β-Bursting as a Sensitive Neural Marker of Inhibitory Control in Healthy Older Adults: A Linear Mixed-Effects Modeling and Threshold-Free Cluster Approach.

Code ↔ Paper

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[1] § Materials and Methods › Recording and preprocessing › Electroencephalography ↔ preprocess.m, the whole file · a weak match · score 0.90 · Hurst exponent, voltage variance, voltage range, blinks, component, runica · Report a mismatch
[2] § Materials and Methods › Experimental design and statistical analysis › LMM ↔ lme_beta_run.m, the whole file · a weak match · score 0.78 · linear regressions, lmeEEG, mass, fits, marginal, intercepts · Report a mismatch
[3] § Materials and Methods › Experimental design and statistical analysis › Muscle bursting ↔ Muscle_Burst_Detection/calcAmplitude.m, the whole file · a weak match · score 0.65 · peak amplitude, CancelTime, partial burst, rectified, SSD, baseline · Report a mismatch
[4] § Materials and Methods › Experimental design and statistical analysis › β-Features ↔ beta_features_extraction.m, lines 244 to 304 · score 0.57 · burst duration, burst volume, datapoints, sum, epochs, bins · Report a mismatch
[5] § Materials and Methods › Experimental design and statistical analysis › β-Features ↔ beta_features_extraction.m, lines 49 to 102 · score 0.56 · Morlet wavelets, 4–10 cycles, 29 Hz, 15, Power · Report a mismatch
[6] § Materials and Methods › Recording and preprocessing › Electromyography ↔ dataimport.m, lines 209 to 268 · score 0.54 · bandpass filtered, EEGLAB, Biosemi, epochs, EMG · Report a mismatch
[7] § Materials and Methods › Experimental design and statistical analysis › Muscle bursting ↔ Muscle_Burst_Detection/FindEMGBursts.m, the whole file · a weak match · score 0.51 · EMG amplitude, window, rectified, SD, baseline, threshold · Report a mismatch

