Physical activity enhances theta-periodicity of visual attentional allocation.

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

[1] § STAR★Methods › Method details › Theta modulation after cue onset ↔ attention_function/circ_wwtest.m, lines 1–76 · score 0.64 · Watson Williams, Circular Statistics Toolbox, concentration, bins
[2] § STAR★Methods › Quantification and statistical analysis ↔ attention_function/circ_wwtest.m, lines 1–76 · score 0.56 · Watson Williams, Circular Statistics Toolbox
[3] § Result › Theta modulation after cue onset ↔ plot_fig4C_corr_cue_theta_behave.m, lines 16–59 · score 0.54 · theta power, cue onset, theta amplitude, frontal, correlated, RTs
[4] § Result › Theta rhythmic modulations in behavioral performance ↔ plot_supfig4_difference_PA_rest_FFT.m, lines 47–91 · score 0.54 · RT variance, FFT, accuracy, power, DA, RTs
[5] § STAR★Methods › Method details › Theta modulation after cue onset ↔ plot_fig4C_corr_cue_theta_behave.m, lines 16–59 · score 0.53 · theta power, theta amplitude, Spearman, frontal, sensors, correlated
[6] § STAR★Methods › Quantification and statistical analysis ↔ plot_supfig7_frontalTheta_occHFA_coupling.m, lines 6–10 · score 0.53 · theta HFA coupling, frontal theta, phases, cue

Paper

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The authors' code

MATLAB · 157 lines · 4.5 KB · no license · 2 matches

function [pval table] = circ_wwtest(varargin)
% [pval, table] = circ_wwtest(alpha, idx, [w])
% [pval, table] = circ_wwtest(alpha1, alpha2, [w1, w2])
% Parametric Watson-Williams multi-sample test for equal means. Can be
% used as a one-way ANOVA test for circular data.
%
% H0: the s populations have equal means
% HA: the s populations have unequal means
%
% Note:
% Use with binned data is only advisable if binning is finer than 10 deg.
% In this case, alpha is assumed to correspond
% to bin centers.
%
% The Watson-Williams two-sample test assumes underlying von-Mises
% distributrions. All groups are assumed to have a common concentration
% parameter k.
%
% Input:
% alpha angles in radians
% idx indicates which population the respective angle in alpha
% comes from, 1:s
% [w number of incidences in case of binned angle data]
%
% Output:
% pval p-value of the Watson-Williams multi-sample test. Discard H0 if
% pval is small.
% table cell array containg the ANOVA table
%
% PHB 3/19/2009
%
% References:
% Biostatistical Analysis, J. H. Zar
%
% Circular Statistics Toolbox for Matlab
% By Philipp Berens, 2009
% [email hidden] - www.kyb.mpg.de/~berens/circStat.html
[alpha, idx, w] = processInput(varargin{:});
% number of groups
u = unique(idx);
s = length(u);
% number of samples
n = sum(w);
% compute relevant quantitites
pn = zeros(s,1); pr = pn;
for t=1:s
pidx = idx == u(t);
pn(t) = sum(pidx.*w);
pr(t) = circ_r(alpha(pidx),w(pidx));
end
r = circ_r(alpha,w);
rw = sum(pn.*pr)/n;
% make sure assumptions are satisfied
checkAssumption(rw,mean(pn))
% test statistic
kk = circ_kappa(rw);
beta = 1+3/(8*kk); % correction factor
A = sum(pr.*pn) - r*n;
B = n - sum(pr.*pn);
F = beta * (n-s) * A / (s-1) / B;
pval = 1 - fcdf(F,s-1,n-s);
na = nargout;
% if na < 2
% printTable;
% end
prepareOutput;
function printTable
fprintf('\nANALYSIS OF VARIANCE TABLE (WATSON-WILLIAMS TEST)\n\n');
fprintf('%s\t\t\t\t%s\t%s\t\t%s\t\t%s\t\t\t%s\n', ' ' ,'d.f.', 'SS', 'MS', 'F', 'P-Value');
fprintf('--------------------------------------------------------------------\n');
fprintf('%s\t\t\t%u\t\t%.2f\t%.2f\t%.2f\t\t%.4f\n', 'Columns', s-1 , A, A/(s-1), F, pval);
fprintf('%s\t\t%u\t\t%.2f\t%.2f\n', 'Residual ', n-s, B, B/(n-s));
fprintf('--------------------------------------------------------------------\n');
fprintf('%s\t\t%u\t\t%.2f', 'Total ',n-1,A+B);
fprintf('\n\n')
end
function prepareOutput
if na > 1
table = {'Source','d.f.','SS','MS','F','P-Value'; ...
'Columns', s-1 , A, A/(s-1), F, pval; ...
'Residual ', n-s, B, B/(n-s), [], []; ...
'Total',n-1,A+B,[],[],[]};
end
end
end
function checkAssumption(rw,n)
if n > 10 && rw<.45
warning('Test not applicable. Average resultant vector length < 0.45.') %#ok<WNTAG>
elseif n > 6 && rw<.5
warning('Test not applicable. Average number of samples per population < 11 and average resultant vector length < 0.5.') %#ok<WNTAG>
elseif n >=5 && rw<.55
warning('Test not applicable. Average number of samples per population < 7 and average resultant vector length < 0.55.') %#ok<WNTAG>
elseif n < 5
warning('Test not applicable. Average number of samples per population < 5.') %#ok<WNTAG>
end
end
function [alpha, idx, w] = processInput(varargin)
if nargin == 4
alpha1 = varargin{1}(:);
alpha2 = varargin{2}(:);
w1 = varargin{3}(:);
w2 = varargin{4}(:);
alpha = [alpha1; alpha2];
idx = [ones(size(alpha1)); ones(size(alpha2))];
w = [w1; w2];
elseif nargin==2 && sum(abs(round(varargin{2})-varargin{2}))>1e-5
alpha1 = varargin{1}(:);
alpha2 = varargin{2}(:);
alpha = [alpha1; alpha2];
idx = [ones(size(alpha1)); 2*ones(size(alpha2))];
w = ones(size(alpha));
elseif nargin==2
alpha = varargin{1}(:);
idx = varargin{2}(:);
if ~(size(idx,1)==size(alpha,1))
error('Input dimensions do not match.')
end
w = ones(size(alpha));
elseif nargin==3
alpha = varargin{1}(:);
idx = varargin{2}(:);
w = varargin{3}(:);
if ~(size(idx,1)==size(alpha,1))
error('Input dimensions do not match.')
end
if ~(size(w,1)==size(alpha,1))
error('Input dimensions do not match.')
end
else
error('Invalid use of circ_wwtest. Type help circ_wwtest.')
end
end

