Analysis of cortical dysplasias using b-tensor encoding diffusion MRI in an animal model.

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[1] § 2 Methods › 2.5 Statistical analysis ↔ displasia_cluster_param.m, the whole file · a weak match · score 0.65 · cluster forming threshold, finding clusters, pclus, permutation, errors, metric

Paper

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The authors' code

MATLAB · 142 lines · 4 KB · no license · 1 match

function clusters = displasia_cluster_param(groupA,groupB,cfthresh,pthresh,nperms,conn,doPlot,datatitle)
% function clusters = displasia_cluster_param(groupA,groupB,pthresh)
% groupA and groupB: [ndepths nstreamlines nsubjects] of one metric.
% cfthres : cluster forming threshold
% pthresh: Cluster threshold, something like 0.01
% nperms: number of permutations to find maximum random cluster size
% conn: 4 or 8 connectivity for bwlabel
% datatitle : a string
if size(groupA,[1 2]) ~= size(groupB,[1,2])
error('First two dimensions of groupA and groupB must agree.')
return
end
tpercentile = (1-cfthresh) * 100;
nr = size(groupA,1);
nc = size(groupA,2);
ngA = size(groupA,3);
ngB = size(groupB,3);
rA = reshape(groupA,nr*nc,ngA);
rB = reshape(groupB,nr*nc,ngB);
rAB = cat(2,rA,rB);
rPvalsPerm = zeros(nr*nc,nperms);
for perm = 1 : nperms
% dividing into two samples
permutation = randperm(size(rAB,2));
idx1 = permutation(1:ngA);
idx2 = permutation(ngA+1:end);
randomSample1 = rAB(:,idx1);
randomSample2 = rAB(:,idx2);
[hp,pp] = ttest2(randomSample1,randomSample2,'dim',2);
rPvalsPerm(:,perm) = pp;
end
% find cluster sizes found by chance
pPerms = reshape(rPvalsPerm,[nr nc nperms]);
clustersizes = [];
for perm = 1 : nperms
CC = bwconncomp(pPerms(:,:,perm) < pthresh,conn);
numPixels = cellfun(@numel,CC.PixelIdxList);
clustersizes = [clustersizes numPixels];
end
clustersizethreshold = round(prctile(clustersizes,tpercentile));
fprintf(1,'Maximum cluster size at %d percentile after %d permutations: %d\n',tpercentile,nperms,clustersizethreshold);
% stats on actual data
[rH,rP] = ttest2(rA,rB,'dim',2);
P = reshape(rP,[nr nc]);
CC = bwconncomp(P < cfthresh,conn);
numPixels = cellfun(@numel,CC.PixelIdxList);
[biggest,idx] = max(numPixels);
L = labelmatrix(CC);
clusters = ones(size(L));
sigcluster = zeros(1,length(numPixels));
pclusters = zeros(1,length(numPixels));
centroids = zeros(length(numPixels),2);
for lab = 1 : length(numPixels)
thisnumpixels = numel(find(L==lab));
nClustersEqualOrLargerThanThisOne = numel(find(clustersizes>=thisnumpixels));
pclus = nClustersEqualOrLargerThanThisOne ./ numel(clustersizes);
pclusters(lab) = pclus;
if pclus <= pthresh
fprintf(1,'Cluster\t%d\t%d pixels\tp=%1.4f is significant\n',lab,thisnumpixels,pclus);
thiscentroid = regionprops(L==lab,'centroid');
centroids(lab,:) = thiscentroid.Centroid;
else
fprintf(1,'Cluster\t%d\t%d pixels\tp=%1.4f\n',lab,thisnumpixels,pclus);
end
clusters(L==lab) = pclus;
end
if doPlot
cmap_div = uint8(cbrewer('div','PuOr',128, 'spline') .* 255);
cmap_warm = uint8(cbrewer('seq','YlOrBr',128,'spline') .* 255);
cmap_cool = uint8(cbrewer('seq','PuBuGn',128,'spline') .* 255); cmap_cool = flip(cmap_cool,1);
cmap_pval = hot(128); cmap_pval = flip(cmap_pval,1);
cmap_flag = prism(50); cmap_flag(1,:) = [1 1 1];
subplot(2,3,1)
imagesc(mean(groupA,3)');
set(gca,'colormap',cmap_cool); colorbar;
title([datatitle ' | Mean of Group A'])
subplot(2,3,2)
imagesc(mean(groupB,3)');
set(gca,'colormap',cmap_cool); colorbar;
title([datatitle ' | Mean of Group B'])
subplot(2,3,3)
DIFF = mean(groupA,3) - mean(groupB,3);
imagesc(DIFF');
lims = get(gca,'Clim');
newlims = [abs(max(lims))*-1 max(lims)];
set(gca,'colormap',cmap_div); colorbar;
set(gca,'Clim',newlims);
title('A-B')
subplot(2,3,4)
imagesc(P');
set(gca,'colormap',cmap_pval); colorbar;
set(gca,'Clim',[0 cfthresh]);colorbar
title('P values');
subplot(2,3,5)
histogram(clustersizes);
title(sprintf('Cluster threshold\n(%d permutations)',nperms))
vline(clustersizethreshold,'r',num2str(clustersizethreshold))
subplot(2,3,6)
imagesc(clusters' < pthresh);
set(gca,'colormap',cmap_flag); colorbar;
hold on
for c = 1 : length(pclusters)
thispclus = pclusters(c);
if thispclus < pthresh
text(centroids(c,2),centroids(c,1),num2str(pclusters(c)));
end
end
title('clusters')
end

