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The cross-site reproducibility of MRI morphometric phenotypes in psychiatric disorders.

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 · 4 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Quantifying the effects of sample and MRI data characteristics ↔ SBM/analysis/step10_covariates/step10k_corr_zmap_covariate_combine.m, the whole file · a weak match · score 0.78 · female ratio, illness duration, scanner model, control ratio, onset, voxel
  2. [2] § Methods › Quantifying the effects of sample and MRI data characteristics ↔ VBM/analysis/step10_figures/figure_covariates.m, the whole file · a weak match · score 0.76 · illness duration, scanner model, control ratio, properties, female, onset
  3. [3] § Methods › Measuring gray matter differences › SBM › Quality control procedures ↔ SBM/preprocessing/step2_autoQC/step2d_sub.mriqc_PCA.py, lines 138–148 · score 0.64 · Image quality, principal component, PCA, score, MRIQC, outliers
  4. [4] § Methods › Measuring gray matter differences › VBM › Quality control procedures ↔ VBM/preprocessing/step1_CAT12/CAT12_preprocessing_job.m, the whole file · a weak match · score 0.60 · CAT12 preprocessing, native, SPM12, MNI, VBM
  5. [5] § Methods › Measuring gray matter differences › SBM › Statistical analysis ↔ SBM/analysis/step6_statistical_analysis/step6_sub_make_input_run_mri_glmfit.sh, lines 57–122 · score 0.54 · mri_glmfit, Thresholded maps, FreeSurfer, permutation, ComBat
  6. [6] § Methods › Measuring gray matter differences › VBM › Region-level analyses ↔ VBM/analysis/step7_parcellation/step7b_sub_combine_parcellation.m, the whole file · a weak match · score 0.53 · Buckner cerebellar, subcortical, network, parcellated, Schaefer

