52 · switching rates, fractional occupancies, lifetimes, intervals
  36. [36] § Methods › Replay data analysis ↔ replay/03_analyze_replay_network.py, lines 132–174 · score 0.51 · Active RSN States, Replay Intervals

Paper

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The authors' code

Python · 151 lines · 5.1 KB · MIT · 4 matches

  1. """Script for analyzing model ability to learn long-range dependencies."""
  3. # Import packages
  4. import os
  5. import numpy as np
  6. from scipy.stats import ks_2samp
  7. from osl_dynamics.analysis import post_hoc
  8. from utils import validate_nd_arrays
  9. from utils import data as ud
  10. from utils import plotting as up
  13. if __name__ == "__main__":
  14. # -------------- [1] Settings -------------- #
  15. print("Step 1: Setting up ...")
  17. # Set user-defined parameters
  18. dyneste_run_ids = 0
  19. hmm_run_ids = 0
  21. # Validate user inputs
  22. if dyneste_run_ids != hmm_run_ids:
  23. raise ValueError(
  24. "Run IDs for DyNeStE and HMM must match to enable a valid comparison."
  25. )
  27. # Set directories
  28. BASE_DIR = "/well/woolrich/users/olt015/Cho2026_DyNeStE/simulation"
  29. MODEL_DIR = os.path.join(BASE_DIR, "results")
  30. FIG_DIR = os.path.join(BASE_DIR, "figures")
  31. os.makedirs(FIG_DIR, exist_ok=True)
  33. # Set colors for visualization
  34. cmap = ["#E69F00", "#56B4E9", "#009E73"] # Okabe-Ito color palette
  36. # -------------- [2] Single Simulation -------------- #
  37. print("Step 2: Analyzing a representative simulation ...")
  39. # Load inferred parameters
  40. dyneste_inf_params = ud.load_inf_params(
  41. MODEL_DIR, "dyneste", dyneste_run_ids
  42. )
  43. hmm_inf_params = ud.load_inf_params(
  44. MODEL_DIR, "hmm", hmm_run_ids
  45. )
  47. # Get state time courses
  48. dyneste_sim_stc = dyneste_inf_params["sim_stc"]
  49. dyneste_inf_stc = dyneste_inf_params["inf_stc"]
  50. dyneste_sam_stc = dyneste_inf_params["sam_stc"]
  51. hmm_sim_stc = hmm_inf_params["sim_stc"]
  52. hmm_inf_stc = hmm_inf_params["inf_stc"]
  53. hmm_sam_stc = hmm_inf_params["sam_stc"]
  55. # Match number of samples
  56. n_samples = dyneste_sam_stc.shape[0]
  57. dyneste_sim_stc = dyneste_sim_stc[:n_samples]
  58. dyneste_inf_stc = dyneste_inf_stc[:n_samples]
  59. hmm_sim_stc = hmm_sim_stc[:n_samples]
  60. hmm_inf_stc = hmm_inf_stc[:n_samples]
  61. # *_stc.shape = (n_samples, n_states)
  63. # Get dice coefficients
  64. dices = [
  65. dyneste_inf_params["dice_coefficient"],
  66. hmm_inf_params["dice_coefficient"],
  67. ]
  69. # Get state-specific covariance matrices
  70. dyneste_sim_cov = dyneste_inf_params["sim_cov"]
  71. dyneste_inf_cov = dyneste_inf_params["inf_cov"]
  72. hmm_sim_cov = hmm_inf_params["sim_cov"]
  73. hmm_inf_cov = hmm_inf_params["inf_cov"]
  74. # *_cov.shape = (n_states, n_channels, n_channels)
  76. # Validate simulation data
  77. validate_nd_arrays(dyneste_sim_stc, hmm_sim_stc)
  78. validate_nd_arrays(dyneste_sim_cov, hmm_sim_cov)
  79. sim_stc = dyneste_sim_stc
  80. sim_cov = dyneste_sim_cov
  81. n_states = sim_stc.shape[-1]
  83. # Plot state time courses
  84. print("(Step 2-1) Plotting state time courses ...")
  86. fig, axes = up.plot_alpha(
  87. sim_stc,
  88. dyneste_inf_stc,
  89. hmm_inf_stc,
  90. n_samples=4000,
  91. plot_kwargs={"alpha": 0.75},
  92. colors=cmap,
  93. )
  94. titles = ["Ground Truth", "DyNeStE", "HMM"]
  95. axes[-1].set_xlabel("Time (samples)", fontsize=14)
  96. for i, ax in enumerate(axes):
  97. title = f"{titles[i]}"
  98. if i > 0:
  99. title += f" (Dice Sørensen Coefficient: {dices[i - 1]:.4f})"
  100. ax.set_ylabel("Probabilities", fontsize=14)
  101. ax.set_title(title, fontsize=14)
  102. ax.tick_params(axis="both", which="both", labelsize=14)
  103. up.save(fig, os.path.join(FIG_DIR, "state_time_courses.png"))
  105. # Plot covariance matrices
  106. print("(Step 2-2) Plotting covariance matrices ...")
  108. covs = np.concatenate([
  109. sim_cov, dyneste_inf_cov, hmm_inf_cov,
  110. ], axis=0)
  111. # covs.shape = (n_total_states, n_channels, n_channels)
  113. fig, _ = up.plot_matrices(covs, cbar_label="Covariance")
  114. up.save(fig, os.path.join(FIG_DIR, "covariance_matrices.png"))
  116. # Plot state lifetime distributions
  117. print("(Step 2-3) Plotting state lifetime distributions ...")
  119. stcs = [sim_stc, dyneste_inf_stc, hmm_inf_stc, dyneste_sam_stc, hmm_sam_stc]
  120. stc_names = ["sim", "dyneste_inf", "hmm_inf", "dyneste_sam", "hmm_sam"]
  122. for stc, name in zip(stcs, stc_names):
  123. filename = os.path.join(FIG_DIR, f"{name}_lt.png")
  124. up.plot_state_lifetime_dist(
  125. stc,
  126. colors=cmap,
  127. gamma_shape=10,
  128. gamma_scale=5,
  129. filename=filename,
  130. )
  132. # Perform statistical testing on state lifetime distributions
  133. print("(Step 2-4) Two-Sample Kolmogorov-Smirnov test for goodness of fit")
  135. bonferroni_n_tests = n_states * 4
  136. alpha_thr = 0.05 / bonferroni_n_tests
  137. print(f"\tBonferroni-corrected alpha threshold: {alpha_thr:.4e}")
  139. sim_lt = post_hoc.lifetimes(sim_stc)
  140. for name, stc in zip(stc_names[1:], stcs[1:]):
  141. print(f"\tComparing '{name}' with simulated data ...")
  142. stc_lt = post_hoc.lifetimes(stc)
  143. # stc_lt.shape = (n_states, n_activations)
  144. for i, lt in enumerate(stc_lt):
  145. res = ks_2samp(sim_lt[i], lt, method="auto")
  146. print(
  147. f"\t\tState {i + 1}: test statistic = {res.statistic:.4f}, "
  148. f"p-value = {res.pvalue:.4e}, significance = {res.pvalue < alpha_thr}"
  149. )
  151. print("Analysis complete.")

