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Capturing the multimodal dynamics of acute stress responses: Evidence from the Montreal Imaging Stress Task (MIST).

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The authors' code

MATLAB · 160 lines · 5.1 KB · GPL-3.0

  1. function batchFileConverter_Dataset1()
  2. % batchFileConverter_Dataset1 Example of how to converts EDF files to MATLAB.
  3. %
  4. % This function is specifically written for the accompanying dataset, use
  5. % it only as a guide on how to implement the edfDataConverter function
  6. % and the RawFileModel class.to convert your files.
  7. %
  8. %--------------------------------------------------------------------------
  9. %
  10. % This code is part of the supplement material to the article:
  11. %
  12. % Preprocessing Pupil Size Data. Guideline and Code.
  13. % Mariska Kret & Elio Sjak-Shie. 2018.
  14. %
  15. %--------------------------------------------------------------------------
  16. %
  17. % Pupil Size Preprocessing Code (v1.1)
  18. % Copyright (C) 2018 Elio Sjak-Shie
  19. % [email hidden].
  20. %
  21. % This program is free software: you can redistribute it and/or
  22. % modify it under the terms of the GNU General Public License as
  23. % published by the Free Software Foundation, either version 3 of
  24. % the License, or (at your option) any later version.
  25. %
  26. % This program is distributed in the hope that it will be useful,
  27. % but WITHOUT ANY WARRANTY; without even the implied warranty of
  28. % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
  29. % General Public License for more details.
  30. %
  31. % You should have received a copy of the GNU General Public License
  32. % along with this program. If not, see
  33. % <http://www.gnu.org/licenses/>.
  34. %
  35. %--------------------------------------------------------------------------
  38. %% Process Directory:
  40. % Get files:
  41. sourceFolderName = './rawData/';
  42. destFolderName = './matlabData/';
  43. rawFiles = dir([sourceFolderName '*.edf']);
  45. % Process subset of files:
  46. nFiles = length(rawFiles);
  48. % Disp file information:
  49. printToConsole('L1');
  50. printToConsole(1, 'Processing %i files...\n', nFiles);
  52. % Loop through files:
  53. for fileIndx = 1:nFiles
  54. edfDataConverter([sourceFolderName rawFiles(fileIndx).name]...
  55. ,[destFolderName rawFiles(fileIndx).name(1:end-4) '.mat'])
  56. printToConsole(2, 'Done with file %i.\n',fileIndx);
  57. end
  58. printToConsole(2, 'Done with folder.\n');
  59. printToConsole('L2');
  61. end
  64. %% edfDataConverter Function:
  66. function edfDataConverter(edfFilename,matFilename)
  67. % edfDataConverter Converts a single edf file to a matlab file.
  69. % Read raw edf file:
  70. hMex = @edfmex; %#ok<NASGU>
  71. [~,rawEDF] = evalc(['edfmex(''' edfFilename ''');']);
  73. t_ms = double(rawEDF.FSAMPLE.time)';
  74. zeroTime_ms = t_ms(1);
  75. t_ms = (t_ms - zeroTime_ms);
  76. L_raw = double(rawEDF.FSAMPLE.pa(1,:)');
  77. R_raw = double(rawEDF.FSAMPLE.pa(2,:)');
  79. % Only keep the correct eye, and label it the left for convenience:
  80. if all(L_raw == intmin('int16'))
  81. L = R_raw;
  82. curEye = 'right';
  83. else
  84. L = L_raw;
  85. curEye = 'left';
  86. end
  88. % Remove the 0 samples:
  89. L(L==0) = NaN;
  91. % Process events (only extract relevant events):
  92. evtRows = arrayfun(@(f) ~isempty(f.message),rawEDF.FEVENT);
  93. eventData.t = (double(vertcat(rawEDF.FEVENT(evtRows).sttime))...
  94. -zeroTime_ms)/1000;
  95. eventData.name = {rawEDF.FEVENT(evtRows).message}';
  97. % Find trials start and end times:
  98. trailStartRows = find(strcmp(eventData.name,'pictureTrial_Start'));
  99. assert(length(trailStartRows)==36);
  100. segmentStart = eventData.t(trailStartRows);
  101. segmentEnd = eventData.t(trailStartRows+9);
  103. % Extract trial metadata:
  104. trialNum = regexp(eventData.name...
  105. ,'!V TRIAL_VAR trial (\d{2,3})','tokens','once');
  106. assert(isequal(find(~cellfun(@isempty,trialNum)),trailStartRows+5))
  107. trial = str2double([trialNum{:}]');
  109. % Extract CA metadata:
  110. trialCA = regexp(eventData.name...
  111. ,'!V TRIAL_VAR CA (\d|?)','tokens','once');
  112. assert(isequal(find(~cellfun(@isempty,trialCA)),trailStartRows+7))
  113. CA = str2double([trialCA{:}]');
  115. % Extract Condition metadata:
  116. trialCondition = regexp(eventData.name...
  117. ,'!V TRIAL_VAR Condition (\d)','tokens','once');
  118. assert(isequal(find(~cellfun(@isempty,trialCondition)),trailStartRows+8))
  119. Condition = str2double([trialCondition{:}]');
  121. % Extract picture metadata:
  122. trialPicture = regexp(eventData.name...
  123. ,'!V TRIAL_VAR picture (.)+$','tokens','once');
  124. assert(isequal(find(~cellfun(@isempty,trialPicture)),trailStartRows+6))
  125. picture = [trialPicture{:}]';
  127. % Make table:
  128. segmentName = strcat('pictureSeq_'...
  129. ,strrep(cellstr(num2str((1:36)')),' ','0'));
  130. SegmentSource = segmentName;
  131. [~,justFileName,~] = fileparts(edfFilename);
  132. SegmentSource(:) = {justFileName};
  133. fileType = SegmentSource;
  134. fileType(:) = regexp(edfFilename...
  135. ,'.+(F|B|iCB1|iCB2).edf','tokens','once');
  136. eyeUsed = segmentName;
  137. eyeUsed(:) = {curEye};
  138. segmentData = table(...
  139. segmentStart,segmentEnd,segmentName,SegmentSource...
  140. ,fileType,trial,CA,Condition,picture,eyeUsed);
  142. % Build RawFileModel instance, which saves the data to a mat file that is
  143. % compatible with the other data models:
  144. diameterUnit = 'px';
  145. diameter = struct('t_ms',t_ms,'L',L,'R',[]);
  146. RawFileModel(diameterUnit,diameter,segmentData...
  147. ,zeroTime_ms,matFilename);
  149. end