Paper

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The authors' code

MATLAB · 126 lines · 4.9 KB · no license · 1 match

  1. function step10k_corr_zmap_covariate_combine(config)
  2. % STEP10K: Stack confound_*.mat from step10a-j into confound_combine.mat for figures.
  3. % Usage: step10k_corr_zmap_covariate_combine('config_hpc.json')
  4. % Prereq: run step10a through step10j first.
  5. % --- Load config and set paths ---
  6. if nargin < 1 || isempty(config)
  7. config = 'config_hpc.json';
  8. end
  9. this_dir = fileparts(mfilename('fullpath'));
  10. repo_root = fullfile(this_dir, '..', '..', '..');
  11. addpath(this_dir);
  12. addpath(genpath(fullfile(repo_root, 'utils')));
  13. if ischar(config) || isstring(config)
  14. config = pipeline_load_config(char(config));
  15. end
  17. % --- Paths from config ---
  18. data_root = config.data_directories.dataset_root;
  19. output_dir = fullfile(data_root, 'results', 'SBM', 'analysis', 'output');
  20. if ~exist(output_dir, 'dir'); mkdir(output_dir); end
  22. diagnosisString = {'BD', 'SCA', 'SCZ', 'ASD', 'MDD', 'AD'};
  23. conName = {'mean age','var age','male','female','sex ratio','patients','controls','subjects','patient HC ratio','treatment','mean EN','var EN','mean age onset','var age onset','mean illness duration','var illness duration','scanner brand','scanner model','voxel volume'};
  24. nCon = length(conName);
  25. nDiag = length(diagnosisString);
  27. % --- Load confound_*.mat and build combined tables ---
  28. % contoplot = table;
  29. for iDiag = 1:nDiag
  31. iCon = 0;
  32. iSite = 1;
  33. load(fullfile(output_dir, 'confound_age.mat'), 'varTable','meanAge', 'stdAge','nSite');
  34. if ~isempty(varTable{iDiag})
  35. contoplot(iDiag,iCon+1:iCon+2) = varTable{iDiag}{1,:};
  36. ptoplot(iDiag,iCon+1:iCon+2) = varTable{iDiag}{2,:};
  37. nSiteToPlot(iDiag,iCon+1:iCon+2) = nSite{iDiag}*ones(1,2);
  38. end
  39. iCon = iCon+2;
  40. iSite = iSite+1;
  42. load(fullfile(output_dir, 'confound_sex.mat'), 'varTable', 'maleRatio', 'femaleRatio', 'subjectRatio', 'maleFemaleRatio','nSite');
  43. if ~isempty(varTable{iDiag})
  44. contoplot(iDiag,iCon+1:iCon+3) = varTable{iDiag}{1,[1,2,4]};
  45. ptoplot(iDiag,iCon+1:iCon+3) = varTable{iDiag}{2,[1,2,4]};
  46. nSiteToPlot(iDiag,iCon+1:iCon+3) = nSite{iDiag}*ones(1,3);
  47. end
  48. iCon = iCon+3;
  49. iSite = iSite+1;
  52. load(fullfile(output_dir, 'confound_nPC.mat'), 'varTable','patientRatio', 'controlRatio', 'subjectRatio', 'patientControlRatio','nSite');
  53. if ~isempty(varTable{iDiag})
  54. contoplot(iDiag,iCon+1:iCon+4) = varTable{iDiag}{1,:};
  55. ptoplot(iDiag,iCon+1:iCon+4) = varTable{iDiag}{2,:};
  56. nSiteToPlot(iDiag,iCon+1:iCon+4) = nSite{iDiag}*ones(1,4);
  57. end
  58. iCon = iCon+4;
  59. iSite = iSite+1;
  61. load(fullfile(output_dir, 'confound_treatment.mat'), 'varTable','medRatio', 'nSite');
  62. if ~isempty(varTable{iDiag})
  63. contoplot(iDiag,iCon+1) = varTable{iDiag}{1,:};
  64. ptoplot(iDiag,iCon+1) = varTable{iDiag}{2,:};
  65. nSiteToPlot(iDiag,iCon+1) = nSite{iDiag};
  66. end
  67. iCon = iCon+1;
  68. iSite = iSite+1;
  70. load(fullfile(output_dir, 'confound_EN.mat'), 'varTable','meanEN','varEN','nSite');
  71. if ~isempty(varTable{iDiag})
  72. contoplot(iDiag,iCon+1:iCon+2) = varTable{iDiag}{1,:};
  73. ptoplot(iDiag,iCon+1:iCon+2) = varTable{iDiag}{2,:};
  74. nSiteToPlot(iDiag,iCon+1:iCon+2) = nSite{iDiag}*ones(1,2);
  75. end
  76. iCon = iCon+2;
  77. iSite = iSite+1;
  79. load(fullfile(output_dir, 'confound_ageonset.mat'), 'varTable', 'meanAgeOnset', 'stdAgeOnset','nSite');
  80. if ~isempty(varTable{iDiag})
  81. contoplot(iDiag,iCon+1:iCon+2) = varTable{iDiag}{1,:};
  82. ptoplot(iDiag,iCon+1:iCon+2) = varTable{iDiag}{2,:};
  83. nSiteToPlot(iDiag,iCon+1:iCon+2) = nSite{iDiag}*ones(1,2);
  84. end
  85. iCon = iCon+2;
  86. iSite = iSite+1;
  88. load(fullfile(output_dir, 'confound_illnessDuration.mat'), 'varTable','meanIllness', 'varIllness','nSite');
  89. if ~isempty(varTable{iDiag})
  90. contoplot(iDiag,iCon+1:iCon+2) = varTable{iDiag}{1,:};
  91. ptoplot(iDiag,iCon+1:iCon+2) = varTable{iDiag}{2,:};
  92. nSiteToPlot(iDiag,iCon+1:iCon+2) = nSite{iDiag}*ones(1,2);
  93. end
  94. iCon = iCon+2;
  95. iSite = iSite+1;
  97. load(fullfile(output_dir, 'confound_scanner.mat'), 'varTable','scannerSim','nSite');
  98. if ~isempty(varTable{iDiag})
  99. contoplot(iDiag,iCon+1) = varTable{iDiag}{1,:};
  100. ptoplot(iDiag,iCon+1) = varTable{iDiag}{2,:};
  101. nSiteToPlot(iDiag,iCon+1) = nSite{iDiag};
  102. end
  103. iCon = iCon+1;
  104. iSite = iSite+1;
  106. load(fullfile(output_dir, 'confound_scannerModel.mat'), 'varTable','modelSim','nSite');
  107. if ~isempty(varTable{iDiag})
  108. contoplot(iDiag,iCon+1) = varTable{iDiag}{1,:};
  109. ptoplot(iDiag,iCon+1) = varTable{iDiag}{2,:};
  110. nSiteToPlot(iDiag,iCon+1) = nSite{iDiag};
  111. end
  112. iCon = iCon+1;
  113. iSite = iSite+1;
  115. load(fullfile(output_dir, 'confound_vol.mat'), 'varTable','volRatio','nSite');
  116. if ~isempty(varTable{iDiag})
  117. contoplot(iDiag,iCon+1) = varTable{iDiag}{1,:};
  118. ptoplot(iDiag,iCon+1) = varTable{iDiag}{2,:};
  119. nSiteToPlot(iDiag,iCon+1) = nSite{iDiag};
  120. end
  121. pvals_bonf(iDiag,:) = min(ptoplot(iDiag,:).* size(ptoplot,2), 1);
  123. end
  124. % --- Save confound_combine.mat ---
  125. save(fullfile(output_dir, 'confound_combine.mat'),'ptoplot','pvals_bonf','contoplot','nSiteToPlot');
  126. end

