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Linking movement-related beta oscillations to cortical excitability, structural damage, and fatigue in multiple sclerosis.

Code ↔ Paper

8 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 8 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and methods › Statistical analysis ↔ nested_lasso_ms_fatigue.m, the whole file · a weak match · score 0.98 · full nested CV, PR AUC, Brier score, logistic regression, selection frequencies, balanced accuracy
  2. [2] § Materials and methods › Statistical analysis ↔ nested_lasso_ms_fatigue.m, the whole file · a weak match · score 0.93 · odds ratios, pseudo R2, logistic regression, descriptive penalized, ridge, coefficients
  3. [3] § Materials and methods › EEG data collection and analysis ↔ Compute_TFR_TAPPING_MS.m, lines 1–62 · score 0.88 · 0.5–1.5 s, 1–90 Hz, 10–20, cap, amplifier, FieldTrip
  4. [4] § Materials and methods › Movement-related beta ERD and ERS analysis ↔ plot_beta_roi_topography.m, lines 1–31 · score 0.81 · ERS ERD, beta power, Right ROIs, movement related beta, topography, maximal
  5. [5] § Results › EMG activity during motor performance and movement-Related Beta ERD and ERS Modulation ↔ plot_beta_roi_topography.m, lines 1–31 · score 0.76 · scalp topography, beta band, ERS ERD, beta power, movement related beta, beta modulation
  6. [6] § Materials and methods › Movement-related beta ERD and ERS analysis ↔ Compute_TFR_TAPPING_MS.m, lines 1–62 · score 0.63 · Morlet Wavelets, 1–90 Hz, ft, preprocessed, EEG, Movement
  7. [7] § Materials and methods › EEG data collection and analysis ↔ Fix_chanlocs_order.m, lines 1–40 · score 0.62 · 10–20, cap, ICA, amplifier, FieldTrip, notch
  8. [8] § Materials and methods › Movement-related beta ERD and ERS analysis ↔ extract_beta_modulation.m, lines 161–183 · score 0.56 · Morlet Wavelets, 3–10 cycles, resolution, ft, 90 Hz, beta

