Liew, S-L;
Zavaliangos-Petropulu, A;
Jahanshad, N;
Lang, CE;
Hayward, KS;
Lohse, KR;
Juliano, JM;
... Thompson, PM; + view all
(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.
10.1002/hbm.25015.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/hbm.25015 |
| Publisher version: | http://dx.doi.org/10.1002/hbm.25015 |
| Language: | English |
| Additional information: | © 2020 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
| Keywords: | MRI, big data, lesions, neuroinformatics, stroke |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Clinical and Movement Neurosciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10096390 |
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