displasia_cluster_param.m at commit 243c6a9, no license · at the source

Overview

Authors: Olimpia Ortega-Fimbres¹, Ricardo Ríos-Carrillo², Edith Gaspar-Martínez¹, Priscila Ruiz-Acosta¹, Mirelta Regalado¹, Hiram Luna-Munguía¹, Alonso Ramírez-Manzanares³, Luis Concha¹

ORCID iDs: Olimpia Ortega-Fimbres, Ricardo Ríos-Carrillo, Priscila Ruiz-Acosta, Alonso Ramírez-Manzanares, Luis Concha

Institutions: Universidad Nacional Autónoma de México (Mexico); Western University (Canada); Mathematics Research Center (Mexico)

Journal: PloS one, volume 21, issue 4, article e0346132

Dates: received 4 December 2025; accepted 16 March 2026; published online 3 April 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1371/journal.pone.0346132 · PMID 41931540 · PMCID PMC13048379 · OpenAlex W7148933938

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: structural MRI / diffusion (modality), histology / microscopy (modality), rat (organism), clinical / translational (subfield)

Methods: Statistics, Connectivity, fMRI & imaging, Physiology & signal measures

MeSH: Diffusion Magnetic Resonance Imaging*, Diffusion Tensor Imaging*, Malformations of Cortical Development*, Animals, Anisotropy, Carmustine, Cerebral Cortex, Disease Models, Animal, Female, Neocortex, Pregnancy, Rats, Rats, Sprague-Dawley (* major topic)

Journal subjects: Medicine and Health Sciences, Diagnostic Medicine, Diagnostic Radiology, Magnetic Resonance Imaging, Research and Analysis Methods, Imaging Techniques, Radiology and Imaging, Physical Sciences, Physics, Condensed Matter Physics, Anisotropy, Materials Science, Material Properties, Biology and Life Sciences, Neuroscience, Brain Mapping, Brain Morphometry, Diffusion Magnetic Resonance Imaging, Neuroimaging, Anatomy, Nervous System, Central Nervous System, Histology, Diffusion Tensor Imaging, Animal Studies, Experimental Organism Systems, Animal Models, Clinical Medicine, Signs and Symptoms, Dysplasia

Topic: Advanced Neuroimaging Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex

Funding: Conahcyt/Secihti (CF-2023-I-218); UNAM-DGAPA (IN204720, IN213423, IN211326); SECIHTI (1019538)

Citations: cited by 1 paper (Europe PMC); 98 references in the paper

Abstract

Cortical dysplasias are malformations of cortical development characterized by disorganization of the cyto- and myeloarchitecture of the neocortex. They are a common cause of epilepsy and their diagnosis through conventional imaging can often be challenging, hindering surgical treatments. Diffusion-weighted magnetic resonance imaging (dMRI) has the ability to infer tissue properties at the microscopic scale, making it a promising technique for detection of cortical dysplasias. This study aims to assess the microarchitecture of the cerebral cortex in a murine model of cortical dysplasia using dMRI acquired with b-tensor encoding. Pregnant Sprague-Dawley rats were administered either carmustine (BCNU) or saline solution on day 15 of gestation. Their offspring were imaged at 120 days of age using a 7 tesla scanner, acquiring diffusion-sensitive images with b-tensor encoding. Images were processed with Q-space trajectory imaging with positivity constraints (QTI+) to derive various metrics along a curvilinear coordinate system across the neocortex. After scanning, the brains were processed for immunofluorescence and histological examinations. Experimental animals exhibited a significant reduction of microscopic fractional anisotropy (µFA) and anisotropic kurtosis (Kshear) in the middle and lateral cortical layers compared to the control animals. Immunofluorescence and histological analysis showed decreased and dysorganized myelinated fibers, and an increase of glial processes in BCNU-treated animals. Given the applicability of b-tensor encoding in clinical scanners, this approach holds promise for improving detection of focal cortical dysplasias in patients with epilepsy.

