OSCR

Bed nucleus of the stria terminalis connectivity during food cue and taste processing under stress.

Code ↔ Paper

9 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 9 matches · 5 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Parametric empirical Bayes ↔ supp_analyses/gusto_group_peb_bmc.m, the whole file · a weak match · score 0.73 · Bayesian model reduction, spm_dcm_peb_bmc, Plausible, utilised, probability, posterior
  2. [2] § Methods › Leave-one-out cross-validation ↔ analyses/Run_PEB_HS_Cue_LOOCV_deltastress.m, lines 50–51 · score 0.66 · spm_dcm_loo, cross validation, stress cue, OFC, PEB, BNST
  3. [3] § Methods › In silico validation of dynamic causal models ↔ in_silico_validation/validate_task_model.m, lines 1–42 · score 0.64 · noise ratios, SNRs, silico, simulation, inversion, inferred
  4. [4] § Methods › General linear models of beverage cue and taste processing ↔ supp_analyses/gusto_log_convert.m, the whole file · a weak match · score 0.63 · pleasantness ratings, fMRI, events, rinse, onsets, GLMs
  5. [5] § Methods › Physiological measures ↔ scr/stress_irr.m, the whole file · a weak match · score 0.59 · inter rater reliability, discardable, stress
  6. [6] § Methods › Physiological measures ↔ hrv/R2_HRV_comp.m, lines 86–129 · score 0.59 · pNN20, pNN50, Poincar, SD1, SD2, HRV
  7. [7] § Results › Beverage task and stress induction ↔ hrv/R2_HRV_comp.m, lines 86–129 · score 0.58 · pNN20, pNN50, Poincar, ratio, SD1, SD2
  8. [8] § Methods › Parametric empirical Bayes ↔ analyses/Run_PEB_HS_Taste.m, the whole file · a weak match · score 0.57 · spm_dcm_peb_bmc, effective connectivity parameters, PEB model, delta, quantified, Bayes
  9. [9] § Methods › Physiological measures ↔ hrv/HRV_combined_R1.m, the whole file · a weak match · score 0.52 · Post hoc, ANOVA, vectors, HRV, segmented, model

Paper

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

The paper is loaded when this pane is shown.

