OSCR

Visualizing cortical laminar architecture in the living human brain using next-generation ultra-high-gradient diffusion MRI.

Code ↔ Paper

14 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 14 matches
  1. [1] § Methods and materials › Data processing ↔ fitting/SANDI_fitting.m, lines 21 to 32 · score 0.77 · intra neurite diffusivity, soma radius, soma diffusivity, ms, Din, noise · Report a mismatch
  2. [2] § Methods and materials › Data processing ↔ super-resolution/A_diffsupres.m, lines 1 to 19 · score 0.75 · super resolution, MRtrix3, FSL, preprocessed, diffusivity · Report a mismatch
  3. [3] § Methods and materials › Data acquisition ↔ supra and infragranular/B1_gran_process.m, lines 1 to 18 · score 0.74 · Massachusetts General Hospital, Martinos Center, Biomedical Imaging, dMRI, Athinoula, intensity · Report a mismatch
  4. [4] § Methods and materials › Data acquisition ↔ super-resolution/A_diffsupres.m, lines 1 to 19 · score 0.70 · Massachusetts General Hospital, Martinos Center, Biomedical Imaging, voxel, Athinoula, MRI · Report a mismatch
  5. [5] § Methods and materials › Data processing ↔ von Economo/A1_vonEconomo_fsa5tofsa.m, lines 1 to 18 · score 0.69 · ENIGMA toolbox, von Economo, FreeSurfer, parcellation, fsaverage, surface · Report a mismatch
  6. [6] § Methods and materials › Data processing ↔ von Economo/A2_vonEconomo_laminar_fis.m, lines 1 to 19 · score 0.66 · von Economo, FreeSurfer, annot, parcellation, fsaverage, profiles · Report a mismatch
  7. [7] § Results › Regional differences in intra-soma signal fraction fis across cortical depths ↔ motor vs visual/A1_motor_visual_comparison.m, lines 1 to 19 · score 0.66 · visual cortices, depth profiles, intra soma signal, surface maps, motor, box · Report a mismatch
  8. [8] § Methods and materials › Data processing ↔ supra and infragranular/A1_fit_lh_infra_supra_surface.py, lines 1 to 51 · score 0.64 · layer fractions, cortical surface, FreeSurfer, template, curvature, fsaverage · Report a mismatch
  9. [9] § Methods and materials › Data processing ↔ Laminar SANDI/S1_Vol2Surf.m, lines 1 to 17 · score 0.64 · mri_vol2surf, FreeSurfer, cortical surface, bbregister, laminar, depth · Report a mismatch
  10. [10] § Results ↔ Laminar SANDI/S1_Vol2Surf.m, lines 1 to 17 · score 0.62 · mri_vol2surf, SANDI fitting, FreeSurfer, S1, surface, laminar · Report a mismatch
  11. [11] § Results › Regional differences in intra-soma signal fraction fis across cortical depths ↔ von Economo/A2_vonEconomo_laminar_fis.m, lines 1 to 19 · score 0.60 · von Economo, intra soma signal, atlas, profiles, cytoarchitectonic, fis · Report a mismatch
  12. [12] § Methods and materials › Statistics and reproducibility ↔ motor vs visual/A1_motor_visual_comparison.m, lines 1 to 19 · score 0.58 · visual cortices, intra soma signal, signal fraction, MATLAB, motor, depth · Report a mismatch
  13. [13] § Results ↔ fitting/SANDI_fitting.m, lines 21 to 32 · score 0.55 · intra neurite diffusivity, soma radius, Din, model, SANDI · Report a mismatch
  14. [14] § Results › SANDI metrics across cortical depths ↔ supra and infragranular/B1_gran_process.m, lines 1 to 18 · score 0.51 · infragranular layers, Cortical surface, supragranular, maps, SANDI · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