step10k_corr_zmap_covariate_combine.m at commit 2260406, no license · at the source

Overview

Authors: Trang Cao1, James C Pang1, Mehul Gajwani1, Ashlea Segal1,2,3, Alexander Holmes4, Joshua F Wiley1, Sidhant Chopra5,6, Juan Helen Zhou7,8,9, Christopher L H Chen10, Fang Ji7,8, Ben J Harrison11, Christopher G Davey11, Toby Constable1, Jeggan Tiego1, Bree Hartshorn1, Jessica Kwee1, Mark A Bellgrove1, Alex Fornito1
ORCID iDs: Trang Cao, James C Pang, Sidhant Chopra, Juan Helen Zhou, Christopher L H Chen, Fang Ji, Christopher G Davey, Toby Constable, Jeggan Tiego, Jessica Kwee, Mark A Bellgrove, Alex Fornito
  1. Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Clayton, Victoria Australia
  2. Department of Neuroscience, Yale School of Medicine, New Haven, CT USA
  3. Wu Tsai Institute, Yale University, New Haven, CT USA
  4. Centre for Integrative Neuroimaging (OxCIN), FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
  5. Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, Victoria Australia
  6. Centre for Youth Mental Health, The University of Melbourne, Parkville, Victoria Australia
  7. Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
  8. Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
  9. Department of Electrical and Computer Engineering & Integrative Sciences and Engineering Programme (ISEP), NUS Graduate School, National University of Singapore, Singapore, Singapore
  10. Memory Aging and Cognition Centre, Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
  11. Department of Psychiatry, The University of Melbourne, Parkville, Victoria Australia
Institutions: Monash University (Australia); Yale University (United States); University of Oxford (United Kingdom); Wellcome Centre for Integrative Neuroimaging (United Kingdom); The University of Melbourne (Australia); Orygen (Australia); National University of Singapore (Singapore)
Journal: Nature neuroscience, volume 29, issue 9, pages 2273-2282
Dates: received 16 October 2025; accepted 8 June 2026; published online 30 July 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41593-026-02359-0 · PMID 42533130 · PMCID PMC13533841 · OpenAlex W7171773801
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), schizophrenia / psychosis (population)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging, Preprocessing
Keywords: Neuroscience, Anatomy
MeSH: Brain*, Magnetic Resonance Imaging*, Mental Disorders*, Adult, Female, Gray Matter, Humans, Male, Phenotype, Reproducibility of Results (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Department of Health | National Health and Medical Research Council (NHMRC) (1146292, 1197431, 2034000, 2033976); European Research Council (866533); Monash FMNHS Early Career Research Excellence Program; Sylvia and Charles Viertel Charitable Foundation; Department of Education and Training | Australian Research Council (DP200103509, FL220100184); Australian government Research Training Program scholarship; The University of Melbourne McKenzie Fellowship, The Brain &amp; Behaviour Research Foundation Young Investigator Grant
Citations: not cited yet (Europe PMC); 117 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.

