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Dynamic, state-dependent characteristics of cognitive fluctuations in Lewy body dementia: a magnetoencephalography study.

Code ↔ Paper

7 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 7 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Materials and methods › MEG data acquisition and preprocessing ↔ 1_osl_ephys/1_preprocessing.py, lines 18–90 · score 0.91 · notch filtered, Bad segments, Bad channels, MNE, osl ephys, 125 Hz
  2. [2] § Materials and methods › Hidden Markov models › Implementation ↔ 2_osl_dynamics/1_prepare_data.py, lines 57–58 · score 0.64 · TFRecord, sequence length, OSL Dynamics, training, HMM
  3. [3] § Materials and methods › Hidden Markov models › Implementation ↔ 2_osl_dynamics/2_train_hmm_revision.py, lines 14–54 · score 0.59 · sequence length, OSL Dynamics, TFRecord, fitting, training, batch
  4. [4] § Materials and methods › Data analysis ↔ 2_osl_dynamics/5_calc_post_hoc.py, lines 78–93 · score 0.58 · Power spectral densities, multitaper, tapered, weighted
  5. [5] § Materials and methods › Hidden Markov models › Implementation ↔ 2_osl_dynamics/2_train_hmm_revision.py, lines 14–54 · score 0.57 · free energy, epoch, probabilities, training, batches, covariances
  6. [6] § Materials and methods › MEG data acquisition and preprocessing ↔ 1_osl_ephys/3_source_reconstruction.py, lines 47–96 · score 0.50 · symmetric, beamforming, orthogonalization, rank, reconstruct, space
  7. [7] § Materials and methods › MEG data acquisition and preprocessing ↔ 1_osl_ephys/3_source_reconstruction_revise.py, the whole file · a weak match · score 0.50 · symmetric, beamforming, orthogonalization, rank, reconstruct, space

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 54 lines · 1.4 KB · no license · 2 matches

  1. from sys import argv
  3. if len(argv) != 3:
  4. print("Please pass the number of states and run id, e.g. python 2_train_hmm.py 8 1")
  5. exit()
  6. n_states = int(argv[1])
  7. run = int(argv[2])
  8. output_dir = f"/HMM/resultsCC/models/{n_states:02d}_states/run{run:02d}"
  9. print("Importing packages")
  10. import pickle
  11. from osl_dynamics.data import load_tfrecord_dataset
  12. from osl_dynamics.models.hmm import Config, Model
  14. # Settings
  15. config = Config(
  16. n_states=n_states,
  17. n_channels=120,
  18. sequence_length=400,
  19. learn_means=False,
  20. learn_covariances=True,
  21. learn_trans_prob=True,
  22. batch_size=32,
  23. learning_rate=0.001,
  24. n_epochs=10,
  25. )
  26. # Load training data
  27. dataset = load_tfrecord_dataset(
  28. "/HMM/cc60_training_dataset",
  29. config.batch_size,
  30. buffer_size=5000,
  31. )
  32. # Build model
  33. model = Model(config)
  34. model.summary()
  35. # Initialization
  36. model.random_state_time_course_initialization(
  37. dataset,
  38. n_init=3,
  39. n_epochs=1,
  40. )
  41. # Full training
  42. print("Training model")
  43. history = model.fit(dataset)
  44. # Save the trained model
  45. model.save(output_dir)
  46. # Get free energy
  47. free_energy = model.free_energy(dataset)
  48. history["free_energy"] = free_energy
  49. # Save training history
  50. with open(f"{output_dir}/history.pkl", "wb") as file:
  51. pickle.dump(history, file)
  52. with open(f"{output_dir}/loss.dat", "w") as file:
  53. file.write(f"ll_loss = {history['loss'][-1]}\n")
  54. file.write(f"free_energy = {free_energy}\n")

2_train_hmm_revision.py at commit f3cb5a0, no license · at the source

Overview

Authors: Hojjatollah Sadeqi1,2, Babak Ahmadi1,2,3, Zohreh Morshedizad1,2, Rachael Burke1,2, Bhavana Patel1,2, Nikolaus R McFarland1,2, Melissa J Armstrong1,2, Abbas Babajani-Feremi1,2
ORCID iDs: Zohreh Morshedizad
  1. Department of Neurology, University of Florida, Gainesville, FL 32611, USA
  2. The Norman Fixel Institute of Neurological Diseases, University of Florida Health, Gainesville, FL 32608, USA
  3. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USA
Institutions: University of Florida Health (United States); University of Florida (United States)
Journal: Brain communications, volume 8, issue 4, article fcag236
Dates: received 23 September 2025; accepted 7 May 2026; published online 24 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag236 · PMID 42491938 · PMCID PMC13378770 · OpenAlex W7165801027
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: MEG (modality), human (organism), Alzheimer's / dementia (population)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging
Keywords: cognitive fluctuations (CF), dynamic functional connectivity, hidden Markov modelling (HMM), magnetoencephalography (MEG), Lewy body dementia (LBD)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NINDS NIH HHS (R01 NS121099); NIA NIH HHS (R44 AG062072, P30 AG066506, R01 AG068128, R01 AG089380, R01 AG083828); Biotechnology and Biological Sciences Research Council (BB/H008217/1)
Citations: not cited yet (Europe PMC); 83 references in the paper

