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 fiel...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04154-7 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Carolyn B. McNabb |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c76da6b5d0854af2b6515d8d3289c35b |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-c76da6b5d0854af2b6515d8d3289c35b2025-02-09T12:11:24ZengNature PortfolioScientific Data2052-44632025-02-0112111910.1038/s41597-024-04154-7WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysisCarolyn B. McNabb0Ian D. Driver1Vanessa Hyde2Garin Hughes3Hannah L. Chandler4Hannah Thomas5Christopher Allen6Eirini Messaritaki7Carl J. Hodgetts8Craig Hedge9Maria Engel10Sophie F. Standen11Emma L. Morgan12Elena Stylianopoulou13Svetla Manolova14Lucie Reed15Matthew Ploszajski16Mark Drakesmith17Michael Germuska18Alexander D. Shaw19Lars Mueller20Holly Rossiter21Christopher W. Davies-Jenkins22Tom Lancaster23C. John Evans24David Owen25Gavin Perry26Slawomir Kusmia27Emily Lambe28Adam M. Partridge29Allison Cooper30Peter Hobden31Hanzhang Lu32Kim S. Graham33Andrew D. Lawrence34Richard G. Wise35James T. R. Walters36Petroc Sumner37Krish D. Singh38Derek K. Jones39Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityDepartment of Psychology, Durham UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversitySchool of Psychology, Aston UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityDepartment of Radiology, University of California Davis Medical CenterWashington Singer Laboratories, University of ExeterLeeds Institute of Cardiovascular and Metabolic Medicine, University of LeedsCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of MedicineDepartment of Psychology, University of BathCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of MedicineCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversitySchool of Medicine, Centre for Neuropsychiatric Genetics and Genomics, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityAbstract 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.https://doi.org/10.1038/s41597-024-04154-7 |
| spellingShingle | 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 WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis Scientific Data |
| title | WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis |
| title_full | WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis |
| title_fullStr | WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis |
| title_full_unstemmed | WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis |
| title_short | WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis |
| title_sort | wand a multi modal dataset integrating advanced mri meg and tms for multi scale brain analysis |
| url | https://doi.org/10.1038/s41597-024-04154-7 |
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