OSCR

Varying patterns of association between cortical large-scale networks and subthalamic nucleus activity in Parkinson's disease.

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
  1. [1] § Methods › MEG preprocessing ↔ preproc/03_preproc.py, lines 1–30 · score 0.77 · 0.5–125 Hz, bad channel detection, notch filters, pipeline, segments, signal
  2. [2] § Methods › MEG preprocessing ↔ replication/preproc/01_maxwell_filter.py, lines 82–150 · score 0.73 · Maxwell filtering, bad channel detection, flat, tSSS, noisy, head
  3. [3] § Methods › MEG preprocessing ↔ osl/source_recon/parcellation/parcellation.py, lines 698–790 · score 0.61 · symmetrical multivariate leakage, parcel, MEG
  4. [4] § Methods › MEG preprocessing ↔ osl/source_recon/beamforming.py, lines 57–206 · score 0.58 · forward model, field, variance, beamformer, LCMV, rank
  5. [5] § Methods › Statistics ↔ state-specific_coherence/02_stn_ctx_coh_adjusted_colors.py, lines 53–115 · score 0.55 · cluster forming thresholds, regressors, covariate, permutation, GLMs, spectra
  6. [6] § Methods › Statistics ↔ state-specific_STN_psd/03_plot_stateSpecific_STN_psds_and_overlap.py, lines 65–127 · score 0.54 · cluster forming thresholds, regressors, covariate, overlaps, permutation, GLMs

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Python · 128 lines · 3.7 KB · MIT · 1 match

  1. #!/usr/bin/env python3
  2. # -*- coding: utf-8 -*-
  3. """
  4. Created on Wed Jul 17 17:15:46 2024
  6. @author: okohl
  8. Sub-RSN Project - Preprocessing 3
  10. Run OSL Preprocessing Pipeline:
  11. Broadband (0.5 to 125Hz) and notch filters (50 & 100Hz)
  12. Reasmple to 250Hz
  13. Bad Segment detection in Mag, Grad, and STN channels
  14. Bad channel detection in Mag and Grad channels
  15. Interpolate bad channels
  17. Curcially, preproc is run separately for peri condition because
  18. in peri condition we have STN recordings in EEG channels. Running the peri
  19. condition separately allows to also identify bad segments based on bad_segments
  20. in STN-channels. This may be helpful to get cleaner STN signals.
  21. """
  23. from glob import glob
  24. from dask.distributed import Client
  25. from osl import preprocessing, utils
  26. import os
  28. import sys
  29. sys.path.append(".../helper/")
  30. from params import data_root
  32. # Input dir
  33. maxfilter_root = '.../data'
  35. # Outdir
  36. preproc_dir = f"{data_root}/preproc" # output directory containing the preprocess files
  38. #%% Run Preprocessing for HCs
  40. # Get File names
  41. inputs = []
  42. for condition in ['HC']:
  43. inputs.extend(sorted(glob(f'{maxfilter_root}/maxfilter/*/*{condition}*/*_task-RestingState_*_raw_tsss.fif')))
  45. # Settings
  46. config = """
  47. preproc:
  48. - filter: {l_freq: 0.5, h_freq: 125, method: iir, iir_params: {order: 5, ftype: butter}}
  49. - notch_filter: {freqs: 50 100}
  50. - resample: {sfreq: 250}
  51. - bad_segments: {segment_len: 500, picks: mag}
  52. - bad_segments: {segment_len: 500, picks: grad}
  53. - bad_segments: {segment_len: 500, picks: mag, mode: diff}
  54. - bad_segments: {segment_len: 500, picks: grad, mode: diff}
  55. - bad_channels: {picks: mag}
  56. - bad_channels: {picks: grad}
  57. #- ica_raw: {picks: meg, n_components: 40}
  58. #- ica_autoreject: {apply: False}
  59. - interpolate_bads: {}
  60. """
  62. if __name__ == "__main__":
  63. utils.logger.set_up(level="INFO")
  65. # Setup parallel processing
  66. #
  67. # n_workers is the number of CPUs to use,
  68. # we recommend less than half the total number of CPUs you have
  69. client = Client(n_workers=12, threads_per_worker=1)
  71. # Main preprocessing
  72. preprocessing.run_proc_batch(
  73. config,
  74. inputs,
  75. outdir=preproc_dir,
  76. overwrite=True,
  77. dask_client=True,
  78. )
  81. #%% Run preprocessing for peri condition separately to allow for additional bad
  82. # segment detection on STN channels
  84. # Get File Names
  85. inputs = []
  86. for condition in ['peri']:
  87. inputs.extend(sorted(glob(f'{maxfilter_root}/maxfilter/*/*{condition}*/*_task-RestingState_*_raw_tsss.fif')))
  89. # Outdir
  90. preproc_dir = f"{data_root}/preproc" # output directory containing the preprocess files
  92. # Settings
  93. config = """
  94. preproc:
  95. - filter: {l_freq: 0.5, h_freq: 125, method: iir, iir_params: {order: 5, ftype: butter}}
  96. - notch_filter: {freqs: 50 100}
  97. - resample: {sfreq: 250}
  98. - bad_segments: {segment_len: 500, picks: mag}
  99. - bad_segments: {segment_len: 500, picks: grad}
  100. - bad_segments: {segment_len: 500, picks: eeg}
  101. - bad_segments: {segment_len: 500, picks: mag, mode: diff}
  102. - bad_segments: {segment_len: 500, picks: grad, mode: diff}
  103. - bad_segments: {segment_len: 500, picks: eeg, mode: diff}
  104. - bad_channels: {picks: mag}
  105. - bad_channels: {picks: grad}
  106. - ica_raw: {picks: meg, n_components: 40}
  107. - ica_autoreject: {apply: False}
  108. - interpolate_bads: {}
  109. """
  111. if __name__ == "__main__":
  112. utils.logger.set_up(level="INFO")
  114. # Setup parallel processing
  115. #
  116. # n_workers is the number of CPUs to use,
  117. # we recommend less than half the total number of CPUs you have
  118. client = Client(n_workers=12, threads_per_worker=1)
  120. # Main preprocessing
  121. preprocessing.run_proc_batch(
  122. config,
  123. inputs,
  124. outdir=preproc_dir,
  125. overwrite=True,
  126. dask_client=True,
  127. )

