OSCR

Testosterone, cortisol, and fNIRS-based cortical activation associated with competitive task persistence and difficulty.

Code ↔ Paper

4 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 4 matches
  1. [1] § METHODS › Data preprocessing and analysis › fNIRS data preprocessing ↔ Data and Code/analyzeExp_ABC_FirstLevel.m, lines 176–293 · score 0.94 · optical density, wavelet filter, pre baseline, post baseline, fNIRS, preprocessing
  2. [2] § METHODS › Data preprocessing and analysis › Chromophores ↔ Data and Code/analyzeExp_ABC_FirstLevel.m, lines 176–293 · score 0.85 · Beer Lambert Law, optical density, hemodynamic response, hemoglobin, concentrations, stimulus
  3. [3] § METHODS › Data preprocessing and analysis › General linear model (GLM) level 1 ↔ Data and Code/analyzeExp_ABC_FirstLevel.m, lines 296–347 · score 0.81 · nirs.modules.RemoveOutlierSubjects, AR IRLS, removed outliers, HRF, cutoff, polynomial
  4. [4] § METHODS › Assessing neural activity with fNIRS › Head cap and Optode layout ↔ Data and Code/analyzeExp_ABC_FirstLevel.m, lines 68–174 · score 0.63 · physiological noise, short channel, NIRx, distance