The authors' code

MATLAB · 154 lines · 5 KB · no license · 2 matches

  1. clear; clc;
  3. %% Load data
  4. S = load('all_cpulse_values.mat');
  5. acv = S.all_cpulse_values; % 1x1 struct of participants
  6. P = fieldnames(acv); % {'P100','P101',...}
  8. % Exclude (poor trace)
  9. exclude = {'Sub107','Sub133','Sub139','Sub147','Sub151'};
  10. P = setdiff(P, exclude);
  12. %% Analysis parameters
  13. fs_tacho = 4; % Hz for tachogram resampling
  14. rr_min_s = 0.3; % RR lower bound
  15. rr_max_s = 2.5; % RR upper bound
  16. lf_band = [0.04 0.15]; % Hz
  17. hf_band = [0.15 0.40]; % Hz
  18. min_len_psd_s = 20; % >=20 s to attempt PSD
  20. metrics_names = {'pNN20','pNN50','SD1_SD2','LF_HF'};
  21. %% Collect per-participant metrics
  22. rows = cell(0, 2 + numel(metrics_names)); % {Participant, Condition, metrics...}
  24. for i = 1:numel(P)
  25. pid = P{i};
  26. pdata = acv.(pid); % struct with GUSTO_LS / GUSTO_HS
  28. ls = double(pdata.GUSTO_LS(:));
  29. hs = double(pdata.GUSTO_HS(:));
  31. mLS = comp_metrics(ls, fs_tacho, rr_min_s, rr_max_s, lf_band, hf_band, min_len_psd_s);
  32. mHS = comp_metrics(hs, fs_tacho, rr_min_s, rr_max_s, lf_band, hf_band, min_len_psd_s);
  34. if isempty(mLS) || isempty(mHS), continue; end
  36. vals_ls = cellfun(@(nm) mLS.(nm), metrics_names, 'UniformOutput', false);
  37. rows(end+1,:) = [{pid,'LS'}, vals_ls];
  39. vals_hs = cellfun(@(nm) mHS.(nm), metrics_names, 'UniformOutput', false);
  40. rows(end+1,:) = [{pid,'HS'}, vals_hs];
  41. end
  43. T = cell2table(rows, 'VariableNames', ['Participant','Condition', metrics_names]);
  45. %% Paired tests (HS vs LS) on the four metrics
  46. stats_rows = {};
  47. for k = 1:numel(metrics_names)
  48. metric = metrics_names{k};
  49. W = unstack(T(:,{'Participant','Condition',metric}), metric, 'Condition', 'GroupingVariables','Participant');
  50. if ~all(ismember({'LS','HS'}, W.Properties.VariableNames)), continue; end
  52. x = W.LS; y = W.HS; % LS vs HS
  53. mask = ~(isnan(x) | isnan(y));
  54. x = x(mask); y = y(mask);
  55. if numel(x) < 3, continue; end
  57. diffv = y - x;
  58. [~, p, ~, st] = ttest(x, y); % paired t-test
  59. dz = mean(diffv) / std(diffv, 0); % Cohen's dz
  60. stats_rows(end+1,1:9) = {metric, numel(x), st.tstat, st.df, p, dz, mean(x), mean(y), mean(diffv)}; %#ok<AGROW>
  61. end
  63. Stats = cell2table(stats_rows, 'VariableNames', ...
  64. {'Metric','n','t','df','p','dz','mean_LS','mean_HS','diff_mean'});
  65. Stats = sortrows(Stats,'p');
  67. disp('--- Paired t-tests (HS vs LS) ---');
  68. disp(Stats);
  70. % Display
  71. report_metrics = {'pNN20','pNN50','LF_HF','SD1_SD2'};
  72. for r = 1:numel(report_metrics)
  73. m = report_metrics{r};
  74. row = Stats(strcmp(Stats.Metric, m), :);
  75. if ~isempty(row)
  76. fprintf('%s: t(%d)=%.6f, p=%.6f, dz=%.6f, meanΔ=HS-LS=%.6f\n', ...
  77. row.Metric{1}, row.df(1), row.t(1), row.p(1), row.dz(1), row.diff_mean(1));
  78. end
  79. end
  81. % Save tables
  82. writetable(T, 'hrv_comp_by_participant.csv');
  83. writetable(Stats, 'hrv_comp_paired_tests.csv');
  85. %% ---------- Local Functions (minimal) ----------
  86. function M = comp_metrics(cpulse_secs, fs, rr_min_s, rr_max_s, lf_band, hf_band, min_len_psd_s)
  87. % Build RR from cumulative pulse times (seconds) with sanity bounds
  88. [rr, tmid] = local_ibi(cpulse_secs, rr_min_s, rr_max_s);
  89. if isempty(rr), M = []; return; end
  91. % Time-domain differences (ms) for pNN20/pNN50
  92. rr_ms = rr * 1000;
  93. diffs = diff(rr_ms);
  94. M.pNN20 = mean(abs(diffs) > 20) * 100;
  95. M.pNN50 = mean(abs(diffs) > 50) * 100;
  97. % Poincaré ratio SD1/SD2 (population variance convention)
  98. if numel(rr_ms) >= 3
  99. rr1 = rr_ms(1:end-1);
  100. rr2 = rr_ms(2:end);
  101. d12 = rr2 - rr1;
  102. sd1 = sqrt(var(d12, 1)/2);
  103. sd2 = sqrt(2*var(rr_ms,1) - var(d12,1)/2);
  104. M.SD1_SD2 = sd1 / sd2;
  105. else
  106. M.SD1_SD2 = NaN;
  107. end
  109. % LF/HF from HR tachogram Welch PSD
  110. hr = 60 ./ rr;
  111. if numel(rr) >= 4 && (tmid(end) - tmid(1)) >= min_len_psd_s
  112. t = tmid(:);
  113. t_uniform = (t(1):1/fs:t(end))';
  114. hr_uniform = interp1(t, hr(:), t_uniform, 'linear', 'extrap');
  115. hr_uniform = detrend(hr_uniform, 'linear');
  116. nperseg = min(numel(hr_uniform), fs*64);
  117. if nperseg < 32
  118. M.LF_HF = NaN;
  119. else
  120. [Pxx, f] = pwelch(hr_uniform, hamming(nperseg), [], [], fs);
  121. LF = bandpow(f, Pxx, lf_band(1), lf_band(2));
  122. HF = bandpow(f, Pxx, hf_band(1), hf_band(2));
  123. M.LF_HF = (HF > 0) * (LF / HF);
  124. if HF <= 0, M.LF_HF = NaN; end
  125. end
  126. else
  127. M.LF_HF = NaN;
  128. end
  129. end
  131. function [rr, tmid] = local_ibi(cpulse_secs, rr_min_s, rr_max_s)
  132. x = sort(double(cpulse_secs(:)));
  133. x = x(~isnan(x));
  134. if numel(x) < 3
  135. rr = []; tmid = []; return;
  136. end
  137. rr0 = diff(x);
  138. tmid0 = x(1:end-1) + rr0/2;
  139. mask = (rr0 > rr_min_s) & (rr0 < rr_max_s);
  140. rr = rr0(mask);
  141. tmid = tmid0(mask);
  142. if nnz(mask) < 3
  143. rr = []; tmid = [];
  144. end
  145. end
  147. function bp = bandpow(f, Pxx, fmin, fmax)
  148. mask = (f >= fmin) & (f < fmax);
  149. if ~any(mask)
  150. bp = NaN;
  151. else
  152. bp = trapz(f(mask), Pxx(mask));
  153. end
  154. end

