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In vivo mapping of striatal neurodegeneration in Huntington's disease with Soma and Neurite Density Imaging.

Code ↔ Paper

6 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 6 matches · 5 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and methods › Image processing › SANDI analysis ↔ SANDI_batch_analysis.m, the whole file · a weak match · score 0.88 · noise floor, simulated signals, Rician noise, ground truth, SANDI model, realistic
  2. [2] § Materials and methods › Image processing › SANDI analysis ↔ functions/ML_fitting/setup_and_run_model_training.m, lines 58–207 · score 0.86 · noise floor, Rician noise, uniformly sampled, simulated signals, ground truth, SANDI model
  3. [3] § Materials and methods › Participants ↔ SANDI_batch_analysis.m, the whole file · a weak match · score 0.65 · Cardiff University Brain, Research Imaging Centre, Neuroimaging, CUBRIC, Database, training
  4. [4] § Materials and methods › Participants ↔ functions/ML_fitting/build_training_set.m, the whole file · a weak match · score 0.60 · Cardiff University Brain, Research Imaging Centre, CUBRIC, training, Database
  5. [5] § Materials and methods › Image processing › SANDI analysis ↔ functions/support_functions/FromParamsToSignal_RicianBiased.m, the whole file · a weak match · score 0.57 · intra soma, model parameters, um, Din, ms, signal
  6. [6] § Materials and methods › Image acquisition ↔ functions/support_functions/FromParamsToSignal_RicianBiased.m, the whole file · a weak match · score 0.57 · gradient pulse duration, gradient pulses separation, diffusion