Paper

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The authors' code

MATLAB · 373 lines · 13 KB · no license · 4 matches

  1. clear;
  2. %% set constants
  3. %run subject level glm graphs
  4. runSLglmGraphs = false;
  5. %run subject level connectivity graphs
  6. runSLconGraphs = false;
  7. % do you want to remove bad channels based on sci? Ted recommends doing so
  8. % only if you have a small number of subjects.
  9. removeBadChannelsSCI = true;
  10. % the SCI threshold for removing channels
  11. % this will remove only the worst channels. I will let AR-IRLS handle the
  12. % bad, but not terrible issues
  13. minSCI = 0.75;
  14. % the percent of bad channels threshold for removing subjects
  15. minPbadChannels = 0.5;
  16. % Run BandPassFilter
  17. runBandPassFilter = false;
  18. %apply wavelet filter
  19. applyWaveletFilter = true;
  21. %if(strcmp(getenv('USER'), 'dalejcohen'))
  22. % root_dir = '/Users/dalejcohen/Library/CloudStorage/Dropbox/UNCW/active uncw/Collaborations/UNCW/Faculty/Casto Cohen Collaboration/fNIRS/Study_1_All/ABC';
  23. %else
  24. % root_dir = '/Users/cohend/Dropbox/UNCW/active uncw/Collaborations/UNCW/Faculty/Casto Cohen Collaboration/fNIRS/Study_1_All/ABC';
  25. %end
  27. root_dir = '.';
  29. expStatsOut = [root_dir filesep 'expStatsOut.txt'];
  30. %% read in files
  32. dataFolder = '../data';
  34. rawFilename = 'raw.mat';
  36. if exist(rawFilename, 'file') == 2
  37. % File exists, load it
  38. load(rawFilename);
  39. disp(['Loaded data from ', rawFilename]);
  40. else
  41. raw = nirs.io.loadDirectory(dataFolder, {'Group', 'Subject'});
  43. j = nirs.modules.DiscardStims();
  44. j.listOfStims = {'0'; '5'}; %Remove stim condition '0' which was an error
  45. raw = j.run(raw);
  47. save('raw.mat', 'raw','-v7.3');
  49. end
  50. demographics = nirs.createDemographicsTable(raw);
  51. outfile = [root_dir filesep 'rawDemographics.csv'];
  52. writetable(demographics, outfile);
  55. fid = fopen(expStatsOut, "w");
  56. fprintf(fid, "\n **** NEW RUN ***\n");
  57. fclose(fid);
  59. % Start a diary
  60. diary(expStatsOut);
  61. disp("number of participants");
  62. disp(height(demographics));
  63. disp(demographics(:,["Subject"]));
  64. diary off;
  66. disp('files loaded');
  68. %% correct the stimulusEvents and remove bad channels
  70. sessFilename = 'sessions.mat';
  72. if exist(sessFilename, 'file') == 2
  73. % File exists, load it
  74. load(sessFilename);
  75. disp(['Loaded data from ', sessFilename]);
  76. else
  77. demographics = nirs.createDemographicsTable(raw);
  78. sessions = raw;
  80. % correct the stimulusEvents
  81. % the onset of the stimulus is transposed when there are 3 repeats of a
  82. % condition in NIRx data
  83. % we need to rotate it
  84. outTable = [];
  85. badSubs = {[]};
  86. badSubsSesionNum = [];
  87. for i = 1:numel(sessions)
  88. sub = cell2mat(demographics.Subject(i));
  89. grp = cell2mat(demographics.Group(i));
  90. for j = 1: numel(sessions(i).stimulus.values)
  91. tmpName = sessions(i).stimulus.values{j}.name;
  92. % test to see if it is a 3x3 matrix
  93. if(numel(sessions(i).stimulus.values{j}.onset) == 3)
  94. % test if onset(3) takes the amp value (i.e., 1)
  95. if(sessions(i).stimulus.values{j}.onset(3) == 1)
  96. ons1 = sessions(i).stimulus.values{j}.onset(1);
  97. ons2 = sessions(i).stimulus.values{j}.dur(1);
  98. ons3 = sessions(i).stimulus.values{j}.amp(1);
  99. sessions(i).stimulus(tmpName).onset = [ons1; ons2; ons3];
  100. sessions(i).stimulus(tmpName).metadata(:,1) = {ons1; ons2; ons3};
  101. %set duration while we are at it.
  102. sessions(i).stimulus(tmpName).dur = [30; 30; 30];
  103. sessions(i).stimulus(tmpName).metadata(:,2) = {30; 30; 30};
  104. sessions(i).stimulus(tmpName).amp = [1; 1; 1];
  105. sessions(i).stimulus(tmpName).metadata(:,3) = {1; 1; 1};
  106. end
  107. end
  109. try
  110. tmp.table = sessions(i).stimulus.values{j}.metadata;
  111. tmpVal = [j; j; j];
  112. % Add the new column to the left side of the table
  113. tmp.table = addvars(tmp.table, tmpVal, 'Before', 1, 'NewVariableNames', 'Condition');
  114. tmpSess = [i; i; i];
  115. tmp.table = addvars(tmp.table, tmpSess, 'Before', 1, 'NewVariableNames', 'Session');