Paper

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The authors' code

MATLAB · 368 lines · 14 KB · no license · 2 matches

function beta_features_extraction(subjname)
% Based on https://osf.io/jfer7
% Edited by Damian Cruse and Aliya Warden 2023
%% import the data
filepath = './Data';
EEG = pop_loadset('filename', sprintf('%s_preproc3.set', subjname), 'filepath', fullfile(filepath,subjname));
%% remove SSDs that are too long
% fix formatting issue first if they're stored as cells within cells (interlinked!)
if iscell(EEG.epoch(1).eventssd)
for e = 1:length(EEG.epoch)
EEG.epoch(e).eventssd = char(EEG.epoch(e).eventssd);
end
end
ssd_lim = .7;
badssds = find(cellfun(@str2num,{EEG.epoch.eventssd})>ssd_lim);
EEG = pop_select(EEG,'notrial',badssds);
%% some parameters
binsize_pnts = 32; % has to be power of 2 so that it fits equally into the epochs
% calc integrated SSRT first
load All_SSRTs.mat;
thispid = find(strcmp(subjname,all_SSRTs(:,1)));
subj_ssrt = all_SSRTs{thispid,2} / 1000;
%% CSD transform
% save the channel locations as .locs
writelocs(EEG.chanlocs(1:128),'Biosemi_128_ABC_noEMG.locs','filetype','loc')
% convert to .ced
chanlabels = {EEG.chanlocs.labels};
ConvertLocations('Biosemi_128_ABC_noEMG.locs');
M = ExtractMontage('Biosemi_128_ABC_noEMG.csd',chanlabels(1:128)');
MapMontage(M);
[G,H] = GetGH(M);
% Delete external channels from current dataset
EEG = pop_select(EEG, 'nochannel', {'EMGL','EMGR'});
dataCSD = single(repmat(NaN,size(EEG.data))); % use single data precision
for ne = 1:length(EEG.epoch) % loop through all epochs
myEEG = single(EEG.data(:,:,ne)); % reduce data precision to reduce memory demand
MyResults = CSD(myEEG,G,H); % compute CSD for <channels-by-samples> 2-D epoch
dataCSD(:,:,ne) = MyResults; % assign data output
end
EEG.data = dataCSD; % Replace data with CSD transformed data
%% Morlet wavlet transformation
% Calculate Morlet Wavelets for all trials
min_freq = 15;
max_freq = 29;
num_frex = 15;
frex = linspace(min_freq,max_freq,num_frex);
% other wavelet parameters
range_cycles = [4 10];
s = logspace(log10(range_cycles(1)),log10(range_cycles(end)),num_frex) ./ (2*pi*frex);
wavtime = -2:1/EEG.srate:2;
half_wave = (length(wavtime)-1)/2;
% FFT parameters
nWave = length(wavtime);
nData = EEG.pnts;
nConv = nWave + nData - 1;
% Loop over channels
channels = chanlabels(1:128);
% initialize output time-frequency data
tf = zeros(length(frex),EEG.pnts,EEG.trials,length(channels));
% loop over frequencies
for fi=1:length(frex)
% create wavelet and get its FFT
% the wavelet doesn't change on each trial...
wavelet = exp(2*1i*pi*frex(fi).*wavtime) .* exp(-wavtime.^2./(2*s(fi)^2));
waveletX = fft(wavelet,nConv);
waveletX = waveletX ./ max(waveletX);
% loop over channels
for ci=1:length(channels)
channel2use = channels{ci};
% now loop over trials...
for triali=1:EEG.trials
dataX = fft(squeeze(EEG.data(strcmpi(channel2use,{EEG.chanlocs.labels}),:,triali)), nConv);
% run convolution
as = ifft(waveletX .* dataX);
as = as(half_wave+1:end-half_wave);
% put power data into big matrix
tf(fi,:,triali,ci) = abs(as).^2;
end
end
end
%% baseline normalise
tf_bslnorm = tf;
% define baseline
baseline_window = [-400 -100];
% convert baseline time into indices
baseidx = reshape( dsearchn(EEG.times',baseline_window(:)), [],2);
%loop over trials
for triali=1:EEG.trials
%loop over channels
for channeli=1:length(channels)
activity = tf_bslnorm(:,:,triali,channeli);
baseline = mean(tf_bslnorm(:,baseidx(1):baseidx(2),triali,channeli),2);
% decibel
tf_bslnorm(:,:,triali,channeli) = 10*log10( bsxfun(@rdivide, activity, baseline) );
end
end
%% trim data around each trial's RT or SSD
% go trials - we lock to fastest RT and look before
% stop trials - we lock to the SSRT (relative to SSD) and look before
timelim = (10*32)/1024; % 312.5ms(ish) = 320 samples = 10 bins of 32 samples each
go_toi_lims = [-timelim 0]; % always this much pre RT; Stop is more complicated
stop_toi_lims = [-timelim 0]; % always this much pre SSD+SSRT
