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

Carolyn 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 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 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 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 (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.

Original language	English
Article number	220
Number of pages	19
Journal	Scientific Data
Volume	12
Early online date	6 Feb 2025
DOIs	https://doi.org/10.1038/s41597-024-04154-7
Publication status	Published - 6 Feb 2025

Bibliographical note

Copyright © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/

Data Access Statement

WAND data 22 are available for download from GIN (https://gin.g-node.org/CUBRIC/WAND). BIDS metadata and data organisation: All imaging data are organised using the Brain Imaging Data Structure (BIDS 32 ) or, where no official BIDS recommendation exists, data are structured according to recommendations from BIDS extension proposals current at the time of acquisition. Details of data structure and decision-making related to non-official BIDS formats are included in the README file of the GitLab repository(https://git.cardiff.ac.uk/cubric/wand).
Data are organised first by subject, then session, then data type (e.g., “meg”, “func”, “dwi”, etc.). Sessions are organised such that a single session contains imaging data from only one device on one occasion (see Table S2 for details).
MRI data are stored in NifTI (Neuroimaging Informatics Technology Initiative) format with accompanying metadata stored in JSON format. MRS data are stored as Siemens TWIX files, using a BIDS-like structure but will not be “BIDS-valid”. MEG data are stored in .ds format according to CTF’s data organisation standards. Continuous recordings from physiological monitoring are stored as tab-separated values (TSV) files with accompanying metadata stored in JSON format. Timing and other properties of events recorded during functional MRI tasks are stored as TSV files with accompanying metadata in JSON format. Cognitive and questionnaire data are stored in TSV format with accompanying metadata/data dictionaries in JSON format.

[see full paper for Technical Validation, Usage Notes and Code availability]

Keywords

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

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10.1038/s41597-024-04154-7Licence: CC BY 4.0

CB McNabb et al_WaND_multi-modal dataset integrating advanced MRI_MEG_tMS for multi-scale brain analysis
Copyright © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/
Final published version, 2.78 MBLicence: CC BY 4.0

Cite this

McNabb, C., 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., 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, 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 (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.",

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

author = "Carolyn McNabb 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 Sophie Standen 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}",

note = "Copyright {\textcopyright} The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article{\textquoteright}s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article{\textquoteright}s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ ",

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language = "English",

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McNabb, C, Driver, ID, Hyde, V, Hughes, G, Chandler, HL, Thomas, H, Allen, C, Messaritaki, E, Hodgetts, CJ, Hedge, C, Engel, M, Standen, S, 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, 220. https://doi.org/10.1038/s41597-024-04154-7

TY - JOUR

T1 - WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis

AU - McNabb, Carolyn

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

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

N1 - Copyright © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/

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 (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.

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 (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.

KW - Adolescent

KW - Adult

KW - Brain/diagnostic imaging

KW - Databases, Factual

KW - Female

KW - Humans

KW - Magnetic Resonance Imaging

KW - Magnetoencephalography

KW - Male

KW - Middle Aged

KW - Neuroimaging

KW - Transcranial Magnetic Stimulation

KW - Young Adult

UR - https://www.nature.com/articles/s41597-024-04154-7

U2 - 10.1038/s41597-024-04154-7

DO - 10.1038/s41597-024-04154-7

M3 - Article

C2 - 39915473

VL - 12

JO - Scientific Data

JF - Scientific Data

M1 - 220

ER -

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

Abstract

Bibliographical note

Data Access Statement

Keywords

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