  116. tmpSub = [{grp}; {grp}; {grp}];
  117. tmp.table = addvars(tmp.table, tmpSub, 'Before', 1, 'NewVariableNames', 'Group');
  118. tmpSub = [{sub}; {sub}; {sub}];
  119. tmp.table = addvars(tmp.table, tmpSub, 'Before', 1, 'NewVariableNames', 'Subject');
  121. if(exist('outTable', 'var'))
  122. outTable = vertcat(outTable, tmp.table);
  123. else
  124. outTable =tmp.table;
  125. end
  126. catch ME % ME is the error object
  127. if(isempty(badSubs{1}))
  128. badSubs = {sub};
  129. else
  130. badSubs = [badSubs; {sub}]; % Store the iteration number
  131. end
  132. badSubsSesionNum = [badSubsSesionNum, i];
  133. continue; % Go to the next iteration
  134. end
  135. end
  136. end
  138. % remove bad subjects
  139. if(~isempty(badSubs{1}))
  140. badSubs = unique(badSubs);
  141. lst = find(ismember(nirs.createDemographicsTable(sessions).Subject, badSubs));
  142. demoBadSubs = nirs.createDemographicsTable(sessions(lst));
  143. sessions(lst) = [];
  144. end
  146. %output bad subjects that were removed, and the conditions information
  147. outfile = [root_dir filesep 'Conditions.csv'];
  148. writetable(outTable, outfile);
  149. if(~isempty(badSubs{1}))
  150. outfile = [root_dir filesep 'badSubsBecauseExperimentError.csv'];
  151. writetable(demoBadSubs, outfile);
  152. end
  154. % label short channels
  155. % to reduce the systemic physiological noises is by recording from
  156. % additional dedicated SS measurements
  157. % label the short seperation
  158. j = nirs.modules.LabelShortSeperation();
  160. % set the max distance for a short channel - here I use 15.
  161. j.max_distance = 15;
  163. sessions = j.run( sessions );
  165. save('sessions.mat', 'sessions','-v7.3');
  166. end
  167. demographics = nirs.createDemographicsTable(sessions);
  168. outfile = [root_dir filesep 'sessionsDemographics.csv'];
  169. writetable(demographics, outfile);
  171. diary(expStatsOut);
  172. disp('stimulus fixed');
  173. disp('short channels labeled');
  174. diary off;
  176. %% PREPROCESSING
  178. hbFilename = 'hb.mat';
  180. if exist(hbFilename, 'file') == 2
  181. % File exists, load it
  182. load(hbFilename);
  183. disp(['Loaded data from ', hbFilename]);
  184. else
  185. demographics = nirs.createDemographicsTable(sessions);
  187. % remove bad channels based on SCI
  188. % for each subject
  189. if(removeBadChannelsSCI)
  191. [sessionsWithoutBadChannels, badSCIsubjects, badSCIchannels] = removeSciBadChannelsAndSubjects(sessions, demographics, minSCI, minPbadChannels);
  193. if(~isempty(badSCIsubjects))
  194. % remove bad subjects
  195. badSCIsubjects = unique(badSCIsubjects);
  196. lst = find(ismember(nirs.createDemographicsTable(sessions).Subject, badSCIsubjects));
  197. sessionsWithoutBadChannels(lst) = [];
  199. % save bad SCI subjects to a file
  200. outfile = [root_dir filesep 'badSubsBecauseTooManyPoorSCI.csv'];
  201. writecell(badSCIsubjects, outfile);
  202. end
  204. if(~isempty(badSCIchannels))
  205. % remove bad subjects
  206. badSCIchannels = unique(badSCIchannels);
  207. % save bad SCI subjects to a file
  208. outfile = [root_dir filesep 'badSCIchannels.xlsx'];
  209. writetable(badSCIchannels,outfile,'Sheet',1);
  211. end
  213. sessionsPostSCI = sessionsWithoutBadChannels;
  214. else
  215. sessionsPostSCI = sessions;
  216. end
  218. save('sessionsPostSCI.mat', 'sessionsPostSCI','-v7.3');
  220. % removejunk files
  221. j = nirs.modules.RemoveStimless( );
  223. j = nirs.modules.FixNaNs(j);
  225. % rename conditions
  226. j = nirs.modules.RenameStims( j );
  228. j.listOfChanges = {
  229. '1', 'control';
  230. '2', 'forward';
  231. '3', 'backward';
  232. '4', 'repetition'};
  234. % reduce the frequency of samples. Should be above 4. The current
  235. % dataset has a Hz = 5. So there is no need to downsample
  236. j = nirs.modules.Resample( j );
  237. j.Fs = 5;
  239. % trim the data so that there is a max of 30 seconds of pre and post
  240. % baseline. This will cut off the time points earlier then 30s before
  241. % the first stim event in the data and 30s AFTER the last stim event.
  242. j = nirs.modules.TrimBaseline( j );
  243. % 10 seconds is enough to establish a good pre-condition baseline
