BOLD response delays represent local cortical processing.

Code ↔ Paper

3 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 3 matches

[1] § Materials and methods › Analysis › Spatial feature selection › A. Cortical representation of the visual stimuli ↔ HRdelay.m, lines 19–51 · score 0.63 · probabilistic retinotopic atlas, stimulus field, axis
[2] § Materials and methods › Analysis › Cross-validation between sessions ↔ fun/runDecoding.m, lines 133–195 · score 0.55 · SVM training, feature selection, crossed, validation, models
[3] § Materials and methods › Analysis › Spatial feature selection › B. Stimulus-driven voxels ↔ fun/manova2.m, lines 1–62 · score 0.52 · dependent variables, manova, intercept, MATLAB, model

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The paper is loaded by your browser, with JavaScript: read it at doi.org or on Europe PMC.

The authors' code

MATLAB · 88 lines · 2.7 KB · CC-BY-4.0 · 1 match

%% Info
% This script reproduces figures from S. Proulx et al., Cerebral Cortex, in
% preperation.
% figure('MenuBar','none','ToolBar','none');
clear all
close all
%% Initiation
initAnalysis; % where to define paths, analysis parameters and some other general configurations
%% Get data (preprocessed BOLD volumes, only V1 ROI voxels, one .mat file per subject, total<600MB)
downloadData;
%% Extract sinusoidal and model-free responses from timeseries in the V1 ROI (all V1 voxels included at this stage)
extractResponses(p);
%% Define the retinotopic representation of the stimulus field of view (data-driven with priors from a probabilistic retinotopic atlas)
doWhat = 'download';
% 'run' -> run it yourself, but will not run when locally saved data is available (several minutes; uses some more Matlab proprietary toolboxes, see initAnalysis.m)
% 'run_forced' -> same as above, but forces to rerun instead of loading locally saved data
% 'download' -> download precomputed data from repository, but will not download when locally saved data is available
% 'download_forced' -> same as above, but forces to redownload instead of loading locally saved data
% 'run_forced_butSkipFlattenEccDist' -> same as run_forced, but skips forced run of flattenEccDist (very long step)
processFov(p,doWhat);
% % Figure 3A histogram inset
% open('/Users/sebastienproulx/HRdelay/figures/myAnalysis/Fig3Ahist.fig')
% f = gcf;
% ax = gca;
% ax.Children(3).FaceColor = 'k';
% ax.Children(3).EdgeColor = 'none';
% f.Color = 'none';
% ax.Color = 'none';
% ax.Box = 'off';
% ax.YAxis.Visible = 'off';
% ax.XTick = [];
% % saveas(f, 'Fig3Ahist.svg')
% % Figure SuppFig1B histograms
% open('/Users/sebastienproulx/HRdelay/figures/myAnalysis/SuppFig1hist.fig')
% f = gcf;
% ax = f.Children;
% hHist = findobj([ax.Children],'Type','Histogram');
% set(hHist,'FaceColor','k','EdgeColor','none');
% f.Color = 'none';
% set(ax,'Color','none');
% set(ax,'XTick',-pi/2:pi/2:(pi+pi/2));
% set(ax,'XTickLabel',-3:3:9);
% % saveas(f, 'SuppFig1hist.svg')
%% Feature selection
processFeatSel(p);
%% Decoding
runAllDecoding(p);
%% Visualize and print stats on BOLD responses
visualizeResponses(p)
%% Visualize and print stats on decoding
plotAllDecoding(p);
statsAllDecoding(p);
%% Run permutation test (will take long time)
runPermDecoding(p)
plotAllDecoding(p);
return
%% Old code that will not run
chan = processChanHr(p,resBShr,info);
f = plotChanHr(p,chan);
statsChanHr(p,chan);
groupAna(p,figOption,verbose)
% runFit(verbose,figOption)
runFit2(verbose,figOption)
runWave2(verbose,figOption)
preprocAndShowMasks(p,figOption,verbose)
inspectSubjAndExclude(figOption,verbose)
runGroupAnalysis_sin(figOption,verbose)
runGroupAnalysis_hr(figOption,verbose)

