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

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

doi:10.1038/s41597-024-04154-7

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

Carolyn B. McNabb, Ian D. Driver, Vanessa Hyde, Garin Hughes, Hannah L. Chandler, Hannah Thomas, Christopher Allen, Eirini Messaritaki, Carl J. Hodgetts, Craig Hedge, Maria Engel, Sophie F. Standen, Emma L. Morgan, Elena Stylianopoulou, Svetla Manolova, Lucie Reed, Matthew Ploszajski, Mark Drakesmith, Michael Germuska, Alexander D. ShawLars Mueller, Holly Rossiter, Christopher W. Davies-Jenkins, Tom Lancaster, C. John Evans, David Owen, Gavin Perry, Slawomir Kusmia, Emily Lambe, Adam M. Partridge, Allison Cooper, Peter Hobden, Hanzhang Lu, Kim S. Graham, Andrew D. Lawrence, Richard G. Wise, James T.R. Walters, Petroc Sumner, Krish D. Singh, Derek K. Jones

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	220
Number of pages	1
Journal	Scientific Data
Volume	12
Issue number	1
DOIs	https://doi.org/10.1038/s41597-024-04154-7
Publication status	Published - 6 Feb 2025

Acknowledgements

Many researchers in the Cardiff University Brain Research Imaging Centre (CUBRIC) contributed their time and expertise throughout the project. The authors would like to especially thank Mara Cercignani and Marco Palombo for their help with quantitative and diffusion imaging questions, the CUBRIC IT team for their help with storage, and data organisation, and the CUBRIC reception team for their help with participant admin. The authors would also like to thank: Thomos Wizel (formally MGH, now GBIO) for his help setting up the diffusion sequences on the Connectom scanner; Olivier Mougin (University of Nottingham) developed the phase-sensitive inversion recovery and T1 code; William Clarke (Oxford University) provided the image reconstruction code for the MP2RAGE; University of Minnesota Center for Magnetic Resonance Research for providing the multiband (simultaneous multislice) sequence; Malgorzata Marjanska and Edward Auerbach from the University of Minnesota Center for Magnetic Resonance Research for providing spectroscopy sequences; Elena Kleban for providing the 7 T mGRE sequence; German Centre Neurodegenerative Diseases (DZNE) for providing the 3DREAM sequence; Fabrizio Fasano from Siemens Healthineers for developing/implementing the qMT sequence; Thomas Okell from the University of Oxford for providing code used in the local implementation of the pCASL sequence (implemented by Michael Germuska).

Funding

The WAND data were acquired at the UK National Facility for In Vivo MR Imaging of Human Tissue Microstructure funded by the EPSRC (grant EP/M029778/1), and The Wolfson Foundation, and supported by a Wellcome Trust Investigator Award (096646/Z/11/Z) and a Wellcome Trust Strategic Award (104943/Z/14/Z). The UK Medical Research Council (MR/M008932/1), the Welsh Government and Cardiff University provide funding support for the CUBRIC ultra-high field imaging facility.

Funders	Funder number
Engineering and Physical Sciences Research Council	EP/M029778/1

ASJC Scopus subject areas

Statistics and Probability
Information Systems
Education
Computer Science Applications
Statistics, Probability and Uncertainty
Library and Information Sciences

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1038/s41597-024-04154-7Licence: CC BY

Cite this

McNabb, C. B., Driver, I. D., Hyde, V., Hughes, G., Chandler, H. L., Thomas, H., Allen, C., Messaritaki, E., Hodgetts, C. J., Hedge, C., Engel, M., Standen, S. F., Morgan, E. L., Stylianopoulou, E., Manolova, S., Reed, L., Ploszajski, M., Drakesmith, M., Germuska, M., ... Jones, D. K. (2025). WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis. Scientific Data, 12(1), Article 220. https://doi.org/10.1038/s41597-024-04154-7

