WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis

2025 · Carolyn McNabb, Ian Driver, Vanessa Hyde, Garin Hughes, Hannah Chandler, Hannah Thomas, Christopher Allen, Eirini Messaritaki, Carl Hodgetts, Craig Hedge, Maria Engel, Sophie Standen, Emma Morgan, Elena Stylianopoulou, Svetla Manolova, Lucie Reed, Matthew Ploszajski, Mark Drakesmith, Michael Germuska, Alexander Shaw, Lars Mueller, Holly Rossiter, Christopher Davies-Jenkins, Tom Lancaster, John Evans, David Owen, Gavin Perry, Slawomir Kusmia, Emily Lambe, Adam Partridge, Allison Cooper, Peter Hobden, Hanzhang Lu, Kim Graham, Andrew Lawrence, Richard Wise, James Walters, Petroc Sumner, Krish Singh, Derek Jones · Scientific Data

Abstract

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.

Publication Details

Journal
Scientific Data
Volume
12
Issue
1
Publisher
Springer Science and Business Media LLC
ISSN
2052-4463