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Reduced pre-movement subthalamic beta desynchronization marks motor deficit in Parkinson's disease.

Code ↔ Paper

6 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 6 matches · all 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 ↔ RestingBeta_Analysis.m, the whole file · a weak match · score 0.75 · Pairwise comparisons, linear mixed, resting beta power, Bonferroni corrections, models, coefficients
  2. [2] § Materials and methods › Data acquisition and analysis › Kinematic data ↔ Manual_MovOnset_Detection.m, the whole file · a weak match · score 0.70 · movement onset detection, semi automated, threshold, angular, peak, slope
  3. [3] § Materials and methods › Data acquisition and analysis › Neural data ↔ BetaDesyncAnalysis_S2S_S2W.m, the whole file · a weak match · score 0.69 · Power spectral density, pre movement initiation, movement onset, Welch, 13 Hz, 30 Hz
  4. [4] § Materials and methods › Data acquisition and analysis › Neural data ↔ BetaDesyncAnalysis_rWFE.m, the whole file · a weak match · score 0.69 · Power spectral density, pre movement initiation, movement onset, Welch, 13 Hz, 30 Hz
  5. [5] § Results › Association between pre-movement subthalamic beta desynchronization and severity of motor impairment ↔ BetaDesyncAnalysis_rWFE.m, the whole file · a weak match · score 0.65 · Repetitive wrist flexion, confidence interval, pre movement initiation, rWFE, Beta desynchronization, STN
  6. [6] § Results › Association between pre-movement subthalamic beta desynchronization and severity of motor impairment ↔ RestingBeta_Analysis.m, the whole file · a weak match · score 0.60 · Repetitive wrist flexion, linear mixed, resting beta power, UPDRS III, model, score

