OSCR

A prognostic human brain network for diffuse midline glioma.

Code ↔ Paper

9 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 9 matches · 5 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Neuroimaging acquisition, segmentation and preprocessing ↔ ea_normalize_spmshoot.m, the whole file · a weak match · score 0.66 · MNI space, brain MRI, ITK, coronal, neuro, medicine
  2. [2] § Methods › Neuroimaging acquisition, segmentation and preprocessing ↔ ext_libs/OSS-DBS/genvat_butenko/ea_prepare_DTI.m, the whole file · a weak match · score 0.64 · nearest neighbour interpolation, MNI space, Python, template, ANTs, transformed
  3. [3] § Methods › Tumour location mapping › Identifying tumour voxels associated with patient overall survival ↔ ea_normalize_fsl.m, the whole file · a weak match · score 0.59 · FMRIB Software Library, MNI space, FSL, ANTs, maps, patient
  4. [4] § Methods › Tumour network mapping › Examination of DMG network robustness ↔ ea_genvat_horn.m, lines 2829–2977 · score 0.57 · cerebrospinal fluid, CSF, head, binary, segmentations, anatomic
  5. [5] § Methods › Patient cohorts ↔ ext_libs/dicm2nii/dicm_dict.m, lines 146–205 · score 0.57 · inversion recovery, spatial resolution, dimensional, slice, sequence, Patient
  6. [6] § Methods › Tumour network mapping › Examination of DMG network robustness ↔ ea_normalize_spmshoot.m, the whole file · a weak match · score 0.54 · deformation fields, MNI space, CSF, segmentations, mask, mapping
  7. [7] § Methods › Tumour network mapping › Identifying functional connections associated with overall survival ↔ explorers/networkmapping_explorer/ea_networkmapping.m, lines 1–74 · score 0.52 · lesion network mapping, flip, covariate, permutation, peak, threshold
  8. [8] § Tumour network mapping ↔ explorers/fiberfiltering_explorer/ea_disctract.m, lines 1–113 · score 0.51 · dMRI, structural connections, pathways, tracts, covariate, weighted
  9. [9] § Methods › Tumour location mapping › Identifying tumour voxels associated with patient overall survival ↔ ea_get_MNI_field_from_csv.m, the whole file · a weak match · score 0.50 · MNI space, ANTs, binary, vector, linear, voxels

