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Geometry of the cumulant series in diffusion MRI.

Code ↔ Paper

5 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 5 matches
  1. [1] § Methods › Parameter estimation ↔ RICEtools.m, lines 99–160 · score 0.61 · cumulant expansion, full DKI, full RICE, fit, Weighted, LTE
  2. [2] § Methods › Parameter estimation ↔ RICEtools.m, lines 99–160 · score 0.61 · cumulant expansion, full DKI, full RICE, fit, Weighted, LTE
  3. [3] § Results › Multiple sclerosis classification based on clinical dMRI ↔ RICE_logistic_regression_AUC_stratified.m, lines 1–28 · score 0.58 · Logistic regression, predictors, trained, models, matched, age
  4. [4] § Results › Multiple sclerosis classification based on clinical dMRI ↔ example_RICE_logistic_regression.m, lines 1–35 · score 0.57 · Logistic regression, predictors, trained, age, sex, DTI
  5. [5] § Results › Multiple sclerosis classification based on clinical dMRI ↔ example_RICE_logistic_regression.m, lines 37–90 · score 0.57 · confidence interval, logistic regressions, bars, bootstrap, AUCs, classification

Paper

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The authors' code

MATLAB · 90 lines · 3.4 KB · no license · 2 matches

  1. %% Analyzing RICE summary statistics
  3. % Recomputing bootstrapped AUC values from RICE summary statistics (~2.5 minutes in a normal desktop)
  5. clc,clear,close all
  7. % Load .mat file with summary statistics
  8. root = '/Users/coelhs01/Documents/SantiagoCoelho/Git/RICE';
  9. load(fullfile(root,'RICE_MS_summary_statistics.mat'))
  11. rng default
  13. subsets_all{1} = [1 2 3];
  14. subset_tags{1,1} = 'DTI'; % DTI invariants
  15. subsets_all{2} = [1 2 3 4 5 7];
  16. subset_tags{2,1} = 'DKI'; % DKI invariants
  17. subsets_all{3} = 1:15;
  18. subset_tags{3,1} = ' RICE$_\mathrm{LTE}$'; % RICE intrinsic+mixed invariants
  20. Nboot = 100;
  21. tic
  22. AUC_logReg = zeros(Nr, length(subsets_all),Nboot);
  23. for id_subset = 1:length(subsets_all)
  24. subset_idx = subsets_all{id_subset};
  25. for id_roi = 1:Nr
  26. X_keep = squeeze(rice_median(subset_idx,id_roi,:))';
  27. X_keep_agesex = [ X_keep age_all(:) sex_all(:) ];
  28. stratify.sex = sex_all(:); stratify.ms = flag_ms(:); stratify.age = age_all(:); stratify.train = 0.8;
  29. % [AUC_logReg(id_roi,id_subset,:),current_coeffs, output_pred(id_roi,id_subset,:,:)] = compute_logistic_regression_AUC_stratified(X_keep_agesex,flag_ms(:),Nboot,stratify);
  30. [AUC_logReg(id_roi,id_subset,:),current_coeffs, output_pred(id_roi,id_subset,:,:)] = RICE_logistic_regression_AUC_stratified(X_keep_agesex,flag_ms(:),Nboot,stratify);
  31. coeffs_mean{id_roi,id_subset} = mean(current_coeffs,2);
  32. coeffs_std{id_roi,id_subset} = std(current_coeffs,[],2);
  33. end
  34. end
  35. t = toc; fprintf('Time for %d AUC computations = %.4f seconds \n',Nboot,t)
  37. %% Plot the above AUCs (combined features + age + sex)
  38. clc,close all
  40. % rois_keep = [ 1:19 ]; % All delateralized ROIs
  41. rois_keep = [ 1 3 10 11 12 13 14 19 ]; % Larger ROIs + involved in MS
  43. subsets_keep = [1 2 3]; % Which sets of invariants are plotted
  45. % Create bar plot with 95% confidence intervals
  46. ROI_name_abbrev = {'GCC','BCC','SCC','CST','ML','ICP','SCP','CP','ALIC','PLIC','ACR','SCR','PCR','PTR','C','SLF','T','EC','TWM'};
  47. names = ROI_name_abbrev(rois_keep);
  49. % Mean and standard deviation across bootstrap resamples
  50. AUC_means = mean(AUC_logReg(rois_keep,subsets_keep,:), 3);
  51. AUC_std = std(AUC_logReg(rois_keep,subsets_keep,:), 0, 3);
  53. % 95% CI for the mean AUC across bootstrap resamples (normal approximation)
  54. CI_95 = 1.96 * AUC_std ./ sqrt(Nboot);
  57. % Grouped bar plot
  58. figure('Position',[2963 590 1024 629]), hold on
  59. h = bar(AUC_means,'grouped');
  60. % Set colors
  61. blue_shade = [ 167 202 236 ; 78 149 217 ; 33 95 154 ]/256;
  62. for k = 1:length(subsets_keep)
  63. h(k).FaceColor = blue_shade(k,:);
  64. end
  66. % Add errorbars
  67. numGroups = size(AUC_means,1);
  68. numBars = size(AUC_means,2);
  70. % X positions of the bars
  71. groupWidth = min(0.8, numBars/(numBars+1.5));
  72. for i = 1:numBars
  73. % Get center of each bar
  74. x = (1:numGroups) - groupWidth/2 + (2*i-1) * groupWidth / (2*numBars);
  75. errorbar(x, AUC_means(:,i), CI_95(:,i), 'k', 'linestyle', 'none', 'LineWidth',1.5);
  76. end
  78. % Adjust x-axis labels: one label per subject
  79. set(gca,'XTick',1:length(rois_keep),'XTickLabel',names);
  80. set(gca,'TickLength',[0 0]) % removes the actual tick marks
  81. ax = gca; % get current axes
  82. ax.TitleFontSizeMultiplier = 1.5; % scales relative to default
  84. % Legend
  85. legend(subset_tags(subsets_keep),'Location','northwest','interpreter','latex');
  86. title('MS classification AUC - logistic regression','interpreter','latex');
  87. set(gca,'FontSize',15)
  88. set(gca, 'TickLabelInterpreter', 'latex');
  89. ylim([0.6 0.95]), box on
  90. set(gca, 'LineWidth', 0.8, 'XColor', 'k', 'YColor', 'k')

