Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI.


Journal

Journal of visualized experiments : JoVE
ISSN: 1940-087X
Titre abrégé: J Vis Exp
Pays: United States
ID NLM: 101313252

Informations de publication

Date de publication:
19 03 2021
Historique:
entrez: 5 4 2021
pubmed: 6 4 2021
medline: 11 5 2021
Statut: epublish

Résumé

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), EEG-fMRI, combines the complementary properties of scalp EEG (good temporal resolution) and fMRI (good spatial resolution) to measure neuronal activity during an electrographic event, through hemodynamic responses known as blood-oxygen-level-dependent (BOLD) changes. It is a non-invasive research tool that is utilized in neuroscience research and is highly beneficial to the clinical community, especially for the management of neurological diseases, provided that proper equipment and protocols are administered during data acquisition. Although recording EEG-fMRI is apparently straightforward, the correct preparation, especially in placing and securing the electrodes, is not only important for safety but is also critical in ensuring the reliability and analyzability of the EEG data obtained. This is also the most experience-demanding part of the preparation. To address these issues, a straightforward protocol that ensures data quality was developed. This article provides a step-by-step guide for acquiring reliable EEG data during EEG-fMRI using this protocol that utilizes readily available medical products. The presented protocol can be adapted to different applications of EEG-fMRI in research and clinical settings, and may be beneficial to both inexperienced and expert operators.

Identifiants

pubmed: 33818571
doi: 10.3791/62247
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Video-Audio Media

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Hui Ming Khoo (HM)

Department of Neurosurgery, Osaka University Graduate School of Medicine; hui.ming.khoo@mail.mcgill.ca.

Yuya Fujita (Y)

Department of Neurosurgery, Osaka University Graduate School of Medicine.

Naoki Tani (N)

Department of Neurosurgery, Osaka University Graduate School of Medicine.

Tetsuya Shimokawa (T)

Center for Information and Neural Networks, National Institute of Information and Communications Technology.

Natalja Zazubovits (N)

Montreal Neurological Institute and Hospital, McGill University.

Satoru Oshino (S)

Department of Neurosurgery, Osaka University Graduate School of Medicine.

Jean Gotman (J)

Montreal Neurological Institute and Hospital, McGill University.

Haruhiko Kishima (H)

Department of Neurosurgery, Osaka University Graduate School of Medicine.

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Classifications MeSH