McNabb, C.B. orcid.org/0000-0002-6434-5177, Driver, I.D. orcid.org/0000-0001-6815-0134, Hyde, V. et al. (37 more authors) (2025) WAND: a multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis. Scientific Data, 12. 220.
Abstract
This paper introduces the Welsh Advanced Neuroimaging Database (WAND), a multi-scale, multi-modal imaging dataset comprising in vivo brain data from 170 healthy volunteers (aged 18–63 years), including 3 Tesla (3 T) magnetic resonance imaging (MRI) with ultra-strong (300 mT/m) magnetic field gradients, structural and functional MRI and nuclear magnetic resonance spectroscopy at 3 T and 7 T, magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS), together with trait questionnaire and cognitive data. Data are organised using the Brain Imaging Data Structure (BIDS). In addition to raw data, we provide brain-extracted T1-weighted images, and quality reports for diffusion, T1- and T2-weighted structural data, and blood-oxygen level dependent functional tasks. Reasons for participant exclusion are also included. Data are available for download through our GIN repository, a data access management system designed to reduce storage requirements. Users can interact with and retrieve data as needed, without downloading the complete dataset. Given the depth of neuroimaging phenotyping, leveraging ultra-high-gradient, high-field MRI, MEG and TMS, this dataset will facilitate multi-scale and multi-modal investigations of the healthy human brain.</jats:p>
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Cognitive neuroscience; Human behaviour; Neuroscience |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Research Services (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Feb 2025 10:47 |
Last Modified: | 11 Feb 2025 10:47 |
Published Version: | https://doi.org/10.1038/s41597-024-04154-7 |
Status: | Published |
Publisher: | Springer Science and Business Media LLC |
Refereed: | Yes |
Identification Number: | 10.1038/s41597-024-04154-7 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:223126 |