circ_wwtest.m at commit ad9ddd2, no license · at the source

Overview

Authors: Xinyun Che¹, Christoph Reichert¹, Robert T Knight^2,3, Stefan Dürschmid^1,4

ORCID iDs: Xinyun Che, Stefan Dürschmid

Leibniz Institute for Neurobiology, Brenneckestr. 6, 39118 Magdeburg, Germany
Departments of Neuroscience and Psychology, University of California, Berkeley, 245 Warren Hall, Berkeley CA 94720, USA
Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley CA 94720, USA
Department of Psychiatry and Psychotherapy, Otto-von-Guericke University Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany

Institutions: Leibniz Institute for Neurobiology (Germany); University of California, Berkeley (United States); Otto-von-Guericke-Universität Magdeburg (Germany)

Journal: iScience, volume 29, issue 6, article 116240

Dates: received 15 November 2025; accepted 19 May 2026; published online 8 June 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1016/j.isci.2026.116240 · PMID 42291248 · PMCID PMC13264037 · OpenAlex W7163923957

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: cognitive (subfield)

Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging

Keywords: Physical activity, Cellular neuroscience, Sensory neuroscience

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

Funding: German Research Foundation (SFB 1436)

Citations: not cited yet (Europe PMC); 74 references in the paper

Abstract

The traditional notion of a sustained visual spotlight of attention has been challenged by behavioral and neural evidence showing that attention fluctuates rhythmically in the theta band (3–8 Hz). In rodents, locomotion drives hippocampal theta oscillations that support cue sampling during navigation, enhancing sensory encoding and decision-making. This raises the question of whether physical activity (PA) similarly modulates theta-based attentional sampling in humans. Using magnetoencephalography (MEG), we found that discrimination accuracy (DA) increased following PA, accompanied by stronger theta-band modulation of both DA and reaction times (RTs) in trial-by-trial spectral analyses. Critically, PA elevated frontal theta power after cue presentation and shifted the coupling of visual high-frequency activity (HFA; 80–150 Hz) toward an encoding-sensitive theta phase. Moreover, target-evoked HFA exhibited stronger theta-range fluctuations after PA. Together, these findings demonstrate that short bouts of movement can tune the brain’s intrinsic theta-band sampling mechanism, thereby linking PA to enhanced visual attention.