Paper

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The authors' code

MATLAB · 140 lines · 5.3 KB · MIT · 2 matches

  1. %% Resting beta analysis & ploting (for all Sit-to-Stand, Stand-to-Walk, and repetitive wrist flexion/extension)
  3. dataDir='C:\Users\seog\Desktop\Data';
  4. datafile={'beta_updrs3_all'};% Master file containing beta power values and other scores (UPDRS III scores and Acceleration Index) for all participants
  5. tasknames={'Sit2Stand','Stand2Walk','WFE'};
  6. allRestBetaValues = [];
  7. allRestTaskLabels = [];
  9. for t=1:length(tasknames)
  10. fname=strcat(datafile, '_', tasknames{t});
  11. datapath= fullfile(dataDir,fname);
  12. mainData{t}=readtable(datapath{1});
  13. subnumuniq{t}=unique(mainData{t}.PatientNumber);
  14. end
  16. rest_avg=nan([1,16]);
  17. rest_std=nan([1,16]);
  19. for s=1:16
  20. restingBtemp=[];
  21. for t2=1:length(tasknames)
  22. if ismember(s,subnumuniq{t2})
  23. restingBtemp=[restingBtemp; mainData{t2}.RestBeta(mainData{t2}.PatientNumber==s)];
  24. end
  25. end
  26. if ~isempty(restingBtemp)
  27. rest_avg(s)=mean(restingBtemp);
  28. rest_std(s)=std(restingBtemp);
  29. end
  30. end
  32. for t=1:length(tasknames)
  33. for s=1:length(subnumuniq{t})
  34. subtemp=subnumuniq{t}(s);
  35. mainData{t}.RestBeta(mainData{t}.PatientNumber==subtemp)=(mainData{t}.RestBeta(mainData{t}.PatientNumber==subtemp)-rest_avg(subtemp))/rest_std(subtemp);
  36. end
  37. allRestBetaValues = [allRestBetaValues; mainData{t}.RestBeta];
  38. allRestTaskLabels = [allRestTaskLabels; repmat({tasknames{t}}, height(mainData{t}), 1)];
  39. end
  41. taskColors = [0.1588, 0.1451, 0.2275; 0.7647, 0.7490, 0.6000; 0.2980, 0.5137, 0.4784];
  42. taskColors2 = [0.2980, 0.5137, 0.4784 ;0.7647, 0.7490, 0.6000;0.1588, 0.1451, 0.2275];
  44. figure;
  45. boxplot(allRestBetaValues, allRestTaskLabels, 'Notch', 'off', 'Labels', tasknames, 'Widths', 0.5, 'Symbol', '');
  46. hold on;
  48. % Overlay scatter plot with random jitter
  49. numTasks = length(tasknames);
  50. for t = 1:numTasks
  51. taskData = allRestBetaValues(strcmp(allRestTaskLabels, tasknames{t}));
  52. jitter = (rand(size(taskData)) - 0.5) * 0.2; % Random jitter within [-0.1, 0.1]
  53. scatter(repmat(t, size(taskData)) + jitter, taskData, 36, 'filled', 'MarkerFaceColor', taskColors(t, :), 'MarkerFaceAlpha', 0.5);
  54. end
  56. % Customize box plot appearance
  57. h = findobj(gca, 'Tag', 'Box');
  58. for j = 1:length(h)
  59. patch(get(h(j), 'XData'), get(h(j), 'YData'), taskColors2(j, :), 'FaceAlpha', 0.2, 'EdgeColor', taskColors2(j, :), 'LineWidth', 2);
  60. end
  62. % Customize other box plot elements
  63. set(findobj(gca, 'Tag', 'Median'), 'Color', 'k', 'LineWidth', 2);
  64. set(findobj(gca, 'Tag', 'Whisker'), 'LineStyle', '-', 'LineWidth', 2, 'Color', 'k');
  65. set(findobj(gca, 'Tag', 'Cap'), 'LineWidth', 2, 'Color', 'k');
  66. set(findobj(gca, 'Tag', 'Outliers'), 'MarkerEdgeColor', 'k', 'MarkerFaceColor', 'k');
  68. % Customize max and min lines (whiskers)
  69. hWhiskers = findobj(gca, 'Tag', 'Whisker');
  70. for j = 1:length(hWhiskers)
  71. set(hWhiskers(j), 'LineWidth', 2);
  72. end
  74. set(gca, 'LineWidth', 1.5, 'FontSize', 14, 'FontWeight', 'bold');
  75. ylabel('Beta Power (normalized)');
  76. box off;
  77. hold off;
  79. % Calculate and display mean and standard deviation for each task
  80. disp('Mean and Standard Deviation for each task:');
  81. for t = 1:numTasks
  82. taskData = allRestBetaValues(strcmp(allRestTaskLabels, tasknames{t}));
  83. meanValue = mean(taskData);
  84. stdValue = std(taskData);
  85. fprintf('%s: Mean = %.2f, Std = %.2f\n', tasknames{t}, meanValue, stdValue);
  86. end
  89. % Prepare data table for LMEM analysis (resting beta)
  90. restDataTable = table(allRestBetaValues, categorical(allRestTaskLabels), ...
  91. 'VariableNames', {'RestBeta', 'Task'});
  93. % Add Participant IDs to the data table
  94. ParticipantIDs = [];
  95. for t = 1:length(tasknames)
  96. ParticipantIDs = [ParticipantIDs; mainData{t}.PatientNumber];
  97. end
  98. restDataTable.PatientNumber = categorical(ParticipantIDs);
  100. % Fit Linear Mixed-Effects Model (LMEM) for Resting Beta Power
  101. restLME = fitlme(restDataTable, 'RestBeta ~ Task + (1|PatientNumber)');
  102. disp(restLME);
  104. % Display fixed effects coefficients and statistics
  105. fixedEffectsResults = dataset2table(restLME.Coefficients);
  106. disp('Fixed Effects Coefficients (Resting Beta Power):');
  107. disp(fixedEffectsResults);
  109. % Perform pairwise comparisons between tasks using contrasts
  110. disp('Pairwise comparisons (Resting Beta Power, Bonferroni corrected):');
  112. % Sit2Stand vs Stand2Walk
  113. [p1,~,DF1] = coefTest(restLME, [0 1 0]);
  114. beta1 = fixedEffectsResults.Estimate(2);
  115. SE1 = fixedEffectsResults.SE(2);
  116. CI1 = [beta1 - 1.96*SE1, beta1 + 1.96*SE1];
  117. t1 = beta1 / SE1;
  118. p1_corr = min(p1 * 3, 1);
  119. fprintf('Sit2Stand vs Stand2Walk: β = %.3f [%.3f, %.3f], t(%d) = %.2f, corrected p = %.4f\n', ...
  120. beta1, CI1(1), CI1(2), DF1, t1, p1_corr);
  122. % Sit2Stand vs WFE
  123. [p2,~,DF2] = coefTest(restLME, [0 0 1]);
  124. beta2 = fixedEffectsResults.Estimate(3);
  125. SE2 = fixedEffectsResults.SE(3);
  126. CI2 = [beta2 - 1.96*SE2, beta2 + 1.96*SE2];
  127. t2 = beta2 / SE2;
  128. p2_corr = min(p2 * 3, 1);
  129. fprintf('Sit2Stand vs WFE: β = %.3f [%.3f, %.3f], t(%d) = %.2f, corrected p = %.4f\n', ...
  130. beta2, CI2(1), CI2(2), DF2, t2, p2_corr);
  132. % Stand2Walk vs WFE
  133. [p3,~,DF3] = coefTest(restLME, [0 1 -1]);
  134. beta3 = beta1 - beta2;
  135. SE3 = sqrt(SE1^2 + SE2^2);
  136. CI3 = [beta3 - 1.96*SE3, beta3 + 1.96*SE3];
  137. t3 = beta3 / SE3;
  138. p3_corr = min(p3 * 3, 1);
  139. fprintf('Stand2Walk vs WFE: β = %.3f [%.3f, %.3f], t(%d) = %.2f, corrected p = %.4f\n', ...
  140. beta3, CI3(1), CI3(2), DF3, t3, p3_corr);