Paper

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The authors' code

MATLAB · 110 lines · 5.7 KB · BSD-2-Clause · 2 matches

  1. function SANDIinput = SANDI_batch_analysis(ProjectMainFolder, Delta, smalldelta, SNR)
  3. % Main script to perform the SANDI analysis (on one or more datasets) using machine learning, as described in Palombo M. et al. Neuroimage 2020: https://doi.org/10.1016/j.neuroimage.2020.116835
  5. % The code assumes that data are organized in the following way:
  6. %
  7. % - ProjectMainFolder
  8. % |-> - derivatives
  9. % |--> - preprocessed
  10. % |---> - sub-01
  11. % |----> - ses-01
  12. % |-----> sub-<>_ses-<>_acq-<>_run-<>_desc-preproc_dwi.nii.gz
  13. % |-----> sub-<>_ses-<>_acq-<>_run-<>_desc-preproc_dwi.bval
  14. % |-----> sub-<>_ses-<>_acq-<>_run-<>_desc-preproc_dwi.bvec
  15. % |-----> sub-<>_ses-<>_acq-<>_run-<>_desc-preproc_mask.nii.gz
  16. % |-----> sub-<>_ses-<>_acq-<>_run-<>_desc-preproc_noisemap.nii.gz
  17. % ...
  18. % |----> - ses-n
  19. % |-----> sub-<>_ses-<>_acq-<>_run-<>_desc-preproc_dwi.nii.gz
  20. % |-----> sub-<>_ses-<>_acq-<>_run-<>_desc-preproc_dwi.bval
  21. % |-----> sub-<>_ses-<>_acq-<>_run-<>_desc-preproc_dwi.bvec
  22. % |-----> sub-<>_ses-<>_acq-<>_run-<>_desc-preproc_mask.nii.gz
  23. % |-----> sub-<>_ses-<>_acq-<>_run-<>_desc-preproc_noisemap.nii.gz
  24. % ...
  25. % |---> - sub-n
  26. % |----> - ses-01
  27. % |-----> sub-<>_ses-<>_acq-<>_run-<>_desc-preproc_dwi.nii.gz
  28. % |-----> sub-<>_ses-<>_acq-<>_run-<>_desc-preproc_dwi.bval
  29. % |-----> sub-<>_ses-<>_acq-<>_run-<>_desc-preproc_dwi.bvec
  30. % |-----> sub-<>_ses-<>_acq-<>_run-<>_desc-preproc_mask.nii.gz
  31. % |-----> sub-<>_ses-<>_acq-<>_run-<>_desc-preproc_noisemap.nii.gz
  32. % ...
  33. % |----> - ses-n
  34. % |-----> sub-<>_ses-<>_acq-<>_run-<>_desc-preproc_dwi.nii.gz
  35. % |-----> sub-<>_ses-<>_acq-<>_run-<>_desc-preproc_dwi.bval
  36. % |-----> sub-<>_ses-<>_acq-<>_run-<>_desc-preproc_dwi.bvec
  37. % |-----> sub-<>_ses-<>_acq-<>_run-<>_desc-preproc_mask.nii.gz
  38. % |-----> sub-<>_ses-<>_acq-<>_run-<>_desc-preproc_noisemap.nii.gz
  40. % The OUTPUT of the analysis will be stored in a new folder
  41. % 'ProjectMainFolder -> derivatives -> SANDI_analysis -> sub-XXX -> ses-XXX -> SANDI_Output'
  42. % for each subject and session
  44. % NOTE: several improvements have been introduced since the original work
  45. % on Neuroimage 2020
  46. % These comprises:
  47. % 1. Calculation of the Spherical Mean signal using Spherical Harmonics
  48. % fitting (zeroth-order SH coefficient)
  49. % 2. The training set is built in a more accurate way:
  50. % (i) Neurite and soma signal fraction are now sampled to cover
  51. % uniformily the simplex fneurite + fsoma <=1
  52. % (ii) The noise is added to simulated signals in a more realistic way:
  53. % first the noiseless signal for a random fibre direction is simulated
  54. % using the SANDI model, then the Rician noise floor is added using the
  55. % RiceMean function with sigma as estimated by MPPCA (noisemap), if
  56. % provided, then Gaussian noise is added with sigma equal to the std. of
  57. % the residuals from the SH fit, finally the spherical mean signal is computed averaging the signal over all the directions.
  58. % (iii) The training can be done in two ways now: minimizing the MSE
  59. % between a) the ground truth model parameters used to simulate the training
  60. % set and the ML prediction, or b) the model parameters estimated by NLLS with Rician likelihood and the ML prediction
  62. % Author:
  63. % Dr. Marco Palombo
  64. % Cardiff University Brain Research Imaging Centre (CUBRIC)
  65. % Cardiff University, UK
  66. % March 2024
  67. % Email: [email hidden]
  69. % Add the path to main and support functions used for SANDI analysis
  71. addpath(genpath(fullfile(pwd, 'functions')));
  73. %% Initialize analysis
  74. SANDIinput = InitializeSANDIinput(ProjectMainFolder, Delta, smalldelta, SNR); % Edit this function to change the default options of the SANDI Toolbox
  76. disp('***** SANDI analysis using Machine Learning based fitting method ***** ')
  77. dt = char(datetime("now"));
  78. disp(['***** ' dt ' ***** '])
  79. fprintf(SANDIinput.LogFileID,'***** SANDI analysis using Machine Learning based fitting method ***** \n');
  80. fprintf(SANDIinput.LogFileID,'***** %s ***** \n', dt);
  82. %% STEP 1 - Preprocess the data: calculate the spherical mean signal and estimate noise distributions
  83. SANDIinput = ProcessAllDatasets(SANDIinput); % Process all the datasets, one by one
  85. %% STEP 2 - Train the Machine Learning (ML) model
  86. SANDIinput = TrainMachineLearningModel(SANDIinput); % trains the ML model on synthetic data
  88. %SANDIinput =
  89. %investigate_exchange_effectes_NEXI_SANDI_RicianNoise(SANDIinput); % Runs
  90. %tests to estimate the bias due to unaccounted exchange between SANDI
  91. %compartments, using NEXI model https://doi.org/10.1016/j.neuroimage.2022.119277
  93. % Saving the Training Set
  94. Signals_train = SANDIinput.database_train_noisy;
  95. Params_train = SANDIinput.params_train;
  96. Performance_train = SANDIinput.train_perf;
  97. Bvals_train = SANDIinput.model.bvals;
  98. Sigma_mppca_train = SANDIinput.model.sigma_mppca;
  99. Sigma_SHresiduals_train = SANDIinput.model.sigma_SHresiduals;
  101. mkdir(fullfile(SANDIinput.StudyMainFolder, 'Report_ML_Training_Performance'));
  103. save(fullfile(SANDIinput.StudyMainFolder, 'Report_ML_Training_Performance','TrainingSet.mat'), 'Signals_train',...
  104. 'Params_train','Performance_train','Bvals_train', 'Sigma_mppca_train', 'Sigma_SHresiduals_train');
  106. %% STEP 3 - SANDI fit each subject
  107. SANDIinput = AnalyseAllDatasets(SANDIinput); % Analyse all the datasets, one by one
  109. fclose(SANDIinput.LogFileID);
  110. end

