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

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-06 DOI:10.1038/s41597-024-04154-7
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
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引用次数: 0

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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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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