Paper

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The authors' code

MATLAB · 125 lines · 6.8 KB · GPL-3.0 · 2 matches

  1. function varargout=ea_normalize_spmshoot(options)
  2. % This is a function that normalizes both a copy of transversal and coronal
  3. % images into MNI-space. The goal was to make the procedure both robust and
  4. % automatic, but still, it must be said that normalization results should
  5. % be taken with much care because all reconstruction results heavily depend
  6. % on these results. Normalization of DBS-MR-images is especially
  7. % problematic since usually, the field of view doesn't cover the whole
  8. % brain (to reduce SAR-levels during acquisition) and since electrode
  9. % artifacts can impair the normalization process. Therefore, normalization
  10. % might be best archieved with other tools that have specialized on
  11. % normalization of such image data.
  12. %
  13. % The procedure used here uses the SPM DARTEL approach to map a patient's
  14. % brain to MNI space directly. Unlike the usual DARTEL-approach, which is
  15. % usually used for group studies, here, DARTEL is used for a pairwise
  16. % co-registration between patient anatomy and MNI template. It has been
  17. % shown that DARTEL also performs superior to many other normalization approaches
  18. % also in a pair-wise setting e.g. in
  19. % Klein, A., et al. (2009). Evaluation of 14 nonlinear deformation algorithms
  20. % applied to human brain MRI registration. NeuroImage, 46(3), 786?802.
  21. % doi:10.1016/j.neuroimage.2008.12.037
  22. %
  23. % Since a high resolution is needed for accurate DBS localizations, this
  24. % function applies DARTEL to an output resolution of 0.5 mm isotropic. This
  25. % makes the procedure quite slow.
  27. % The function uses some code snippets written by Ged Ridgway.
  28. % __________________________________________________________________________________
  29. % Copyright (C) 2014 Charite University Medicine Berlin, Movement Disorders Unit
  30. % Andreas Horn
  33. if ischar(options) % return name of method.
  34. varargout{1}='SPM12 SHOOT (Ashburner 2011)';
  35. varargout{2}=1; % dummy output
  36. varargout{3}=0; % hassettings.
  37. varargout{4}=1; % is multispectral
  38. return
  39. end
  41. disp('Segmenting preoperative version (Import to DARTEL-space)');
  42. preopImages = struct2cell(options.subj.coreg.anat.preop);
  43. ea_newseg(preopImages, 1, 1);
  44. disp('Segmentation of preoperative MRI done.');
  46. [directory, preopAnchorName] = fileparts(preopImages{1});
  47. directory = [directory, filesep];
  49. % Check if SHOOT template is available
  50. if exist([ea_space(options,'dartel'),'shootmni_6.nii'],'file')
  51. % There is a DARTEL-Template. Check if it will match:
  52. Vt=spm_vol([ea_space(options,'dartel'),'shootmni_6.nii']);
  53. Vp=spm_vol([directory, 'rc1', preopAnchorName, '.nii']);
  54. if ~isequal(Vp.dim,Vt(1).dim) || ~isequal(Vp.mat,Vt(1).mat) % Dartel template not matching. -> create matching one.
  55. ea_create_tpm_darteltemplate;
  56. end
  57. else % no dartel template present. -> Create matching dartel templates from highres version.
  58. keyboard
  59. ea_create_tpm_darteltemplate;
  60. end
  62. % Forward
  63. matlabbatch{1}.spm.tools.shoot.warp1.images = {
  64. {[directory,'rc1',preopAnchorName,'.nii,1']}
  65. {[directory,'rc2',preopAnchorName,'.nii,1']}
  66. {[directory,'rc3',preopAnchorName,'.nii,1']}}';
  67. matlabbatch{1}.spm.tools.shoot.warp1.templates = {[ea_space(options,'dartel'),'shootmni_1.nii']
  68. [ea_space(options,'dartel'),'shootmni_2.nii']
  69. [ea_space(options,'dartel'),'shootmni_3.nii']
  70. [ea_space(options,'dartel'),'shootmni_4.nii']
  71. [ea_space(options,'dartel'),'shootmni_5.nii']
  72. [ea_space(options,'dartel'),'shootmni_6.nii']};
  74. spm_jobman('run',{matlabbatch});
  75. disp('*** Shoot coregistration of preoperative version worked.');
  76. clear matlabbatch;
  78. movefile([directory,'y_rc1', preopAnchorName, '_Template.nii'], [directory,'y_ea_normparams.nii']);
  80. % Inverse
  81. matlabbatch{1}.spm.util.defs.comp{1}.inv.comp{1}.def = {[directory,'y_ea_normparams.nii']};
  82. matlabbatch{1}.spm.util.defs.comp{1}.inv.space = {[directory, preopAnchorName, '.nii']};
  83. matlabbatch{1}.spm.util.defs.out{1}.savedef.ofname = 'ea_inv_normparams.nii';
  84. matlabbatch{1}.spm.util.defs.out{1}.savedef.savedir.saveusr = {directory};
  85. spm_jobman('run',{matlabbatch});
  86. disp('*** Exported normalization parameters to y_ea_inv_normparams.nii');
  87. clear matlabbatch;
  89. % Delete rc* files and u_rc1* file
  90. ea_delete([directory, 'rc*', preopAnchorName, '.nii']);
  91. ea_delete([directory, '*_rc1', preopAnchorName, '_Template.nii']);
  93. % Rename Segmentations (c1, c2, c3)
  94. mod = replace(options.subj.AnchorModality, textBoundary('start') + alphanumericsPattern + "_", "");
  95. movefile([directory, 'c1', preopAnchorName, '.nii'], setBIDSEntity(preopImages{1}, 'mod', mod, 'label', 'GM', 'suffix', 'mask'));
  96. movefile([directory, 'c2', preopAnchorName, '.nii'], setBIDSEntity(preopImages{1}, 'mod', mod, 'label', 'WM', 'suffix', 'mask'));
  97. movefile([directory, 'c3', preopAnchorName, '.nii'], setBIDSEntity(preopImages{1}, 'mod', mod, 'label', 'CSF', 'suffix', 'mask'));
  99. % Deformation fields to itk
  100. ea_mkdir(fileparts(options.subj.norm.transform.forwardBaseName));
  101. ea_spm_fwd_displacement_field_to_ants([directory, 'y_ea_normparams.nii'], [options.subj.norm.transform.forwardBaseName, 'ants.nii.gz']);
  102. ea_slicer_invert_transform([options.subj.norm.transform.forwardBaseName, 'ants.nii.gz'], options.subj.coreg.anat.preop.(options.subj.AnchorModality), [options.subj.norm.transform.inverseBaseName, 'ants.nii.gz']);
  103. delete([directory, 'y_ea_normparams.nii'])
  104. delete([directory, 'y_ea_inv_normparams.nii'])
  106. ea_apply_normalization(options)
  108. % add methods dump:
  109. [scit, lcit] = ea_getspacedefcit;
  110. cits={
  111. 'Ashburner, J., & Friston, K. J. (2005). Unified segmentation., 26(3), 839?851. http://doi.org/10.1016/j.neuroimage.2005.02.018'
  112. 'Ashburner, J., & Friston, K. J. (2011). Diffeomorphic registration using geodesic shooting and Gauss?Newton optimisation. NeuroImage, 55(3), 954?967. http://doi.org/10.1016/j.neuroimage.2010.12.049'
  113. 'Horn, A., & Kuehn, A. A. (2015). Lead-DBS: a toolbox for deep brain stimulation electrode localizations and visualizations. NeuroImage, 107, 127?135. http://doi.org/10.1016/j.neuroimage.2014.12.002'};
  115. if ~isempty(lcit)
  116. cits = [cits; {lcit}];
  117. end
  119. modality = regexp(preopImages, '(?<=_)[^\W_]+(?=\.nii(\.gz)?$)', 'match', 'once');
  121. ea_methods(options,['Pre- (and post-) operative acquisitions were spatially normalized into ',ea_getspace,' space ',scit,' based on preoperative acquisition(s) (',strjoin(modality, ', '),') using a'...
  122. ' diffeomorphic registration algorithm using geodesic shooting and Gauss-Neuwton optimisation (SHOOT) as implemented in SPM12 (Ashburner 2011; www.fil.ion.ucl.ac.uk/spm/software/).',...
  123. ' SHOOT registration was performed by directly registering tissue segmentations of preoperative acquisitions (obtained using the unified Segmentation approach as implemented in SPM12 (Ashburner 2005)',...
  124. ' to a SHOOT template created from tissue priors defined by the MNI (ICBM 152 Nonlinear asymmetric 2009b atlas; http://nist.mni.mcgill.ca/?p=904)',...
  125. ' supplied within Lead-DBS software (Horn 2015; www.lead-dbs.org).'],cits);

