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Converging metabolic and functional networks for tremor expression and deep brain stimulation-mediated control.

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 · all tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Local and network effects ↔ custom_scripts/Networkcorrelation_withTremortreatmentMap.m, the whole file · a weak match · score 0.75 · Spearman correlation, tremor treatment network, SPM, metric, sum, map
  2. [2] § Methods › FDG-PET Imaging, preprocessing and functional connectivity estimation ↔ custom_scripts/Networkcorrelation_withTremortreatmentMap.m, the whole file · a weak match · score 0.58 · PET scans, SPM, space, Volumes, patients, mask

Paper

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The authors' code

MATLAB · 71 lines · 2.8 KB · no license · 2 matches

  1. % Script to compute similarity between individual PET scans and a reference tremor network map.
  2. % Outputs: Spearman correlation and voxelwise dot product (sum of multiplications).
  4. % -------------------- File Selection --------------------
  5. % Select the tremor treatment network map (NIfTI)
  6. [tremor_map_file, tremor_map_path] = uigetfile('*.nii', 'Select the tremor map');
  7. tremor_map_pathfile = fullfile(tremor_map_path, tremor_map_file);
  8. tremor_map_img = spm_vol(tremor_map_pathfile);
  10. % Select PET scans (NIfTI files)
  11. [pet_files, pet_path] = uigetfile('*.nii', 'Select PET scans', 'MultiSelect', 'on');
  12. if ischar(pet_files)
  13. pet_files = {pet_files}; % Convert to cell array if only one file selected
  14. end
  16. % Initialize results table
  17. similarity_table = cell(length(pet_files), 3);
  19. % ---------------------------------------
  20. for i = 1:length(pet_files)
  21. pet_file = fullfile(pet_path, pet_files{i});
  22. pet_img = spm_vol(pet_file);
  23. pet_data = spm_read_vols(pet_img);
  25. % Check for dimension mismatch
  26. if isequal(tremor_map_img.dim, pet_img.dim)
  27. resliced_data = spm_read_vols(tremor_map_img);
  28. else
  29. % Reslice tremor map into PET image space if necessary
  30. warning('Dimension mismatch detected with %s. Reslicing tremor map.', pet_files{i});
  31. flags = struct('interp', 4, 'mask', false, 'mean', false, ...
  32. 'which', 1, 'wrap', [0 0 0], 'prefix', 'r_');
  33. spm_reslice({pet_file, tremor_map_pathfile}, flags);
  35. % Load resliced tremor map
  36. resliced_file = fullfile(tremor_map_path, ['r_', tremor_map_file]);
  37. if ~isfile(resliced_file)
  38. warning('Reslicing failed for %s. Skipping.', pet_files{i});
  39. similarity_table(i, :) = {pet_files{i}, NaN, NaN};
  40. continue;
  41. end
  42. resliced_data = spm_read_vols(spm_vol(resliced_file));
  43. end
  45. % Flatten and mask valid voxels
  46. map_vector = resliced_data(:);
  47. pet_vector = pet_data(:);
  48. valid_voxels = ~isnan(map_vector) & ~isnan(pet_vector) & map_vector ~= 0;
  50. % Compute similarity metrics
  51. similarity_table{i, 1} = pet_files{i};
  52. similarity_table{i, 2} = corr(map_vector(valid_voxels), pet_vector(valid_voxels), 'Type', 'Spearman');
  53. similarity_table{i, 3} = sum(map_vector(valid_voxels) .* pet_vector(valid_voxels));
  55. disp(['Processed: ', pet_files{i}]);
  56. end
  58. % -------------------- Save Results --------------------
  59. similarity_results = cell2table(similarity_table, ...
  60. 'VariableNames', {'Patient', 'SpearmanCorrelation', 'Voxelwise_Mult_Sum'});
  62. output_file = fullfile(pet_path, 'ET_diffPET_FDGchange_Similarity.xlsx');
  63. writetable(similarity_results, output_file, 'FileType', 'spreadsheet');
  65. % Clean up resliced file
  66. resliced_file = fullfile(tremor_map_path, ['r_', tremor_map_file]);
  67. if isfile(resliced_file)
  68. delete(resliced_file);
  69. end
  71. disp(['Results saved to: ', output_file]);

Networkcorrelation_withTremortreatmentMap.m at commit 25b9502, no license · at the source

