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Mapping the multiscale neuroanatomy of GRN-related frontotemporal dementia using mode-based morphometry.

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 · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Mode-Based Morphometry (MBM) analysis ↔ mbm_demo_book_chapter.m, lines 1–35 · score 0.66 · surface mesh, FreeSurfer, cortex, vtk, fsaverage, binary
  2. [2] § Methods › Surface-Based Morphometry (SBM) analysis ↔ func/mbm_perm_test_map.m, the whole file · a weak match · score 0.63 · mri_glmfit, FreeSurfer, design matrix, mapped, threshold, vertices
  3. [3] § Methods › Surface-Based Morphometry (SBM) analysis ↔ mbm_demo_sim.m, the whole file · a weak match · score 0.58 · sim, cortical thickness, design matrix, LH, threshold, vertices
  4. [4] § Methods › Analysis of the relationship between SBM/MBM cortical alterations and cognitive features ↔ func/mbm_perm_test_map.m, the whole file · a weak match · score 0.52 · mri_glmfit, FreeSurfer, threshold, vertex, MBM

Paper

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The authors' code

MATLAB · 112 lines · 5 KB · Apache-2.0 · 2 matches

  1. function [statMapNull, output1, output2] = mbm_perm_test_map(inputMap, stat, observedMap)
  2. % Permutation tests on the statitical map
  3. %
  4. %% Inputs:
  5. % inputMap - Matrix of rows of anatomical maps.
  6. %
  7. % stat - structure having the input fields:
  8. % stat.test - Statistical test to be used:
  9. % 'one sample' one-sample t-test,
  10. % 'two sample' two-sample t-test,
  11. % 'one way ANOVA' one-way ANOVA.
  12. % 'ANCOVA_F' ANCOVA with two groups (f-test).
  13. % 'ANCOVA_Z' ANCOVA with two groups (z-test, producing z-map from FreeSurfer).
  14. %
  15. % stat.designMatrix - Design matrix [m subjects by k effects].
  16. % - For the design matrix in the statistical test:
  17. % 'one sample': one column, '1' or '0' indicates a subject in the group or not.
  18. % 'two sample': two columns, '1' or '0' indicates a subject in a group or not.
  19. % 'one way ANOVA': k columns, '1' or '0' indicates a subject in a group or not, number of subjects in each group must be equal.
  20. % 'ANCOVA_F': first column: '1' or another number (e.g., '2'): group effect (similar to input file for mri_glmfit in freesurfer)
  21. % second to k-th columns: covariates (discrete or continous numbers)
  22. % 'ANCOVA_Z': first column: '1' or another number (e.g., '2'): group effect (similar to input file for mri_glmfit in freesurfer)
  23. % second to k-th columns: covariates (discrete or continous numbers)
  24. %
  25. %
  26. % stat.nPer - Number of permutations in the
  27. % statistical test.
  28. %
  29. % stat.pThr - Threshold of p-values. If the
  30. % p-values are below stat.pThr,
  31. % these are refined further using a
  32. % tail approximation from the
  33. % Generalise Pareto Distribution (GPD).
  34. %
  35. % stat.thres - Threshold of p-values. When the
  36. % p-value is below stat.thres,
  37. % the statitical test is considered
  38. % significant.
  39. %
  40. % stat.fdr - Option ('true' or 'false') to
  41. % correct multiple test with FDR or not.
  42. %
  43. % observedMap - Vector of a statistical map.
  44. %
  45. %% Outputs:
  46. % statMapNull - Matrix of rows of null statistical maps
  47. %
  48. % stat - structure having the output fields:
  49. %
  50. % stat.pMap - Vector of p-values of the
  51. % statistical map.
  52. %
  53. % stat.revMap - Vector of "false" or "true"
  54. % indicating the observed value of an
  55. % element in the statistical map on
  56. % the right or left tail of the null
  57. % distribution.
  59. % Trang Cao, Neural Systems and Behaviour Lab, Monash University, 2024.
  61. [nSub, nVertice] = size(inputMap); % number of subjects and number of vertices
  63. statMapNull = zeros(stat.nPer, size(inputMap,2)); % preallocation space
  64. for iPer = 1:stat.nPer
  66. if strcmp(stat.test, 'one sample')
  67. % null input maps
  68. inputMapNull = inputMap.* sign(rand(nSub,1) - 0.5);
  70. % statistical map of the null inputs
  71. statMapNull(iPer,:) = mbm_stat_map(inputMapNull, stat);
  73. elseif ismember(stat.test, {'two sample', 'one way ANOVA'})
  75. %suffling the labels of the groups
  76. iNull = randperm(nSub);
  77. statNull = stat;
  78. statNull.designMatrix = stat.designMatrix(iNull,:);
  80. % statistical map of the null inputs
  81. statMapNull(iPer,:) = mbm_stat_map(inputMap, statNull);
  83. else
  85. %suffling the labels of the groups
  86. iNull = randperm(nSub);
  87. statNull = stat;
  88. statNull.designMatrix(:,1) = stat.designMatrix(iNull,1);
  90. % statistical map of the null inputs
  91. statMapNull(iPer,:) = mbm_stat_map(inputMap, statNull);
  93. end
  95. end
  97. % calculate p-value of the t-map and obtain the thresholded map
  98. for iVertice = 1:nVertice
  100. [pMap(iVertice), revMap(iVertice)] = mbm_estimate_p_val_tail(statMapNull(:,iVertice),...
  101. observedMap(iVertice), stat.pThr); % stat.revMap with value "false" or "true" indicates the observed value is on the right or left tail of the null distribution.
  103. end
  105. % correction with fdr if wishing
  106. if stat.fdr == 1
  107. [h, crit_p, adj_ci_cvrg, pMap] = fdr_bh(pMap, stat.thres, 'pdep');
  108. end
  110. output1 = pMap;
  111. output2 = revMap;
  112. end