ea_normalize_spmshoot.m at commit 5b1008d, under GPL-3.0 · at the source

Overview

Authors: Jai Sidpra1, Valentina Lind1,2,3, Alexander L Cohen4,5,6,7, Frederic L W V J Schaper4,5,8, Thomas J Stone1,9, Yura Grabovska10, Asthik Biswas11, Sniya Sudhakar11, Francisco Sepulveda11,12,13, Bruno S Peres11,14, Greta Veronese2,3, Cristina Alemán-Charlet15,16, Olumide Ogunbiyi9, Kiarash Shamardani17, Jiaqi Zhao6, Alberto Castro Palacin6, Gillian Miller6, Raffaella S Opipari11,18, Enrico De Vita11,19,20, Deborah Ridout21
and 31 other authorsSuely F Ferraciolli14, Leandro T Lucato14, Jernej Avsenik22,23, Eleonora Piccirilli24,25, Andrés Morales-La Madrid26, Jordi Muchart27,28, Maura E Ryan29, Rajan Patel30, Parthiv Haldipur31, Ciaran S Hill3,32, Marie T Krüger2,3,33, Ludvic Zrinzo2,3, Noor ul Owase Jeelani1,34, Juan Pedro Martinez-Barbera1, Andrew M Donson35,36, Kathleen Dorris35,36, Paul S Morgan37,38,39, Alan Mackay10, Humsa S Venkatesh4,5,8, Andreas Horn4,40, Sabine Mueller41, Adam L Green35,36, David M Mirsky42, Harith Akram2,3, Chris Jones10, Kristian Aquilina34, Kshitij Mankad11,20, Michelle Monje17,43, Thomas S Jacques1,9, Michael D Fox4,5,8,44, Darren R Hargrave1,45
ORCID iDs: Jai Sidpra, Valentina Lind, Alexander L Cohen, Frederic L W V J Schaper, Yura Grabovska, Francisco Sepulveda, Greta Veronese, Gillian Miller, Raffaella S Opipari, Enrico De Vita, Jernej Avsenik, Eleonora Piccirilli, Jordi Muchart, Ciaran S Hill, Noor ul Owase Jeelani, Juan Pedro Martinez-Barbera, Paul S Morgan, Humsa S Venkatesh, Andreas Horn, Sabine Mueller, Chris Jones, Kristian Aquilina, Michelle Monje, Thomas S Jacques, Darren R Hargrave
45 affiliations
  1. Developmental Biology and Cancer Section, University College London Great Ormond Street Institute of Child Health, London, UK
  2. Unit of Functional Neurosurgery, University College London Queen Square Institute of Neurology, London, UK
  3. Victor Horsley Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London, UK
  4. Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry, Neurosurgery, and Radiology, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA USA
  5. Harvard Medical School, Harvard University, Boston, MA USA
  6. Department of Neurology, Boston Children’s Hospital, Boston, MA USA
  7. Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA USA
  8. Department of Neurology, Brigham and Women’s Hospital, Boston, MA USA
  9. Department of Histopathology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
  10. Centre for Children and Young People’s Cancer, Division of Cancer Biology, Institute of Cancer Research, London, UK
  11. Department of Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
  12. Department of Radiology, Clínica Alemana de Santiago, Facultad de Medicina Clínica Alemana, Universidad del Desarrollo, Santiago, Chile
  13. Department of Neuroradiology, Institute of Neurosurgery Dr. Alfonso Asenjo, National Health Service, Santiago, Chile
  14. Neuroradiology Section, Hospital das Clinicas (HCFMUSP), Faculdade de Medicina, Universidade de São Paulo, Sao Paulo, Brazil
  15. Genetics and Genomic Medicine Section, University College London Great Ormond Street Institute of Child Health, London, UK
  16. Specialist Integrated Haematology and Malignancy Diagnostic Service—Acquired Genomics, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
  17. Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA USA
  18. Department of Neuroscience, Neuroradiology Unit, San Bortolo Hospital, Vicenza, Italy
  19. MR Physics Group, Department of Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
  20. Developmental Imaging and Biophysics, Developmental Neurosciences, University College London Great Ormond Street Institute of Child Health, London, UK
  21. Population, Policy, and Practice Programme, University College London Great Ormond Street Institute of Child Health, London, UK
  22. Clinical Institute of Radiology, University Medical Centre Ljubljana, Ljubljana, Slovenia
  23. Department of Radiology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
  24. Department of Imaging, Oncological and Advanced Neuroradiology Unit, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
  25. Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, Chieti, Italy
  26. Neuro-Oncology Unit, Paediatric Cancer Center, Sant Joan de Déu Barcelona Children’s Hospital, University of Barcelona, Esplugues de Llobregat, Spain
  27. Department of Radiology, Sant Joan de Déu Barcelona Children’s Hospital, University of Barcelona, Esplugues de Llobregat, Spain