McNabb, CB, Driver, ID, Hyde, V, Hughes, G, Chandler, HL, Thomas, H, Allen, C, Messaritaki, E, Hodgetts, CJ, Hedge, C, Engel, M, Standen, SF, Morgan, EL, Stylianopoulou, E, Manolova, S, Reed, L, Ploszajski, M, Drakesmith, M, Germuska, M, Shaw, AD, Mueller, L, Rossiter, H, Davies-Jenkins, CW, Lancaster, T, Evans, CJ, Owen, D, Perry, G, Kusmia, S, Lambe, E, Partridge, AM, Cooper, A, Hobden, P, Lu, H, Graham, KS, Lawrence, AD, Wise, RG, Walters, JTR, Sumner, P, Singh, KD & Jones, DK 2025, 'WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis', Scientific Data, vol. 12, no. 1, 220. https://doi.org/10.1038/s41597-024-04154-7

@article{3d4b78d1e3734275976185a1aedad540,

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

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.",

author = "McNabb, {Carolyn B.} and Driver, {Ian D.} and Vanessa Hyde and Garin Hughes and Chandler, {Hannah L.} and Hannah Thomas and Christopher Allen and Eirini Messaritaki and Hodgetts, {Carl J.} and Craig Hedge and Maria Engel and Standen, {Sophie F.} and Morgan, {Emma L.} and Elena Stylianopoulou and Svetla Manolova and Lucie Reed and Matthew Ploszajski and Mark Drakesmith and Michael Germuska and Shaw, {Alexander D.} and Lars Mueller and Holly Rossiter and Davies-Jenkins, {Christopher W.} and Tom Lancaster and Evans, {C. John} and David Owen and Gavin Perry and Slawomir Kusmia and Emily Lambe and Partridge, {Adam M.} and Allison Cooper and Peter Hobden and Hanzhang Lu and Graham, {Kim S.} and Lawrence, {Andrew D.} and Wise, {Richard G.} and Walters, {James T.R.} and Petroc Sumner and Singh, {Krish D.} and Jones, {Derek K.}",

year = "2025",

month = feb,

day = "6",

doi = "10.1038/s41597-024-04154-7",

language = "English",

volume = "12",

journal = "Scientific Data",

issn = "2052-4463",

publisher = "Nature Research",

number = "1",

}

TY - JOUR

T1 - WAND

T2 - A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis

AU - McNabb, Carolyn B.

AU - Driver, Ian D.

AU - Hyde, Vanessa

AU - Hughes, Garin

AU - Chandler, Hannah L.

AU - Thomas, Hannah

AU - Allen, Christopher

AU - Messaritaki, Eirini

AU - Hodgetts, Carl J.

AU - Hedge, Craig

AU - Engel, Maria

AU - Standen, Sophie F.

AU - Morgan, Emma L.

AU - Stylianopoulou, Elena

AU - Manolova, Svetla

AU - Reed, Lucie

AU - Ploszajski, Matthew

AU - Drakesmith, Mark

AU - Germuska, Michael

AU - Shaw, Alexander D.

AU - Mueller, Lars

AU - Rossiter, Holly

AU - Davies-Jenkins, Christopher W.

AU - Lancaster, Tom

AU - Evans, C. John

AU - Owen, David

AU - Perry, Gavin

AU - Kusmia, Slawomir

AU - Lambe, Emily

AU - Partridge, Adam M.

AU - Cooper, Allison

AU - Hobden, Peter

AU - Lu, Hanzhang

AU - Graham, Kim S.

AU - Lawrence, Andrew D.

AU - Wise, Richard G.

AU - Walters, James T.R.

AU - Sumner, Petroc

AU - Singh, Krish D.

AU - Jones, Derek K.

PY - 2025/2/6

Y1 - 2025/2/6

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85218290661&partnerID=8YFLogxK

U2 - 10.1038/s41597-024-04154-7

DO - 10.1038/s41597-024-04154-7

M3 - Article

C2 - 39915473

AN - SCOPUS:85218290661

SN - 2052-4463

VL - 12

JO - Scientific Data

JF - Scientific Data

IS - 1

M1 - 220

ER -

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

Abstract

Acknowledgements

Funding

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this