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The authors' code

MATLAB · 82 lines · 3.8 KB · MIT · 2 matches

  1. % Main script for the SANDI analysis using random forest regression
  2. %
  3. % Reference:
  4. % Lee H, ..., Huang S, Communications Biology, 2026
  5. % Based on Palombo M. et al. Neuroimage 2020: https://doi.org/10.1016/j.neuroimage.2020.116835
  6. %
  7. % Requirements:
  8. % - SANDI-Matlab-Toolbox v1.0 (https://github.com/palombom/SANDI-Matlab-Toolbox-v1.0)
  9. % Please make sure the functions are installed and the path is correctly set.
  10. %
  11. % Author: Hansol Lee (0000-0003-2112-1197)
  12. % Athinoula A. Martinos Center for Biomedical Imaging,
  13. % Massachusetts General Hospital / Harvard Medical School
  15. clear all; close all; clc;
  17. %% Add paths to SANDI functions
  18. restoredefaultpath % Restore default MATLAB path
  19. addpath(genpath('/autofs/cluster/connectome2/Bay8_C2/bids/code/SANDI/functions')); % Add SANDI functions
  21. %% User-defined parameters
  22. sub = 'sub-001'; % List of subjects
  23. rf = '/autofs/cluster/connectome2/Bay8_C2/bids'; % Root folder
  24. delta = 13; % Diffusion gradient separation (ms)
  25. smalldelta = 6; % Diffusion gradient duration (ms)
  26. Dsoma = 2; % Soma diffusivity (µm^2/ms)
  27. Din_UB = 3; % Intra-neurite diffusivity UB (µm^2/ms)
  28. Rsoma_UB = 12; % Soma radius UB (µm)
  29. De_UB = 3; % Extra-cellular diffusivity UB (µm^2/ms)
  30. seed_rng = 1; % Random seed
  31. MLmodel = 'RF'; % Machine learning model: 'RF', 'MLP', 'GRNN'
  32. Nset = 1e4; % Number of noise samples for SNR estimation
  34. %% Start timer
  35. overallTimer = tic;
  37. %% Define data paths
  38. data_f = fullfile(rf, sub, 'dwi_process/s13_SANDI'); % Path to DWI processed data
  39. noise_f = fullfile(rf, 'derivatives/processed_dwi', sub); % Path to estimated noise
  40. out_f = fullfile(rf, 'derivatives/SANDI', sub); % Output folder for SANDI results
  41. if ~exist(out_f, 'dir')
  42. mkdir(out_f); % Create output folder if it does not exist
  43. end
  45. %% Load b-values and acquisition parameters
  46. bvalues_filename = fullfile(data_f, 'bvals_D13ms.txt'); % b-values file (FSL format)
  47. data_filename = fullfile(data_f, 'D13ms.nii.gz'); % Preprocessed DWI data file
  48. mask_filename = fullfile(noise_f, [sub '_brain_mask.nii.gz']); % Brain mask file
  49. noisemap_filename = fullfile(noise_f, [sub '_sigma.nii.gz']); % Noise map file from MP-PCA denoising
  51. %% Calculate SNR
  52. noisemap = normalize_noisemap(noisemap_filename, data_filename, mask_filename, bvalues_filename); % Normalize noise map by b=0 image
  54. sigma = noisemap(:);
  55. sigma = sigma(sigma > 0);
  56. sigma = min(max(sigma, 0), 1);
  58. sigma_sampled = sigma(randi(numel(sigma), [Nset, 1]));
  59. sigma_median = median(sigma_sampled);
  60. SNR = 1 / sigma_median;
  62. %% Train Machine Learning model
  63. disp(['Training SANDI ML model for ' sub '...']);
  65. load_acquisition_parameters(bvalues_filename, delta, smalldelta, out_f); % Save acquisition parameters
  67. schemefile = fullfile(out_f, 'diravg.scheme');
  68. [Mdl, train_perf] = setup_and_run_model_training_rician( ...
  69. schemefile, SNR, out_f, Dsoma, Din_UB, Rsoma_UB, De_UB, seed_rng, MLmodel); % Train random forest regression model
  71. %% Preprocess: Compute direction-averaged signal
  72. make_direction_average(out_f, data_filename, bvalues_filename, delta, smalldelta); % Compute direction-averaged DWI
  74. %% Fit SANDI model
  75. disp(['Fitting SANDI model for ' sub '...']);
  77. img_data = fullfile(out_f, 'diravg_signal.nii.gz');
  78. run_model_fitting(img_data, mask_filename, schemefile, out_f, Mdl, MLmodel, sub); % Perform model fitting using trained ML model
  80. %% Display total execution time
  81. elapsedTime = toc(overallTimer);
  82. disp(['Finished SANDI analysis for ' sub ' in ' num2str(elapsedTime, '%.2f') ' seconds.']);