go_bin_times = 1:binsize_pnts:diff(go_toi_lims)*EEG.srate+1;
stop_bin_times = 1:binsize_pnts:diff(stop_toi_lims)*EEG.srate+1;
go_times = go_toi_lims(1):1/EEG.srate:go_toi_lims(2);
stop_times = stop_toi_lims(1):1/EEG.srate:stop_toi_lims(2);
% tf_Epoch = zeros(length(frex),round(diff(toi_lims)*EEG.srate),size(tf,3),length(channels));
tf_Epoch = cell(1,size(tf,3));
tf_Epoch_bslnorm = cell(1,size(tf_bslnorm,3));
% snap ssrt to nearest sample to avoid unequal numbers of datapoints
subj_ssrt = EEG.times(min(find(EEG.times>=subj_ssrt*1000)))/1000;
for t = 1:EEG.trials
switch char(EEG.epoch(t).eventtype)
case 'go'
tle = min([str2num(char(EEG.epoch(t).eventleft_rt)) str2num(char(EEG.epoch(t).eventright_rt))]);
toi_lims = go_toi_lims;
case 'stop'
tle = str2num(char(EEG.epoch(t).eventssd)) + subj_ssrt;
toi_lims = stop_toi_lims;
end
% snap tle to closest sample
tle = EEG.times(min(find(EEG.times>=tle*1000))) / 1000;
% catch missing response trials
if isempty(tle)
tf_Epoch{t} = NaN(size(tf,1),round(diff(toi_lims)*EEG.srate),1,size(tf,4));
tf_Epoch_bslnorm{t} = NaN(size(tf,1),round(diff(toi_lims)*EEG.srate),1,size(tf,4));
else
this_toi = min(find(EEG.times/1000 >= tle+toi_lims(1))):min(find(EEG.times/1000 >= tle+toi_lims(2))-1);
tf_Epoch{t} = tf(:,this_toi,t,:);
tf_Epoch_bslnorm{t} = tf_bslnorm(:,this_toi,t,:);
end
end
%% catch any issues with time-locking events
if length(unique(cell2mat(cellfun(@size,tf_Epoch,'UniformOutput',false)))) ~= 4
error('Issue with number of time-points in tle')
end
%% calculate beta burst stuff
numBursts = cell(size(tf,3),length(channels));
ampBursts = cell(size(tf,3),length(channels));
volBursts = cell(size(tf,3),length(channels));
durBursts = cell(size(tf,3),length(channels));
meanBetaPower = cell(size(tf,3),length(channels));
for trialNr=1:size(tf,3) % Loop over single trials
fprintf('Processing trial %d\n',trialNr);
if sum(isnan(tf_Epoch{trialNr})) > 0
continue;
end
for channelNr=1:length(channels) % Loop over single channels
% Extract the correct trial and channel
tf_Epoch_OneTrial = tf_Epoch{trialNr}(:,:,:,channelNr);
tf_Epoch_OneTrial_bslnorm = tf_Epoch_bslnorm{trialNr}(:,:,:,channelNr);
%% 4. Extract Beta burst rate and amplitude
tf_Epoch_OneTrial_BurstDetection = tf_Epoch_OneTrial;
% Calculate local maxima in the longer time-frequency power matrix
% (this will be a matrix of 0 and 1, where 1 are the local maxima)
LocalMaxima_long = imregionalmax(tf_Epoch_OneTrial_BurstDetection);
% Calculate one threshold for bursts over the whole matrix
Threshold_OneTrial = median(tf_Epoch_OneTrial(:))*2;
% Find the location of local maxima
[LocalMaxima_row, LocalMaxima_col] = find(LocalMaxima_long==1);
% Find local maxima that are smaller than the threshold and set
% those to 0, leaving the local maxima larger than the threshold at 1
for i=1:length(LocalMaxima_row)
if tf_Epoch_OneTrial(LocalMaxima_row(i),LocalMaxima_col(i))<Threshold_OneTrial
LocalMaxima_long(LocalMaxima_row(i),LocalMaxima_col(i))=0;
end
end
% Now we want to count the number of bursts within each bin over
% all frequencies
% Initialise variables
bintimes = 1:binsize_pnts:size(tf_Epoch_OneTrial,2)+1;
tmpnumBursts = [];
tmpampBursts = [];
% Loop over all bins
for i=1:length(bintimes)-1
binStart = bintimes(i);
binEnd = bintimes(i+1)-1;
tmpnumBursts(i)=sum(sum(LocalMaxima_long(:,binStart:binEnd)));
tmpampBursts(i)=mean(nonzeros(LocalMaxima_long(:,binStart:binEnd).*tf_Epoch_OneTrial(:,binStart:binEnd)));
if isnan(tmpampBursts(i))
tmpampBursts(i) = 0;
end
end
% Update big variable
numBursts{trialNr,channelNr} = tmpnumBursts;
ampBursts{trialNr,channelNr} = tmpampBursts;
%% 5. Calculate Beta burst volume and duration
% Now we want to calculate the volume under the bursts in each time
% bin. We will take the sum of the volume of all bursts in one time bin.
% First we need to create a matrix with all datapoints that are
% smaller than the threshold = 0, so that just the power values of
% the bursts are left
tf_Epoch_OneTrial_Burst=tf_Epoch_OneTrial;
tf_Epoch_OneTrial_Burst(tf_Epoch_OneTrial<Threshold_OneTrial)=0;