HRdelay.m at commit 5ab79f1, under CC-BY-4.0 · at the source

Overview

Authors: Sébastien Proulx^1,2, Reza Farivar^1,2

ORCID iDs: Sébastien Proulx, Reza Farivar

Institutions: Montreal General Hospital (Canada); McGill University Health Centre (Canada); Research Institute of the McGill University Health Centre (Canada); McGill University (Canada)

Journal: Cerebral cortex (New York, N.Y. : 1991), volume 36, issue 4, article bhag040

Dates: received 25 November 2025; accepted 8 March 2026; published online 22 April 2026; in print April 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1093/cercor/bhag040 · PMID 42015405 · PMCID PMC13099394 · OpenAlex W7155173621

Open access: hybrid, a free copy (OpenAlex)

Status: code verified

Categories: fMRI (modality), human (organism), stroke (population), cognitive (subfield)

Methods: Connectivity, Statistics, Machine learning, Preprocessing, Evoked potentials, fMRI & imaging

Keywords: BOLD, cross-orientation suppression, fMRI, hemodynamic response function, V1

MeSH: Visual Cortex*, Visual Perception*, Adult, Brain Mapping, Cerebrovascular Circulation, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Oxygen, Photic Stimulation, Young Adult (* major topic)

Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Natural Sciences and Engineering Research Council (RGPIN 419235-2013); NSERC and Fonds de la recherche en santé du Québec

Citations: cited by 1 paper (Europe PMC); 73 references in the paper

Abstract

A number of studies showed that stimulus or task conditions can alter the shape of the hemodynamic response (HR). Contrary to variations across brains and brain regions, vascular factors alone cannot account for within-voxel HR waveform variations. Instead, different neuron types may contribute differently to shaping the HR, suggesting that beyond detecting neural activations, measurements of stimulus- or task-specific HRs could inform on the nature of underlying neural processes. To assess this hypothesis, we measured HR apparent delays to oriented visual stimuli with 1 mm and 1-s resolution Blood Oxygenation Level Dependent (BOLD) functional MRI (fMRI) in healthy humans. As expected, decoding V1 patterns of HR amplitudes allowed robust cross-validated predictions of stimulus conditions, ie two orthogonal gratings and an overlay of the two. More interestingly, this was also true using patterns of HR delays alone, and predictions using both delay and amplitude information outperformed those using amplitude alone. Finally, while all stimuli evoked similar V1-averaged HR amplitudes, the overlay stimulus’ HR waveform lagged ~180 ms behind that of grating stimuli. We interpret this increased HR delay as reflecting different neural computations, here more cross-orientation suppression with overlay stimuli, and conclude that neurally relevant information can be obtained from the HR waveform in addition to its commonly used amplitude.

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 3 matches between paragraphs and lines of code.

kendrickkay.net/glmdenoise

License: none: the authors keep all their rights

State: the link answers, verified on 28 September 2026

Evidence: the link answers

Software Heritage: not checked

Found in: the acknowledgements

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

Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)

28 September 2026: the link answers (HTTP 200)

At the source: kendrickkay.net/GLMdenoise/

proulx-s/hrdelay

License: CC-BY-4.0

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: 5ab79f1132d71c07c637e2843eda209540f2e876, 23 March 2026

Languages: MATLAB (41)

Size: 45 files, 41 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 (7 files), fdr_bh (Benjamini-Hochberg FDR) (2 files), Curve Fitting Toolbox (1 file), Image Processing Toolbox (1 file), shadedErrorBar (1 file)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