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 1 match between paragraphs and lines of code.

lconcha/Displasias

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 243c6a9d4a00b53166ef945ce656eb2000fd5a42, 11 March 2026

Languages: MATLAB (79), Shell (38), Python (6), R (2)

Size: 149 files, 125 scripts

Software Heritage: not archived

Found in: the text, “2.4 Spatial analysis”

Holds: README, environment (environment.yml)

Not found: license file, CITATION.cff, tests, continuous integration, documentation

Tools: MRtrix3 (25 files), Statistics and Machine Learning Toolbox (16 files), Image Processing Toolbox (13 files), NumPy (5 files), NiBabel (4 files), FSL (3 files), SciPy (3 files), DIPY (2 files), Matplotlib (1 file), NetworkX (1 file), reticulate (1 file), scikit-image (1 file), scikit-learn (1 file), shadedErrorBar (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

126 files

Streamlines/analisisCaracteristicas.R, R, 685 lines
Streamlines/analisisPermutaciones.R, R, 673 lines
Streamlines/crear_mapas_streamlines_20_para_1_caracteristica.sh, Shell, 29 lines
Streamlines/deNiftiObtenerParalelayPerpendicular.py, Python, 147 lines
batch_displasia_txt2tsf.m, MATLAB, 49 lines
batch_make_tckfigs.sh, Shell, 14 lines
cbrewer/cbrewer/._cbrewer.m, MATLAB, not shown here
cbrewer/cbrewer/._interpolate_cbrewer.m, MATLAB, not shown here
cbrewer/cbrewer/._plot_brewer_cmap.m, MATLAB, not shown here
cbrewer/cbrewer/cbrewer.m, MATLAB, 128 lines
cbrewer/cbrewer/change_jet.m, MATLAB, 64 lines
cbrewer/cbrewer/interpolate_cbrewer.m, MATLAB, 36 lines
cbrewer/cbrewer/plot_brewer_cmap.m, MATLAB, 50 lines
cluster_perm_2D.m, MATLAB, 111 lines
crameri.m, MATLAB, 173 lines
dcohen.m, MATLAB, 8 lines
dcohen2D.m, MATLAB, 18 lines
displasia_Oli_compare_vectorDirections.m, MATLAB, 114 lines
displasia_Oli_compare_vectorgrids.m, MATLAB, 134 lines
displasia_Oli_plot_coherency_energy_dot.m, MATLAB, 122 lines
displasia_add_RD_to_results.m, MATLAB, 36 lines
displasia_anat_preproc_and_register.sh, Shell, 127 lines
displasia_anat_streamline_lengths.sh, Shell, 20 lines
displasia_aylin_check_streamlines.sh, Shell, 59 lines
displasia_batch_DKI.sh, Shell, 32 lines
displasia_batch_anat_lengths.sh, Shell, 18 lines
displasia_batch_averagemaps.m, MATLAB, 167 lines
displasia_batch_corticalmasks.sh, Shell, 23 lines
displasia_batch_fit_noddi.sh, Shell, 35 lines
displasia_batch_gardneraltman.m, MATLAB, 85 lines
displasia_batch_get_normals.m, MATLAB, 77 lines
displasia_batch_movedwifolders.sh, Shell, 57 lines
displasia_batch_mrds.sh, Shell, 20 lines
displasia_batch_organize_all.sh, Shell, 81 lines
displasia_batch_plot_streamlines.m, MATLAB, 66 lines
displasia_batch_prevalence_maps.m, MATLAB, 22 lines
displasia_batch_remove_dwi_outliers.sh, Shell, 29 lines
displasia_batch_streamlines.sh, Shell, 61 lines
displasia_boxplot.m, MATLAB, 61 lines
displasia_calculate_mahalanobis.m, MATLAB, 77 lines
displasia_cluster_param.m, MATLAB, 142 lines, 1 match
displasia_cluster_permutations.m, MATLAB, 55 lines
displasia_collect_anat_lengths.sh, Shell, 39 lines
displasia_do_pointwise_stats.m, MATLAB, 80 lines
displasia_fit_noddi.py, Python, 43 lines
displasia_get_and_show_data.m, MATLAB, 14 lines
displasia_get_data.m, MATLAB, 71 lines
displasia_gridline_animation.m, MATLAB, 94 lines
displasia_load_nii.m, MATLAB, 22 lines