  244. j.preBaseline = 10;
  245. % 20 seconds is needed to allow for the hemodynamic responses return to baseline
  246. j.postBaseline = 20;
  248. % Before doing regression we must convert to optical density and then
  249. % hemoglobin. The two modules must be done in order.
  250. j = nirs.modules.OpticalDensity( j );
  252. if(runBandPassFilter)
  253. % Add band-pass filtering
  254. % apply filtering after converting to optical density but before
  255. % converting to hemoglobin concentrations using BeerLambertLaw.
  256. j=nirs.modules.BandPassFilter(j); % This is a bandpass filter.
  257. j.lowpass=0.9; % Remove very slow drifts
  258. %j.highpass= 0.01; % Remove cardiac, respiration, and high-frequency noise
  259. j.do_downsample=true;
  260. end
  262. if(applyWaveletFilter)
  263. j=nirs.modules.WaveletFilter(j);
  264. j.sthresh= 16; %Barker JW, Aarabi A, Huppert TJ. Autoregressive model based algorithm
  265. % for correcting motion and serially correlated errors in fNIRS. Biomed Opt Express.
  266. % 2013 Jul 17;4(8):1366-79. doi: 10.1364/BOE.4.001366. PMID: 24009999; PMCID: PMC3756568.
  267. j.removeScaling = true;
  268. end
  271. % Convert to hemoglobin.
  272. j = nirs.modules.BeerLambertLaw( j );
  274. % I am going to include the SS as a regressor in the group level GLM as recommended by hubbert
  276. % add hbt and sto2
  277. % j=nirs.modules.CalculateTotalHb( j );
  279. % Finally, run the pipeline on the raw data and save to anew variable.
  280. % This will run through all the steps we just created and output the
  281. % processed nirs.core.Data class (which is now hemoglobin since the MBLL
  282. % was now run);
  283. hb = j.run( sessionsPostSCI );
  286. save('hb.mat', 'hb','-v7.3');
  287. end
  289. demographics = nirs.createDemographicsTable(hb);
  290. outfile = [root_dir filesep 'hbDemographics.csv'];
  291. writetable(demographics, outfile);
  293. disp('preprocessing done');
  296. %% run GLM
  298. glmFilename = 'hb_GLM.mat';
  300. if exist(glmFilename, 'file') == 2
  301. % File exists, load it
  302. load(glmFilename);
  303. disp(['Loaded data from ', glmFilename]);
  304. else
  305. % Initialize the GLM analysis
  306. j = nirs.modules.GLM();
  307. j.type = 'AR-IRLS';
  308. j.AddShortSepRegressors = true;
  310. % (OPTIONAL) This turns on optional progress output.
  311. j.verbose = true;
  313. % order polynomial as above.
  314. j.trend_func = @(t) nirs.design.trend.legendre(t, 3);
  316. % (OPTIONAL) A temporal basis can be specified. The Canonical HRF basis
  317. % used by default.
  318. j.basis = Dictionary();
  320. % This is a default function
  321. j.basis('default') = nirs.design.basis.Canonical();
  322. % j.basis('default') = nirs.design.basis.nonlinearHRF();
  324. % output data
  325. j = nirs.modules.ExportData(j);
  326. j.Output= 'SubjStatsOut';
  328. % run the GLM analysis
  329. % run the GLM analysis
  330. hb_GLMpre = j.run(hb);
  332. %remove statistical outliers
  333. j = nirs.modules.RemoveOutlierSubjects();
  334. j.allow_partial_removal=true;
  335. j.cutoff=0.05; %default 0.05
  336. hb_GLM = j.run(hb_GLMpre);
  338. save('hb_GLM.mat', 'hb_GLM', '-v7.3');
  339. end
  341. demographics = nirs.createDemographicsTable(hb_GLM);
  342. outfile = [root_dir filesep 'hb_GLMDemographics.csv'];
  343. writetable(demographics, outfile);
  345. diary(expStatsOut);
  346. disp('GLM Done');
  347. diary off;
  349. %% graph results
  351. if exist(glmFilename, 'file') == 2
  352. % File exists, load it
  353. load(glmFilename);
  354. disp(['Loaded data from ', glmFilename]);
  355. end
  356. %graph
  357. if runSLglmGraphs
  358. demographics = nirs.createDemographicsTable(hb_GLM);
  359. for i = 1:numel(hb_GLM)
  360. sub = cell2mat(demographics.Subject(i));
  361. folder = [root_dir filesep 'figures/FirstLevelGLM' filesep sub filesep 'tstat'];
  362. hb_GLM(i).printAll('tstat', [], 'q < 0.05', folder, 'jpg');
  363. folder = [root_dir filesep 'figures/GLM' filesep sub filesep 'beta'];
  364. hb_GLM(i).printAll('beta', [], 'q < 0.05', folder, 'jpg');
  365. end
  366. close all;
  368. diary(expStatsOut);
  369. disp('GLM Graph Done');
  370. diary off;
  371. end
  373. disp("First Level Analysis Complete")

