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

Title: WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis
Authors: McNabb, Carolyn; Driver, Ian D; Hyde, Vanessa; Hughes, Garin; Chandler, Hannah L; Thomas, Hannah; Allen, Christopher; Messaritaki, Eirini; Hodgetts, Carl J; Hedge, Craig; Engel, Maria; Standen, Sophie; Morgan, Emma L; Stylianopoulou, Elena; Manolova, Svetla; Reed, Lucie; Ploszajski, Matthew; Drakesmith, Mark; Germuska, Michael; Shaw, Alexander D; Mueller, Lars; Rossiter, Holly; Davies-Jenkins, Christopher W; Lancaster, Tom; Evans, C John; Owen, David; Perry, Gavin; Kusmia, Slawomir; Lambe, Emily; Partridge, Adam M; Cooper, Allison; Hobden, Peter; Lu, Hanzhang; Graham, Kim S; Lawrence, Andrew D; Wise, Richard G; Walters, James T R; Sumner, Petroc; Singh, Krish D; Jones, Derek K
Publication Year: 2025
Collection: Aston University: Aston Publications Explorer
Description: 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 (3T) magnetic resonance imaging (MRI) with ultra-strong (300 mT/m) magnetic field gradients, structural and functional MRI and nuclear magnetic resonance spectroscopy at 3T and 7T, 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.
Document Type: article in journal/newspaper
File Description: text
Language: English
Relation: https://publications.aston.ac.uk/id/eprint/47183/6/CB_McNabb_et_al_WaND_multi-modal_dataset_integrating_advanced_MRI_MEG_tMS_for_multi-scale_brain_analysis.pdf; McNabb, Carolyn, Driver, Ian D, Hyde, Vanessa, Hughes, Garin, Chandler, Hannah L, Thomas, Hannah, Allen, Christopher, Messaritaki, Eirini, Hodgetts, Carl J, Hedge, Craig , Engel, Maria, Standen, Sophie, Morgan, Emma L, Stylianopoulou, Elena, Manolova, Svetla, Reed, Lucie, Ploszajski, Matthew, Drakesmith, Mark, Germuska, Michael, Shaw, Alexander D, Mueller, Lars, Rossiter, Holly, Davies-Jenkins, Christopher W, Lancaster, Tom, Evans, C John, Owen, David, Perry, Gavin, Kusmia, Slawomir, Lambe, Emily, Partridge, Adam M, Cooper, Allison, Hobden, Peter, Lu, Hanzhang, Graham, Kim S, Lawrence, Andrew D, Wise, Richard G, Walters, James T R, Sumner, Petroc, Singh, Krish D and Jones, Derek K (2025). WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis. Scientific Data, 12
Availability: https://publications.aston.ac.uk/id/eprint/47183/
Rights: cc_by_4
Accession Number: edsbas.39524CC9
Database: BASE