The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke

Sook Lei Liew, Artemis Zavaliangos-Petropulu, Neda Jahanshad, Catherine E. Lang, Kathryn S. Hayward, Keith R. Lohse, Julia M. Juliano, Francesca Assogna, Lee A. Baugh, Anup K. Bhattacharya, Bavrina Bigjahan, Michael R. Borich, Lara A. Boyd, Amy Brodtmann, Cathrin M. Buetefisch, Winston D. Byblow, Jessica M. Cassidy, Adriana B. Conforto, R. Cameron Craddock, Michael A. DimyanAdrienne N. Dula, Elsa Ermer, Mark R. Etherton, Kelene A. Fercho, Chris M. Gregory, Shahram Hadidchi, Jess A. Holguin, Darryl H. Hwang, Simon Jung, Steven A. Kautz, Mohamed Salah Khlif, Nima Khoshab, Bokkyu Kim, Hosung Kim, Amy Kuceyeski, Martin Lotze, Bradley J. MacIntosh, John L. Margetis, Feroze B. Mohamed, Fabrizio Piras, Ander Ramos-Murguialday, Geneviève Richard, Pamela Roberts, Andrew D. Robertson, Jane M. Rondina, Natalia S. Rost, Nerses Sanossian, Nicolas Schweighofer, Na Jin Seo, Mark S. Shiroishi, Surjo R. Soekadar, Gianfranco Spalletta, Cathy M. Stinear, Anisha Suri, Wai Kwong W. Tang, Gregory T. Thielman, Daniela Vecchio, Arno Villringer, Nick S. Ward, Emilio Werden, Lars T. Westlye, Carolee Winstein, George F. Wittenberg, Kristin A. Wong, Chunshui Yu, Steven C. Cramer, Paul M. Thompson

Research output: Contribution to journalReview articlepeer-review

40 Scopus citations


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 languageEnglish (US)
Pages (from-to)129-148
Number of pages20
JournalHuman Brain Mapping
Issue number1
StatePublished - Jan 2022
Externally publishedYes


  • MRI
  • big data
  • lesions
  • neuroinformatics
  • stroke

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology


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