Open AccessThe ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke
Author(s) -
Liew SookLei,
ZavaliangosPetropulu 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.,
Dula Adrienne N.,
Ermer Elsa,
Etherton Mark R.,
Fercho Kelene A.,
Gregory Chris M.,
Hadidchi Shahram,
Holguin Jess A.,
Hwang Darryl H.,
Jung Simon,
Kautz Steven A.,
Khlif Mohamed Salah,
Khoshab Nima,
Kim Bokkyu,
Kim Hosung,
Kuceyeski Amy,
Lotze Martin,
MacIntosh Bradley J.,
Margetis John L.,
Mohamed Feroze B.,
Piras Fabrizio,
RamosMurguialday Ander,
Richard Geneviève,
Roberts Pamela,
Robertson Andrew D.,
Rondina Jane M.,
Rost Natalia S.,
Sanossian Nerses,
Schweighofer Nicolas,
Seo Na Jin,
Shiroishi Mark S.,
Soekadar Surjo R.,
Spalletta Gianfranco,
Stinear Cathy M.,
Suri Anisha,
Tang Wai Kwong W.,
Thielman Gregory T.,
Vecchio Daniela,
Villringer Arno,
Ward Nick S.,
Werden Emilio,
Westlye Lars T.,
Winstein Carolee,
Wittenberg George F.,
Wong Kristin A.,
Yu Chunshui,
Cramer Steven C.,
Thompson Paul M.
Publication year - 2022
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.25015
Subject(s) - neuroimaging , stroke (engine) , neuroinformatics , harmonization , demographics , data collection , stroke recovery , medicine , psychology , physical medicine and rehabilitation , neuroscience , data science , computer science , rehabilitation , mechanical engineering , statistics , physics , demography , mathematics , sociology , acoustics , engineering
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.