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Disability profiles in progressive multiple sclerosis reflect pathology distribution, independent of clinical phenotype.

Code ↔ Paper

2 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 2 matches
  1. [1] § Materials and methods › Network disconnection quantification ↔ extract_tractogram.py, lines 7–73 · score 0.79 · DSI Studio, streamlines passing, lesion masks, ROI, tracts, template
  2. [2] § Materials and methods › Network disconnection quantification ↔ runner.py, lines 17–55 · score 0.65 · structurally disconnected, streamlines passing, lesion masks, tractogram, matrix, atlas

Paper

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The authors' code

Python · 98 lines · 4.4 KB · CC-BY-NC-SA-4.0 · 1 match

  1. import os
  2. import glob
  3. import subprocess
  4. import argparse
  7. def extract_tractogram(lesion_mask, output_folder, dependencies_directory='./dependencies', overwrite=False) :
  9. """
  10. First step of disconnectome analysis :
  11. Uses DSIStudio to extract the disconnectome and its connectivity matrix. This is done in two calls of dsi_studio command line :
  12. firstly, we isolate the streamlines of the template whole brain tractogram that pass though the lesion mask,
  13. secondly, we extract the connectivity matrix characterizing these streamlines, using a predefined parcellation atlas
  15. Args :
  16. lesion_mask : path to the binary lesion segmentation mask
  17. output_folder : path to the output_folder where the results will be saved
  18. dependencies_directory : path to the dependencies directory
  19. overwrite : if True will overwrite the results if they already exist. If False will not run
  20. """
  22. docker_image_dsi = 'manishka/dsi-studio-docker:v19.10'
  23. docker_run = 'docker run -u $(id -u):$(id -g) --rm -e DISPLAY=' + os.environ['DISPLAY'] + ' -v /tmp/.X11-unix/:/tmp/.X11-unix'
  25. # path to the tractogram of affected streamlines in trk format
  26. output_trk = os.path.join(output_folder,'connectivity_loss.trk.gz')
  27. # path to the connectivity matrix of the affected streamlines
  28. output_cm = os.path.join(output_folder,'connectivity_loss.mat')
  30. # checks if the lesion mask exists in MNI
  31. try :
  32. lesion_roi = glob.glob(lesion_mask)[0]
  33. except:
  34. print('No lesion mask or multiple were found')
  35. return
  37. # definition of volumes to be mounted for docker image
  38. vol = ' -v ' + os.path.abspath(dependencies_directory) + ':/dependencies/ -v ' +\
  39. os.path.split(lesion_roi)[0] + ':/input/ -v ' +\
  40. output_folder + ':/output/ '
  43. docker_base_command = docker_run + vol + docker_image_dsi + ' ./opt/dsistudio/dsi_studio '
  45. # if streamlines passing through lesions were not isolated yet
  46. if not os.path.isfile(output_trk) or overwrite :
  48. # extract affected streamlines
  49. # define output
  50. os.makedirs( output_folder, exist_ok=True)
  51. # command line options to extract streamlines passing through lesion mask from whole brain tractogram
  52. docker_command = docker_base_command +\
  53. '--action=ana --source=/dependencies/HCP842_1mm.fib.gz' +\
  54. ' --tract=/dependencies/whole_brain.trk.gz' +\
  55. ' --roi=/input/' + os.path.split(lesion_roi)[1] +\
  56. ' --output=/output/connectivity_loss.trk.gz'
  58. subprocess.check_output(docker_command,stderr=subprocess.STDOUT,shell= True)
  61. # if the connectivity matrix wasn't already extracted
  62. if not os.path.isfile(output_cm) or overwrite:
  64. # extract connectivity of affected streamlines using the Brainnetome atlas
  65. # command line options to extract connectivity of affected streamlines
  66. docker_command = docker_base_command + \
  67. ' --action=ana --source=/dependencies/HCP842_1mm.fib.gz'+ \
  68. ' --connectivity="/dependencies/BN_Atlas_274_combined.nii.gz' + \
  69. '" --connectivity_value=count --connectivity_type=pass' + \
  70. ' --tract=' + output_trk.replace(output_folder, '/output/') +\
  71. ' --output=/output/connectivity_loss.mat'
  73. subprocess.check_output(docker_command,stderr=subprocess.STDOUT,shell= True)
  78. if __name__ == '__main__':
  80. PARSER = argparse.ArgumentParser(description="Extracting streamlines passing through lesions")
  82. PARSER.add_argument('-d', '--dependencies_directory', dest='dependencies_directory', action='store', required=False, type=str, default ='./dependencies',
  83. help='Path to the dependencies directory. Default is ./dependencies ')
  85. PARSER.add_argument('-o', '--output_directory', dest='output_folder', action='store', required=True, type=str,
  86. help='Output folder of disconnectome')
  88. PARSER.add_argument('-f', '--force', dest='overwrite', action='store_true', default=False,
  89. help='Force overwriting results')
  91. PARSER.add_argument('-l', '--lesion_mask', dest='lesion_mask', action='store', required=True, type=str,
  92. help='Lesion mask in MNI sace')
  94. ARGS = PARSER.parse_args()
  96. extract_tractogram(lesion_mask=ARGS.lesion_mask, dependencies_directory=ARGS.dependencies_directory, output_folder=ARGS.output_folder, overwrite=ARGS.overwrite)