batchFileConverter_Dataset1.m at commit 944523b, under GPL-3.0 · at the source

Overview

Authors: Wei Zhao1, Pengrui Li1, Dezhong Yao1,2, Yun Qin1,2, Tiejun Liu1,2
ORCID iDs: Wei Zhao
  1. The Clinical Hospital of Chengdu Brain Science Institute, MOE-K Lab for NeuroInformation, Brain-Apparatus Communication Institute, University of Electronic Science and Technology of China, Chengdu, China
  2. Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China
Institutions: University of Electronic Science and Technology of China (China)
Journal: International journal of clinical and health psychology : IJCHP, volume 26, issue 2, article 100713
Dates: received 27 February 2026; accepted 1 August 2026; published online 7 August 2026; in print April 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1016/j.ijchp.2026.100713 · PMID 42603950 · PMCID PMC13476487 · OpenAlex W7201866644
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Evoked potentials, Physiology & signal measures
Keywords: Acute stress, Montreal Imaging Stress Task (MIST), Electroencephalogram (EEG), Pupil diameter, Multimodal dynamics
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China; Science and Technology Department of Sichuan Province
Citations: not cited yet (Europe PMC); 41 references in the paper

Abstract

Background: Capturing multimodal acute stress responses is important for understanding how stress is expressed across psychophysiological systems. Pupil diameter and electroencephalogram (EEG) are important indices for assessing acute stress responses. However, the relationships among behavioral, pupillary, and EEG responses during sustained stress exposure remain less well understood.