Overview

Authors: Benedikt Weigl1, Regina Pistorius1, Joachim Brumberg2, Nicoló G Pozzi1, Andreas Buck3, Muthuraman Muthuraman1,4, Ioannis U Isaias1,5, Jens Volkmann1, Juho Joutsa6,7, Martin M Reich1
  1. Department of Neurology, University Hospital and Julius-Maximilian-University Würzburg, Würzburg, Germany
  2. Department of Nuclear Medicine, Medical Center – University of Freiburg, Freiburg, Germany
  3. Department of Nuclear Medicine, University Hospital and Julius-Maximilian-University Würzburg, Würzburg, Germany
  4. Informatics for Medical Technology, Institute of Computer Science, University Augsburg, Augsburg, Germany
  5. Parkinson Institute, ASST Gaetano Pini-CTO, Milan, Italy
  6. Turku Brain and Mind Center, Clinical Neurosciences, University of Turku, Turku, Finland
  7. Turku PET Center, Neurocenter, Turku University Hospital, Turku, Finland
Institutions: University of Würzburg (Germany); Universitätsklinikum Würzburg (Germany); University of Freiburg (Germany); University Medical Center Freiburg (Germany); University of Augsburg (Germany); University of Turku (Finland); Turku University Hospital (Finland); Turku PET Centre (Finland)
Journal: NPJ Parkinson's disease, volume 12, issue 1, article 119
Dates: received 23 November 2025; accepted 6 May 2026; published online 20 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41531-026-01388-7 · PMID 42162003 · PMCID PMC13190717 · OpenAlex W7161795639
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: PET / SPECT (modality)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging
Keywords: Diseases, Neurology, Neuroscience
Topic: Neurological disorders and treatments (Neurology, Medicine), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (424778381)
Citations: not cited yet (Europe PMC); 60 references in the paper

Abstract

Emerging evidence indicates that movement disorders arise from symptom-specific rather than disease-specific brain network dysfunctions that can be influenced through targeted neuromodulation. Such networks are widely mapped using normative connectome analyses from lesion and stimulation sites. Here, we used [18F]-fluorodeoxyglucose (FDG)-PET in 14 essential tremor patients undergoing thalamic deep brain stimulation (DBS) to identify stimulation-induced and tremor related regional metabolic changes in a within-subject design and combined this with normative connectome results. Stimulation increased metabolism in motor cortical and cerebellar regions - key hubs of the previously proposed tremor treatment network as derived from normative functional connectivity. Importantly, individual alignment with this network predicted clinical tremor improvement (R2 = 0.593, p = 0.007). These same regions showed higher metabolism during tremor expression in the untreated condition, suggesting overlap between the circuits involved in symptom generation and therapeutic response. These findings support indirect connectome-based models by linking them to brain glucose metabolism changes and suggest that DBS relieves tremor by modulating the same circuit that underlies symptom expression.

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 2 matches between paragraphs and lines of code.

BenediktWeigl/Converging-metabolic-and-functional-tremor-networks

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 25b95029622e2e1af18aeee8a5df081c809ce28d, 18 May 2026
Languages: MATLAB (3)
Size: 7 files, 3 scripts
Software Heritage: not archived
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SPM (2 files), Image Processing 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
3 files
At the source: github.com/BenediktWeigl/Converging-metabolic-and-functional-tremor-networks

Code availability

Scripts beyond the cited published analysis pipelines are publicly available in the same GitHub repository [https://github.com/BenediktWeigl/Converging-metabolic-and-functional-tremor-networks].

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

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;
  • 3 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 Statement

Final group-level statistical maps are publicly available in a GitHub repository under [https://github.com/BenediktWeigl/Converging-metabolic-and-functional-tremor-networks]. Individual Patient Data are available upon reasonable request pending institutional approval.

Scripts beyond the cited published analysis pipelines are publicly available in the same GitHub repository [https://github.com/BenediktWeigl/Converging-metabolic-and-functional-tremor-networks].

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 3 keywords, 1 funder, 59 references.

Cite

This paper

Weigl, B., Pistorius, R., Brumberg, J., Pozzi, N. G., Buck, A., Muthuraman, M., Isaias, I. U., Volkmann, J., Joutsa, J., & Reich, M. M. (2026). Converging metabolic and functional networks for tremor expression and deep brain stimulation-mediated control. NPJ Parkinson's disease, 12(1), 119. https://doi.org/10.1038/s41531-026-01388-7

BibTeX

@article{weigl2026converging,
author = {Weigl, Benedikt and Pistorius, Regina and Brumberg, Joachim and Pozzi, Nicoló G and Buck, Andreas and Muthuraman, Muthuraman and Isaias, Ioannis U and Volkmann, Jens and Joutsa, Juho and Reich, Martin M},
title = {{Converging metabolic and functional networks for tremor expression and deep brain stimulation-mediated control}},
journal = {NPJ Parkinson's disease},
year = {2026},
month = may,
volume = {12},
number = {1},
pages = {119},
publisher = {Nature Publishing Group},
issn = {2373-8057},
doi = {10.1038/s41531-026-01388-7},
url = {https://doi.org/10.1038/s41531-026-01388-7},
pmid = {42162003},
pmcid = {PMC13190717}
}

RIS

TY - JOUR
AU - Weigl, Benedikt
AU - Pistorius, Regina
AU - Brumberg, Joachim
AU - Pozzi, Nicoló G
AU - Buck, Andreas
AU - Muthuraman, Muthuraman
AU - Isaias, Ioannis U
AU - Volkmann, Jens
AU - Joutsa, Juho
AU - Reich, Martin M
TI - Converging metabolic and functional networks for tremor expression and deep brain stimulation-mediated control
T2 - NPJ Parkinson's disease
J2 - NPJ Parkinsons Dis
PY - 2026
DA - 2026/05/20
VL - 12
IS - 1
SP - 119
SN - 2373-8057
PB - Nature Publishing Group
DO - 10.1038/s41531-026-01388-7
UR - https://doi.org/10.1038/s41531-026-01388-7
LA - en
ER -

CSL-JSON

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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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