% Here we extract burst duration
tmpburstduration = double(LocalMaxima_long); % to fill in with duration values
[burst_ix, burst_iy] = find(LocalMaxima_long); % find bursts first
for bi = 1:length(burst_ix)
tmpfreqburst = tf_Epoch_OneTrial_Burst(burst_ix(bi),:); % extract only peak burst freq
tmpbwlabel = bwlabel(tmpfreqburst); % cluster bursts in that frequency
tmpburstduration(burst_ix(bi),burst_iy(bi)) = sum(tmpbwlabel == tmpbwlabel(burst_iy(bi))); % count time-points within that burst at peak freq
end
% Now we need to loop over each frequency in each time bin and
% calculate the AUC of each frequency in each time bin and then sum
% over all frequencies for each time bin. This gives us the volume
% for each time bin. Here we ignore differnt bursts and just add
% up the volume of all bursts in one time bin.
% Re-define start numbers
burstSize=[]; % initialise matrix
tmpdurBurst=[];
nFreqs=size(tf_Epoch_OneTrial_Burst,1); % number of frequencies
% Loop over all bins
for i=1:length(bintimes)-1
binStart = bintimes(i);
binEnd = bintimes(i+1)-1;
% Extract mean burst duration for current bin
tmpdurBurst(i) = mean(nonzeros(tmpburstduration(:,binStart:binEnd))) / EEG.srate;
if isnan(tmpdurBurst(i))
tmpdurBurst(i) = 0;
end
% Extract data of current bin
dataOneBin=tf_Epoch_OneTrial_Burst(:,binStart:binEnd);
oneBinAUC=[]; % initialise matrix for every bin
for freq=1:nFreqs % Loop over all frequencies
dataOneBinOneFreq=dataOneBin(freq,:); % Extract data from current frequency in current time bin
dataOneBinOneFreqBurst=dataOneBinOneFreq(find(dataOneBinOneFreq>0)); % Find datapoints that are above the threshold
dataOneBinOneFreqAUC=trapz(dataOneBinOneFreqBurst); % Calculate AUC for datapoints above the threshold for current frequency
oneBinAUC=[oneBinAUC,dataOneBinOneFreqAUC]; % Vector with all AUCs for current bin
end
sumOneBinAUC=sum(oneBinAUC); % Sum up all AUCs of each frequency for current bin
burstSize=[burstSize,sumOneBinAUC]; % Vector with volume of bursts for each bin
end
% Save to correct variable
volBursts{trialNr,channelNr} = burstSize;
durBursts{trialNr,channelNr} = tmpdurBurst;
%% 6. Calculate Averaged Beta Power
% Average over all frequencies
tf_Epoch_OneTrial_bslnorm_avg = mean(tf_Epoch_OneTrial_bslnorm,1);
% Initialise variables
BetaPower=[]; % initialise matrix
% Loop over all bins
for i=1:length(bintimes)-1
binStart = bintimes(i);
binEnd = bintimes(i+1)-1;
BetaPower(1,i)=mean(tf_Epoch_OneTrial_bslnorm_avg(:,binStart:binEnd),2); % Average over power values from current time bin
end
% Save to correct variable
meanBetaPower{trialNr,channelNr} = BetaPower;
end %End of single channel
end %End of single trial loop
savefilename = sprintf('%s_beta_features.mat',subjname);
save(savefilename,'meanBetaPower','durBursts','volBursts','numBursts','ampBursts','EEG','go_times','go_bin_times','stop_times','stop_bin_times');
% %% plot some stuff for verification
% trials2plot = {'go','stop'};
%
% for t2p = 1:length(trials2plot)
%
% switch trials2plot{t2p}
% case 'go'
% binlims = round(go_times(go_bin_times)*1000);
% case 'stop'
% binlims = round(stop_times(stop_bin_times)*1000);
% end
%
% datlen = length(binlims) - 1;
%
% trials2use = find(strcmp({EEG.event.type},trials2plot{t2p}));
% trialbeta = meanBetaPower(trials2use,:);
% % trialbeta = volBursts(trials2use,:);
% trialbetamat = zeros(size(trialbeta,1),size(trialbeta,2),datlen);
%
% for trialNr = 1:length(trials2use)
% for channelNr = 1:length(channels)
% if isempty(trialbeta{trialNr,channelNr})
% trialbetamat(trialNr,channelNr,:) = nan(1,datlen);
% else
% trialbetamat(trialNr,channelNr,:) = trialbeta{trialNr,channelNr};
% end
% end
% end
%
% figure;title(sprintf('%s trials',trials2plot{t2p}));
% plotlim = max(max(squeeze(nanmean(abs(trialbetamat),1)),[],1));
% for i = 1:datlen
% subplot(2,datlen/2,i);
% topoplot(squeeze(nanmean(trialbetamat(:,:,i),1)),EEG.chanlocs,'maplimits',[-plotlim plotlim])
% title(sprintf('%dms - %dms',binlims(i),(binlims(i+1))));
% end
%
% end

