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The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke: 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. Dimyan
  • Adrienne 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, Artemis Zavaliangos-Petropulu, Ander Ramos-Murguialday
  • University of Southern California
  • Washington University St. Louis
  • University of Melbourne
  • University of Utah
  • IRCCS Fondazione Santa Lucia - Roma
  • University of South Dakota
  • Sioux Falls VA Health Care System
  • Emory University
  • University of British Columbia
  • The University of Auckland
  • University of North Carolina at Chapel Hill
  • Universidade de São Paulo
  • Hospital Israelita Albert Einstein
  • University of Texas at Austin
  • University of Maryland, Baltimore
  • VA Medical Center
  • Massachusetts General Hospital
  • Harvard University
  • Federal Aviation Administration
  • Medical University of South Carolina
  • Wayne State University
  • University of Bern
  • Department of Veterans Affairs
  • University of California at Irvine
  • SUNY Upstate Medical University
  • Cornell University
  • University of Greifswald
  • University of Toronto
  • Sunnybrook Health Sciences Centre
  • Thomas Jefferson University
  • University of Tübingen
  • University of Oslo
  • Cedars-Sinai Medical Center
  • University of Waterloo
  • University College London
  • Charité – Universitätsmedizin Berlin
  • Baylor College of Medicine
  • University of Pittsburgh
  • Chinese University of Hong Kong
  • University of Sciences in Philadelphia
  • Samson College
  • Max Planck Institute for Human Cognitive and Brain Sciences
  • Leipzig University
  • Tianjin Medical University
  • University of California at Los Angeles

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

67 Citas (Scopus)
6 Descargas (Pure)

Resumen

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.
Idioma originalInglés
Páginas (desde-hasta)129-148
Número de páginas20
PublicaciónHuman Brain Mapping
Volumenunknown
N.º1
DOI
EstadoPublicada - 2020

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

  1. ODS 3: Salud y bienestar
    ODS 3: Salud y bienestar

Palabras clave

  • Big data
  • Lesions
  • MRI
  • Neuroinformatics
  • Stroke

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