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Multivariate age-related variations in quantitative MRI maps: widespread age-related differences revisited.

Code ↔ Paper

7 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 7 matches · all tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Participants and spatial preprocessing ↔ crc_qMRIage_defaults.m, the whole file · a weak match · score 0.78 · Tissue weighted smoothing, tissue class, qMRI, MTsat, DARTEL, VBQ
  2. [2] § Methods › ROI analyses ↔ SM_code/step7-extractROIsBasedOnAAL3Atlas/sm_extract_ROIs.m, the whole file · a weak match · score 0.77 · AAL3 atlas, ROI masks, heschl, precentral, thalamus, pallidum
  3. [3] § Methods › ROI analyses ↔ SM_code/step8-calculateMediansWithinEachROI/SM_create_median_age_table.m, the whole file · a weak match · score 0.68 · quantitative map, heschl, precentral, thalamus, pallidum, motor
  4. [4] § Results › ROI analyses › Canonical vectors ↔ SM_code/step11-canonical_clusters/SM_calc_canonical_val_mean.m, the whole file · a weak match · score 0.56 · canonical vectors, quantitative map, MTsat, voxel, GM
  5. [5] § Methods › Multivariate GLM (mGLM) › Canonical correlation analysis ↔ SM_code/step11-canonical_clusters/SM_calc_canonical_val_mean.m, the whole file · a weak match · score 0.55 · canonical variates, Canonical vectors
  6. [6] § Methods › Participants and spatial preprocessing ↔ crc_qMRIage_main.m, the whole file · a weak match · score 0.54 · tissue class, qMRI, MNI, masks, WM, GM
  7. [7] § Results › ROI analyses › Canonical vectors ↔ SM_code/step13-maskCanonicalvect_ROIs/SM_extract_peak_from_MSPM.m, the whole file · a weak match · score 0.52 · canonical vectors, peak voxel, ROI, GM, map

Paper

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The authors' code

MATLAB · 64 lines · 2.7 KB · GPL-2.0 · 2 matches

  1. function SM_calc_canonical_val_mean(quant_map_pattern, base_path, canonical_path, out_path)
  2. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  3. % This function generates and saves canonical variate images based on the
  4. % z-scored quantitative maps. It uses the canonical vector image, derived from
  5. % the multivariateSPM in MSPM, and multiply it by the quantitative map values,
  6. % Then, it takes the mean out of all cacnonical variates. The output image
  7. % is the mean canonical vector lenght (canonical variate) for each voxel,
  8. % that represents the contribution of the quantitative map in the
  9. % MultivariateSPM model
  10. %
  11. % Parameters:
  12. % quant_map_pattern - regex pattern to select quantitative maps (e.g., '^z_sub.*GMsmo_PDmap.nii')
  13. % base_path - base directory of the project
  14. % canonical_path - full path to the canonical vectors (SPM .nii images)
  15. % out_path - output directory to save canonical variate images
  16. %
  17. % Example usage:
  18. %
  19. % quant_map_pattern = '^z_sub.*GMsmo_MTsat.nii';
  20. % base_path = fullfile('/Users/smoallemian/Desktop/Manuscripts/Moallemian_et_al_Aging_2025');
  21. % canonical_path = fullfile(base_path,'statistical analysis','MultivariateSPM', 'GM', 'L_01_c01');
  22. % out_path = fullfile(base_path, 'statistical analysis','CanonicalVariates', 'MTsat');
  23. %%%%%%%%%%%%%%%%%%%%%%%
  26. % Select quantitative maps
  27. quant_maps = spm_select('FPListRec', fullfile(base_path, 'data', 'derivatives', 'VBQ_TWsmooth'), ...
  28. quant_map_pattern);
  30. % Select canonical variate images
  31. canonical_vecs = spm_select('FPListRec', canonical_path, '^spm_CVL.*.nii');
  33. % Create output directory if it doesn't exist
  34. if ~exist(out_path, 'dir')
  35. mkdir(out_path);
  36. end
  38. % Process each canonical vector
  39. for can_jj = 1:size(canonical_vecs, 1)
  40. can_vol = spm_vol(canonical_vecs(can_jj, :));
  41. can_mat = spm_read_vols(can_vol);
  42. can_variate_sum = zeros(size(can_mat)); % Initialize volume
  44. % Multiply each quant map with the canonical vector
  45. for map_ii = 1:size(quant_maps, 1)
  46. map_vol = spm_vol(quant_maps(map_ii, :));
  47. map_mat = spm_read_vols(map_vol);
  48. can_variate_sum = can_variate_sum + (map_mat .* can_mat);
  49. end
  51. % Average across maps
  52. can_variate_mean = can_variate_sum / size(quant_maps, 1);
  54. % Prepare output file
  55. out_vol = can_vol;
  56. [~, can_vec_name, ~] = fileparts(canonical_vecs(can_jj, :));
  57. out_vol.fname = fullfile(out_path, ['CanonicalVariate_' can_vec_name '.nii']);
  59. % Save the NIfTI file
  60. spm_write_vol(out_vol, can_variate_mean);
  61. end
  63. fprintf('Canonical variate images saved to: %s\n', out_path);
  64. end