beta_features_extraction.m, no license · at the source

Overview

Authors: Aliya C. M. Warden^1,2, Damian Cruse^2,3, Craig J. McAllister^1,2, Hayley J. MacDonald^1,4

ORCID iDs: Aliya C. M. Warden, Damian Cruse, Craig J. McAllister, Hayley J. MacDonald

School of Sport, Exercise and Rehabilitation Sciences, College of Life and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, United Kingdom
Centre for Human Brain Health, University of Birmingham, Birmingham B15 2TT, United Kingdom
School of Psychology, College of Life and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, United Kingdom
Department of Biological and Medical Psychology, University of Bergen, Bergen 5009, Norway

Institutions: University of Birmingham (United Kingdom); University of Bergen (Norway)

Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience, volume 46, issue 12, article e1151252026

Dates: received 21 June 2025; accepted 17 February 2026; published online 25 March 2026; in print 25 March 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1523/jneurosci.1151-25.2026 · PMID 41735056 · PMCID PMC13022523 · OpenAlex W7131261368

Open access: hybrid, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: EEG (modality), other (modality), human (organism), healthy (population)

Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Complexity, Physiology & signal measures

Keywords: β-bursts, β-oscillations, electroencephalography, impulse control, response inhibition

MeSH: Aging*, Beta Rhythm*, Inhibition, Psychological*, Neural Inhibition*, Aged, Electroencephalography, Electromyography, Female, Humans, Linear Models, Male, Middle Aged, Muscle, Skeletal, Psychomotor Performance (* major topic)

Journal subjects: Behavioral/Cognitive

Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: HRT (Les Rhoades Memorial PhD Studentship)

