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<p>This paper introduces the Welsh Advanced Neuroimaging Database (WAND), a multi-scale, multi-modal imaging dataset comprising <i>in vivo</i> 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月6日

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  • 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

摘要

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.