03_preproc.py at commit d470281, under MIT · at the source

Overview

Authors: Oliver Kohl1, Chetan Gohil2,3, Matthias Sure1, Alfons Schnitzler1,4, Esther Florin1
ORCID iDs: Esther Florin
  1. Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University,Düsseldorf, Germany
  2. School of Computer Science, University of Sydney,Sydney, NSW Australia
  3. Brain and Mind Centre, University of Sydney,Sydney, NSW Australia
  4. Department of Neurology, Centre for Movement Disorders and Neuromodulation, Medical Faculty, Heinrich-Heine-University,Düsseldorf, Germany
Institutions: Heinrich Heine University Düsseldorf (Germany); The University of Sydney (Australia)
Journal: NPJ Parkinson's disease, volume 12, issue 1, article 106
Dates: received 27 November 2025; accepted 20 April 2026; published online 2 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41531-026-01372-1 · PMID 42069716 · PMCID PMC13135510 · OpenAlex W4417478344
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.21203/rs.3.rs-8222792/v1
Status: code verified
Categories: Parkinson's (population), systems (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions
Keywords: Neurology, Neuroscience
Topic: Neurological disorders and treatments (Neurology, Medicine), according to OpenAlex
Funding: Volkswagen Foundation (VolkswagenStiftung) (89387); Deutsche Forschungsgemeinschaft (DFG) (424778381 – TRR 295)
Citations: not cited yet (Europe PMC); 60 references in the paper