Paper

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The authors' code

MATLAB · 138 lines · 5.6 KB · MIT · 2 matches

  1. function nested_lasso_ms_fatigue()
  2. % NESTED_LASSO_MS_FATIGUE
  3. %
  4. % Nested elastic-net / LASSO logistic regression for MS fatigue classification.
  5. %
  6. % - Outcome coding (IMPORTANT):
  7. % y = 1 -> fatigued (MS_F)
  8. % y = 0 -> non-fatigued (MS_NF)
  9. % - Theory-driven candidate predictors; NO outcome-based pre-screening
  10. % - Stratified Kouter x Kinner nested CV for unbiased performance
  11. % - Repeated nested CV for confidence intervals and selection stability
  12. % - Descriptive penalized (near-ridge) refit on features with selection
  13. % frequency >= 0.50
  14. % - Reports β, odds ratios (per 1 SD), 95% bootstrap CIs, and pseudo-R²
  15. %
  16. % This script was used in:
  17. % Tatti et al., "Insights into Central Fatigue in Multiple Sclerosis:
  18. % Movement-Related Beta Oscillatory Activity and its Association with
  19. % Brain Neurophysiology and Structure" (Brain Communications).
  20. %
  21. % DEPENDENCIES
  22. % - Statistics and Machine Learning Toolbox (for lassoglm, cvpartition, perfcurve)
  23. %
  24. % USAGE (typical):
  25. % 1. Place your analysis CSV in: data/lasso_with_caudateandSLF.csv
  26. % 2. Edit the 'dataFile' path below if needed.
  27. % 3. From MATLAB: run nested_lasso_ms_fatigue
  28. %
  29. % The script prints performance metrics and writes:
  30. % - ElasticNet_Firth_Wald.csv : Firth refit coefficients, SEs, z, p, OR, CIs
  32. clear; clc; rng(7,'twister');
  34. %% 1) Load data -----------------------------------------------------------
  36. % Relative or absolute path to the CSV file (edit for your machine)
  37. dataFile = fullfile('data','lasso_with_caudateandSLF.csv');
  39. if ~exist(dataFile, 'file')
  40. error('Data file not found: %s. Please check the path.', dataFile);
  41. end
  43. data = readtable(dataFile, 'VariableNamingRule','preserve');
  45. % Outcome (assumes first column is the binary label)
  46. outcomeVar = data.Properties.VariableNames{1}; % e.g., 'Dummy MS'
  47. y_all = data{:, outcomeVar}; % REQUIRED coding: 1=fatigued, 0=non-fatigued
  48. assert(all(ismember(unique(y_all(~isnan(y_all))), [0 1])), ...
  49. 'Outcome must be coded 0/1 (0=non-fatigued, 1=fatigued).');
  51. % Candidate predictors (must match CSV headers exactly)
  52. clin_vars = {'age','sex_num','disease_duration','education','MADRS', ...
  53. 'MSQOL_54_PH','MSQOL_54_MH','9-HPT_right','NUM_LES','VOL_LES'};
  54. vol_vars = {'WM','CAU','THAL'};
  55. tms_vars = {'CSP','SICI','ICF','RMT','MEP latency'};
  56. eeg_vars = {'Bp2p_left_pow_avg','Bp2p_right_pow_avg','Bp2p_front_pow_avg'};
  57. fa_vars = {'FA_CST_R','FA_CST_L','FA_SLF_R','FA_SLF_L'};
  59. predictorNames = [clin_vars, vol_vars, tms_vars, eeg_vars, fa_vars];
  61. % Keep only predictors that exist in the file
  62. existsMask = ismember(predictorNames, data.Properties.VariableNames);
  63. if ~all(existsMask)
  64. warning('Missing predictors removed: %s', ...
  65. strjoin(predictorNames(~existsMask), ', '));
  66. predictorNames = predictorNames(existsMask);
  67. end
  69. % Design matrix
  70. Xmat_all = data{:, predictorNames};
  72. % ---- COMPLETE-CASE (listwise deletion), as in the primary analysis ------
  73. rowKeep = all(isfinite(Xmat_all), 2) & isfinite(y_all);
  74. Xmat = Xmat_all(rowKeep, :);
  75. y = y_all(rowKeep);
  77. fprintf('N included after listwise deletion: %d (of %d total)\n', numel(y), numel(y_all));
  78. % -------------------------------------------------------------------------
  80. %% 2) CV configuration ----------------------------------------------------
  82. Kouter = 5; % outer folds
  83. Kinner = 5; % inner folds for tuning
  84. AlphaGrid = [0.1 0.3 0.5 0.7 0.9 1]; % elastic-net to LASSO (α=1)
  85. NumLambda = 100;
  86. useWeights = false; % set true for class weighting
  88. %% 3) One full nested CV run (pooled held-out metrics + selection) --------
  90. res = run_one_cv(Xmat, y, Kouter, Kinner, AlphaGrid, NumLambda, useWeights);
  92. fprintf('\n=== Single nested-CV run (pooled outer folds) ===\n');
  93. fprintf('Overall AUC: %.3f\n', res.AUC);
  94. fprintf('PR-AUC: %.3f\n', res.PRAUC);
  95. fprintf('Accuracy: %.3f, Balanced accuracy: %.3f\n', res.accuracy, res.bal_accuracy);
  96. fprintf('Brier score: %.4f\n', res.Brier);
  98. selFreq = res.selFreq(:);
  99. tblSel = table(predictorNames(:), selFreq, ...
  100. 'VariableNames', {'Predictor','SelectionFreq'});
  101. disp(tblSel);
  103. %% 4) Repeated outer CV for CIs and stability -----------------------------
  105. nRepeats = 50; % increase for tighter CIs
  106. metrics = zeros(nRepeats,5); % [AUC PRAUC acc balacc Brier]
  107. selMat = zeros(nRepeats, numel(predictorNames));
  109. rng(7,'twister');
  110. for r = 1:nRepeats
  111. rr = run_one_cv(Xmat, y, Kouter, Kinner, AlphaGrid, NumLambda, useWeights);
  112. metrics(r,:) = [rr.AUC, rr.PRAUC, rr.accuracy, rr.bal_accuracy, rr.Brier];
  113. selMat(r,:) = rr.selFreq(:)'; % selection freq for this run
  114. end
  116. avg = mean(metrics,1);
  117. ci = prctile(metrics,[2.5 97.5],1); % 95% CI
  119. fprintf('\n=== Repeated nested-CV (n=%d repeats) ===\n', nRepeats);
  120. fprintf('AUC mean=%.3f [%.3f, %.3f]\n', avg(1), ci(1,1), ci(2,1));
  121. fprintf('PR-AUC mean=%.3f [%.3f, %.3f]\n',avg(2), ci(1,2), ci(2,2));
  122. fprintf('Acc mean=%.3f BalAcc mean=%.3f Brier mean=%.3f\n', ...
  123. avg(3), avg(4), avg(5));
  125. meanSel = mean(selMat,1)'; % average selection frequency across repeats
  126. selTable = table(predictorNames(:), meanSel, ...
  127. 'VariableNames', {'Predictor','MeanSelFreq'});
  128. selTable = sortrows(selTable, 'MeanSelFreq', 'descend');
  129. disp(selTable);
  131. %% 5) Descriptive penalized refit on ≥50% selection frequency ------------
  132. % Outcome coding reminder:
  133. % y = 1 -> fatigued (MS_F)
  134. % y = 0 -> non-fatigued (MS_NF)
  135. % So OR < 1 corresponds to lower odds of fatigue (potentially "protective").
  137. % Choose selection vector:
  138. % keep single-run frequencies if you wan