SANDI_fitting.m at commit d826225, under MIT · at the source

Overview

Authors: Hansol Lee1,2,3, Yixin Ma1,2, Kwok-Shing Chan1,2, Eva A Krijnen4,5, Laleh Eskandarian1,2, Aneri Bhatt1,2, Julianna Gerold1,2, Mirsad Mahmutovic6, Oula Puonti1,7, Xiangrui Zeng1,2, Lucas Jacob Deden Binder1,2, Bruce Fischl1,2, Boris Keil6,8,9, Gabriel Ramos-Llordén1,2, Eric C Klawiter4, Hong-Hsi Lee1,2, Susie Y Huang1,2
ORCID iDs: Hansol Lee, Kwok-Shing Chan, Aneri Bhatt, Julianna Gerold, Oula Puonti, Bruce Fischl, Hong-Hsi Lee, Susie Y Huang
  1. Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA USA
  2. Harvard Medical School, Boston, MA USA
  3. Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
  4. Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
  5. MS Center Amsterdam, Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
  6. Institute of Medical Physics and Radiation Protection, TH-Mittelhessen University of Applied Sciences, Giessen, Germany
  7. Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, Copenhagen, Denmark
  8. LOEWE Research Cluster for Advanced Medical Physics in Imaging and Therapy (ADMIT), TH-Mittelhessen University of Applied Sciences, Giessen, Germany
  9. Department of Diagnostic and Interventional Radiology, University Hospital Marburg, Philipps University of Marburg, Marburg, Germany
Institutions: Harvard University (United States); Massachusetts General Hospital (United States); Athinoula A. Martinos Center for Biomedical Imaging (United States); Ulsan National Institute of Science and Technology (South Korea); Amsterdam Neuroscience (Netherlands); Amsterdam UMC Location Vrije Universiteit Amsterdam (Netherlands); Technische Hochschule Mittelhessen (Germany); Amager Hospital (Denmark); Hvidovre Hospital (Denmark); Copenhagen University Hospital (Denmark); Philipps University of Marburg (Germany); Universitätsklinikum Gießen und Marburg (Germany)
Journal: Communications biology, volume 9, issue 1, article 651
Dates: received 22 May 2025; accepted 6 March 2026; published online 23 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s42003-026-09887-2 · PMID 41872514 · PMCID PMC13172339 · OpenAlex W7140107183
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: structural MRI / diffusion (modality), human (organism), systems (subfield)
Methods: Statistics, Connectivity, Preprocessing
Keywords: Magnetic resonance imaging, Translational research, Brain, Biomarkers
MeSH: Brain*, Cerebral Cortex*, Diffusion Magnetic Resonance Imaging*, Adult, Connectome, Female, Humans, Male (* major topic)
Topic: Advanced Neuroimaging Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB) (P41EB015896, P41EB030006, U01EB026996); U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) (R01NS118187, U24NS137077); U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) (R21AG082377, R21AG085795); ZonMw (04520232330012); U.S. Department of Health & Human Services | NIH | National Institute of Dental and Craniofacial Research (NIDCR) (DP5OD031854)
Citations: cited by 3 papers (Europe PMC); 118 references in the paper

Abstract

Characterizing cortical laminar microstructure is essential for understanding the organization of the human brain. Leveraging the next-generation Connectome MRI scanner (maximum gradient strength=500mT/m, slew rate=600 T/m/s), we characterized in vivo cortical laminar cytoarchitecture and myeloarchitecture through cortical depth-dependent analyses of soma and neurite density imaging (SANDI) metrics derived from 1 mm diffusion MRI generated using a super-resolution technique. SANDI revealed distinct laminar profiles: intra-soma signal fraction peaked at ~55% cortical depth, while the intra-neurite signal fraction increased toward deeper cortical layers, consistent with known histological patterns. The visual cortex showed higher intra-soma signal fraction than the motor cortex, particularly in deeper layers. Intra-soma signal fraction correlated positively with cortical curvature in superficial layers and negatively in deeper layers, indicating layer-specific relationships between cortical microstructure and geometry. These findings demonstrate the feasibility of noninvasive mapping of laminar architecture, offering a potential in vivo surrogate for histology and enabling future studies of cortical laminar organization using high-performance gradient MRI.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 14 matches between paragraphs and lines of code.