  28. Pediatric Computational Imaging Center (PeCIC), Institut de Recerca Sant Joan de Déu (IRSJD), Esplugues de Llobregat, Spain
  29. Department of Medical Imaging, Ann & Robert H. Lurie Children’s Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL USA
  30. Department of Pediatric Radiology, Texas Children’s Hospital, Baylor College of Medicine, Houston, TX USA
  31. Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, WA USA
  32. Samantha Dickson Brain Cancer Unit, University College London Cancer Institute, London, UK
  33. Department of Functional Neurosurgery, Albert-Ludwigs-Universität Freiburg, Freiburg im Breisgau, Germany
  34. Department of Neurosurgery, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
  35. Morgan Adams Foundation Pediatric Brain Tumour Research Program, Department of Pediatrics, University of Colorado Anschutz School of Medicine, Aurora, CO USA
  36. Neuro-Oncology Program, Center for Cancer and Blood Disorders, Children’s Hospital Colorado, Aurora, CO USA
  37. Department of Medical Physics and Clinical Engineering, Nottingham University Hospitals, Nottingham, UK
  38. School of Medicine, University of Nottingham, Nottingham, UK
  39. NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals, Nottingham, UK
  40. Institute for Network Stimulation, Department of Stereotactic and Functional Neurosurgery, University Hospital Cologne, Cologne, Germany
  41. Department of Neurology, Neurosurgery and Pediatrics, University of California, San Francisco, San Francisco, CA USA
  42. Department of Neuroradiology, Children’s Hospital Colorado, University of Colorado School of Medicine, Aurora, CO USA
  43. Howard Hughes Medical Institute, Stanford, CA USA
  44. Department of Psychiatry, Brigham and Women’s Hospital, Boston, MA USA
  45. Department of Haematology and Oncology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
Institutions: University College London (United Kingdom); National Hospital for Neurology and Neurosurgery (United Kingdom); UCL Queen Square Institute of Neurology (United Kingdom); Brigham and Women's Hospital (United States); Boston Children's Hospital (United States); Harvard University (United States); Great Ormond Street Hospital (United Kingdom); Great Ormond Street Hospital for Children NHS Foundation Trust (United Kingdom); Institute of Cancer Research (United Kingdom); Universidad del Desarrollo (Chile); Clínica Alemana (Chile); Universidade de São Paulo (Brazil); Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (Brazil); Stanford Medicine (United States); Stanford University (United States); Ljubljana University Medical Centre (Slovenia); Bambino Gesù Children's Hospital (Italy); Ospedale San Bortolo (Italy); Istituti di Ricovero e Cura a Carattere Scientifico (Italy); Hospital Sant Joan de Déu Barcelona (Spain); Northwestern University (United States); Lurie Children's Hospital (United States); Baylor College of Medicine (United States); Texas Children's Hospital (United States); University of Ljubljana (Slovenia); Seattle Children's Research Institute (United States); University of Chieti-Pescara (Italy); Universitat de Barcelona (Spain); Children's Hospital Colorado (United States); Institut de Recerca Sant Joan de Déu; Nottingham University Hospitals NHS Trust (United Kingdom); University of Freiburg (Germany); National Health Service (United Kingdom); University of Colorado Anschutz (United States); Morgan Adams Foundation (United States); University of Nottingham (United Kingdom); Nottingham Biomedical Research Centre (United Kingdom); University Hospital Cologne (Germany); University of California, San Francisco (United States)
Journal: Nature, volume 655, issue 8123, pages 769-779
Dates: received 26 February 2025; accepted 6 May 2026; published online 10 June 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41586-026-10631-3 · PMID 42271051 · PMCID PMC13372695 · OpenAlex W4413928338
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), other condition (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
Keywords: Paediatric cancer, Cancer in the nervous system, CNS cancer, Cancer genomics, Cancer imaging
MeSH: Brain*, Brain Neoplasms*, Glioma*, Nerve Net*, Child, Child, Preschool, Female, Humans, Infant, Male, Pons, Prognosis, Synapses, Thalamus (* major topic)
Topic: Glioma Diagnosis and Treatment (Genetics, Medicine), according to OpenAlex
Citations: cited by 3 papers (Europe PMC); 108 references in the paper