nested_lasso_ms_fatigue.m at commit a70acd9, under MIT · at the source

Overview

Authors: Elisa Tatti1, Alberto Benelli2, Alessandra Cinti1,2, Rosa Cortese3, Anna Serbina1, Anna J Kulapurathazhe1, Sophia Saed1, Ludovico Luchetti3,4, Javier Cudeiro5, Jian Zhang6, Anna de Mauro3, Francesco Neri2,7, Maria Laura Stromillo3, Tommaso Lisini Baldi8, Nicole d'Aurizio8, Marco Battaglini3,4, Domenico Plantone3, Delia Righi3, Elisa Massucco2, Alessandro Giannotta2
and 7 other authorsFrancesco Lomi2, Adriano Scoccia2, Giuseppe Lai9, Monica Ulivelli3, Maria Felice Ghilardi1, Nicola De Stefano3, Simone Rossi2,7
ORCID iDs: Elisa Tatti, Alberto Benelli, Rosa Cortese, Jian Zhang, Francesco Neri, Nicole d'Aurizio, Domenico Plantone, Giuseppe Lai, Nicola De Stefano, Simone Rossi
  1. Department of Molecular, Cellular and Biomedical Sciences, City University of NewYork, School of Medicine, New York, NY 10031, United States
  2. Department of Medicine, Surgery and Neuroscience, Siena Brain Investigation and Neuromodulation Lab (Si-BIN Lab), Unit of Neurology and Clinical Neurophysiology, University of Siena, Siena 53100, Italy
  3. Department of Medicine, Surgery and Neuroscience, University of Siena, Siena 53100, Italy
  4. Siena Imaging SRL, Siena 53100, Italy
  5. Department of Physiotherapy, Medicine and Biomedical Sciences, NEUROcom (Neuroscience and Motor Control Group), Universidade da Coruña, Biomedical Institute of A Coruña (INIBIC), and Galician Brain Stimulation Center, A Coruña 15006, Spain
  6. Department of Mental Health, The First Affiliated Hospital, Guangxi Medical University, Nanning 530021, China
  7. Oto-Neuro-Tech Conjoined Lab, Policlinico Le Scotte, University of Siena, Siena 53100, Italy
  8. Department of Information Engineering and Mathematics, University of Siena, Siena 53100, Italy
  9. Goldsmiths, University of London, London SE14 6NW, UK
Institutions: City University of New York (United States); University of Siena (Italy); Universidade da Coruña (Spain); Instituto de Investigación Biomédica de A Coruña (Spain); Guangxi Medical University (China); First Affiliated Hospital of GuangXi Medical University (China); Goldsmiths University of London (United Kingdom)
Journal: Brain communications, volume 8, issue 2, article fcag043
Dates: received 23 June 2025; accepted 9 March 2026; published online 12 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag043 · PMID 41835133 · PMCID PMC12980577 · OpenAlex W7135177296
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), EEG (modality), human (organism), multiple sclerosis (population)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging, Physiology & signal measures
Keywords: fatigue, EEG, beta oscillations, multiple sclerosis, beta ERD/ERS
Topic: Multiple Sclerosis Research Studies (Pathology and Forensic Medicine, Medicine), according to OpenAlex
Funding: Fondazione Italiana Sclerosi Multipla; Neurophysiological and Structural Hallmarks to Non-Invasive Brain Stimulation Treatment
Citations: cited by 1 paper (Europe PMC); 106 references in the paper