RestingBeta_Analysis.m at commit c473109, under MIT · at the source

Overview

Authors: Gang Seo1, Kevin B Wilkins1, Helen M Bronte-Stewart1,2
ORCID iDs: Kevin B Wilkins
  1. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
  2. Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA
Institutions: Stanford Medicine (United States); Stanford University (United States)
Journal: Brain communications, volume 8, issue 4, article fcag245
Dates: received 9 September 2025; accepted 3 May 2026; published online 25 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag245 · PMID 42422531 · PMCID PMC13343376 · OpenAlex W7165963157
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: intracranial EEG (iEEG / ECoG / SEEG) (modality), human (organism), Parkinson's (population), systems (subfield)
Methods: Spectral & time-frequency, Statistics, Preprocessing, Evoked potentials
Keywords: electrophysiology, motor control, movement disorder, intracranial recording, parkinsonian
Topic: Neurological disorders and treatments (Neurology, Medicine), according to OpenAlex
Funding: NINDS NIH HHS (UH3 NS107709, R21 NS096398, UH3 NS128150); Medtronic Inc; Robert and Ruth Halperin Foundation; NINDS (R21NS096398, UH3NS107709, UH3NS128150); John A. Blume Foundation; National Institute of Neurological Disorders and Stroke; John E. Cahill Family Foundation; Michael J Fox Foundation (9605)
Citations: not cited yet (Europe PMC); 47 references in the paper

Abstract

Abnormal beta-band (13–30 Hz) oscillations in the subthalamic nucleus are a well-established biomarker of motor dysfunction in Parkinson's disease. While most prior work has focused on beta activity during rest or sustained movement, far less is known about its transient dynamics during movement preparation—a critical phase in which suppression of beta-band activity (beta desynchronization) is thought to facilitate motor circuit readiness. Here, we delineate subthalamic beta-band activity specifically in the pre-movement initiation window across diverse motor tasks and demonstrate its association with both clinical motor impairment severity and subsequent movement acceleration. We recorded subthalamic nucleus local field potentials and kinematics in sixteen individuals with Parkinson's disease (10 males, 6 females; mean age 57.9 ± 10.5 years) implanted with sensing deep brain stimulation systems (Medtronic Activa® PC+S). While off medication and stimulation, participants performed cued motor tasks, including sit-to-stand, stand-to-walk and wrist flexion-extension. Pre-movement beta desynchronization, quantified as beta power normalized to resting baseline, was extracted and analysed using linear mixed-effects models to determine its relationship with clinical impairment severity and movement acceleration. Across all tasks, we observed robust pre-movement beta desynchronization in the subthalamic nucleus (P < 0.001). Critically, reduced desynchronization was associated with greater motor impairment, particularly bradykinesia (P < 0.001). This association appeared stronger during the more complex stand-to-walk task and was linked to reduced movement acceleration, as measured by acceleration indices (P < 0.05). A strong link between greater pre-movement beta desynchronization, less severe bradykinesia and more vigorous movement suggests that impaired beta modulation reflects disruptions in motor planning, delayed recruitment of motor networks and excessive basal ganglia inhibition. These circuit-level abnormalities likely contribute to the difficulties individuals with Parkinson's disease face in initiating and executing movements, offering valuable insight into the neurophysiological basis of motor dysfunction in Parkinson's disease.

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 6 matches between paragraphs and lines of code.

slowriver322/pMI_BetaDesync_BrainCom

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: c473109c54c2b68cdeba051e334f6dd0f9ae80c9, 19 December 2025
Languages: MATLAB (9)
Size: 11 files, 9 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 (3 files), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
11 files
At the source: github.com/slowriver322/pMI_BetaDesync_BrainCom.git

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;
  • 9 scripts, each with its path and the digest of its content;
  • 6 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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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 data supporting the findings of this study are available from the corresponding author upon reasonable request. Analysis codes are available at the following GitHub repository: https://github.com/slowriver322/pMI_BetaDesync_BrainCom.git

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

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 8 funders, 47 references.

Cite

This paper

Seo, G., Wilkins, K. B., & Bronte-Stewart, H. M. (2026). Reduced pre-movement subthalamic beta desynchronization marks motor deficit in Parkinson's disease. Brain communications, 8(4), fcag245. https://doi.org/10.1093/braincomms/fcag245

BibTeX

@article{seo2026reduced,
author = {Seo, Gang and Wilkins, Kevin B and Bronte-Stewart, Helen M},
title = {{Reduced pre-movement subthalamic beta desynchronization marks motor deficit in Parkinson's disease}},
journal = {Brain communications},
year = {2026},
month = jun,
volume = {8},
number = {4},
pages = {fcag245},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag245},
url = {https://doi.org/10.1093/braincomms/fcag245},
pmid = {42422531},
pmcid = {PMC13343376}
}

RIS

TY - JOUR
AU - Seo, Gang
AU - Wilkins, Kevin B
AU - Bronte-Stewart, Helen M
TI - Reduced pre-movement subthalamic beta desynchronization marks motor deficit in Parkinson's disease
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/06/25
VL - 8
IS - 4
SP - fcag245
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag245
UR - https://doi.org/10.1093/braincomms/fcag245
LA - en
ER -

CSL-JSON

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