analyzeExp_ABC_FirstLevel.m, no license · at the source

Overview

Authors: Kathleen V Casto1, Dale J Cohen2, Cameron Hicks1, Andrew Bowser1
ORCID iDs: Kathleen V Casto
  1. Department of Psychological Sciences, Kent State University, Kent, Ohio, USA
  2. Department of Psychology, University of North Carolina Wilmington, Wilmington, North Carolina, USA
Institutions: Kent State University (United States); University of North Carolina Wilmington (United States)
Journal: Journal of neuroendocrinology, volume 38, issue 4, article e70185
Dates: received 27 October 2025; accepted 2 April 2026; published online 19 April 2026; in print April 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1111/jne.70185 · PMID 42001854 · PMCID PMC13092350 · OpenAlex W7154913446
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: fNIRS (modality), human (organism)
Methods: Statistics, fMRI & imaging
Keywords: cortisol, dual‐hormone, fNIRS, testosterone, competition
MeSH: Competitive Behavior*, Hydrocortisone*, Testosterone*, Adult, Female, Humans, Male, Motivation, Prefrontal Cortex, Reward, Saliva, Spectroscopy, Near-Infrared, Young Adult (* major topic)
Topic: Evolutionary Psychology and Human Behavior (Experimental and Cognitive Psychology, Psychology), according to OpenAlex
Funding: Kent State University
Citations: not cited yet (Europe PMC); 120 references in the paper

Abstract

Motivation for social or resource‐related rewards is regulated by areas of the brain that control executive functioning and regulate attention, including the prefrontal cortex (PFC) and temporoparietal junction (TPJ). Testosterone and cortisol are two steroid hormones that influence behaviors related to motivation in social competition and are thought to do so via their independent and interactive effects on these same brain networks. Yet there remains relatively limited evidence for functional hormone–brain correspondence during status contests in humans. In ~120–130 participants, we measured frontal‐temporal cortical patterns of neural activity via functional near‐infrared spectroscopy (fNIRS), salivary testosterone and cortisol, and task performance in a competitive key‐pressing contest for rewards in which the cognitive difficulty of the task was varied. Participants completed the task under one of three conditions for incentivizing performance: a cash prize, positive social judgement, or negative social judgement. The competitive task was associated with increased neural activity bilaterally across the PFC and decreased TPJ activity, especially as task difficulty increased. Individuals who performed better showed greater frontal cortical activation overall and were more likely to have increasing testosterone across the task, but only if cortisol levels simultaneously declined. While hormone change across the task had limited direct ties to brain activity, basal testosterone predicted right vlPFC activation, while the interaction of basal testosterone and cortisol predicted activity in the right TPJ depending on task difficulty. Incentive condition had no clear effects on patterns of brain activity or hormone–brain relationships. These findings support an emerging model of testosterone and cortisol's influence on implicit brain processes underlying attention and goal salience when pursuing social goals. More broadly, this research raises new directions for understanding the neuroendocrine mechanisms behind social and reward‐seeking behavior.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

