Liew, Sook-Lei; Zavaliangos-Petropulu, Artemis; Jahanshad, Neda; Lang, Catherine E; Hayward, Kathryn S; Lohse, Keith R; Juliano, Julia M; Assogna, Francesca; Baugh, Lee A; Bhattacharya, Anup K; Bigjahan, Bavrina; Borich, Michael R; Boyd, Lara A; Brodtmann, Amy; Buetefisch, Cathrin M; Byblow, Winston D; Cassidy, Jessica M; Conforto, Adriana B; Craddock, R Cameron; Dimyan, Michael A; ... (2022). The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke. Human brain mapping, 43(1), pp. 129-148. Wiley-Blackwell 10.1002/hbm.25015
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Liew, 2020, The ENIGMA STroke Recovery Working Group.pdf - Published Version Available under License Creative Commons: Attribution-Noncommercial-No Derivative Works (CC-BY-NC-ND). Download (2MB) | Preview |
The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.
Item Type: |
Journal Article (Review Article) |
|---|---|
Division/Institute: |
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology |
UniBE Contributor: |
Jung, Simon |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
1065-9471 |
Publisher: |
Wiley-Blackwell |
Language: |
English |
Submitter: |
Chantal Kottler |
Date Deposited: |
17 Nov 2020 16:56 |
Last Modified: |
05 Dec 2022 15:41 |
Publisher DOI: |
10.1002/hbm.25015 |
PubMed ID: |
32310331 |
Uncontrolled Keywords: |
MRI big data lesions neuroinformatics stroke |
BORIS DOI: |
10.7892/boris.147708 |
URI: |
https://boris.unibe.ch/id/eprint/147708 |
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