mbm_perm_test_map.m at commit 92c69c6, under Apache-2.0 · at the source

Overview

Authors: Enrico Premi1, Giada Bianchetti2, Valeria Bracca3,4, Giulia Campana3, Elena Gatti2, Trang Cao5, Alex Fornito5, Valentina Cantoni4, Sonia Bellini6, Daniele Corbo7, Mauro Magoni1, Roberto Gasparotti7, Roberta Ghidoni6, Michela Pievani2, Barbara Borroni4,6
ORCID iDs: Valeria Bracca, Michela Pievani, Barbara Borroni
  1. Stroke Unit, ASST Spedali Civili, Brescia, Italy
  2. Laboratory of Alzheimer’s Neuroimaging and Epidemiology (LANE), IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
  3. Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
  4. Department of Clinical and Experimental Sciences, University of Brescia, Italy
  5. The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
  6. Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
  7. Department of Medical Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
Institutions: Azienda Socio Sanitaria Territoriale degli Spedali Civili di Brescia (Italy); Centro San Giovanni di Dio Fatebenefratelli (Italy); University of Brescia (Italy); Monash University (Australia)
Journal: NeuroImage. Clinical, volume 50, article 103994
Dates: received 23 December 2025; accepted 13 April 2026; published online 15 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.nicl.2026.103994 · PMID 41996765 · PMCID PMC13101774 · OpenAlex W7154458348
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), Alzheimer's / dementia (population)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity, fMRI & imaging, Preprocessing
Keywords: Mode-based morphometry, Surface-based morphometry, GRN, Asymmetry, Frontotemporal dementia
MeSH: Brain*, Brain Mapping*, Frontotemporal Dementia*, Progranulins*, Adult, Aged, Atrophy, Female, Gray Matter, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged (* major topic)
Topic: Cerebrospinal fluid and hydrocephalus (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Ministero della Salute (2025-2027)
Citations: not cited yet (Europe PMC); 43 references in the paper

Abstract

Background: Individuals carrying Progranulin (GRN) mutations show asymmetrical grey matter atrophy, which could be used for early detection in the long asymptomatic phase. To capture these alterations, we employed both conventional Surface-Based Morphometry (SBM) and Mode-Based Morphometry (MBM). While the former provides high-resolution, location-specific estimates of cortical thickness (CT) differences, the latter has recently been introduced as a novel framework that decomposes CT maps into geometric eigenmodes, allowing a multiscale characterization of brain structural variability. Using both approaches enables the detection of complementary aspects of GRN-related neurodegeneration across spatial scales.

Methods: SBM and MBM were applied to CT maps to quantify structural alterations in individuals, 15 presymptomatic and 27 symptomatic, compared to 19 healthy controls (HC). SBM was used to assess vertex-wise CT differences, whereas MBM was used to decompose individual CT maps into geometric eigenmodes and quantify alterations across spatial scales. From both pipelines asymmetry indices (SBM-AI and MBM-AI) were computed. Associations between SBM/MBM-derived measures and domain-specific cognitive performance as well as global disease severity scores were then assessed.