extract_tractogram.py at commit 0716b1e, under CC-BY-NC-SA-4.0 · at the source

Overview

Authors: Ermelinda De Meo1,2, Amy E Jolly1, Marco Ganzetti3, Ferran Prados Carrasco1,4,5, Baris Kanber1, Jonathan Stutters1, David McManus1, Agne Kazlauskaite3, Licinio Craveiro3, Arman Eshaghi1, James Cole6, Declan Chard1,4, Frederik Barkhof1,6,7
ORCID iDs: Ermelinda De Meo, Ferran Prados Carrasco, Baris Kanber, Jonathan Stutters, James Cole, Frederik Barkhof
  1. NMR Research Unit, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, Queen Square MS Centre, University College London, London WC1B5EH, United Kingdom
  2. NEUROFARBA, University of Florence, Florence 50100, Italy
  3. Hoffman-La Roche Ltd., Roche-Pharma, Basel 4070, Switzerland
  4. National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre, London NW1 2PG, United Kingdom
  5. eHealth Center, Universitat Oberta de Catalunya, Barcelona 08018, Spain
  6. Queen Square Institute of Neurology and UCL Hawkes Institute, University College London, London WC1V 6LJ, United Kingdom
  7. Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, 22660, 1100 DD, Netherlands
Institutions: UCL Queen Square Institute of Neurology (United Kingdom); University of Florence (Italy); University College London (United Kingdom); Roche (Switzerland) (Switzerland); Universitat Oberta de Catalunya (Spain); National Institute for Health and Care Research (United Kingdom); UCL Biomedical Research Centre (United Kingdom); Amsterdam University Medical Centers (Netherlands); Vrije Universiteit Amsterdam (Netherlands)
Journal: Brain communications, volume 8, issue 3, article fcag162
Dates: received 4 August 2025; accepted 30 April 2026; published online 2 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag162 · PMID 42205160 · PMCID PMC13201091 · OpenAlex W7160443367
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), multiple sclerosis (population), clinical / translational (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
Keywords: multiple sclerosis, progression, disability profiles, MRI, cognition
Topic: Multiple Sclerosis Research Studies (Pathology and Forensic Medicine, Medicine), according to OpenAlex
Funding: Roche
Citations: not cited yet (Europe PMC); 62 references in the paper

