Behaviourally driven closed-loop beta-tACS enhances beta activity and motor behaviour.
The 7 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
- [1] § Results › Behaviourally triggered beta tACS enhances post-movement beta ERS but not ERD ↔ code.zip/analyse_movement_beta_ERD.m, lines 101–126 · score 0.86 · pre movement baseline, 0–1.5 s, beta band, beta ERD, beta power, window
- [2] § Methods › Statistical analyses ↔ code.zip/linear_mixed_models.R, lines 19–86 · score 0.75 · linear mixed, lme4, post hoc, pairwise, LMMs, models
- [3] § Methods › EEG recording and analysis ↔ code.zip/plot_movement_beta_ERD.m, lines 15–39 · score 0.75 · 0–1.5 s, 15–30 Hz, Beta ERD, 2.5 s, 15 Hz, windows
- [4] § Methods › EEG recording and analysis ↔ code.zip/plot_postmovement_beta_ERS.m, lines 16–48 · score 0.67 · 15–30 Hz, early stimulation, late stimulation, Beta ERS, 1.5 s, 15 Hz
- [5] § Results › Behaviourally triggered beta tACS enhances post-movement beta ERS but not ERD ↔ code.zip/plot_movement_beta_ERD.m, lines 97–110 · score 0.62 · 0–1.5 s, beta ERD, window, power, movement
- [6] § Methods › Two-state model fitting ↔ code.zip/simulate_two_state_model.m, the whole file · a weak match · score 0.60 · state model, motor adaptation, error, fast
- [7] § Results › Beta tACS increases retention of a visuomotor adaptation task ↔ code.zip/simulate_two_state_model.m, the whole file · a weak match · score 0.57 · state model, motor adaptation, simulated, Af, Bf, fast
Paper
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The authors' code
MATLAB · 168 lines · 5.3 KB · no license · 2 matches
- %% Plot movement-related beta ERD time-frequency responses
- %
- % Input:
- % group_ERD_ERS_data.mat
- %
- % Dependency:
- % EEGLAB (topoplot)
- %
- % Outputs:
- % FigS1.pdf
- % FigS1.png
- clc; clear; close all;
- %% Load group data
- scriptDir = fileparts(mfilename('fullpath'));
- repoDir = fileparts(scriptDir);
- dataDir = fullfile(repoDir, 'data');
- outputDir = fullfile(repoDir, 'outputs');
- if ~exist(outputDir, 'dir')
- mkdir(outputDir);
- end
- inputFile = fullfile(dataDir, 'group_ERD_ERS_data.mat');
- load(inputFile, 'ERD_data_all', 'all_movTime', 'conditions_new_name');
- assert(exist('topoplot', 'file') == 2, ...
- 'EEGLAB is required. Add the EEGLAB folder to the MATLAB path.');
- roiChannels = {'C1', 'C5', 'CP1', 'CP3', 'CP5', 'P1', 'P3', 'P5'};
- channelLabels = {ERD_data_all{1}.chns.labels};
- roiIdx = find(ismember(channelLabels, roiChannels));
- betaRange = [15, 30];
- erdWindowMs = [0, 1500];
- timeLimits = [-0.5, 2.5];
- colorLimitsTfr = [-2.5, 2.5];
- colorLimitsTopo = [-1, 1];
- densityTime = linspace(timeLimits(1), timeLimits(2), 200);
- %% Create the movement-related beta ERD overview
- fig = figure('Color', 'w', 'Units', 'centimeters', ...
- 'Position', [1, 1, 18, 12]);
- tiledlayout(fig, 3, 3, 'TileSpacing', 'compact', 'Padding', 'compact');
- tfrSource = cell(3, 1);
- movementSource = cell(3, 1);
- topoSource = cell(3, 1);
- for condIdx = 1:3
- condition = string(conditions_new_name{condIdx});
- erd = ERD_data_all{condIdx};
- groupTfr = mean(erd.data, 4, 'omitnan');
- timeSeconds = erd.time ./ 1000;
- %% Row 1: time-frequency response
- nexttile(condIdx);
- roiTfr = squeeze(mean(groupTfr(roiIdx, :, :), 1, 'omitnan'));
- contourf(timeSeconds, erd.freq, roiTfr, 40, 'LineColor', 'none');
- colormap(gca, redBlueMap(100));
- colorbarWithLabel('Power (dB)');
- clim(colorLimitsTfr);
- set(gca, 'YDir', 'normal', 'XLim', timeLimits, 'FontSize', 8);
- yticks(10:10:40);
- xline(0, 'k:', 'LineWidth', 0.5);
- rectangle('Position', [erdWindowMs(1) / 1000, betaRange(1), ...