Abstract

Cognitive fluctuations are a hallmark clinical feature of Lewy body dementia (LBD), yet their underlying neural mechanisms remain poorly understood. This study aimed to identify dynamic, state-dependent neural signatures of cognitive fluctuations in LBD using magnetoencephalography and dynamic functional connectivity based on hidden Markov modelling. Resting-state magnetoencephalography data were acquired from individuals with LBD, Parkinson’s disease without dementia and cognitively normal controls. Hidden Markov modelling was used to identify transient brain states followed by spectral analyses across regions and states. Additionally, associations between regional spectral power and cognitive fluctuations severity, measured by the Clinician Assessment of Fluctuation, were assessed. Patients with LBD exhibited a distinct pattern of brain dynamics, particularly in two states (States 2 and 6), characterized by increased fractional occupancy of State 2 and markedly reduced occupancy of State 6, contrasting with the more distributed state engagement observed in Parkinson’s disease and normal controls. Spectral analyses revealed widespread slowing in LBD, with elevated theta/beta power ratios in frontal, parietal and visual cortices—most pronounced in States 2 and 6. Region-specific theta/beta power ratio elevations were identified in the anterior cingulate, medial prefrontal cortex, posterior cingulate, dorsal visual stream and auditory cortex. Critically, Clinician Assessment of Fluctuation scores correlated positively with spectral power in low frequency (δ and θ) and negatively with power in the high frequency (α and β), particularly in the ventral visual stream, default mode network hubs and sensorimotor regions. These findings reveal dynamic and spatially specific electrophysiological abnormalities in LBD closely linked to cognitive fluctuations severity, suggesting that magnetoencephalography-hidden-Markov-model characteristics hold promise as biomarkers for diagnosis, monitoring and therapeutic targeting in LBD.

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 7 matches between paragraphs and lines of code.

sadeqi60/MEG_HMM_LBD

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f3cb5a03419aea21585549eabd3492360e210030, 1 August 2026
Languages: Python (12), R (10)
Size: 24 files, 22 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: OHBA Software Library (OSL) (6 files), broom (4 files), NumPy (4 files), ggseg (2 files), pandas (2 files), tidyverse (2 files), caret (1 file), cowplot (1 file), easystats (1 file), ggpubr (1 file), igraph (1 file), MNE-Python (1 file), patchwork (1 file), Plotly (1 file), pROC (1 file), psych (1 file), randomForest (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
23 files
At the source: github.com/sadeqi60/MEG_HMM_LBD

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;
  • 22 scripts, each with its path and the digest of its content;
  • 7 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

No dataset and no data link were found in the paper.

Data availability

The preprocessing scripts, hidden Markov model (HMM) analysis code and visualization pipelines used in this study are openly available in a dedicated GitHub repository: https://github.com/sadeqi60/MEG_HMM_LBD.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 5 keywords, 3 funders, 77 references.

Cite

This paper

Sadeqi, H., Ahmadi, B., Morshedizad, Z., Burke, R., Patel, B., McFarland, N. R., Armstrong, M. J., & Babajani-Feremi, A. (2026). Dynamic, state-dependent characteristics of cognitive fluctuations in Lewy body dementia: a magnetoencephalography study. Brain communications, 8(4), fcag236. https://doi.org/10.1093/braincomms/fcag236

BibTeX

@article{sadeqi2026dynamic,
author = {Sadeqi, Hojjatollah and Ahmadi, Babak and Morshedizad, Zohreh and Burke, Rachael and Patel, Bhavana and McFarland, Nikolaus R and Armstrong, Melissa J and Babajani-Feremi, Abbas},
title = {{Dynamic, state-dependent characteristics of cognitive fluctuations in Lewy body dementia: a magnetoencephalography study}},
journal = {Brain communications},
year = {2026},
month = jun,
volume = {8},
number = {4},
pages = {fcag236},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag236},
url = {https://doi.org/10.1093/braincomms/fcag236},
pmid = {42491938},
pmcid = {PMC13378770}
}

RIS

TY - JOUR
AU - Sadeqi, Hojjatollah
AU - Ahmadi, Babak
AU - Morshedizad, Zohreh
AU - Burke, Rachael
AU - Patel, Bhavana
AU - McFarland, Nikolaus R
AU - Armstrong, Melissa J
AU - Babajani-Feremi, Abbas
TI - Dynamic, state-dependent characteristics of cognitive fluctuations in Lewy body dementia: a magnetoencephalography study
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/06/24
VL - 8
IS - 4
SP - fcag236
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag236
UR - https://doi.org/10.1093/braincomms/fcag236
LA - en
ER -

CSL-JSON

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"container-title": "Brain communications",
"author": [
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"given": "Hojjatollah"
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"PMCID": "PMC13378770",
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