Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, California, USA.
Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Hum Brain Mapp. 2022 Jan;43(1):129-148. doi: 10.1002/hbm.25015. Epub 2020 Apr 20.
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.
通过荟萃分析增强神经影像学遗传学(ENIGMA)中风康复工作组的目标是利用功能强大的荟萃分析和超级分析方法来了解大脑与行为的关系。ENIGMA 中风康复拥有来自全球 10 个国家的 39 项研究中超过 2100 名中风患者的数据,是迄今为止最大的多站点回顾性中风数据合作。本文概述了 ENIGMA 中风康复工作组为开发神经信息学协议和方法以管理多站点中风脑磁共振成像、行为和人口统计学数据所做的努力。具体而言,描述了可扩展的数据摄入和预处理、多站点数据协调以及大规模中风损伤分析的过程,并讨论了中风研究中这种大数据协作所特有的挑战。最后,提供了未来的方向和局限性,以及通过前瞻性数据收集和数据管理改善数据协调的建议。