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

Department of Psychology

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	19
Journal	Scientific Data
Volume	12
Issue number	1
DOIs	https://doi.org/10.1038/s41597-024-04154-7
Publication status	Published - 6 Feb 2025

Keywords

Humans
Brain/diagnostic imaging
Magnetic Resonance Imaging
Transcranial Magnetic Stimulation
Adult
Magnetoencephalography
Adolescent
Young Adult
Middle Aged
Neuroimaging
Databases, Factual
Male
Female

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

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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.",

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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}",

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

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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

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.

KW - Humans

KW - Brain/diagnostic imaging

KW - Magnetic Resonance Imaging

KW - Transcranial Magnetic Stimulation

KW - Adult

KW - Magnetoencephalography

KW - Adolescent

KW - Young Adult

KW - Middle Aged

KW - Neuroimaging

KW - Databases, Factual

KW - Male

KW - Female

U2 - 10.1038/s41597-024-04154-7

DO - 10.1038/s41597-024-04154-7

M3 - Article

C2 - 39915473

SN - 2052-4463

VL - 12

JO - Scientific Data

JF - Scientific Data

IS - 1

M1 - 220

ER -