Abstract

Multiple sclerosis (MS) is a clinically heterogeneous disease affecting both neurological and cognitive functions. In this study, we defined clinical profiles based on combined cognitive and motor assessments in progressive MS patients, examined their overlap with classical progressive phenotypes and linked each profile to MRI measures of network disconnection and grey matter atrophy. We analyzed baseline data from 580 participants [277 primary progressive (PP)MS, 303 secondary progressive (SP)MS] in the phase 3b CONSONANCE (NCT03523858) study. Clinical assessments included Expanded Disability Status Scale, 9-Hole Peg Test, timed 25-Foot Walk Test, Symbol Digit Modalities Test and Brief Visuospatial Memory Test Revised. MRI acquisition comprised Fluid Attenuated Inversion Recovery for lesion segmentation and 3D-T1 weighted images for disconnectome reconstruction and volumetry. Independent component analysis was applied to regional disconnection and volumes to, respectively, identify patterns (ICs) of disconnection and grey matter atrophy. Latent profile analysis (LPA) identified disability profiles from multimodal clinical data. To identify the variables most strongly associated with each LPA-defined profile, we trained one-versus-all eXtreme Gradient Boosting classifiers and computed SHapley Additive exPlanations values. Based on prevailing clinical manifestation, LPA delineated three profiles: motor disability (n = 138, 23.8%), cognitive disability (n = 181, 31.2%) and global disability (n = 261, 45.0%). Profile prevalence did not differ by clinical phenotype (χ2, P = 0.675). Compared with the other profiles, motor disability one was associated with relatively preserved connectivity and tissue volume in the limbic and frontal networks and preserved volumes in the supplementary motor cortex and postcentral gyrus. The cognitive disability profile had relatively lower connectivity and volumes in the cerebellar vermis, temporal pole, posterior cingulate gyrus parietal operculum, lower default-mode-network connectivity and prefrontal–visual integration network volumes. The global disability profile had lower fronto-parietal-network connectivity and lower grey matter, superior frontal gyrus, pallidum, cuneus and cerebellum volumes. Our findings reveal that progressive MS encompasses at least three clinically and biologically distinct subtypes transcending the traditional PPMS/SPMS divide. The cognitive disability profile is driven by focal network disconnection and targeted grey matter loss in critical cognitive hubs, whereas the global disability profile reflects widespread grey matter atrophy across motor and associative regions. Identifying these profiles could be useful in clinical trials by matching outcome measures to underlying patterns of pathology and their clinical manifestations.

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 2 matches between paragraphs and lines of code.

data-edu.github.io/tidylpa

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: the text, “Latent profile analysis”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: data-edu.github.io/tidyLPA/

acit-lausanne/lesion-disconnectomics

License: CC-BY-NC-SA-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 0716b1e3490585bae5e1e8c97639b5585fd5b710, 5 October 2021
Languages: Python (3), R (2)
Size: 38 files, 5 scripts
Software Heritage: not archived
Found in: the text, “Network disconnection quantification”
Holds: README, license file, environment (environment.yml, renv.lock)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: circlize (1 file), ComplexHeatmap (1 file), NetworkX (1 file), NumPy (1 file), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
7 files
At the source: gitlab.com/acit-lausanne/lesion-disconnectomics

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:

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

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

Data availability

Data are available from Roche upon reasonable request.

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 2, 28 September 2026

  • Funding: added F. Hoffmann-La Roche

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 5 keywords, 49 references.

Cite

This paper

De Meo, E., Jolly, A. E., Ganzetti, M., Prados Carrasco, F., Kanber, B., Stutters, J., McManus, D., Kazlauskaite, A., Craveiro, L., Eshaghi, A., Cole, J., Chard, D., & Barkhof, F. (2026). Disability profiles in progressive multiple sclerosis reflect pathology distribution, independent of clinical phenotype. Brain communications, 8(3), fcag162. https://doi.org/10.1093/braincomms/fcag162

BibTeX

@article{demeo2026disability,
author = {De Meo, Ermelinda and Jolly, Amy E and Ganzetti, Marco and Prados Carrasco, Ferran and Kanber, Baris and Stutters, Jonathan and McManus, David and Kazlauskaite, Agne and Craveiro, Licinio and Eshaghi, Arman and Cole, James and Chard, Declan and Barkhof, Frederik},
title = {{Disability profiles in progressive multiple sclerosis reflect pathology distribution, independent of clinical phenotype}},
journal = {Brain communications},
year = {2026},
month = may,
volume = {8},
number = {3},
pages = {fcag162},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag162},
url = {https://doi.org/10.1093/braincomms/fcag162},
pmid = {42205160},
pmcid = {PMC13201091}
}

RIS

TY - JOUR
AU - De Meo, Ermelinda
AU - Jolly, Amy E
AU - Ganzetti, Marco
AU - Prados Carrasco, Ferran
AU - Kanber, Baris
AU - Stutters, Jonathan
AU - McManus, David
AU - Kazlauskaite, Agne
AU - Craveiro, Licinio
AU - Eshaghi, Arman
AU - Cole, James
AU - Chard, Declan
AU - Barkhof, Frederik
TI - Disability profiles in progressive multiple sclerosis reflect pathology distribution, independent of clinical phenotype
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/05/02
VL - 8
IS - 3
SP - fcag162
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag162
UR - https://doi.org/10.1093/braincomms/fcag162
LA - en
ER -

CSL-JSON

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