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.
Original language | English |
---|---|
Pages (from-to) | 129-148 |
Number of pages | 20 |
Journal | Human Brain Mapping |
Volume | 43 |
Issue number | 1 |
DOIs | |
Publication status | Published - Jan 2022 |
Externally published | Yes |
Keywords
- big data
- lesions
- MRI
- neuroinformatics
- stroke
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}
In: Human Brain Mapping, Vol. 43, No. 1, 01.2022, p. 129-148.
Research output: Contribution to journal › Review Article › Research › peer-review
TY - JOUR
T1 - The ENIGMA Stroke Recovery Working Group
T2 - Big data neuroimaging to study brain–behavior relationships after stroke
AU - Liew, Sook Lei
AU - Zavaliangos-Petropulu, Artemis
AU - Jahanshad, Neda
AU - Lang, Catherine E.
AU - Hayward, Kathryn S.
AU - Lohse, Keith R.
AU - Juliano, Julia M.
AU - Assogna, Francesca
AU - Baugh, Lee A.
AU - Bhattacharya, Anup K.
AU - Bigjahan, Bavrina
AU - Borich, Michael R.
AU - Boyd, Lara A.
AU - Brodtmann, Amy
AU - Buetefisch, Cathrin M.
AU - Byblow, Winston D.
AU - Cassidy, Jessica M.
AU - Conforto, Adriana B.
AU - Craddock, R. Cameron
AU - Dimyan, Michael A.
AU - Dula, Adrienne N.
AU - Ermer, Elsa
AU - Etherton, Mark R.
AU - Fercho, Kelene A.
AU - Gregory, Chris M.
AU - Hadidchi, Shahram
AU - Holguin, Jess A.
AU - Hwang, Darryl H.
AU - Jung, Simon
AU - Kautz, Steven A.
AU - Khlif, Mohamed Salah
AU - Khoshab, Nima
AU - Kim, Bokkyu
AU - Kim, Hosung
AU - Kuceyeski, Amy
AU - Lotze, Martin
AU - MacIntosh, Bradley J.
AU - Margetis, John L.
AU - Mohamed, Feroze B.
AU - Piras, Fabrizio
AU - Ramos-Murguialday, Ander
AU - Richard, Geneviève
AU - Roberts, Pamela
AU - Robertson, Andrew D.
AU - Rondina, Jane M.
AU - Rost, Natalia S.
AU - Sanossian, Nerses
AU - Schweighofer, Nicolas
AU - Seo, Na Jin
AU - Shiroishi, Mark S.
AU - Soekadar, Surjo R.
AU - Spalletta, Gianfranco
AU - Stinear, Cathy M.
AU - Suri, Anisha
AU - Tang, Wai Kwong W.
AU - Thielman, Gregory T.
AU - Vecchio, Daniela
AU - Villringer, Arno
AU - Ward, Nick S.
AU - Werden, Emilio
AU - Westlye, Lars T.
AU - Winstein, Carolee
AU - Wittenberg, George F.
AU - Wong, Kristin A.
AU - Yu, Chunshui
AU - Cramer, Steven C.
AU - Thompson, Paul M.
N1 - Funding Information: American Heart Association; AMORSA, Grant/Award Number: FKZ 16SV7754; Brain and Behavior Research Foundation, Grant/Award Numbers: NARSAD Young Investigator Grant, P&S Fund Investigator; BrightFocus Foundation, Grant/Award Number: A2019052S; Canadian Institutes of Health Research, Grant/Award Number: PJT‐153330; Canadian Partnership for Stroke Recovery; Center for Integrated Healthcare, U.S. Department of Veterans Affairs, Grant/Award Numbers: IO1RX001667, N‐1667; Deutsche Forschungsgemeinschaft, Grant/Award Numbers: LO795/22‐1, LO795/5‐1; Einstein Stiftung Berlin; Fortüne‐Program of the University of Tübingen, Grant/Award Number: 2422‐0‐1; H2020 European Research Council, Grant/Award Numbers: ERC‐2017‐STG‐759370, ERC‐STG‐802998; Health Research Council of New Zealand, Grant/Award Numbers: 09/164R, 11/270, 14/136; Italian Ministry of Health, Grant/Award Number: RC 15‐16‐17‐18‐19/A; Leon Levy Foundation Fellowship; Lone Star Stroke Research Consortium; Max‐Planck‐Gesellschaft; National Health and Medical Research Council, Grant/Award Numbers: 1020526, 1088449, 1094974; National Institutes of Health, Grant/Award Numbers: 5P2CHD086851, HD065438, HD086844, K01HD091283, K23NS088107, P20 GM109040, P2CHD06570, R00HD091375, R01AG059874, R01HD065438, R01HD095137, R01MH117601, R01NR015591, R01NS076348, R01NS082285, R01NS086905, R01NS090677, R01NS115845, R21HD067906, R56‐NS100528, U19NS115388, U54EB020403; National Key Research and Development Program of China, Grant/Award Number: 2018YFC1314300; Norges Forskningsråd, Grant/Award Number: 249795; Norwegian ExtraFoundation for Health and Rehabilitation, Grant/Award Number: 2015/FO5146; South‐Eastern Norway Regional Health Authority, Grant/Award Number: 2018076; Stroke Association, Grant/Award Number: TSA 2017/04; the Bundesministerium für Bildung und Forschung BMBF MOTORBIC, Grant/Award Number: FKZ 13GW0053 Funding information Publisher Copyright: © 2020 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.
PY - 2022/1
Y1 - 2022/1
N2 - 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.
AB - 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.
KW - big data
KW - lesions
KW - MRI
KW - neuroinformatics
KW - stroke
UR - http://www.scopus.com/inward/record.url?scp=85083706027&partnerID=8YFLogxK
U2 - 10.1002/hbm.25015
DO - 10.1002/hbm.25015
M3 - Review Article
C2 - 32310331
AN - SCOPUS:85083706027
SN - 1065-9471
VL - 43
SP - 129
EP - 148
JO - Human Brain Mapping
JF - Human Brain Mapping
IS - 1
ER -