SANDI_batch_analysis.m at commit 2589339, under BSD-2-Clause · at the source

Overview

Authors: Vasileios Ioakeimidis1,2, Marco Palombo1, Chiara Casella3,4,5, Lucy Layland1, Carolyn McNabb1, Robin Schubert6, Philip Pallmann7, Monica Busse7, Cheney Drew7, Sundus Alusi8, Timothy Harrower9,10, Jane Davies11, Anne Rosser12,13, Claudia Metzler-Baddeley1
ORCID iDs: Vasileios Ioakeimidis, Chiara Casella, Carolyn McNabb, Philip Pallmann, Monica Busse, Timothy Harrower, Jane Davies, Claudia Metzler-Baddeley
13 affiliations
  1. Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
  2. Danish Research Centre for Magnetic Resonance, Department for Radiology and Nuclear Medicine, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
  3. Early Life Imaging Research Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
  4. London Collaborative Ultra high field System (LoCUS), Kings College London, London, United Kingdom
  5. Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
  6. George Huntington Institut (GHI), Muenster, Germany
  7. Centre for Trials Research, School of Medicine, Cardiff University, Cardiff, United Kingdom
  8. The Walton Centre for Neurology and Neurosurgery, Fazakerley, Liverpool, United Kingdom
  9. Royal Devon and Exeter NHS Trust, Exeter, United Kingdom
  10. Neurology, Exeter NIHR Biomedical Research Centre, Exeter, United Kingdom
  11. Cardiff and Vale University Health Board, Main University Hospital Wales Building, Cardiff University, Health Park Campus, Cardiff, United Kingdom
  12. Cardiff University Brain Repair Group, School of Biosciences, Cardiff University, Cardiff, United Kingdom
  13. Advanced Neurotherapeutics Centre (ANTC), Department of Neurology and Psychological Medicine, School of Medicine, Cardiff University, Cardiff, United Kingdom
Institutions: Amager Hospital (Denmark); Copenhagen University Hospital (Denmark); Cardiff University (United Kingdom); King's College London (United Kingdom); George Huntington Institute (Germany); Walton Centre (United Kingdom); Royal Devon & Exeter NHS Foundation Trust (United Kingdom); Cardiff and Vale University Health Board (United Kingdom)
Journal: eLife, volume 14, article RP107661
Dates: published online 11 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.107661 · PMID 42576606 · PMCID PMC13461150 · OpenAlex W4414238985
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.7554/elife.107661.2
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), other condition (population), clinical / translational (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity, fMRI & imaging
Keywords: Human
MeSH: Corpus Striatum*, Huntington Disease*, Neurites*, Adult, Aged, Basal Ganglia, Diffusion Magnetic Resonance Imaging, Female, Humans, Male, Middle Aged (* major topic)
Topic: Genetic Neurodegenerative Diseases (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Wellcome Trust (317797/Z/24/Z, 096646/Z/11/Z, 104943, 104943/Z/14/Z, 227882/Z/23/Z, 10.35802/104943, 204005/Z/16/Z, 10.35802/204005); HCRW_ (NIHR-FS(A)-2022); UKRI (MR/T020296/2)
Citations: not cited yet (Europe PMC); 89 references in the paper
Research resources: SANDI Matlab Toolbox RRID:SCR_028525

Abstract

Huntington’s disease (HD) is an inherited neurodegenerative disorder characterised by progressive cognitive and motor decline driven by basal ganglia (BG) atrophy. Clinical trials of novel disease-modifying therapies are ongoing, creating a need for sensitive non-invasive imaging biomarkers. Soma and Neurite Density Imaging (SANDI) is a multi-shell diffusion MRI model that estimates intracellular signal fractions from sphere-shaped soma and shows promise as a marker of neurodegeneration. The objectives of this study were to characterise HD-related microstructural abnormalities in the BG using SANDI and to examine relationships between SANDI and volumetric measurements and motor performance. T1- and diffusion-weighted images (b-values 200–6000 s/mm²) were acquired on a 3T Siemens Connectom scanner (300 mT/m) in 56 individuals with HD and 57 age- and sex-matched controls. HD participants completed Quantitative Motor (Q-Motor) tasks, summarised using principal component analysis. SANDI estimated apparent soma and neurite density, apparent soma size, and extracellular signal fraction. Microstructural and volumetric indices were extracted from bilateral caudate, putamen, pallidum and thalamus regions, compared between groups, and correlated with Q-Motor performance. HD was associated with reduced apparent soma density and increased apparent soma size and extracellular signal fraction in the BG but not the thalami. No group differences were present for apparent neurite density. SANDI metrics correlated with Q-Motor performance and explained up to 63% of striatal atrophy in HD. SANDI indices detected HD-related striatal neurodegeneration, explained atrophy, and correlated with motor impairments, demonstrating its potential as an in vivo biomarker and surrogate clinical outcome measure for HD and other neurodegenerative diseases.