- diff(erdWindowMs) / 1000, diff(betaRange)], ...
- 'EdgeColor', 'k', 'LineStyle', ':', 'LineWidth', 0.5);
- xlabel('Time (s)');
- ylabel('Frequency (Hz)');
- title(condition);
- [timeGrid, freqGrid] = meshgrid(timeSeconds, erd.freq);
- nTfr = numel(roiTfr);
- tfrSource{condIdx} = table( ...
- repmat(condition, nTfr, 1), timeGrid(:), freqGrid(:), roiTfr(:), ...
- 'VariableNames', {'Condition', 'Time_s', 'Frequency_Hz', 'Power_dB'});
- %% Row 2: movement-time distribution
- nexttile(condIdx + 3);
- movementTime = all_movTime{condIdx}(:);
- density = ksdensity(movementTime, densityTime, 'Bandwidth', 0.1);
- density = density ./ trapz(densityTime, density);
- plot(densityTime, density, 'Color', [223, 101, 176] ./ 255, ...
- 'LineWidth', 1);
- xline(0, 'k--', 'LineWidth', 0.5);
- xlim(timeLimits);
- ylim([0, max(density)]);
- xlabel('Time (s)');
- ylabel('Distribution');
- subtitle('Movement window');
- movementSource{condIdx} = table( ...
- repmat(condition, numel(movementTime), 1), movementTime, ...
- 'VariableNames', {'Condition', 'MovementTime_s'});
- %% Row 3: beta ERD scalp topography
- nexttile(condIdx + 6);
- betaIdx = erd.freq >= betaRange(1) & erd.freq <= betaRange(2);
- timeIdx = erd.time >= erdWindowMs(1) & erd.time <= erdWindowMs(2);
- topoPower = squeeze(mean(groupTfr(:, betaIdx, timeIdx), [2, 3], ...
- 'omitnan'));
- plotTopoPanel(topoPower, erd.chns, roiChannels, colorLimitsTopo);
- subtitle('0-1.5 s');
- topoSource{condIdx} = table( ...
- repmat(condition, numel(channelLabels), 1), ...
- string(channelLabels(:)), topoPower(:), ...
- 'VariableNames', {'Condition', 'Channel', 'Power_dB'});
- end
- %% Save figure and source data
- exportgraphics(fig, fullfile(outputDir, 'FigS1.pdf'), ...
- 'ContentType', 'vector', 'BackgroundColor', 'white');
- exportgraphics(fig, fullfile(outputDir, 'FigS1.png'), ...
- 'Resolution', 300, 'BackgroundColor', 'white');
- %% Local plotting functions
- function plotTopoPanel(power, chanlocs, roiChannels, mapLimits)
- labels = {chanlocs.labels};
- roiIdx = find(ismember(labels, roiChannels));
- stimIdx = find(ismember(labels, {'C3', 'Pz'}));
- topoplot(power, chanlocs, ...
- 'electrodes', 'on', ...
- 'emarker2', {roiIdx, 'o', 'k', 3, 1}, ...
- 'maplimits', mapLimits, ...
- 'style', 'map', ...
- 'numcontour', 5, ...
- 'shrink', 'on', ...
- 'colormap', redBlueMap(100));
- hold on;
- theta = deg2rad([chanlocs(stimIdx).theta]);
- radius = [chanlocs(stimIdx).radius];
- [x, y] = pol2cart(theta, radius);
- plot(x, y, 'wo', 'MarkerFaceColor', 'w', ...