Results: Compared with HC, symptomatic GRN showed significant alterations in seven eigenmodes in the left hemisphere, while only two modes contributed to CT differences in the right hemisphere. For MBM-AI and SBM-AI symptomatic GRN exhibited significantly different values compared to HC and presymptomatic GRN (p < 0.001). Although both asymmetry indices showed significant differences across disease stages (p = 1.3 × 10−5 SBM-AI; p = 3.5 × 10−5 MBM-AI), only the MBM-AI revealed a U-shaped trajectory across disease progression, characterized by an early increase in asymmetry followed by a partial re-symmetrisation in later stages.

Conclusions: MBM revealed multiscale cortical alterations in symptomatic GRN mutation carriers, capturing both large-scale hemispheric differences and more localized regional variations in CT that are less apparent with conventional SBM. These findings indicate that GRN-related neurodegeneration involves complex spatial pattern across multiple anatomical scales. Brain asymmetry remains a core hallmark of GRN-related pathology, supporting the use of asymmetry indices (derived from both SBM and MBM) as potential markers of disease progression at the symptomatic stage.

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 4 matches between paragraphs and lines of code.

NSBLab/MBM

License: Apache-2.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 92c69c6edcb2cde9d002fe68f3091e6b4a0a3521, 29 September 2026
Languages: MATLAB (304), C (17), C/C++ (3), Shell (2)
Size: 426 files, 326 scripts
Software Heritage: archived
Found in: the text, “Mode-Based Morphometry (MBM) analysis”
Holds: README, license file, CITATION.cff, tests, 2 notebooks
Not found: environment file, continuous integration, documentation
Tools: SPM (13 files), Image Processing Toolbox (7 files), GIfTI library for MATLAB (6 files), FreeSurfer (5 files), fdr_bh (Benjamini-Hochberg FDR) (2 files), Statistics and Machine Learning Toolbox (2 files), Connectome Workbench (2 files), FSL (1 file), Tools for NIfTI and ANALYZE image (MATLAB) (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
328 files
At the source: github.com/NSBLab/MBM

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;
  • 326 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

Datasets cited

Data availability

The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. The dataset is publicly available at the following DOI: 10.5281/zenodo.17530608.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 15 authors, 5 keywords, 14 MeSH terms, 1 funder, 43 references.

Cite

This paper

Premi, E., Bianchetti, G., Bracca, V., Campana, G., Gatti, E., Cao, T., Fornito, A., Cantoni, V., Bellini, S., Corbo, D., Magoni, M., Gasparotti, R., Ghidoni, R., Pievani, M., & Borroni, B. (2026). Mapping the multiscale neuroanatomy of GRN-related frontotemporal dementia using mode-based morphometry. NeuroImage. Clinical, 50, 103994. https://doi.org/10.1016/j.nicl.2026.103994

BibTeX

@article{premi2026mapping,
author = {Premi, Enrico and Bianchetti, Giada and Bracca, Valeria and Campana, Giulia and Gatti, Elena and Cao, Trang and Fornito, Alex and Cantoni, Valentina and Bellini, Sonia and Corbo, Daniele and Magoni, Mauro and Gasparotti, Roberto and Ghidoni, Roberta and Pievani, Michela and Borroni, Barbara},
title = {{Mapping the multiscale neuroanatomy of GRN-related frontotemporal dementia using mode-based morphometry}},
journal = {NeuroImage. Clinical},
year = {2026},
month = apr,
volume = {50},
pages = {103994},
publisher = {Elsevier},
issn = {2213-1582},
doi = {10.1016/j.nicl.2026.103994},
url = {https://doi.org/10.1016/j.nicl.2026.103994},
pmid = {41996765},
pmcid = {PMC13101774}
}

RIS

TY - JOUR
AU - Premi, Enrico
AU - Bianchetti, Giada
AU - Bracca, Valeria
AU - Campana, Giulia
AU - Gatti, Elena
AU - Cao, Trang
AU - Fornito, Alex
AU - Cantoni, Valentina
AU - Bellini, Sonia
AU - Corbo, Daniele
AU - Magoni, Mauro
AU - Gasparotti, Roberto
AU - Ghidoni, Roberta
AU - Pievani, Michela
AU - Borroni, Barbara
TI - Mapping the multiscale neuroanatomy of GRN-related frontotemporal dementia using mode-based morphometry
T2 - NeuroImage. Clinical
J2 - Neuroimage Clin
PY - 2026
DA - 2026/04/15
VL - 50
SP - 103994
SN - 2213-1582
PB - Elsevier
DO - 10.1016/j.nicl.2026.103994
UR - https://doi.org/10.1016/j.nicl.2026.103994
LA - en
ER -

CSL-JSON

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