03_analyze_simulations.py at commit b78a86c, under MIT · at the source

Overview

Authors: SungJun Cho1,2, Rukuang Huang1,3, Chetan Gohil1, Oiwi Parker Jones1,4, Mark W Woolrich1,3
ORCID iDs: SungJun Cho, Rukuang Huang, Chetan Gohil, Oiwi Parker Jones, Mark W Woolrich
  1. Oxford Centre for Integrative Neuroimaging (OxCIN), University of Oxford, Oxford, United Kingdom
  2. Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
  3. Department of Psychiatry, University of Oxford, Oxford, United Kingdom
  4. Department of Engineering Science, University of Oxford, Oxford, United Kingdom
Institutions: University of Oxford (United Kingdom); Wellcome Centre for Integrative Neuroimaging (United Kingdom)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1237
Dates: received 7 October 2025; accepted 13 April 2026; published online 26 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1237 · PMID 42212226 · PMCID PMC13214575 · OpenAlex W4414552642
Open access: diamond, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.09.25.678554
Status: code verified
Categories: MEG (modality), systems (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Source localization, Machine learning, Physiology & signal measures
Keywords: dynamics, resting-state networks, electrophysiology, MEG, machine learning
Topic: Neural Networks and Applications (Artificial Intelligence, Computer Science), according to OpenAlex
Funding: Wellcome Trust (106183/Z/14/Z, 215573/Z/19/Z); National Institute for Health Research (NIHR) (NIHR203316)
Citations: not cited yet (Europe PMC); 57 references in the paper

