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] § 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] § 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] § 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] § 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] § 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] § 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] § 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

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The authors' code

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

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It can be read at the source: 2_osl_dynamics/2_train_hmm_revision.py.

Overview

Authors: Hojjatollah Sadeqi^1,2, Babak Ahmadi^1,2,3, Zohreh Morshedizad^1,2, Rachael Burke^1,2, Bhavana Patel^1,2, Nikolaus R McFarland^1,2, Melissa J Armstrong^1,2, Abbas Babajani-Feremi^1,2

ORCID iDs: Zohreh Morshedizad

Department of Neurology, University of Florida, Gainesville, FL 32611, USA
The Norman Fixel Institute of Neurological Diseases, University of Florida Health, Gainesville, FL 32608, USA
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, not copied: shown from their source

OSCR keeps no copy of these files: this repository has no license that allows it. The reader above shows each one from its source, fetched by your browser at commit f3cb5a0, when its fingerprint is the one OSCR verified. How this works.

1_osl_ephys/1_preprocessing.py — Python, 90 lines, 1 match, shown from its source
1_osl_ephys/2_coregistration.py — Python, 122 lines, shown from its source
1_osl_ephys/2_coregistration_revise.py — Python, 46 lines, shown from its source
1_osl_ephys/3_source_reconstruction.py — Python, 97 lines, 1 match, shown from its source
1_osl_ephys/3_source_reconstruction_revise.py — Python, 42 lines, 1 match, shown from its source
1_osl_ephys/4_sign.py — Python, 44 lines, shown from its source
2_osl_dynamics/1_prepare_data.py — Python, 61 lines, 1 match, shown from its source
2_osl_dynamics/2_train_hmm.py — Python, 71 lines, shown from its source
2_osl_dynamics/2_train_hmm_revision.py — Python, 54 lines, 2 matches, shown from its source
2_osl_dynamics/3_print_free_energy.py — Python, 34 lines, shown from its source
2_osl_dynamics/4_get_inf_params.py — Python, 44 lines, shown from its source
2_osl_dynamics/5_calc_post_hoc.py — Python, 113 lines, 1 match, shown from its source
3_ggseg_visualizations/0_R_Packages_Installation.R — R, 73 lines, shown from its source
3_ggseg_visualizations/1_Loading_Libraries_and_Data.R — R, 180 lines, shown from its source
3_ggseg_visualizations/2_Fractional_Occupancy_Analysis.R — R, 176 lines, shown from its source
3_ggseg_visualizations/3_Power_Spectoral_Densities_Analysis.R — R, 71 lines, shown from its source
3_ggseg_visualizations/3_TBR_Analysis.R — R, 129 lines, shown from its source
3_ggseg_visualizations/4_LBD_NC_TBR_Difference_Analysis.R — R, 138 lines, shown from its source
3_ggseg_visualizations/5_LBD_NC_Power_Difference_Analysis.R — R, 158 lines, shown from its source
3_ggseg_visualizations/6_PD_NC_Power_Difference_Analysis.R — R, 139 lines, shown from its source
3_ggseg_visualizations/7_LBD_CAF_Correlation_Analysis.R — R, 160 lines, shown from its source
3_ggseg_visualizations/libraries/libraries.R — R, 63 lines, shown from its source
README.md — Text, 65 lines, shown from its source

At the source: github.com/sadeqi60/MEG_HMM_LBD

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

Tracing map

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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);
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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

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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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