Citations: cited by 1 paper (Europe PMC); 68 references in the paper

Abstract

Inhibitory control is essential for adaptive behavior and declines with age, yet the underlying neural dynamics remain poorly understood. The β-rhythm (15–29 Hz) is associated with inhibitory signaling within the frontobasal ganglia network. Recent evidence suggests that transient β-bursts support inhibitory performance but are often masked by conventional trial-averaged β-power analyses. A recently developed analysis approach, combining linear mixed-effects modeling and threshold-free cluster enhancement (LMM–TFCE), was applied to examine trial-by-trial β-bursting activity associated with response inhibition and initiation in older adults. Twenty healthy older adults (nine females) performed a bimanual anticipatory response inhibition task, while electroencephalography and electromyography were recorded to capture β-activity (β-burst rate/volume; averaged β-power) and muscle bursting dynamics, respectively. Our analysis revealed distinct β-bursting signatures absent in averaged β-power data. During bimanual response inhibition, parieto-occipital β-bursting preceded bilateral frontocentral β-bursting, consistent with initial attentional processes prior to broader inhibitory network engagement. Moreover, a link was established between right sensorimotor β-bursting and muscle bursts during stopping, indicating rapid cortical suppression of initiated motor output. β-burst volume proved uniquely sensitive to response withholding, with early left frontal activity supporting preparatory suppression mechanisms. A further link between increased parieto-occipital β-burst volume and muscle bursts aligned with top–down inhibitory signaling to support visuomotor stabilization and prevent premature response release. These results underscore the sensitivity of β-bursting to both the timing and context of inhibitory demands in healthy aging. Future research will help establish the potential of β-bursting, combined with LMM–TFCE analysis, as a clinically relevant marker of impulse control dysfunction.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

sourceforge.net/faster

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: the link answers

Software Heritage: not checked

Found in: the text, “Electroencephalography”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)

30 September 2026: the link answers (HTTP 200)

At the source: sourceforge.net/projects/faster

OSF 4yu9n

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Languages: MATLAB (30)

Size: 112 files, 30 scripts

Software Heritage: not checked

Found in: “Code accessibility”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Statistics and Machine Learning Toolbox (5 files), EEGLAB (3 files), Signal Processing Toolbox (2 files), Image Processing Toolbox (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)

30 September 2026: the link answers (HTTP 200)

30 files

Muscle_Burst_Detection/ConcatenatePartialBursts.m, MATLAB, 56 lines
Muscle_Burst_Detection/ConcatenatePrematureBursts.m, MATLAB, 56 lines
Muscle_Burst_Detection/EMGPreProcess.m, MATLAB, 24 lines
Muscle_Burst_Detection/FindEMGBursts.m, MATLAB, 136 lines, 1 match
Muscle_Burst_Detection/analyzeEMGBehaviouralData.m, MATLAB, 320 lines
Muscle_Burst_Detection/averageCellArray.m, MATLAB, 19 lines
Muscle_Burst_Detection/calcAmplitude.m, MATLAB, 106 lines, 1 match
Muscle_Burst_Detection/calcDuration.m, MATLAB, 46 lines
Muscle_Burst_Detection/calcIncidence.m, MATLAB, 80 lines
Muscle_Burst_Detection/cellStr2Num.m, MATLAB, 28 lines
Muscle_Burst_Detection/checkIfAnotherChannel.m, MATLAB, 36 lines
Muscle_Burst_Detection/combineBehaviouralEMG.m, MATLAB, 27 lines
Muscle_Burst_Detection/findEmgOnsetPeak.m, MATLAB, 154 lines
Muscle_Burst_Detection/getParameters.m, MATLAB, 31 lines
Muscle_Burst_Detection/identifyPartialBursts.m, MATLAB, 55 lines
Muscle_Burst_Detection/loadProcessEmg.m, MATLAB, 109 lines
Muscle_Burst_Detection/loadSignalBehaviouralData.m, MATLAB, 74 lines
Muscle_Burst_Detection/outputToXls.m, MATLAB, 406 lines
Muscle_Burst_Detection/plotGrandAverages.m, MATLAB, 53 lines
Muscle_Burst_Detection/plotTraces.m, MATLAB, 49 lines
Muscle_Burst_Detection/prepareForTraces.m, MATLAB, 145 lines
Muscle_Burst_Detection/returnIndexedValues.m, MATLAB, 47 lines
Muscle_Burst_Detection/summarizeData.m, MATLAB, 515 lines
beta_features_extraction.m, MATLAB, 368 lines, 2 matches
dataimport.m, MATLAB, 313 lines, 1 match
lme_beta_plot_channel.m, MATLAB, 113 lines
lme_beta_prep.m, MATLAB, 133 lines
lme_beta_run.m, MATLAB, 119 lines, 1 match
lme_beta_run_wEMG.m, MATLAB, 168 lines
preprocess.m, MATLAB, 91 lines, 1 match