R2_HRV_comp.m at commit 506df1e, no license · at the source

Overview

Authors: Eva Guerrero-Hreins1,2, Matthew D. Greaves3,4,5, Po-Han Kung5,6, Bradford A. Moffat7, Rebecca K. Glarin7, Stuart B. Murray8, Ben J. Harrison5, Priya Sumithran9,10, Robyn M. Brown1,2, Trevor Steward5,6
ORCID iDs: Eva Guerrero-Hreins, Matthew D. Greaves, Po-Han Kung, Bradford A. Moffat, Robyn M. Brown, Trevor Steward
  1. Department of Biochemistry and Pharmacology, University of Melbourne,Parkville, VIC Australia
  2. Florey Institute of Neuroscience and Mental Health, University of Melbourne,Parkville, VIC Australia
  3. Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University,Clayton, VIC Australia
  4. Monash Biomedical Imaging, Monash University,Clayton, VIC Australia
  5. Department of Psychiatry, The University of Melbourne,Parkville, VIC Australia
  6. Melbourne School of Psychological Sciences, The University of Melbourne,Parkville, VIC Australia
  7. Melbourne Brain Centre Imaging Unit, Department of Radiology, University of Melbourne,Parkville, VIC Australia
  8. Department of Psychiatry and Biobehavioral Sciences, University of California,Los Angeles, CA USA
  9. Department of Surgery, School of Translational Medicine, Monash University,Melbourne, VIC Australia
  10. Department of Endocrinology and Diabetes, Alfred Health,Melbourne, VIC Australia
Institutions: National Academy of Sciences of Belarus (Belarus)
Journal: Nature communications, volume 17, issue 1, article 5004
Dates: received 4 April 2025; accepted 20 March 2026; published online 8 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-71414-y · PMID 41951626 · PMCID PMC13237201 · OpenAlex W7152051041
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, fMRI & imaging, Physiology & signal measures, Smoothing, state filtering, decompositions
Keywords: Neural circuits, Stress and resilience, Feeding behaviour
MeSH: Cues*, Septal Nuclei*, Stress, Psychological*, Taste*, Taste Perception*, Adult, Feeding Behavior, Female, Food, Humans, Magnetic Resonance Imaging, Male, Nucleus Accumbens, Reward, Young Adult (* major topic)
Topic: Biochemical Analysis and Sensing Techniques (Nutrition and Dietetics, Nursing), according to OpenAlex
Funding: National Health and Medical Research Council
Citations: not cited yet (Europe PMC); 113 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

evaghreins/Gustometer_Stress_BNST_DCM

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 506df1e4d6d2ce96e4f6f39cbb5bcd3e2572bf7f, 24 March 2026
Languages: MATLAB (25)
Size: 209 files, 25 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SPM (14 files), Statistics and Machine Learning Toolbox (5 files), Parallel Computing Toolbox (1 file), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
26 files
At the source: github.com/evaghreins/Gustometer_Stress_BNST_DCM

Zenodo 18738387

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
At the source:

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-71414-y.

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;
  • 25 scripts, each with its path and the digest of its content;
  • 9 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.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-71414-y.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 3 keywords, 15 MeSH terms, 1 funder, 112 references.

Cite

This paper

Guerrero-Hreins, E., Greaves, M. D., Kung, P.-H., Moffat, B. A., Glarin, R. K., Murray, S. B., Harrison, B. J., Sumithran, P., Brown, R. M., & Steward, T. (2026). Bed nucleus of the stria terminalis connectivity during food cue and taste processing under stress. Nature communications, 17(1), 5004. https://doi.org/10.1038/s41467-026-71414-y

BibTeX

@article{guerrerohreins2026bed,
author = {Guerrero-Hreins, Eva and Greaves, Matthew D. and Kung, Po-Han and Moffat, Bradford A. and Glarin, Rebecca K. and Murray, Stuart B. and Harrison, Ben J. and Sumithran, Priya and Brown, Robyn M. and Steward, Trevor},
title = {{Bed nucleus of the stria terminalis connectivity during food cue and taste processing under stress}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5004},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71414-y},
url = {https://doi.org/10.1038/s41467-026-71414-y},
pmid = {41951626},
pmcid = {PMC13237201}
}

RIS

TY - JOUR
AU - Guerrero-Hreins, Eva
AU - Greaves, Matthew D.
AU - Kung, Po-Han
AU - Moffat, Bradford A.
AU - Glarin, Rebecca K.
AU - Murray, Stuart B.
AU - Harrison, Ben J.
AU - Sumithran, Priya
AU - Brown, Robyn M.
AU - Steward, Trevor
TI - Bed nucleus of the stria terminalis connectivity during food cue and taste processing under stress
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/08
VL - 17
IS - 1
SP - 5004
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71414-y
UR - https://doi.org/10.1038/s41467-026-71414-y
LA - en
ER -

CSL-JSON

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"family": "Guerrero-Hreins",
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