Abstract

The rising prevalence of Parkinson’s disease has created an urgent need for brain activity markers guiding diagnosis and treatment strategies. While abnormal basal ganglia activity is known to synchronise with specific cortical regions, the temporal dynamics and cortical network architecture of this coupling remain unclear. To address this, we analysed simultaneous magnetoencephalography and subthalamic nucleus (STN) local field potential recordings from 27 individuals with Parkinson’s disease, both on and off dopaminergic medication. Using a time-delay embedded Hidden Markov Model, we identified dynamic large-scale cortical networks whose occurrences fluctuate over time and exhibit distinct STN-cortical coupling. STN-supplementary motor area (SMA) synchrony increased during activations of the sensorimotor network and the posterior default mode network. The former was associated with 9.5–23 Hz power and beta bursts in the STN, and the latter with 5–16.5 Hz power. Dopaminergic medication preferentially reduced STN beta power in networks lacking enhanced STN-SMA synchrony. These findings suggest that large-scale cortical networks have varying patterns of association with STN activity and may provide temporal windows into subcortical processing. Such network signatures in non-invasive recordings offer promising candidates for markers of subcortical-cortical activity in Parkinson’s disease and may provide targets for treatment strategies, including closed-loop stimulation.

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 6 matches between paragraphs and lines of code.

Kohliver/Subcortical-Cortical-Networks-in-PD

License: none: the authors keep all their rights
State: the link is dead, verified on 28 September 2026
Evidence: found in the paper
Software Heritage: not archived
Found in: the text, “Software”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link is dead
  • 28 September 2026: the link is dead
At the source: github.com/Kohliver/Subcortical-Cortical-Networks-in-PD

Kohliver/Subcortical-Cortical-Networks-in-PD-

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: d4702815e359c66565d2b4f419afbcc10d07e75a, 19 January 2026
Languages: Python (34), MATLAB (2)
Size: 41 files, 36 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (26 files), MNE-Python (15 files), OHBA Software Library (OSL) (14 files), Matplotlib (13 files), pandas (12 files), SciPy (8 files), FieldTrip (2 files), NeuroDSP (1 file), NiBabel (1 file), Nilearn (1 file), seaborn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
38 files
At the source: github.com/Kohliver/Subcortical-Cortical-Networks-in-PD-

Zenodo 10401793

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 6 files
Software Heritage: not checked
Found in: the references
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:

Zenodo 6875060

License: BSD-3-Clause
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (40 files), OHBA Software Library (OSL) (28 files), MNE-Python (26 files), Matplotlib (16 files), SciPy (10 files), FSL (8 files), NiBabel (8 files), pandas (5 files), scikit-learn (3 files), Nilearn (2 files), h5py (1 file), Numba (1 file), OpenCV (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
72 files
At the source:

Code availability

All analysis scripts are publicly available on GitHub: https://github.com/Kohliver/Subcortical-Cortical-Networks-in-PD-.

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:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 105 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.

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

The conditions of our ethics approval do not permit public archiving of the data supporting this study. Sharing data requires a formal data-sharing agreement following ethics procedures governing the re-use of sensitive data. Readers seeking access to the data should contact the first author.

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, 5 authors, 2 keywords, 2 funders, 60 references.

Cite

This paper

Kohl, O., Gohil, C., Sure, M., Schnitzler, A., & Florin, E. (2026). Varying patterns of association between cortical large-scale networks and subthalamic nucleus activity in Parkinson's disease. NPJ Parkinson's disease, 12(1), 106. https://doi.org/10.1038/s41531-026-01372-1

BibTeX

@article{kohl2026varying,
author = {Kohl, Oliver and Gohil, Chetan and Sure, Matthias and Schnitzler, Alfons and Florin, Esther},
title = {{Varying patterns of association between cortical large-scale networks and subthalamic nucleus activity in Parkinson's disease}},
journal = {NPJ Parkinson's disease},
year = {2026},
month = may,
volume = {12},
number = {1},
pages = {106},
publisher = {Nature Publishing Group},
issn = {2373-8057},
doi = {10.1038/s41531-026-01372-1},
url = {https://doi.org/10.1038/s41531-026-01372-1},
pmid = {42069716},
pmcid = {PMC13135510}
}

