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|Title:||Connectomes for 40,000 UK Biobank participants: A multi-modal, multi-scale brain network resource.||Austin Authors:||L, Sina Mansour;Di Biase, Maria A;Smith, Robert E;Zalesky, Andrew;Seguin, Caio||Affiliation:||Department of Biomedical Engineering, The University of Melbourne, VIC, Australia.
Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia; Department of Anatomy and Physiology, School of Biomedical Sciences, The University of Melbourne, Parkville, Victoria, Australia; Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, MA, USA.
The Florey Institute of Neuroscience and Mental Health
Department of Biomedical Engineering, The University of Melbourne, VIC, Australia; Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia.
Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia.
|Issue Date:||1-Dec-2023||Date:||2023||Publication information:||NeuroImage 2023-12-01; 283||Abstract:||We mapped functional and structural brain networks for more than 40,000 UK Biobank participants. Structural connectivity was estimated with tractography and diffusion MRI. Resting-state functional MRI was used to infer regional functional connectivity. We provide high-quality structural and functional connectomes for multiple parcellation granularities, several alternative measures of interregional connectivity, and a variety of common data pre-processing techniques, yielding more than one million connectomes in total and requiring more than 200,000 h of compute time. For a single subject, we provide 28 out-of-the-box versions of structural and functional brain networks, allowing users to select, e.g., the parcellation and connectivity measure that best suit their research goals. Furthermore, we provide code and intermediate data for the time-efficient reconstruction of more than 1000 different versions of a subject's connectome based on an array of methodological choices. All connectomes are available via the UK Biobank data-sharing platform and our connectome mapping pipelines are openly available. In this report, we describe our connectome resource in detail for users, outline key considerations in developing an efficient pipeline to map an unprecedented number of connectomes, and report on the quality control procedures that were completed to ensure connectome reliability and accuracy. We demonstrate that our structural and functional connectivity matrices meet a number of quality control checks and replicate previously established findings in network neuroscience. We envisage that our resource will enable new studies of the human connectome in health, disease, and aging at an unprecedented scale.||URI:||https://ahro.austin.org.au/austinjspui/handle/1/33986||DOI:||10.1016/j.neuroimage.2023.120407||ORCID:||Journal:||NeuroImage||Start page:||120407||PubMed URL:||37839728||ISSN:||1095-9572||Type:||Journal Article||Subjects:||Computational modelling and analysis|
|Appears in Collections:||Journal articles|
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