Abstract

Diffuse midline gliomas (DMGs) are near-universally lethal tumours of the childhood central nervous system1,2. In animal models, DMGs form brain-wide integrated networks through neuron-to-glioma synapses3–6 and glioma-to-glioma gap junctional coupling3. This extensive connectivity robustly promotes the growth and invasion of DMG3–9 and other glial malignancies10–12 through paracrine mechanisms and direct neuron-to-glioma synapses. However, the organization and clinical implications of these connections in the living human brain remain to be elucidated. Here, we develop tumour network mapping to compute the brain-wide connectivity profile of DMG, defining a conserved brain network across pontine and thalamic DMG associated with patient short-term survival (DMG network). Tumour functional connectivity with the DMG network was independently predictive of patient overall survival across two external validation cohorts. Tumour growth mapped to DMG network-specific trajectories and peak in-network neurometabolic changes across development spatiotemporally aligned with the peak age incidence of DMG. Analyses of single-nucleus RNA sequencing data confirmed diverse synaptic gene enrichment in high-connectivity DMG. Strikingly, incidental surgical resection of high-connectivity thalamic DMG tissue conferred a significant survival advantage. Collectively, these data define a conserved and prognostically important brain network in children with DMG, consistent with the hypothesis that DMGs exploit otherwise healthy brain circuits to promote tumour growth.