example_RICE_logistic_regression.m at commit 3c327de, no license · at the source

Overview

Authors: Santiago Coelho1, Jenny Chen1, Filip Szczepankiewicz2, Els Fieremans1, Dmitry S Novikov1
ORCID iDs: Santiago Coelho, Jenny Chen, Filip Szczepankiewicz, Els Fieremans, Dmitry S Novikov
  1. Center for Biomedical Imaging and Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY USA
  2. Department of Medical Radiation Physics, Lund University, Lund, Sweden
Institutions: Center for Advanced Imaging Innovation and Research (United States); New York University (United States); Lund University (Sweden)
Journal: Nature communications, volume 17, issue 1, article 4220
Dates: received 28 April 2025; accepted 16 February 2026; published online 10 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-70018-w · PMID 42108286 · PMCID PMC13161373 · OpenAlex W7160772459
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), multiple sclerosis (population)
Methods: Smoothing, state filtering, decompositions, Physiology & signal measures, fMRI & imaging
Keywords: Magnetic properties and materials, Diffusion tensor imaging, Magnetic resonance imaging
MeSH: Brain*, Diffusion Magnetic Resonance Imaging*, Multiple Sclerosis*, Algorithms, Humans, Image Processing, Computer-Assisted (* major topic)
Topic: Advanced Neuroimaging Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: Cancerfonden (22 0592 JIA); NIBIB NIH HHS (K99 EB036080, P41 EB017183, R01 EB027075); NINDS NIH HHS (R01 NS088040); U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (K99 EB036080, R01 EB027075, P41 EB017183); U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (R01 NS088040); Vetenskapsrådet (2021-04844)
Citations: cited by 2 papers (Europe PMC); 118 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 5 matches between paragraphs and lines of code.

NYU-DiffusionMRI/RICE

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 3c327def230d19bc8d966bfaf629cf149e40a16f, 30 July 2026
Languages: MATLAB (5)
Size: 11 files, 5 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Parallel Computing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file), Tools for NIfTI and ANALYZE image (MATLAB) (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
6 files
At the source: github.com/NYU-DiffusionMRI/RICE

Zenodo 19698777

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Parallel Computing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file), Tools for NIfTI and ANALYZE image (MATLAB) (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
6 files
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Read it in the paper: doi.org/10.1038/s41467-026-70018-w.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 3 keywords, 6 MeSH terms, 6 funders, 80 references.

Cite

This paper

Coelho, S., Chen, J., Szczepankiewicz, F., Fieremans, E., & Novikov, D. S. (2026). Geometry of the cumulant series in diffusion MRI. Nature communications, 17(1), 4220. https://doi.org/10.1038/s41467-026-70018-w

BibTeX

@article{coelho2026geometry,
author = {Coelho, Santiago and Chen, Jenny and Szczepankiewicz, Filip and Fieremans, Els and Novikov, Dmitry S},
title = {{Geometry of the cumulant series in diffusion MRI}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {4220},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-70018-w},
url = {https://doi.org/10.1038/s41467-026-70018-w},
pmid = {42108286},
pmcid = {PMC13161373}
}

RIS

TY - JOUR
AU - Coelho, Santiago
AU - Chen, Jenny
AU - Szczepankiewicz, Filip
AU - Fieremans, Els
AU - Novikov, Dmitry S
TI - Geometry of the cumulant series in diffusion MRI
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/10
VL - 17
IS - 1
SP - 4220
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-70018-w
UR - https://doi.org/10.1038/s41467-026-70018-w
LA - en
ER -

CSL-JSON

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