Normative brain mapping using scalp EEG and potential clinical application.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
18 08 2023
Historique:
received: 06 04 2023
accepted: 29 07 2023
medline: 21 8 2023
pubmed: 19 8 2023
entrez: 18 8 2023
Statut: epublish

Résumé

A normative electrographic activity map could be a powerful resource to understand normal brain function and identify abnormal activity. Here, we present a normative brain map using scalp EEG in terms of relative band power. In this exploratory study we investigate its temporal stability, its similarity to other imaging modalities, and explore a potential clinical application. We constructed scalp EEG normative maps of brain dynamics from 17 healthy controls using source-localised resting-state scalp recordings. We then correlated these maps with those acquired from MEG and intracranial EEG to investigate their similarity. Lastly, we use the normative maps to lateralise abnormal regions in epilepsy. Spatial patterns of band powers were broadly consistent with previous literature and stable across recordings. Scalp EEG normative maps were most similar to other modalities in the alpha band, and relatively similar across most bands. Towards a clinical application in epilepsy, we found abnormal temporal regions ipsilateral to the epileptogenic hemisphere. Scalp EEG relative band power normative maps are spatially stable across time, in keeping with MEG and intracranial EEG results. Normative mapping is feasible and may be potentially clinically useful in epilepsy. Future studies with larger sample sizes and high-density EEG are now required for validation.

Identifiants

pubmed: 37596291
doi: 10.1038/s41598-023-39700-7
pii: 10.1038/s41598-023-39700-7
pmc: PMC10439201
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

13442

Subventions

Organisme : Medical Research Council
ID : G0301067
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/T04294X/1
Pays : United Kingdom
Organisme : Department of Health
Pays : United Kingdom
Organisme : NINDS NIH HHS
ID : U01 NS090407
Pays : United States
Organisme : Wellcome Trust
ID : WT106882
Pays : United Kingdom
Organisme : Wellcome Trust
Pays : United Kingdom

Commentaires et corrections

Type : UpdateOf

Informations de copyright

© 2023. Springer Nature Limited.

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Auteurs

Vytene Janiukstyte (V)

CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, NE4 5DG, UK.

Thomas W Owen (TW)

CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, NE4 5DG, UK.

Umair J Chaudhary (UJ)

Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK.

Beate Diehl (B)

Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK.

Louis Lemieux (L)

Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK.

John S Duncan (JS)

Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK.

Jane de Tisi (J)

Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK.

Yujiang Wang (Y)

CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, NE4 5DG, UK.
Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, NE2 4HH, UK.
Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK.

Peter N Taylor (PN)

CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, NE4 5DG, UK. peter.taylor@newcastle.ac.uk.
Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, NE2 4HH, UK. peter.taylor@newcastle.ac.uk.
Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK. peter.taylor@newcastle.ac.uk.

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Classifications MeSH