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 9 matches between paragraphs and lines of code.

bchcohenlab/BIDS_to_CBIG_fMRI_Preproc2016

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: bf0bc902b2005bca33072a903ffc86f8bcf71de1, 22 May 2023
Languages: Shell (18)
Size: 32 files, 18 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FreeSurfer (6 files), FSL (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
19 files
At the source: github.com/bchcohenlab/BIDS_to_CBIG_fMRI_Preproc2016

netneurolab/neuromaps

License: CC-BY-NC-SA-4.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: ffcc2e0f657943ce00a1b6a968396f32250e495c, 4 June 2026
Languages: Python (45), Shell (3)
Size: 90 files, 48 scripts
Software Heritage: archived
Found in: “Data availability”
Holds: README, license file, environment (Dockerfile, pyproject.toml, requirements.txt, setup.py, docs/requirements.txt), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: neuromaps (29 files), NumPy (19 files), NiBabel (8 files), SciPy (7 files), Nilearn (5 files), scikit-learn (3 files), Matplotlib (2 files), BrainSMASH (1 file), BrainSpace (1 file), pandas (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
50 files
At the source: github.com/netneurolab/neuromaps

lead-dbs.org

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
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At the source: lead-dbs.org

netstim/leaddbs

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 5b1008d705e97fe0c8f693dece14455607f4afac, 2 March 2026
Languages: MATLAB (2362), Python (101), C++ (30), C (29), Shell (10), C/C++ (4), Java (3)
Size: 6,260 files, 2,539 scripts
Software Heritage: archived
Found in: “Code availability”
Holds: README, license file, CITATION.cff, environment (ext_libs/PaCER/docs/requirements.txt), documentation
Not found: tests, continuous integration
Tools: SPM (121 files), Statistics and Machine Learning Toolbox (84 files), Tools for NIfTI and ANALYZE image (MATLAB) (38 files), Image Processing Toolbox (36 files), NumPy (35 files), FieldTrip (24 files), h5py (12 files), SciPy (8 files), FreeSurfer (7 files), cifti-matlab (6 files), Signal Processing Toolbox (6 files), Matplotlib (6 files), Parallel Computing Toolbox (5 files), pandas (5 files), TensorFlow (4 files), Keras (3 files), Optimization Toolbox (3 files), ANTs (2 files), export_fig (2 files), GIfTI library for MATLAB (2 files), NiBabel (2 files), Psychtoolbox (2 files), CAT12 (1 file), Curve Fitting Toolbox (1 file), pydicom (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2,000 files
At the source: github.com/netstim/leaddbs

Code availability

All analyses were conducted using freely available code and software using the programming languages Python (v.3.12.1; Python Software Foundation); R (v.4.3.2; R Foundation for Statistical Computing); and MATLAB (v.R2024a; MathWorks). Code used to preprocess functional connectivity data is available at GitHub (https://github.com/bchcohenlab/BIDS_to_CBIG_fMRI_Preproc2016). dMRI data were preprocessed in line with the HCP minimal processing pipelines (https://github.com/Washington-University/HCPpipelines.git). The code for lesion connectivity analyses is freely available in Lead DBS v.3.0 (www.lead-dbs.org; https://github.com/netstim/leaddbs). Voxelwise univariate VLSM was implemented in NiiStat (https://github.com/neurolabusc/NiiStat). Multivariate VLSM was implemented in SVR-LSM (https://github.com/atdemarco/svrlsmgui). The FMRIB Software Library v.6.0.7.18 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/docs/#/) was also used for both resting-state fMRI and dMRI analyses, as reported in the Methods. Fluorescence-activated cell sorting was performed using FACSDiva (v.9.0; BD FACSDiva) and analysed using FlowJo (v.11.0; FlowJo), both of which are commercially available from BD Biosciences. snRNA-seq fastq files were processed with cellranger v.8.0.1, using the GRCh38 2024-A reference obtained from 10x Genomics (https://www.10xgenomics.com/support/software/cell-ranger/downloads#reference-downloads) with the chemistry flag set to ‘threeprime’ with the other arguments set as the default. The resulting filtered matrix h5 files were analysed in R using Seurat v.5.3.1. DNA methylation arrays were processed in minfi and submitted to the molecular neuropathology methylation classifier v.12.8 hosted by Heidelberg Epignostix (https://app.epignostix.com). References for all source code are provided in the Article.