42 files

HRdelay.m, MATLAB, 88 lines, 1 match
downloadData.m, MATLAB, 23 lines
extractResponses.m, MATLAB, 42 lines
fun/cartSpaceNormalization.m, MATLAB, 166 lines
fun/catcell.m, MATLAB, 25 lines
fun/choose.m, MATLAB, 18 lines
fun/constructpolynomialmatrix.m, MATLAB, 43 lines
fun/credibleInt2D.m, MATLAB, 55 lines
fun/defineFeatSel.m, MATLAB, 146 lines
fun/diaryON.m, MATLAB, 5 lines
fun/empiricalFov.m, MATLAB, 1,066 lines
fun/getXYK.m, MATLAB, 44 lines
fun/isrowvector.m, MATLAB, 17 lines
fun/manova2.m, MATLAB, 110 lines, 1 match
fun/mtimescell.m, MATLAB, 23 lines
fun/normalizemax.m, MATLAB, 25 lines
fun/olsmatrix.m, MATLAB, 62 lines
fun/olsmatrix2.m, MATLAB, 67 lines
fun/permuteLabels.m, MATLAB, 72 lines
fun/plotAllDecodingAv.m, MATLAB, 215 lines
fun/plotAllDecodingOriVsPlaid.m, MATLAB, 214 lines
fun/plotNorm.m, MATLAB, 244 lines
fun/polarSpaceNormalization.m, MATLAB, 142 lines
fun/projectionmatrix.m, MATLAB, 33 lines
fun/reorgData.m, MATLAB, 12 lines
fun/runDecoding.m, MATLAB, 1,401 lines, 1 match
fun/runGLMs.m, MATLAB, 1,164 lines
fun/showDelayFovContour.m, MATLAB, 33 lines
fun/squish.m, MATLAB, 20 lines
fun/unitlength.m, MATLAB, 92 lines
fun/vectorlength.m, MATLAB, 40 lines
fun/wrapToPi.m, MATLAB, 2 lines
fun/zerodiv.m, MATLAB, 81 lines
initAnalysis.m, MATLAB, 128 lines
plotAllDecoding.m, MATLAB, 176 lines
processFeatSel.m, MATLAB, 524 lines
processFov.m, MATLAB, 182 lines
runAllDecoding.m, MATLAB, 64 lines
runPermDecoding.m, MATLAB, 36 lines
statsAllDecoding.m, MATLAB, 295 lines
visualizeResponses.m, MATLAB, 1,104 lines
README.md, Text, 20 lines

At the source: github.com/Proulx-S/HRdelay/releases/latest

Zenodo 6568435

License: CC-BY-4.0

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: “Data and code availability”

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

Tools: Statistics and Machine Learning Toolbox (7 files), fdr_bh (Benjamini-Hochberg FDR) (2 files), Curve Fitting Toolbox (1 file), Image Processing Toolbox (1 file), shadedErrorBar (1 file)

Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)

28 September 2026: the link answers (HTTP 200)

42 files

HRdelay.m, MATLAB, 88 lines
downloadData.m, MATLAB, 23 lines
extractResponses.m, MATLAB, 42 lines
fun/cartSpaceNormalization.m, MATLAB, 166 lines
fun/catcell.m, MATLAB, 25 lines
fun/choose.m, MATLAB, 18 lines
fun/constructpolynomialmatrix.m, MATLAB, 43 lines
fun/credibleInt2D.m, MATLAB, 55 lines
fun/defineFeatSel.m, MATLAB, 146 lines
fun/diaryON.m, MATLAB, 5 lines
fun/empiricalFov.m, MATLAB, 1,066 lines
fun/getXYK.m, MATLAB, 44 lines
fun/isrowvector.m, MATLAB, 17 lines
fun/manova2.m, MATLAB, 110 lines
fun/mtimescell.m, MATLAB, 23 lines
fun/normalizemax.m, MATLAB, 25 lines
fun/olsmatrix.m, MATLAB, 62 lines
fun/olsmatrix2.m, MATLAB, 67 lines
fun/permuteLabels.m, MATLAB, 72 lines
fun/plotAllDecodingAv.m, MATLAB, 215 lines
fun/plotAllDecodingOriVsPlaid.m, MATLAB, 214 lines
fun/plotNorm.m, MATLAB, 244 lines
fun/polarSpaceNormalization.m, MATLAB, 142 lines
fun/projectionmatrix.m, MATLAB, 33 lines
fun/reorgData.m, MATLAB, 12 lines
fun/runDecoding.m, MATLAB, 1,401 lines
fun/runGLMs.m, MATLAB, 1,164 lines
fun/showDelayFovContour.m, MATLAB, 33 lines
fun/squish.m, MATLAB, 20 lines
fun/unitlength.m, MATLAB, 92 lines
fun/vectorlength.m, MATLAB, 40 lines
fun/wrapToPi.m, MATLAB, 2 lines
fun/zerodiv.m, MATLAB, 81 lines
initAnalysis.m, MATLAB, 128 lines
plotAllDecoding.m, MATLAB, 176 lines
processFeatSel.m, MATLAB, 524 lines
processFov.m, MATLAB, 182 lines
runAllDecoding.m, MATLAB, 64 lines
runPermDecoding.m, MATLAB, 36 lines
statsAllDecoding.m, MATLAB, 295 lines
visualizeResponses.m, MATLAB, 1,104 lines
README.md, Text, 20 lines