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.

FreyaChe/visual_attention_script

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: ad9ddd29e2a918121b8ff7c9b407c7ecec65e7d4, 10 May 2026

Languages: MATLAB (62)

Size: 87 files, 62 scripts

Software Heritage: not archived

Found in: “Data and code availability”

Holds: README

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

Tools: Statistics and Machine Learning Toolbox (22 files), shadedErrorBar (10 files), CircStat (5 files), EEGLAB (1 file), Curve Fitting Toolbox (1 file), Signal Processing Toolbox (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

63 files

attention_function/CorrelationCI.m, MATLAB, 20 lines
attention_function/FF_color.m, MATLAB, 25 lines
attention_function/FF_cosfit.m, MATLAB, 89 lines
attention_function/FF_scattervolum.m, MATLAB, 68 lines
attention_function/GetOffset4OverlappingDataPoints.m, MATLAB, 26 lines
attention_function/STS.m, MATLAB, 11 lines
attention_function/STS2.m, MATLAB, 11 lines
attention_function/checkconfig.m, MATLAB, 774 lines
attention_function/circ_kappa.m, MATLAB, 57 lines
attention_function/circ_mean.m, MATLAB, 56 lines
attention_function/circ_r.m, MATLAB, 62 lines
attention_function/circ_std.m, MATLAB, 57 lines
attention_function/circ_wwtest.m, MATLAB, 157 lines, 2 matches
attention_function/compare_kappa.m, MATLAB, 65 lines
attention_function/cosfit.m, MATLAB, 141 lines
attention_function/cosfit_w1.m, MATLAB, 83 lines
attention_function/dist.m, MATLAB, 27 lines
attention_function/drawBackgroundPatch.m, MATLAB, 7 lines
attention_function/fieldtripdefs.m, MATLAB, 110 lines
attention_function/filetype.m, MATLAB, 1,035 lines
attention_function/filetype_check_extension.m, MATLAB, 42 lines
attention_function/filetype_check_header.m, MATLAB, 92 lines
attention_function/filetype_check_uri.m, MATLAB, 222 lines
attention_function/inside_contour.m, MATLAB, 48 lines
attention_function/keyval.m, MATLAB, 62 lines
attention_function/myaxis.m, MATLAB, 6 lines
attention_function/mylinearFit.m, MATLAB, 18 lines
attention_function/neuromagGetSensorAOIs.m, MATLAB, 12 lines
attention_function/onezerostandard.m, MATLAB, 12 lines
attention_function/permDist2CI.m, MATLAB, 23 lines
attention_function/plotCorrelationCI.m, MATLAB, 7 lines
attention_function/prepare_layout.m, MATLAB, 852 lines
attention_function/shadedErrorBar.m, MATLAB, 177 lines
attention_function/subtractmean.m, MATLAB, 49 lines
attention_function/topoplot.m, MATLAB, 666 lines
attention_function/topoprepare.m, MATLAB, 23 lines
attention_function/toposhow.m, MATLAB, 11 lines
plot_fig1B_DA_match.m, MATLAB, 38 lines
plot_fig1B_RT_RTvar_match.m, MATLAB, 48 lines
plot_fig1C_DA.m, MATLAB, 30 lines
plot_fig1C_RT_RTvar.m, MATLAB, 53 lines
plot_fig1D_speed_regression.m, MATLAB, 42 lines
plot_fig1E_PA_speed_behave_linearplot.m, MATLAB, 42 lines
plot_fig2A_cosfit.m, MATLAB, 130 lines
plot_fig2BC_DA_FFT.m, MATLAB, 44 lines
plot_fig2BC_RT_FFT.m, MATLAB, 44 lines
plot_fig2BC_RTvar_FFT.m, MATLAB, 44 lines
plot_fig3ABleft_grandHFA.m, MATLAB, 30 lines
plot_fig3B_HFALoco_Rest.m, MATLAB, 54 lines
plot_fig4AB_theta_cueonset.m, MATLAB, 69 lines
plot_fig4C_corr_cue_theta_behave.m, MATLAB, 60 lines, 2 matches
plot_fig5A_HFA_cosfit.m, MATLAB, 44 lines
plot_fig5BC_HFA_FFT.m, MATLAB, 51 lines
plot_fig5D_example_cosfit_HFA_behave.m, MATLAB, 53 lines
plot_fig5E_corr_cue_target_inter_HFA_bahave.m, MATLAB, 77 lines
plot_supfig1_cue_uncue.m, MATLAB, 170 lines
plot_supfig2_rest_speed_behave_linearplot.m, MATLAB, 40 lines
plot_supfig4_difference_PA_rest_FFT.m, MATLAB, 91 lines, 1 match
plot_supfig5left_NoiseHFA_interv.m, MATLAB, 44 lines
plot_supfig5right_NoiseHFA.m, MATLAB, 49 lines
plot_supfig6_theta_cueonset.m, MATLAB, 169 lines
plot_supfig7_frontalTheta_occHFA_coupling.m, MATLAB, 92 lines, 1 match
README.md, Text, 2 lines