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

Tracing map

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  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2,064 scripts, each with its path and the digest of its content;
  • 9 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 Statement

Lifespan HCP (development; HCP-D) and Adolescent Brain Cognitive Development (ABCD1000) study data are available from the National Institute of Mental Health Data Archive subject to appropriate permissions35–37. GSP1000 study data are open access, including our preprocessed distribution through the Harvard Dataverse (10.7910/DVN/ILXIKS)40. Yeo1000 data are subject to restricted access given study participant privacy restrictions39. Developmental [18F]FDG-PET data are available on request from the authors of the study53. Human neurotransmitter PET data are available through neuromaps (https://github.com/netneurolab/neuromaps)56. HERBY trial data are available on request from the authors of the study57,99. PNOC trial data are available on application (https://pnoc.us). UCSF-PDGM data are available open access through The Cancer Imaging Archive (https://www.cancerimagingarchive.net/collection/ucsf-pdgm/)46. Other patient datasets used in this study were curated with institutional permission for the present analyses and are not publicly available due to patient privacy and consent restrictions. Deidentified, individual-level data that can be shared are subject to institutional approvals and data transfer agreements; requests should be directed to the corresponding authors, who will respond within 10 working days. As a condition of local institutional approval, GOSH NHS patient data are not permitted to leave the GOSH environment; accordingly, these data are not available to external researchers. snRNA-seq data generated by this study have been deposited in the European Genome-Phenome Archive (https://ega-archive.org/) under controlled access (EGAD50000002514 (https://ega-archive.org/datasets/EGAD50000002514)). Clinical metadata provided by BRAIN UK Participating Centres are not publicly available due to patient privacy and ethical restrictions96.

All analyses were conducted using freely available code and software using the programming languages Python (v.3.12.1; Python Software Foundation); R (v.4.3.2; R Foundation for Statistical Computing); and MATLAB (v.R2024a; MathWorks). Code used to preprocess functional connectivity data is available at GitHub (https://github.com/bchcohenlab/BIDS_to_CBIG_fMRI_Preproc2016). dMRI data were preprocessed in line with the HCP minimal processing pipelines (https://github.com/Washington-University/HCPpipelines.git). The code for lesion connectivity analyses is freely available in Lead DBS v.3.0 (www.lead-dbs.org; https://github.com/netstim/leaddbs). Voxelwise univariate VLSM was implemented in NiiStat (https://github.com/neurolabusc/NiiStat). Multivariate VLSM was implemented in SVR-LSM (https://github.com/atdemarco/svrlsmgui). The FMRIB Software Library v.6.0.7.18 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/docs/#/) was also used for both resting-state fMRI and dMRI analyses, as reported in the Methods. Fluorescence-activated cell sorting was performed using FACSDiva (v.9.0; BD FACSDiva) and analysed using FlowJo (v.11.0; FlowJo), both of which are commercially available from BD Biosciences. snRNA-seq fastq files were processed with cellranger v.8.0.1, using the GRCh38 2024-A reference obtained from 10x Genomics (https://www.10xgenomics.com/support/software/cell-ranger/downloads#reference-downloads) with the chemistry flag set to ‘threeprime’ with the other arguments set as the default. The resulting filtered matrix h5 files were analysed in R using Seurat v.5.3.1. DNA methylation arrays were processed in minfi and submitted to the molecular neuropathology methylation classifier v.12.8 hosted by Heidelberg Epignostix (https://app.epignostix.com). References for all source code are provided in the Article.

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

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 51 authors, 5 keywords, 14 MeSH terms, 106 references.