Abstract

Fatigue is one of the most disabling symptoms of multiple sclerosis (MS), yet its neurobiology remains unclear, and there are no objective biomarkers. Previous studies using electroencephalography alone have revealed altered movement-related beta Event-Related Desynchronization (ERD) and Synchronization (ERS) dynamics in fatigued patients, but without providing mechanistic insight. In this cross-sectional study, we combined electroencephalography with transcranial magnetic stimulation, structural magnetic resonance imaging with diffusion tensor imaging (DTI), and clinical measures to probe the mechanistic basis of movement-related beta modulation depth (ERS-ERD) and its link with fatigue. Based on the Fatigue Severity Scale score (FSS), we enrolled 41 relapsing-remitting MS patients, 19 with clinically significant fatigue, 22 without (aged 25–55 years, 25 females), alongside 18 age- and sex-matched healthy volunteers. Participants underwent neuropsychological assessment, blood sampling for inflammatory and neurodegeneration-related biomarkers, structural magnetic resonance imaging with DTI to assess grey and white matter integrity, transcranial magnetic stimulation to quantify cortical excitatory and inhibitory balance, and continuous electroencephalography during 300 cued pinch movements to characterize movement-related beta dynamics. Compared with non-fatigued patients and healthy volunteers, fatigued patients exhibited reduced beta peak ERD to ERS modulation (P < 0.001), especially in frontal regions. The modulation depth correlated with fatigue severity (ρ = −0.54, P = 0.0006), intracortical facilitation (ρ = 0.49, P = 0.0009), and caudate nucleus volume (ρ = 0.35, P = 0.010). A nested elastic-net logistic regression integrating demographic, clinical, structural, and functional markers showed robust held-out performance (mean Receiver Operating Characteristic-Area Under the Curve = 0.92, Precision Recall-Area Under the Curve = 0.83, accuracy = 0.89, Brier score = 0.10). Variables with the strongest protective association with fatigue were higher intracortical facilitation, better mental health, larger caudate volume, greater beta modulation over the frontal regions, and higher corticospinal tract and superior longitudinal fasciculus integrity. These findings support frontal beta modulation as a mechanistically grounded, non-invasive biomarker of central fatigue in multiple sclerosis and highlight its potential utility for clinical diagnosis and targeted therapeutic intervention.

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 8 matches between paragraphs and lines of code.