At the source: github.com/FreyaChe/visual_attention_script

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;
62 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);
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 and code availability

All data needed to evaluate the conclusions in the paper are available in the GitHub repository: https://github.com/FreyaChe/visual_attention_script. Raw and preprocessed MEG/EEG data are not publicly available due to participant privacy restrictions and ethics approval requirements. Requests for access should be directed to the lead contact and are subject to institutional data sharing agreements.

All analysis code is available in the GitHub repository listed above https://github.com/FreyaChe/visual_attention_script.

Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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 2, 28 September 2026

Authors: added Stefan Dürschmid (0000-0001-6686-976X); removed Stefan Dürschmid

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 3 keywords, 1 funder, 74 references.

Cite

This paper

Che, X., Reichert, C., Knight, R. T., & Dürschmid, S. (2026). Physical activity enhances theta-periodicity of visual attentional allocation. iScience, 29(6), 116240. https://doi.org/10.1016/j.isci.2026.116240

BibTeX

@article{che2026physical, author = {Che, Xinyun and Reichert, Christoph and Knight, Robert T and Dürschmid, Stefan}, title = {{Physical activity enhances theta-periodicity of visual attentional allocation}}, journal = {iScience}, year = {2026}, month = jun, volume = {29}, number = {6}, pages = {116240}, publisher = {Elsevier}, issn = {2589-0042}, doi = {10.1016/j.isci.2026.116240}, url = {https://doi.org/10.1016/j.isci.2026.116240}, pmid = {42291248}, pmcid = {PMC13264037} }

RIS

TY - JOUR AU - Che, Xinyun AU - Reichert, Christoph AU - Knight, Robert T AU - Dürschmid, Stefan TI - Physical activity enhances theta-periodicity of visual attentional allocation T2 - iScience J2 - iScience PY - 2026 DA - 2026/06/08 VL - 29 IS - 6 SP - 116240 SN - 2589-0042 PB - Elsevier DO - 10.1016/j.isci.2026.116240 UR - https://doi.org/10.1016/j.isci.2026.116240 LA - en ER -

CSL-JSON

{ "id": "10.1016/j.isci.2026.116240", "type": "article-journal", "title": "Physical activity enhances theta-periodicity of visual attentional allocation", "container-title": "iScience", "author": [ { "family": "Che", "given": "Xinyun" }, { "family": "Reichert", "given": "Christoph" }, { "family": "Knight", "given": "Robert T" }, { "family": "Dürschmid", "given": "Stefan" } ], "container-title-short": "iScience", "volume": "29", "issue": "6", "page": "116240", "DOI": "10.1016/j.isci.2026.116240", "PMID": "42291248", "PMCID": "PMC13264037", "ISSN": "2589-0042", "publisher": "Elsevier", "URL": "https://doi.org/10.1016/j.isci.2026.116240", "language": "en", "issued": { "date-parts": [ [ 2026, 6, 8 ] ] } }

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