At the source: doi.org/10.5281/zenodo.6568435

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:

3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
82 scripts, each with its path and the digest of its content;
3 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

Datasets cited

zenodo:5183027, at Zenodo; found in “Data and code availability”

Data and code availability

Intermediary data (preprocessed V1 voxels' timeseries) is available for download at https://doi.org/10.5281/zenodo.5183027. Analysis code is available at https://github.com/Proulx-S/HRdelay/releases/latest (DOI: https://doi.org/10.5281/zenodo.6568435).

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 4 keywords, 13 MeSH terms, 2 funders, 72 references.

Cite

This paper

Proulx, S., & Farivar, R. (2026). BOLD response delays represent local cortical processing. Cerebral cortex (New York, N.Y. : 1991), 36(4), bhag040. https://doi.org/10.1093/cercor/bhag040

BibTeX

@article{proulx2026bold, author = {Proulx, Sébastien and Farivar, Reza}, title = {{BOLD response delays represent local cortical processing}}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, year = {2026}, month = apr, volume = {36}, number = {4}, pages = {bhag040}, publisher = {Oxford University Press}, issn = {1047-3211}, doi = {10.1093/cercor/bhag040}, url = {https://doi.org/10.1093/cercor/bhag040}, pmid = {42015405}, pmcid = {PMC13099394} }

RIS

TY - JOUR AU - Proulx, Sébastien AU - Farivar, Reza TI - BOLD response delays represent local cortical processing T2 - Cerebral cortex (New York, N.Y. : 1991) J2 - Cereb Cortex PY - 2026 DA - 2026/04/01 VL - 36 IS - 4 SP - bhag040 SN - 1047-3211 PB - Oxford University Press DO - 10.1093/cercor/bhag040 UR - https://doi.org/10.1093/cercor/bhag040 LA - en ER -

CSL-JSON

{ "id": "10.1093/cercor/bhag040", "type": "article-journal", "title": "BOLD response delays represent local cortical processing", "container-title": "Cerebral cortex (New York, N.Y. : 1991)", "author": [ { "family": "Proulx", "given": "Sébastien" }, { "family": "Farivar", "given": "Reza" } ], "container-title-short": "Cereb Cortex", "volume": "36", "issue": "4", "page": "bhag040", "DOI": "10.1093/cercor/bhag040", "PMID": "42015405", "PMCID": "PMC13099394", "ISSN": "1047-3211", "publisher": "Oxford University Press", "URL": "https://doi.org/10.1093/cercor/bhag040", "language": "en", "issued": { "date-parts": [ [ 2026, 4, 1 ] ] } }

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BOLD response delays represent local cortical processing.

Code ↔ Paper

Paper

The authors' code

Overview

Abstract

Repositories

kendrickkay.net/glmdenoise

proulx-s/hrdelay

Zenodo 6568435

Tracing map

Data

Datasets cited

Data and code availability

Versions

Version 1, 28 September 2026: the first record

Cite

This paper

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