The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke.
The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke.
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DOI：
10.1002/hbm.25015
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发表时间：
2022-01
影响因子：
4.8
通讯作者：
Thompson PM
中科院分区：
文献类型：
--
作者：
Liew SL;Zavaliangos-Petropulu A;Jahanshad N;Lang CE;Hayward KS;Lohse KR;Juliano JM;Assogna F;Baugh LA;Bhattacharya AK;Bigjahan B;Borich MR;Boyd LA;Brodtmann A;Buetefisch CM;Byblow WD;Cassidy JM;Conforto AB;Craddock RC;Dimyan MA;Dula AN;Ermer E;Etherton MR;Fercho KA;Gregory CM;Hadidchi S;Holguin JA;Hwang DH;Jung S;Kautz SA;Khlif MS;Khoshab N;Kim B;Kim H;Kuceyeski A;Lotze M;MacIntosh BJ;Margetis JL;Mohamed FB;Piras F;Ramos-Murguialday A;Richard G;Roberts P;Robertson AD;Rondina JM;Rost NS;Sanossian N;Schweighofer N;Seo NJ;Shiroishi MS;Soekadar SR;Spalletta G;Stinear CM;Suri A;Tang WKW;Thielman GT;Vecchio D;Villringer A;Ward NS;Werden E;Westlye LT;Winstein C;Wittenberg GF;Wong KA;Yu C;Cramer SC;Thompson PM
通过Meta分析增强神经成像遗传学（ENIGMA）中风恢复工作组的目标是使用强大的Meta - and - mega -分析方法来理解大脑和行为之间的关系。ENIGMA卒中康复项目收集了来自全球10个国家的39项研究中超过2100名卒中患者的数据，包括迄今为止最大的多站点回顾性卒中数据协作。本文概述了ENIGMA卒中恢复工作组为开发神经信息学协议和方法来管理多位点卒中脑磁共振成像、行为和人口统计数据所做的努力。具体来说，本文描述了可扩展数据采集和预处理、多站点数据协调和大规模脑卒中病变分析的过程，并讨论了脑卒中研究中这种类型的大数据协作所面临的独特挑战。最后，提出了未来的方向和限制，以及通过前瞻性数据收集和数据管理改进数据协调的建议。
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.
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影响因子：
6.7
作者：
Bernhardt, Julie;Hayward, Kathryn S.;Cramer, Steven C.
通讯作者：
Cramer, Steven C.
影响因子：
3.3
作者：
Brooks, R
通讯作者：
Brooks, R
DOI：
10.1016/j.jstrokecerebrovasdis.2018.10.009
发表时间：
2019-02-01
影响因子：
2.5
作者：
Auriat, Angela M.;Ferris, Jennifer K.;Boyd, Lara A.
通讯作者：
Boyd, Lara A.
影响因子：
3.1
作者：
Acheson A;Wijtenburg SA;Rowland LM;Winkler A;Mathias CW;Hong LE;Jahanshad N;Patel B;Thompson PM;McGuire SA;Sherman PM;Kochunov P;Dougherty DM
通讯作者：
Dougherty DM
影响因子：
6.7
作者：
Bernhardt, Julie;Hayward, Kathryn S.;Cramer, Steven C.
通讯作者：
Cramer, Steven C.