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Individual cases of Parkinson's disease can be robustly classified using magnetoencephalography.

Overview

Authors: Gillian Roberts1, Samuel Hardy1, Yali Pan1,2, Robert Chen3,4,5,6, Benjamin T. Dunkley1,5,6,7,8,9,10
  1. MYndspan Ltd, London, UK
  2. Centre for Human Brain Health, School of Psychology, University of Birmingham,Birmingham, UK
  3. Division of Neurology, Department of Medicine, University of Toronto,Toronto, Canada
  4. Krembil Research Institute, University Health Network,Toronto, Canada
  5. Institute of Medical Science, University of Toronto,Toronto, Canada
  6. Neurosciences & Mental Health, Hospital for Sick Children Research Institute,Toronto, Canada
  7. Department of Diagnostic & Interventional Radiology, Hospital for Sick Children,Toronto, Canada
  8. Department of Medical Imaging, Temerty Faculty of Medicine, University of Toronto,Toronto, Canada
  9. Pharmacology & Toxicology, University of Toronto,Toronto, Canada
  10. Department of Psychology, University of Nottingham,Nottingham, UK
Institutions: University of Birmingham (United Kingdom); University of Toronto (Canada); University Health Network (Canada); Hospital for Sick Children (Canada); University of Nottingham (United Kingdom)
Journal: NPJ Parkinson's disease, volume 12, issue 1, article 170
Dates: received 17 January 2025; accepted 28 March 2026; published online 6 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41531-026-01345-4 · PMID 42091933 · PMCID PMC13350984 · OpenAlex W7160418155
Open access: gold, a free copy (OpenAlex)
Status: code on request
Categories: MEG (modality), Parkinson's (population)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Evoked potentials, fMRI & imaging
Keywords: Computational biology and bioinformatics, Neurophysiology, Parkinson's disease
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 52 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

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Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it says that the code is available on request

Read it in the paper: doi.org/10.1038/s41531-026-01345-4.

Tracing map

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Data

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Code and data availability statement

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  • it says that the data are available on request
  • it says that the code is available on request

Read it in the paper: doi.org/10.1038/s41531-026-01345-4.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 3 keywords, 50 references.

Cite

This paper

Roberts, G., Hardy, S., Pan, Y., Chen, R., & Dunkley, B. T. (2026). Individual cases of Parkinson's disease can be robustly classified using magnetoencephalography. NPJ Parkinson's disease, 12(1), 170. https://doi.org/10.1038/s41531-026-01345-4

BibTeX

@article{roberts2026individual,
author = {Roberts, Gillian and Hardy, Samuel and Pan, Yali and Chen, Robert and Dunkley, Benjamin T.},
title = {{Individual cases of Parkinson's disease can be robustly classified using magnetoencephalography}},
journal = {NPJ Parkinson's disease},
year = {2026},
month = may,
volume = {12},
number = {1},
pages = {170},
publisher = {Nature Publishing Group},
issn = {2373-8057},
doi = {10.1038/s41531-026-01345-4},
url = {https://doi.org/10.1038/s41531-026-01345-4},
pmid = {42091933},
pmcid = {PMC13350984}
}

RIS

TY - JOUR
AU - Roberts, Gillian
AU - Hardy, Samuel
AU - Pan, Yali
AU - Chen, Robert
AU - Dunkley, Benjamin T.
TI - Individual cases of Parkinson's disease can be robustly classified using magnetoencephalography
T2 - NPJ Parkinson's disease
J2 - NPJ Parkinsons Dis
PY - 2026
DA - 2026/05/06
VL - 12
IS - 1
SP - 170
SN - 2373-8057
PB - Nature Publishing Group
DO - 10.1038/s41531-026-01345-4
UR - https://doi.org/10.1038/s41531-026-01345-4
LA - en
ER -

CSL-JSON

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