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 6 matches between paragraphs and lines of code.

palombom/SANDI-Matlab-Toolbox-Latest-Release

License: BSD-2-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 2589339a2996c1ad573adf9aaaa19d4aafdb6100, 25 February 2025
Languages: MATLAB (88)
Size: 101 files, 88 scripts
Software Heritage: not archived
Found in: the text, “SANDI analysis”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Tools for NIfTI and ANALYZE image (MATLAB) (20 files), Statistics and Machine Learning Toolbox (13 files), Optimization Toolbox (6 files), Image Processing Toolbox (5 files), Deep Learning Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
90 files
At the source: github.com/palombom/SANDI-Matlab-Toolbox-Latest-Release

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;
  • 88 scripts, each with its path and the digest of its content;
  • 6 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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Data

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

Data availability

This research utilised baseline data from the HD-DRUM project that has been endorsed by the Enroll-HD Scientific Oversight Committee (SOC) (14/11/2022). The genetic and clinical data in this study were provided by Enroll-HD (https://www.enroll-hd.org) in accordance with its data access policies. At the end of the HD-DRUM project, the coded study data will be shared and made accessible to the research community via the Enroll-HD specific data request process that is administered by the CHDI Foundation. We are unable to make the data openly available in anonymised form as our study comprises a relatively small number of individuals with a rare genetic disease, and the dataset includes genetic, clinical, and imaging information that, in combination with demographic detail, creates a meaningful risk of re-identification even after standard anonymisation procedures. This risk is heightened for rare disease populations, where the pool of possible participants is inherently limited. Researchers seeking access to the patient data may apply through Enroll-HD. WAND data are publicly available (https://git.cardiff.ac.uk/cubric/wand).

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 14 authors, 1 keyword, 11 MeSH terms, 3 funders, 82 references, 1 RRID.

Cite

This paper

Ioakeimidis, V., Palombo, M., Casella, C., Layland, L., McNabb, C., Schubert, R., Pallmann, P., Busse, M., Drew, C., Alusi, S., Harrower, T., Davies, J., Rosser, A., & Metzler-Baddeley, C. (2026). In vivo mapping of striatal neurodegeneration in Huntington's disease with Soma and Neurite Density Imaging. eLife, 14, RP107661. https://doi.org/10.7554/elife.107661

BibTeX

@article{ioakeimidis2026vivo,
author = {Ioakeimidis, Vasileios and Palombo, Marco and Casella, Chiara and Layland, Lucy and McNabb, Carolyn and Schubert, Robin and Pallmann, Philip and Busse, Monica and Drew, Cheney and Alusi, Sundus and Harrower, Timothy and Davies, Jane and Rosser, Anne and Metzler-Baddeley, Claudia},
title = {{In vivo mapping of striatal neurodegeneration in Huntington's disease with Soma and Neurite Density Imaging}},
journal = {eLife},
year = {2026},
month = aug,
volume = {14},
pages = {RP107661},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.107661},
url = {https://doi.org/10.7554/elife.107661},
pmid = {42576606},
pmcid = {PMC13461150}
}

RIS

TY - JOUR
AU - Ioakeimidis, Vasileios
AU - Palombo, Marco
AU - Casella, Chiara
AU - Layland, Lucy
AU - McNabb, Carolyn
AU - Schubert, Robin
AU - Pallmann, Philip
AU - Busse, Monica
AU - Drew, Cheney
AU - Alusi, Sundus
AU - Harrower, Timothy
AU - Davies, Jane
AU - Rosser, Anne
AU - Metzler-Baddeley, Claudia
TI - In vivo mapping of striatal neurodegeneration in Huntington's disease with Soma and Neurite Density Imaging
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/08/11
VL - 14
SP - RP107661
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.107661
UR - https://doi.org/10.7554/elife.107661
LA - en
ER -

CSL-JSON

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