Cite

This paper

Sidpra, J., Lind, V., Cohen, A. L., Schaper, F. L. W. V. J., Stone, T. J., Grabovska, Y., Biswas, A., Sudhakar, S., Sepulveda, F., Peres, B. S., Veronese, G., Alemán-Charlet, C., Ogunbiyi, O., Shamardani, K., Zhao, J., Palacin, A. C., Miller, G., Opipari, R. S., De Vita, E., . . . Hargrave, D. R. (2026). A prognostic human brain network for diffuse midline glioma. Nature, 655(8123), 769-779. https://doi.org/10.1038/s41586-026-10631-3

BibTeX

@article{sidpra2026prognostic,
author = {Sidpra, Jai and Lind, Valentina and Cohen, Alexander L and Schaper, Frederic L W V J and Stone, Thomas J and Grabovska, Yura and Biswas, Asthik and Sudhakar, Sniya and Sepulveda, Francisco and Peres, Bruno S and Veronese, Greta and Alemán-Charlet, Cristina and Ogunbiyi, Olumide and Shamardani, Kiarash and Zhao, Jiaqi and Palacin, Alberto Castro and Miller, Gillian and Opipari, Raffaella S and De Vita, Enrico and Ridout, Deborah and Ferraciolli, Suely F and Lucato, Leandro T and Avsenik, Jernej and Piccirilli, Eleonora and Morales-La Madrid, Andrés and Muchart, Jordi and Ryan, Maura E and Patel, Rajan and Haldipur, Parthiv and Hill, Ciaran S and Krüger, Marie T and Zrinzo, Ludvic and Jeelani, Noor ul Owase and Martinez-Barbera, Juan Pedro and Donson, Andrew M and Dorris, Kathleen and Morgan, Paul S and Mackay, Alan and Venkatesh, Humsa S and Horn, Andreas and Mueller, Sabine and Green, Adam L and Mirsky, David M and Akram, Harith and Jones, Chris and Aquilina, Kristian and Mankad, Kshitij and Monje, Michelle and Jacques, Thomas S and Fox, Michael D and Hargrave, Darren R},
title = {{A prognostic human brain network for diffuse midline glioma}},
journal = {Nature},
year = {2026},
month = jun,
volume = {655},
number = {8123},
pages = {769--779},
publisher = {Nature Portfolio},
issn = {0028-0836},
doi = {10.1038/s41586-026-10631-3},
url = {https://doi.org/10.1038/s41586-026-10631-3},
pmid = {42271051},
pmcid = {PMC13372695}
}

RIS

TY - JOUR
AU - Sidpra, Jai
AU - Lind, Valentina
AU - Cohen, Alexander L
AU - Schaper, Frederic L W V J
AU - Stone, Thomas J
AU - Grabovska, Yura
AU - Biswas, Asthik
AU - Sudhakar, Sniya
AU - Sepulveda, Francisco
AU - Peres, Bruno S
AU - Veronese, Greta
AU - Alemán-Charlet, Cristina
AU - Ogunbiyi, Olumide
AU - Shamardani, Kiarash
AU - Zhao, Jiaqi
AU - Palacin, Alberto Castro
AU - Miller, Gillian
AU - Opipari, Raffaella S
AU - De Vita, Enrico
AU - Ridout, Deborah
AU - Ferraciolli, Suely F
AU - Lucato, Leandro T
AU - Avsenik, Jernej
AU - Piccirilli, Eleonora
AU - Morales-La Madrid, Andrés
AU - Muchart, Jordi
AU - Ryan, Maura E
AU - Patel, Rajan
AU - Haldipur, Parthiv
AU - Hill, Ciaran S
AU - Krüger, Marie T
AU - Zrinzo, Ludvic
AU - Jeelani, Noor ul Owase
AU - Martinez-Barbera, Juan Pedro
AU - Donson, Andrew M
AU - Dorris, Kathleen
AU - Morgan, Paul S
AU - Mackay, Alan
AU - Venkatesh, Humsa S
AU - Horn, Andreas
AU - Mueller, Sabine
AU - Green, Adam L
AU - Mirsky, David M
AU - Akram, Harith
AU - Jones, Chris
AU - Aquilina, Kristian
AU - Mankad, Kshitij
AU - Monje, Michelle
AU - Jacques, Thomas S
AU - Fox, Michael D
AU - Hargrave, Darren R
TI - A prognostic human brain network for diffuse midline glioma
T2 - Nature
J2 - Nature
PY - 2026
DA - 2026/06/10
VL - 655
IS - 8123
SP - 769
EP - 779
SN - 0028-0836
PB - Nature Portfolio
DO - 10.1038/s41586-026-10631-3
UR - https://doi.org/10.1038/s41586-026-10631-3
LA - en
ER -

CSL-JSON

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}

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