RIS

TY - JOUR
AU - Kohl, Oliver
AU - Gohil, Chetan
AU - Sure, Matthias
AU - Schnitzler, Alfons
AU - Florin, Esther
TI - Varying patterns of association between cortical large-scale networks and subthalamic nucleus activity in Parkinson's disease
T2 - NPJ Parkinson's disease
J2 - NPJ Parkinsons Dis
PY - 2026
DA - 2026/05/02
VL - 12
IS - 1
SP - 106
SN - 2373-8057
PB - Nature Publishing Group
DO - 10.1038/s41531-026-01372-1
UR - https://doi.org/10.1038/s41531-026-01372-1
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41531-026-01372-1",
"type": "article-journal",
"title": "Varying patterns of association between cortical large-scale networks and subthalamic nucleus activity in Parkinson's disease",
"container-title": "NPJ Parkinson's disease",
"author": [
{
"family": "Kohl",
"given": "Oliver"
},
{
"family": "Gohil",
"given": "Chetan"
},
{
"family": "Sure",
"given": "Matthias"
},
{
"family": "Schnitzler",
"given": "Alfons"
},
{
"family": "Florin",
"given": "Esther"
}
],
"container-title-short": "NPJ Parkinsons Dis",
"volume": "12",
"issue": "1",
"page": "106",
"DOI": "10.1038/s41531-026-01372-1",
"PMID": "42069716",
"PMCID": "PMC13135510",
"ISSN": "2373-8057",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41531-026-01372-1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
2
]
]
}
}

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

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1162/imag.a.1190 [code]
Canonical Hidden Markov Model Networks for studying M/EEG.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: OHBA Software Library (OSL), Numba, MNE-Python, 10 other tools, 12 references
[2] doi:10.1002/hbm.70516 [code]
Effects of Age on Resting-State Cortical Networks.
Journal: Human brain mapping
In common: OHBA Software Library (OSL), Numba, MNE-Python, 10 other tools, 11 references
[3] doi:10.1016/j.ebiom.2026.106293 [code]
Dynamic neural states underpin motor symptom severity in Parkinson's disease: a longitudinal analysis of chronic cortico-subthalamic nucleus recordings.
Journal: EBioMedicine
In common: seaborn, pandas, SciPy, 2 other tools, Parkinson's, 11 references
[4] doi:10.1162/imag.a.1237 [code]
Modelling discrete states and long-term dynamics in functional brain networks.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: MNE-Python, seaborn, scikit-learn, 4 other tools, systems, 10 references
[5] doi:10.1162/imag.a.1301 [code]
MEG-GPT: A transformer-based foundation model for magnetoencephalography data.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: MNE-Python, Nilearn, NiBabel, 6 other tools, 8 references
[6] doi:10.1162/imag.a.1188 [code]
Modelling variability in functional brain networks using embeddings.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: OHBA Software Library (OSL), NiBabel, seaborn, 5 other tools, systems, 6 references
[7] doi:10.1093/braincomms/fcag329
A meta-analysis of periodic and aperiodic electrophysiological features in Parkinson's disease.
Journal: Brain communications
In common: Parkinson's, 11 references
[8] doi:10.7554/elife.100605 [code]
Age-related changes in ‘cortical’ 1/f dynamics are linked to cardiac activity
Journal: n/a
In common: NeuroDSP, MNE-Python, h5py, 6 other tools, 5 references
[9] doi:10.1162/imag.a.1226 [code]
A neuroscientist's guide to neural burst detection.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: NeuroDSP, FieldTrip, MNE-Python, 6 other tools, 3 references
[10] doi:10.1093/braincomms/fcag236 [code]
Dynamic, state-dependent characteristics of cognitive fluctuations in Lewy body dementia: a magnetoencephalography study.
Journal: Brain communications
In common: OHBA Software Library (OSL), MNE-Python, pandas, 1 other tool, 7 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.