- 'MarkerEdgeColor', 'k', 'MarkerSize', 5, 'LineWidth', 0.5);
- colorbarWithLabel('Power (dB)');
- clim(mapLimits);
- end
- function colorbarWithLabel(labelText)
- cbar = colorbar;
- cbar.Label.String = labelText;
- cbar.Label.FontSize = 10;
- cbar.Label.FontWeight = 'normal';
- cbar.TickDirection = 'out';
- cbar.FontSize = 8;
- end
- function cmap = redBlueMap(n)
- anchors = [
- 0.02, 0.19, 0.38
- 0.13, 0.40, 0.67
- 0.42, 0.68, 0.84
- 0.82, 0.90, 0.94
- 1.00, 1.00, 1.00
- 0.99, 0.86, 0.78
- 0.96, 0.65, 0.51
- 0.84, 0.24, 0.22
- 0.40, 0.00, 0.12
- ];
- x = linspace(0, 1, size(anchors, 1));
- cmap = interp1(x, anchors, linspace(0, 1, n), 'linear');
- end
plot_movement_beta_ERD.m, no license · at the source
Overview
- Oxford Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
- Department of Physical Therapy and Assistive Technology, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Department of Experimental Psychology, University of Oxford, Oxford, UK
Abstract
Movement-related beta event-related synchronization (ERS) has been linked to motor control and learning, showing potential as a therapeutic target for those with movement deficits, such as stroke survivors. However, whether directly modulating beta ERS can causally influence motor performance remains unclear, largely due to the lack of methods designed to specifically target this neural activity. To address this gap, we developed a behaviourally-driven, closed-loop transcranial alternating current stimulation (tACS) approach to target movement-related beta ERS during a visuomotor adaptation task. We found that the behaviourally-driven, closed-loop beta-tACS specifically enhances beta ERS without affecting beta event-related desynchronization (ERD). Critically, this targeted enhancement significantly improves retention of motor adaptation. These findings support a functional role of beta ERS in motor behaviour and suggest that behaviourally-driven beta-tACS may provide a promising approach to further test the mechanistic links between beta ERS and behavioural outcomes in clinical populations.
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.
OSF r6phw
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
- 27 September 2026: the link answers (HTTP 200)
7 files
- code.zip/
analyse_movement_beta_ER , MATLAB, 352 lines, 1 matchD.m - code.zip/
analyse_postmovement_bet , MATLAB, 340 linesa_ERS.m - code.zip/
linear_mixed_models.R , R, 88 lines, 1 match - code.zip/
plot_example_movement_pa , MATLAB, 111 linesths.m - code.zip/
plot_movement_beta_ERD.m , MATLAB, 168 lines, 2 matches - code.zip/
plot_postmovement_beta_E , MATLAB, 203 lines, 1 matchRS.m - code.zip/
simulate_two_state_model , MATLAB, 30 lines, 2 matches.m
Code availability
The code used to generate the figures and perform the reported analyses is available on OSF at https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Tracing map
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Data
No dataset and no data link were found in the paper.
Data availability
The EEG data supporting the findings of this study have been deposited on OSF at https://
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, 11 authors, 2 keywords, 8 MeSH terms, 4 funders, 53 references.
Cite
This paper
Wu, M., Xu, Z., Fleming, M. K., Shackle, N., Biller, L., Tabone, F., Wong, P.-L., Nettekoven, C., Sharott, A., Zich, C., & Stagg, C. J. (2026). Behaviourally driven closed-loop beta-tACS enhances beta activity and motor behaviour. Nature communications, 17(1), 8908. https://
BibTeX
@article{wu2026behaviour
author = {Wu, Min and Xu, Zeyu and Fleming, Melanie K and Shackle, Nicholas and Biller, Lara and Tabone, Faye and Wong, Pei-Ling and Nettekoven, Caroline and Sharott, Andrew and Zich, Catharina and Stagg, Charlotte J},
title = {{Behaviourally driven closed-loop beta-tACS enhances beta activity and motor behaviour}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {8908},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/
url = {https://
pmid = {42481501},
pmcid = {PMC13503910}
}
RIS
TY - JOUR
AU - Wu, Min
AU - Xu, Zeyu
AU - Fleming, Melanie K
AU - Shackle, Nicholas
AU - Biller, Lara
AU - Tabone, Faye
AU - Wong, Pei-Ling
AU - Nettekoven, Caroline
AU - Sharott, Andrew
AU - Zich, Catharina
AU - Stagg, Charlotte J
TI - Behaviourally driven closed-loop beta-tACS enhances beta activity and motor behaviour
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/
VL - 17
IS - 1
SP - 8908
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/
UR - https://
LA - en
ER -
CSL-JSON
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"container-title": "Nature communications",
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