displasia_load_tck_voxelcoords.m, MATLAB, 35 lines
displasia_make_exampleSubjectMaps.m, MATLAB, 93 lines
displasia_mde_ohbm.m, MATLAB, 55 lines
displasia_merge_lines.sh, Shell, 65 lines
displasia_mrds.sh, Shell, 209 lines
displasia_normalise_DWIsignal.sh, Shell, 64 lines
displasia_oli_4plots.m, MATLAB, 47 lines
displasia_oli_QTI_stats.m, MATLAB, 74 lines
displasia_oli_batch_createAveragePlots.m, MATLAB, 13 lines
displasia_oli_batch_plot_vectors_sta.m, MATLAB, 27 lines
displasia_oli_cluster_perm_2D.m, MATLAB, 187 lines
displasia_oli_create_subplots.m, MATLAB, 71 lines
displasia_oli_lconcha_analyze.m, MATLAB, 116 lines
displasia_oli_load_all_metrics.m, MATLAB, 68 lines
displasia_oli_load_txts.m, MATLAB, 80 lines
displasia_oli_plot_average_oneMetric.m, MATLAB, 100 lines
displasia_oli_plot_d_p.m, MATLAB, 70 lines
displasia_oli_plot_just_boxplot.m, MATLAB, 103 lines
displasia_oli_plot_just_cohen.m, MATLAB, 98 lines
displasia_oli_plot_streamlines.m, MATLAB, 110 lines
displasia_oli_plot_vectors_sta.m, MATLAB, 91 lines
displasia_oli_pruebas.m, MATLAB, 81 lines
displasia_oli_read_txt.m, MATLAB, 26 lines
displasia_oli_table2matrix.m, MATLAB, 75 lines
displasia_organize_data_for_sharing.sh, Shell, 49 lines
displasia_permute_axes_tck.m, MATLAB, 15 lines
displasia_plot_data.m, MATLAB, 66 lines
displasia_plot_streamlines.m, MATLAB, 89 lines
displasia_plot_tck.m, MATLAB, 19 lines
displasia_prepare_corticalmasks.sh, Shell, 89 lines
displasia_prevalence_maps.m, MATLAB, 66 lines
displasia_run_DKI.sh, Shell, 47 lines
displasia_run_mrds.sh, Shell, 37 lines
displasia_save_K_mu_niftis.sh, Shell, 14 lines
displasia_separate_slices_removing_outliers.sh, Shell, 87 lines
displasia_showTensorAngles.m, MATLAB, 67 lines
displasia_show_mahal.m, MATLAB, 188 lines
displasia_show_streamlines_with_values.m, MATLAB, 67 lines
displasia_tckfixelsample.m, MATLAB, 328 lines
displasia_tckfixelsample.sh, Shell, 47 lines
displasia_tckfixelsample_CSD.m, MATLAB, 324 lines
displasia_tckfixelsample_CSD.sh, Shell, 67 lines
displasia_tcksample.m, MATLAB, 71 lines
displasia_tempscript.m, MATLAB, 36 lines
displasia_test_showTensorAngles.m, MATLAB, 54 lines
displasia_test_tckfixelsample_CSD.m, MATLAB, 21 lines
displasia_ttest_2D.m, MATLAB, 170 lines
displasia_txt2tsf.m, MATLAB, 59 lines
displasia_viz_streamlines.sh, Shell, 112 lines
ellipse2D.m, MATLAB, 78 lines
expand_lines.sh, Shell, 12 lines
fn_makesnaps.sh, Shell, 47 lines
get_seeds.py, Python, 141 lines
make_grid.sh, Shell, 76 lines
mask_closing.py, Python, 143 lines
mask_dilation.py, Python, 73 lines
nii2streams.sh, Shell, 124 lines
nii2streams_brkraw_analysis.sh, Shell, 70 lines
nii2streams_prepareOrientation.sh, Shell, 127 lines
nii2streams_toOriginalOrientation.sh, Shell, 22 lines
permutation_test_2D.m, MATLAB, 98 lines
plot_tck.m, MATLAB, 37 lines
prueba_clus.m, MATLAB, 64 lines
prueba_clus2.m, MATLAB, 57 lines
prueba_clus_param.m, MATLAB, 68 lines
rhomboid2D.m, MATLAB, 28 lines
run_grid.sh, Shell, 63 lines
shadedErrorBar.m, MATLAB, 275 lines
single_figure_cohenDofMahal.sh, Shell, 47 lines
slanCM.m, MATLAB, 77 lines
tck_permute_axes.sh, Shell, 67 lines
tckskip.sh, Shell, 22 lines
textprogressbar.m, MATLAB, 60 lines
tmp_Oli_correlations_shearbulkmu.m, MATLAB, 39 lines
tttmmmppp.m, MATLAB, 55 lines
vector2streams.py, Python, 175 lines
README.md, Text, 37 lines

