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A multi-site, multi-participant magnetoencephalography resting-state dataset to study dementia: The BioFIND dataset

Vaghari, Delshad; Bruna, Ricardo; Hughes, Laura E.; Nesbitt, David; Tibon, Roni; Rowe, James B.; Maestu, Fernando; Henson, Richard N.

A multi-site, multi-participant magnetoencephalography resting-state dataset to study dementia: The BioFIND dataset Thumbnail


Authors

Delshad Vaghari

Ricardo Bruna

Laura E. Hughes

David Nesbitt

RONI TIBON Roni.Tibon@nottingham.ac.uk
Assistant Professor in Psychology

James B. Rowe

Fernando Maestu

Richard N. Henson



Abstract

Early detection of Alzheimer's Disease (AD) is vital to reduce the burden of dementia and for developing effective treatments. Neuroimaging can detect early brain changes, such as hippocampal atrophy in Mild Cognitive Impairment (MCI), a prodromal state of AD. However, selecting the most informative imaging features by machine-learning requires many cases. While large publically-available datasets of people with dementia or prodromal disease exist for Magnetic Resonance Imaging (MRI), comparable datasets are missing for Magnetoencephalography (MEG). MEG offers advantages in its millisecond resolution, revealing physiological changes in brain oscillations or connectivity before structural changes are evident with MRI. We introduce a MEG dataset with 324 individuals: patients with MCI and healthy controls. Their brain activity was recorded while resting with eyes closed, using a 306-channel MEG scanner at one of two sites (Madrid or Cambridge), enabling tests of generalization across sites. A T1-weighted MRI is provided to assist source localisation. The MEG and MRI data are formatted according to international BIDS standards and analysed freely on the DPUK platform (https://portal.dementiasplatform.uk/Apply). Here, we describe this dataset in detail, report some example (benchmark) analyses, and consider its limitations and future directions.

Journal Article Type Article
Acceptance Date May 30, 2022
Online Publication Date May 31, 2022
Publication Date Sep 1, 2022
Deposit Date Jun 21, 2022
Publicly Available Date Jun 22, 2022
Journal NeuroImage
Print ISSN 1053-8119
Electronic ISSN 1095-9572
Publisher Elsevier BV
Peer Reviewed Peer Reviewed
Volume 258
Article Number 119344
DOI https://doi.org/10.1016/j.neuroimage.2022.119344
Keywords Cognitive Neuroscience; Neurology
Public URL https://nottingham-repository.worktribe.com/output/8630415
Publisher URL https://www.sciencedirect.com/science/article/pii/S1053811922004633?via%3Dihub
Additional Information This article is maintained by: Elsevier; Article Title: A multi-site, multi-participant magnetoencephalography resting-state dataset to study dementia: The BioFIND dataset; Journal Title: NeuroImage; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.neuroimage.2022.119344; Content Type: article; Copyright: © 2022 The Author(s). Published by Elsevier Inc.

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