Abstract

Functional brain network dynamics underlie fundamental aspects of human cognition and behaviour, including memory, ageing, and a range of clinical disorders. It has been shown that ongoing brain network dynamics can be reliably inferred at fast, sub-second timescales from electrophysiological data using unsupervised machine learning. However, these methods often struggle with inherent trade-offs. For example, Hidden Markov Models (HMMs) have been used to infer categorical brain network states that provide good interpretability but do not model long-range temporal structure. Recently, deep learning approaches using recurrent neural networks (e.g., Dynamic Network Modes) have been proposed to model long-range temporal dependencies, but at the expense of interpretability. In this paper, we introduce Dynamic Network States (DyNeStE) to address this problem. This new model employs amortised Bayesian inference with recurrent neural networks to model long-range temporal structure and uses a Gumbel-Softmax distribution to enforce categorical states for greater interpretability. In both simulations and real resting-state magnetoencephalography data, DyNeStE was able to recover plausible dynamic brain network states and showed superior performance over the HMM in capturing long-range temporal dependencies in network dynamics. These dynamic networks were reproducible across independent data splits and build on established HMM-based findings. Together, these results highlight DyNeStE as an interpretable and temporally informative framework, capable of representing large-scale neural activity as discrete state transitions while capturing transient and long-range brain network dynamics.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 36 matches between paragraphs and lines of code.

OHBA-analysis/Cho2026_DyNeStE

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: b78a86c688b472741067ba8abaa5c14146d77888, 18 February 2026
Languages: Python (38)
Size: 45 files, 38 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (30 files), SciPy (8 files), pandas (6 files), seaborn (6 files), Matplotlib (5 files), MNE-Python (3 files), scikit-learn (1 file), TensorFlow (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
40 files
At the source: github.com/OHBA-analysis/Cho2026_DyNeStE

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 38 scripts, each with its path and the digest of its content;
  • 36 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data and Code Availability

The Nottingham MEGUK dataset is publicly available at https://meguk.ac.uk/database/ (raw sensor-level MEG recordings) and at https://osf.io/by2tc/ (source reconstructed data). The Replay dataset is freely available upon request, subject to participant consent (Higgins et al., 2021; Liu et al., 2019).

The source code for DyNeStE can be found in the osl-dynamics toolbox (Gohil, Huang, et al., 2024), along with example scripts for basic usage. All scripts for reproducing the results, figures, and analyses in this paper are written in Python and available at https://github.com/OHBA-analysis/Cho2026_DyNeStE.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 5 keywords, 2 funders, 54 references.

Cite

This paper

Cho, S., Huang, R., Gohil, C., Jones, O. P., & Woolrich, M. W. (2026). Modelling discrete states and long-term dynamics in functional brain networks. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1237. https://doi.org/10.1162/imag.a.1237

BibTeX

@article{cho2026modelling,
author = {Cho, SungJun and Huang, Rukuang and Gohil, Chetan and Jones, Oiwi Parker and Woolrich, Mark W},
title = {{Modelling discrete states and long-term dynamics in functional brain networks}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = may,
volume = {4},
pages = {IMAG.a.1237},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1237},
url = {https://doi.org/10.1162/imag.a.1237},
pmid = {42212226},
pmcid = {PMC13214575}
}

RIS

TY - JOUR
AU - Cho, SungJun
AU - Huang, Rukuang
AU - Gohil, Chetan
AU - Jones, Oiwi Parker
AU - Woolrich, Mark W
TI - Modelling discrete states and long-term dynamics in functional brain networks
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/05/26
VL - 4
SP - IMAG.a.1237
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1237
UR - https://doi.org/10.1162/imag.a.1237
LA - en
ER -

CSL-JSON

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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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