Connectome20/diffusion_preproc_C2

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 59032d4f5ea70d3fbe37d93ae8d4d049e217d315, 7 August 2025
Languages: Python (15), Shell (1)
Size: 29 files, 16 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NiBabel (4 files), MRtrix3 (3 files), NumPy (3 files), FSL (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
17 files
At the source: github.com/Connectome20/diffusion_preproc_C2

Connectome20/Laminar-SANDI

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: d826225b94bfc2b31108db4433211c4286e36b52, 23 December 2025
Languages: MATLAB (13), Python (1)
Size: 107 files, 14 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FreeSurfer (11 files), Parallel Computing Toolbox (1 file), MRtrix3 (1 file), NiBabel (1 file), NumPy (1 file), pandas (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
16 files
At the source: github.com/Connectome20/Laminar-SANDI

Code availability

The code for diffusion MRI preprocessing and laminar analysis is publicly available on GitHub at https://github.com/Connectome20/diffusion_preproc_C2 and https://github.com/Connectome20/Laminar-SANDI.

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

Tracing map

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What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 30 scripts, each with its path and the digest of its content;
  • 14 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

A Connectome 2.0 diffusion MRI dataset (raw and preprocessed DWIs) is publicly available on OpenNeuro (10.18112/openneuro.ds006181.v1.0.0)117. Connectome 1.0 DWI datasets are publicly available via figshare (10.6084/m9.figshare.c.5315474)118. All other data supporting the findings of this study are available from the corresponding author upon reasonable request, subject to applicable data-sharing agreements and conditions of reuse.

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

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 17 authors, 4 keywords, 8 MeSH terms, 5 funders, 109 references.

Cite

This paper

Lee, H., Ma, Y., Chan, K.-S., Krijnen, E. A., Eskandarian, L., Bhatt, A., Gerold, J., Mahmutovic, M., Puonti, O., Zeng, X., Deden Binder, L. J., Fischl, B., Keil, B., Ramos-Llordén, G., Klawiter, E. C., Lee, H.-H., & Huang, S. Y. (2026). Visualizing cortical laminar architecture in the living human brain using next-generation ultra-high-gradient diffusion MRI. Communications biology, 9(1), 651. https://doi.org/10.1038/s42003-026-09887-2

BibTeX

@article{lee2026visualizing,
author = {Lee, Hansol and Ma, Yixin and Chan, Kwok-Shing and Krijnen, Eva A and Eskandarian, Laleh and Bhatt, Aneri and Gerold, Julianna and Mahmutovic, Mirsad and Puonti, Oula and Zeng, Xiangrui and Deden Binder, Lucas Jacob and Fischl, Bruce and Keil, Boris and Ramos-Llordén, Gabriel and Klawiter, Eric C and Lee, Hong-Hsi and Huang, Susie Y},
title = {{Visualizing cortical laminar architecture in the living human brain using next-generation ultra-high-gradient diffusion MRI}},
journal = {Communications biology},
year = {2026},
month = mar,
volume = {9},
number = {1},
pages = {651},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-09887-2},
url = {https://doi.org/10.1038/s42003-026-09887-2},
pmid = {41872514},
pmcid = {PMC13172339}
}

RIS

TY - JOUR
AU - Lee, Hansol
AU - Ma, Yixin
AU - Chan, Kwok-Shing
AU - Krijnen, Eva A
AU - Eskandarian, Laleh
AU - Bhatt, Aneri
AU - Gerold, Julianna
AU - Mahmutovic, Mirsad
AU - Puonti, Oula
AU - Zeng, Xiangrui
AU - Deden Binder, Lucas Jacob
AU - Fischl, Bruce
AU - Keil, Boris
AU - Ramos-Llordén, Gabriel
AU - Klawiter, Eric C
AU - Lee, Hong-Hsi
AU - Huang, Susie Y
TI - Visualizing cortical laminar architecture in the living human brain using next-generation ultra-high-gradient diffusion MRI
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/03/23
VL - 9
IS - 1
SP - 651
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-09887-2
UR - https://doi.org/10.1038/s42003-026-09887-2
LA - en
ER -

CSL-JSON

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