SM_calc_canonical_val_mean.m at commit 70aa467, under GPL-2.0 · at the source

Overview

Authors: Soodeh Moallemian1,2, Christine Bastin1, Martina F Callaghan3, Christophe Phillips1
ORCID iDs: Soodeh Moallemian
  1. GIGA-CRC Human Imaging, University of Liège, Liège, Belgium
  2. Center for Molecular and Behavioral Neuroscience, Rutgers University–Newark, Newark, NJ, United States
  3. Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
Institutions: Rutgers, The State University of New Jersey (United States); University of Liège (Belgium); UCL Queen Square Institute of Neurology (United Kingdom); University College London (United Kingdom)
Journal: Frontiers in neuroscience, volume 20, article 1770672
Dates: received 18 December 2025; accepted 7 April 2026; published online 18 May 2026
Type: Methods article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fnins.2026.1770672 · PMID 42232654 · PMCID PMC13223175 · OpenAlex W7161565641
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), cellular / molecular (subfield)
Methods: Statistics, fMRI & imaging, Machine learning
Keywords: aging, iron content, multivariate model, myelin, quantitative MRI, water concentration
Topic: Advanced Neuroimaging Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: Wellcome Trust (203147/Z/16/Z)
Citations: not cited yet (Europe PMC); 30 references in the paper

Abstract

This study applied multivariate ANOVA to investigate age-related microstructural changes in the brain tissues driven primarily by myelin, iron, and water content, as observed in MRI (semi-)quantitative R1, R2*, MTsat and PD maps. This is effectively a re-analysis of the data analyzed in a univariate way in a previous publication. Voxel-wise analyses were performed on gray matter (GM) and white matter (WM), in addition to region of interest (ROI) analyses. The multivariate approach identified brain regions showing coordinated alterations in multiple tissue properties and demonstrated bidirectional correlations between age and all examined modalities in various brain regions, including the caudate nucleus, putamen, insula, cerebellum, lingual gyri, hippocampus, and olfactory bulb. The multivariate model was more sensitive than univariate analyses, as evidenced by detecting a larger number of significant voxels within clusters in the supplementary motor area, frontal cortex, hippocampus, amygdala, occipital cortex, and cerebellum bilaterally. Though when cross validating the results by splitting the data into 2 subsets, sensitivity is strongly reduced, even more so for the multivariate approach. The examination of normalized, smoothed, and z-transformed maps within the ROIs revealed concurrent age-dependent alterations in myelin, iron, and water content. These findings contribute to our understanding of age-related brain differences and provide insights into the underlying mechanisms of aging. The study emphasizes the importance of multivariate analysis for detecting subtle microstructural changes associated with aging when dealing with multiple quantitative MRI parameter maps.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

CyclotronResearchCentre/qMRI_MSPM_AgeFx

License: GPL-2.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 70aa4674d7fb5ed328c177bdbdbafcdc88340e67, 28 May 2026
Languages: MATLAB (66)
Size: 75 files, 66 scripts
Software Heritage: not archived
Found in: “Data availability statement”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SPM (58 files), Statistics and Machine Learning Toolbox (5 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
68 files
At the source: github.com/CyclotronResearchCentre/qMRI_MSPM_AgeFx

The paper's code and data availability statement is in the Data section.

Tracing map

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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;
  • 66 scripts, each with its path and the digest of its content;
  • 7 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

Data availability statement

The data supporting this study is available on Open Neuro: https://doi.org/10.18112/openneuro.ds005851.v1.0.0. Moreover, the scripts used to generate the results presented in this study are provided on GitHub (https://github.com/CyclotronResearchCentre/qMRI_MSPM_AgeFx) as part of the replication package.

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, pages, dates, 4 authors, 6 keywords, 1 funder, 29 references.

Cite

This paper

Moallemian, S., Bastin, C., Callaghan, M. F., & Phillips, C. (2026). Multivariate age-related variations in quantitative MRI maps: widespread age-related differences revisited. Frontiers in neuroscience, 20, 1770672. https://doi.org/10.3389/fnins.2026.1770672

BibTeX

@article{moallemian2026multivariate,
author = {Moallemian, Soodeh and Bastin, Christine and Callaghan, Martina F and Phillips, Christophe},
title = {{Multivariate age-related variations in quantitative MRI maps: widespread age-related differences revisited}},
journal = {Frontiers in neuroscience},
year = {2026},
month = may,
volume = {20},
pages = {1770672},
publisher = {Frontiers Media SA},
issn = {1662-4548},
doi = {10.3389/fnins.2026.1770672},
url = {https://doi.org/10.3389/fnins.2026.1770672},
pmid = {42232654},
pmcid = {PMC13223175}
}

RIS

TY - JOUR
AU - Moallemian, Soodeh
AU - Bastin, Christine
AU - Callaghan, Martina F
AU - Phillips, Christophe
TI - Multivariate age-related variations in quantitative MRI maps: widespread age-related differences revisited
T2 - Frontiers in neuroscience
J2 - Front Neurosci
PY - 2026
DA - 2026/05/18
VL - 20
SP - 1770672
SN - 1662-4548
PB - Frontiers Media SA
DO - 10.3389/fnins.2026.1770672
UR - https://doi.org/10.3389/fnins.2026.1770672
LA - en
ER -

CSL-JSON

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"container-title": "Frontiers in neuroscience",
"author": [
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"family": "Moallemian",
"given": "Soodeh"
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"container-title-short": "Front Neurosci",
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"page": "1770672",
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"PMID": "42232654",
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"ISSN": "1662-4548",
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"URL": "https://doi.org/10.3389/fnins.2026.1770672",
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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