ElisaT-Neuro/MS_beta_fatigue

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: a70acd9d4786e9906e2aaa0f328c48b241e3b308, 2 December 2025
Languages: MATLAB (6)
Size: 8 files, 6 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (4 files), FieldTrip (3 files), EEGLAB (2 files), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
8 files
At the source: github.com/ElisaT-Neuro/MS_beta_fatigue

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;
  • 6 scripts, each with its path and the digest of its content;
  • 8 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

Due to privacy and consent restrictions, the individual-level EEG and clinical datasets are not publicly available but can be shared in anonymized form upon request to the corresponding authors and subject to institutional approvals. The MATLAB scripts used for data analysis in this study are deposited in a public GitHub repository (https://github.com/ElisaT-Neuro/MS_beta_fatigue).

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 27 authors, 5 keywords, 2 funders, 104 references.

Cite

This paper

Tatti, E., Benelli, A., Cinti, A., Cortese, R., Serbina, A., Kulapurathazhe, A. J., Saed, S., Luchetti, L., Cudeiro, J., Zhang, J., de Mauro, A., Neri, F., Stromillo, M. L., Baldi, T. L., d'Aurizio, N., Battaglini, M., Plantone, D., Righi, D., Massucco, E., . . . Rossi, S. (2026). Linking movement-related beta oscillations to cortical excitability, structural damage, and fatigue in multiple sclerosis. Brain communications, 8(2), fcag043. https://doi.org/10.1093/braincomms/fcag043

BibTeX

@article{tatti2026linking,
author = {Tatti, Elisa and Benelli, Alberto and Cinti, Alessandra and Cortese, Rosa and Serbina, Anna and Kulapurathazhe, Anna J and Saed, Sophia and Luchetti, Ludovico and Cudeiro, Javier and Zhang, Jian and de Mauro, Anna and Neri, Francesco and Stromillo, Maria Laura and Baldi, Tommaso Lisini and d'Aurizio, Nicole and Battaglini, Marco and Plantone, Domenico and Righi, Delia and Massucco, Elisa and Giannotta, Alessandro and Lomi, Francesco and Scoccia, Adriano and Lai, Giuseppe and Ulivelli, Monica and Ghilardi, Maria Felice and De Stefano, Nicola and Rossi, Simone},
title = {{Linking movement-related beta oscillations to cortical excitability, structural damage, and fatigue in multiple sclerosis}},
journal = {Brain communications},
year = {2026},
month = mar,
volume = {8},
number = {2},
pages = {fcag043},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag043},
url = {https://doi.org/10.1093/braincomms/fcag043},
pmid = {41835133},
pmcid = {PMC12980577}
}

RIS

TY - JOUR
AU - Tatti, Elisa
AU - Benelli, Alberto
AU - Cinti, Alessandra
AU - Cortese, Rosa
AU - Serbina, Anna
AU - Kulapurathazhe, Anna J
AU - Saed, Sophia
AU - Luchetti, Ludovico
AU - Cudeiro, Javier
AU - Zhang, Jian
AU - de Mauro, Anna
AU - Neri, Francesco
AU - Stromillo, Maria Laura
AU - Baldi, Tommaso Lisini
AU - d'Aurizio, Nicole
AU - Battaglini, Marco
AU - Plantone, Domenico
AU - Righi, Delia
AU - Massucco, Elisa
AU - Giannotta, Alessandro
AU - Lomi, Francesco
AU - Scoccia, Adriano
AU - Lai, Giuseppe
AU - Ulivelli, Monica
AU - Ghilardi, Maria Felice
AU - De Stefano, Nicola
AU - Rossi, Simone
TI - Linking movement-related beta oscillations to cortical excitability, structural damage, and fatigue in multiple sclerosis
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/03/12
VL - 8
IS - 2
SP - fcag043
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag043
UR - https://doi.org/10.1093/braincomms/fcag043
LA - en
ER -

CSL-JSON

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