At the source: github.com/lconcha/Displasias

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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;
125 scripts, each with its path and the digest of its content;
1 match between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

No dataset and no data link were found in the paper.

Data Availability

The raw diffusion-weighted images and photomicrographs are available at https://osf.io/n46d8 (https://doi.org/10.17605/OSF.IO/N46D8).

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

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 13 MeSH terms, 3 funders, 97 references.

Cite

This paper

Ortega-Fimbres, O., Ríos-Carrillo, R., Gaspar-Martínez, E., Ruiz-Acosta, P., Regalado, M., Luna-Munguía, H., Ramírez-Manzanares, A., & Concha, L. (2026). Analysis of cortical dysplasias using b-tensor encoding diffusion MRI in an animal model. PloS one, 21(4), e0346132. https://doi.org/10.1371/journal.pone.0346132

BibTeX

@article{ortegafimbres2026analysis, author = {Ortega-Fimbres, Olimpia and Ríos-Carrillo, Ricardo and Gaspar-Martínez, Edith and Ruiz-Acosta, Priscila and Regalado, Mirelta and Luna-Munguía, Hiram and Ramírez-Manzanares, Alonso and Concha, Luis}, title = {{Analysis of cortical dysplasias using b-tensor encoding diffusion MRI in an animal model}}, journal = {PloS one}, year = {2026}, month = apr, volume = {21}, number = {4}, pages = {e0346132}, publisher = {PLOS}, issn = {1932-6203}, doi = {10.1371/journal.pone.0346132}, url = {https://doi.org/10.1371/journal.pone.0346132}, pmid = {41931540}, pmcid = {PMC13048379} }

RIS

TY - JOUR AU - Ortega-Fimbres, Olimpia AU - Ríos-Carrillo, Ricardo AU - Gaspar-Martínez, Edith AU - Ruiz-Acosta, Priscila AU - Regalado, Mirelta AU - Luna-Munguía, Hiram AU - Ramírez-Manzanares, Alonso AU - Concha, Luis TI - Analysis of cortical dysplasias using b-tensor encoding diffusion MRI in an animal model T2 - PloS one J2 - PLoS One PY - 2026 DA - 2026/04/03 VL - 21 IS - 4 SP - e0346132 SN - 1932-6203 PB - PLOS DO - 10.1371/journal.pone.0346132 UR - https://doi.org/10.1371/journal.pone.0346132 LA - en ER -

CSL-JSON

{ "id": "10.1371/journal.pone.0346132", "type": "article-journal", "title": "Analysis of cortical dysplasias using b-tensor encoding diffusion MRI in an animal model", "container-title": "PloS one", "author": [ { "family": "Ortega-Fimbres", "given": "Olimpia" }, { "family": "Ríos-Carrillo", "given": "Ricardo" }, { "family": "Gaspar-Martínez", "given": "Edith" }, { "family": "Ruiz-Acosta", "given": "Priscila" }, { "family": "Regalado", "given": "Mirelta" }, { "family": "Luna-Munguía", "given": "Hiram" }, { "family": "Ramírez-Manzanares", "given": "Alonso" }, { "family": "Concha", "given": "Luis" } ], "container-title-short": "PLoS One", "volume": "21", "issue": "4", "page": "e0346132", "DOI": "10.1371/journal.pone.0346132", "PMID": "41931540", "PMCID": "PMC13048379", "ISSN": "1932-6203", "publisher": "PLOS", "URL": "https://doi.org/10.1371/journal.pone.0346132", "language": "en", "issued": { "date-parts": [ [ 2026, 4, 3 ] ] } }

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