Methods: We collected multimodal data, including self-reported stress, salivary cortisol, electrocardiogram (ECG), pupil diameter, behavioral performance, and EEG from 33 healthy young adults during the Montreal Imaging Stress Task (MIST). Differences in responses between the training and testing conditions across difficulty levels were analyzed to characterize changes in the multimodal dynamics of acute stress responses across MIST blocks, and exploratory correlation analyses were conducted to examine associations between pupil diameter changes and relative EEG power changes

Results: The testing condition successfully elicited an acute stress response profile, reflected by increased self-reported stress, salivary cortisol, heart rate, and pupil diameter, alongside decreased behavioral performance and a shift in relative EEG power from lower to higher frequency bands. During the testing condition, behavioral performance improved and pupil diameter decreased from the medium to the hard difficulty block, accompanied by changes in relative alpha, beta (beta1, beta2, beta3), and gamma power. These findings may reflect adaptive-like changes over the course of the testing condition. Exploratory analyses showed limited associations between pupil diameter changes and relative EEG power changes, restricted to changes from the medium to the hard difficulty block in the testing condition and mainly involving frontal relative beta2 power.

Conclusion: Our findings suggest that acute stress involves coordinated but partly dissociable multimodal dynamics across MIST blocks and highlight the value of integrating behavioral, pupillary, and EEG measures to characterize acute stress responses.

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.

ElioS-S/pupil-size

License: GPL-3.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 944523bff0ca583039039a3008ac1171ab46400a, 9 October 2019
Languages: MATLAB (15)
Size: 42 files, 15 scripts
Software Heritage: not archived
Found in: the text, “Pupillary data processing and analysis”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (2 files), Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
17 files
At the source: github.com/ElioS-S/pupil-size

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;
  • 15 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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 accessibility statement

Data in the present study can be made available upon request to the primary contact author.

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

  • Authors: added Wei Zhao (0009-0000-8091-2728); removed Wei Zhao

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 2 funders, 40 references.

Cite

This paper

Zhao, W., Li, P., Yao, D., Qin, Y., & Liu, T. (2026). Capturing the multimodal dynamics of acute stress responses: Evidence from the Montreal Imaging Stress Task (MIST). International journal of clinical and health psychology : IJCHP, 26(2), 100713. https://doi.org/10.1016/j.ijchp.2026.100713

BibTeX

@article{zhao2026capturing,
author = {Zhao, Wei and Li, Pengrui and Yao, Dezhong and Qin, Yun and Liu, Tiejun},
title = {{Capturing the multimodal dynamics of acute stress responses: Evidence from the Montreal Imaging Stress Task (MIST)}},
journal = {International journal of clinical and health psychology : IJCHP},
year = {2026},
month = apr,
volume = {26},
number = {2},
pages = {100713},
publisher = {Asociacion Espanola de Psicologia Conductual},
issn = {1697-2600},
doi = {10.1016/j.ijchp.2026.100713},
url = {https://doi.org/10.1016/j.ijchp.2026.100713},
pmid = {42603950},
pmcid = {PMC13476487}
}

RIS

TY - JOUR
AU - Zhao, Wei
AU - Li, Pengrui
AU - Yao, Dezhong
AU - Qin, Yun
AU - Liu, Tiejun
TI - Capturing the multimodal dynamics of acute stress responses: Evidence from the Montreal Imaging Stress Task (MIST)
T2 - International journal of clinical and health psychology : IJCHP
J2 - Int J Clin Health Psychol
PY - 2026
DA - 2026/04/01
VL - 26
IS - 2
SP - 100713
SN - 1697-2600
PB - Asociacion Espanola de Psicologia Conductual
DO - 10.1016/j.ijchp.2026.100713
UR - https://doi.org/10.1016/j.ijchp.2026.100713
LA - en
ER -

CSL-JSON

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"DOI": "10.1016/j.ijchp.2026.100713",
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