Repository

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

NSBLab/reproducibility_grey_matter_differences_in_disorders

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 2260406ee295042a6cdb51cb147400e07007f2f6, 2 August 2026
Languages: MATLAB (258), Shell (77), Python (7)
Size: 369 files, 342 scripts
Software Heritage: archived
Found in: “Code availability”
Holds: README, documentation, 55 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration
Tools: SPM (26 files), FreeSurfer (16 files), Image Processing Toolbox (12 files), Statistics and Machine Learning Toolbox (8 files), NumPy (6 files), BrainSMASH (3 files), fdr_bh (Benjamini-Hochberg FDR) (3 files), NiBabel (3 files), pandas (3 files), MRIQC (2 files), neuroCombat (2 files), FSL (1 file), Matplotlib (1 file), scikit-learn (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
343 files
At the source: github.com/NSBLab/reproducibility_grey_matter_differences_in_disorders

Code availability statement

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Read it in the paper: doi.org/10.1038/s41593-026-02359-0.

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.

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 342 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);
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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

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  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41593-026-02359-0.

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 18 authors, 2 keywords, 10 MeSH terms, 7 funders, 107 references.

Cite

This paper

Cao, T., Pang, J. C., Gajwani, M., Segal, A., Holmes, A., Wiley, J. F., Chopra, S., Zhou, J. H., Chen, C. L. H., Ji, F., Harrison, B. J., Davey, C. G., Constable, T., Tiego, J., Hartshorn, B., Kwee, J., Bellgrove, M. A., & Fornito, A. (2026). The cross-site reproducibility of MRI morphometric phenotypes in psychiatric disorders. Nature neuroscience, 29(9), 2273-2282. https://doi.org/10.1038/s41593-026-02359-0

BibTeX

@article{cao2026cross,
author = {Cao, Trang and Pang, James C and Gajwani, Mehul and Segal, Ashlea and Holmes, Alexander and Wiley, Joshua F and Chopra, Sidhant and Zhou, Juan Helen and Chen, Christopher L H and Ji, Fang and Harrison, Ben J and Davey, Christopher G and Constable, Toby and Tiego, Jeggan and Hartshorn, Bree and Kwee, Jessica and Bellgrove, Mark A and Fornito, Alex},
title = {{The cross-site reproducibility of MRI morphometric phenotypes in psychiatric disorders}},
journal = {Nature neuroscience},
year = {2026},
month = jul,
volume = {29},
number = {9},
pages = {2273--2282},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02359-0},
url = {https://doi.org/10.1038/s41593-026-02359-0},
pmid = {42533130},
pmcid = {PMC13533841}
}

RIS

TY - JOUR
AU - Cao, Trang
AU - Pang, James C
AU - Gajwani, Mehul
AU - Segal, Ashlea
AU - Holmes, Alexander
AU - Wiley, Joshua F
AU - Chopra, Sidhant
AU - Zhou, Juan Helen
AU - Chen, Christopher L H
AU - Ji, Fang
AU - Harrison, Ben J
AU - Davey, Christopher G
AU - Constable, Toby
AU - Tiego, Jeggan
AU - Hartshorn, Bree
AU - Kwee, Jessica
AU - Bellgrove, Mark A
AU - Fornito, Alex
TI - The cross-site reproducibility of MRI morphometric phenotypes in psychiatric disorders
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/07/30
VL - 29
IS - 9
SP - 2273
EP - 2282
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02359-0
UR - https://doi.org/10.1038/s41593-026-02359-0
LA - en
ER -

CSL-JSON

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"PMID": "42533130",
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