At the source: osf.io/4yu9n/

Code accessibility

The custom MATLAB scripts used in the current study are available on the Open Science Framework: https://osf.io/4yu9n/.

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

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:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
30 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 anonymized datasets analyzed in the current study are available on the Open Science Framework: https://osf.io/4yu9n/.

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, 4 authors, 5 keywords, 14 MeSH terms, 1 funder, 68 references.

Cite

This paper

Warden, A. C. M., Cruse, D., McAllister, C. J., & MacDonald, H. J. (2026). β-Bursting as a Sensitive Neural Marker of Inhibitory Control in Healthy Older Adults: A Linear Mixed-Effects Modeling and Threshold-Free Cluster Approach. The Journal of neuroscience : the official journal of the Society for Neuroscience, 46(12), e1151252026. https://doi.org/10.1523/jneurosci.1151-25.2026

BibTeX

@article{warden2026bursting, author = {Warden, Aliya C. M. and Cruse, Damian and McAllister, Craig J. and MacDonald, Hayley J.}, title = {{β-Bursting as a Sensitive Neural Marker of Inhibitory Control in Healthy Older Adults: A Linear Mixed-Effects Modeling and Threshold-Free Cluster Approach}}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, year = {2026}, month = mar, volume = {46}, number = {12}, pages = {e1151252026}, publisher = {Society for Neuroscience}, issn = {0270-6474}, doi = {10.1523/jneurosci.1151-25.2026}, url = {https://doi.org/10.1523/jneurosci.1151-25.2026}, pmid = {41735056}, pmcid = {PMC13022523} }

RIS

TY - JOUR AU - Warden, Aliya C. M. AU - Cruse, Damian AU - McAllister, Craig J. AU - MacDonald, Hayley J. TI - β-Bursting as a Sensitive Neural Marker of Inhibitory Control in Healthy Older Adults: A Linear Mixed-Effects Modeling and Threshold-Free Cluster Approach T2 - The Journal of neuroscience : the official journal of the Society for Neuroscience J2 - J Neurosci PY - 2026 DA - 2026/03/25 VL - 46 IS - 12 SP - e1151252026 SN - 0270-6474 PB - Society for Neuroscience DO - 10.1523/jneurosci.1151-25.2026 UR - https://doi.org/10.1523/jneurosci.1151-25.2026 LA - en ER -

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{ "id": "10.1523/jneurosci.1151-25.2026", "type": "article-journal", "title": "β-Bursting as a Sensitive Neural Marker of Inhibitory Control in Healthy Older Adults: A Linear Mixed-Effects Modeling and Threshold-Free Cluster Approach", "container-title": "The Journal of neuroscience : the official journal of the Society for Neuroscience", "author": [ { "family": "Warden", "given": "Aliya C. M." }, { "family": "Cruse", "given": "Damian" }, { "family": "McAllister", "given": "Craig J." }, { "family": "MacDonald", "given": "Hayley J." } ], "container-title-short": "J Neurosci", "volume": "46", "issue": "12", "page": "e1151252026", "DOI": "10.1523/jneurosci.1151-25.2026", "PMID": "41735056", "PMCID": "PMC13022523", "ISSN": "0270-6474", "publisher": "Society for Neuroscience", "URL": "https://doi.org/10.1523/jneurosci.1151-25.2026", "language": "en", "issued": { "date-parts": [ [ 2026, 3, 25 ] ] } }

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