OSF djwkv

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: MATLAB (6), R (6)
Size: 16 files, 12 scripts
Software Heritage: not checked
Found in: “Data availability and code transparency”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (5 files), emmeans (3 files), lmerTest (3 files), ggplot2 (1 file), multcomp (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
12 files
At the source: osf.io/djwkv/

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;
  • 12 scripts, each with its path and the digest of its content;
  • 4 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 statement

The data that support the findings of this study are openly available in The Open Science Framework at https://osf.io/djwkv/.

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

Data Availability Statement

Data and analysis code are publicly available on the open science framework (https://osf.io/djwkv/).

The data that support the findings of this study are openly available in The Open Science Framework at https://osf.io/djwkv/.

Reproduced under the paper's license (CC BY-NC), 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

  • Publisher: n/a → Wiley
  • Funding: added Kent State University

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 13 MeSH terms, 117 references.

Cite

This paper

Casto, K. V., Cohen, D. J., Hicks, C., & Bowser, A. (2026). Testosterone, cortisol, and fNIRS-based cortical activation associated with competitive task persistence and difficulty. Journal of neuroendocrinology, 38(4), e70185. https://doi.org/10.1111/jne.70185

BibTeX

@article{casto2026testosterone,
author = {Casto, Kathleen V and Cohen, Dale J and Hicks, Cameron and Bowser, Andrew},
title = {{Testosterone, cortisol, and fNIRS-based cortical activation associated with competitive task persistence and difficulty}},
journal = {Journal of neuroendocrinology},
year = {2026},
month = apr,
volume = {38},
number = {4},
pages = {e70185},
publisher = {Wiley},
issn = {0953-8194},
doi = {10.1111/jne.70185},
url = {https://doi.org/10.1111/jne.70185},
pmid = {42001854},
pmcid = {PMC13092350}
}

RIS

TY - JOUR
AU - Casto, Kathleen V
AU - Cohen, Dale J
AU - Hicks, Cameron
AU - Bowser, Andrew
TI - Testosterone, cortisol, and fNIRS-based cortical activation associated with competitive task persistence and difficulty
T2 - Journal of neuroendocrinology
J2 - J Neuroendocrinol
PY - 2026
DA - 2026/04/01
VL - 38
IS - 4
SP - e70185
SN - 0953-8194
PB - Wiley
DO - 10.1111/jne.70185
UR - https://doi.org/10.1111/jne.70185
LA - en
ER -

CSL-JSON

{
"id": "10.1111/jne.70185",
"type": "article-journal",
"title": "Testosterone, cortisol, and fNIRS-based cortical activation associated with competitive task persistence and difficulty",
"container-title": "Journal of neuroendocrinology",
"author": [
{
"family": "Casto",
"given": "Kathleen V"
},
{
"family": "Cohen",
"given": "Dale J"
},
{
"family": "Hicks",
"given": "Cameron"
},
{
"family": "Bowser",
"given": "Andrew"
}
],
"container-title-short": "J Neuroendocrinol",
"volume": "38",
"issue": "4",
"page": "e70185",
"DOI": "10.1111/jne.70185",
"PMID": "42001854",
"PMCID": "PMC13092350",
"ISSN": "0953-8194",
"publisher": "Wiley",
"URL": "https://doi.org/10.1111/jne.70185",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
1
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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Alterations in resting-state functional connectivity relate to psychopathology trajectories during emerging adolescence.
Journal: JCPP advances
In common: emmeans, lmerTest, ggplot2, 1 other tool, 1 reference
[10] doi:10.1117/1.nph.13.3.035001 [code]
Understanding variability in full-term newborns' fNIRS data: the impact of birth weight and gestational age on infants' speech perception abilities.
Journal: Neurophotonics
In common: emmeans, lmerTest, ggplot2, 1 other tool, fNIRS

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