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Review
. 2022 Jan;43(1):129-148.
doi: 10.1002/hbm.25015. Epub 2020 Apr 20.

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

Sook-Lei Liew  1   2   3   4 Artemis Zavaliangos-Petropulu  2   4   5 Neda Jahanshad  2   5 Catherine E Lang  6 Kathryn S Hayward  7   8 Keith R Lohse  9   10 Julia M Juliano  4 Francesca Assogna  11 Lee A Baugh  12   13 Anup K Bhattacharya  14 Bavrina Bigjahan  2   15 Michael R Borich  16 Lara A Boyd  17   18 Amy Brodtmann  19 Cathrin M Buetefisch  16   20 Winston D Byblow  21 Jessica M Cassidy  22 Adriana B Conforto  23   24 R Cameron Craddock  25 Michael A Dimyan  26   27 Adrienne N Dula  25   28 Elsa Ermer  26 Mark R Etherton  29   30 Kelene A Fercho  12   31 Chris M Gregory  32 Shahram Hadidchi  33   34 Jess A Holguin  1 Darryl H Hwang  15 Simon Jung  35 Steven A Kautz  32   36 Mohamed Salah Khlif  19 Nima Khoshab  37 Bokkyu Kim  38   39 Hosung Kim  2 Amy Kuceyeski  40   41 Martin Lotze  42 Bradley J MacIntosh  43   44 John L Margetis  1 Feroze B Mohamed  45 Fabrizio Piras  11 Ander Ramos-Murguialday  46   47 Geneviève Richard  48   49   50 Pamela Roberts  51 Andrew D Robertson  52   53 Jane M Rondina  54 Natalia S Rost  55 Nerses Sanossian  56 Nicolas Schweighofer  39 Na Jin Seo  32   36   57 Mark S Shiroishi  58 Surjo R Soekadar  59   60 Gianfranco Spalletta  11   61 Cathy M Stinear  62 Anisha Suri  63 Wai Kwong W Tang  64 Gregory T Thielman  65   66 Daniela Vecchio  11 Arno Villringer  67   68   69 Nick S Ward  70 Emilio Werden  19 Lars T Westlye  48   49 Carolee Winstein  39   71 George F Wittenberg  72   73 Kristin A Wong  74 Chunshui Yu  75   76 Steven C Cramer  77 Paul M Thompson  2   5
Affiliations
Review

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

Sook-Lei Liew et al. Hum Brain Mapp. 2022 Jan.

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.

Keywords: MRI; big data; lesions; neuroinformatics; stroke.

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Conflict of interest statement

Neda Jahanshad and Paul Thompson are MPIs of a research‐related grant from Biogen, Inc. for work unrelated to the contents of this manuscript. Steven C. Cramer is a consultant for Abbvie, Constant Therapeutics, MicroTransponder, Neurolutions, Regenera, SanBio, Stemedica, Fujifilm Toyama Chemical Co., Biogen, and TRCare.

Figures

FIGURE 1
FIGURE 1
ENIGMA Stroke Recovery workflow. Workflow for ENIGMA Stroke Recovery from data intake to data analysis
FIGURE 2
FIGURE 2
Optimized lesion segmentation pipeline. Example of a future neuroinformatics system for lesion segmentation, with only one point of manual input (manual segmentation of failed lesion masks, indicated in bold)
FIGURE A1
FIGURE A1
Examples of quality control decisions. (A) An axial view of a participant's left (blue) and right (red) caudate that are both scored as PASS. (B) An axial view of an under segmented right caudate (FAIL). Lesion overlap in the caudate caused ambiguous gray matter boundaries, resulting in a failed segmentation. (C) FreeSurfer can fail to segment correctly even without the presence of a lesion. This is an axial view of under segmented left and right caudate segmentation that were scored as FAIL. (D) A coronal view of a participant's left and right caudate that are both scored as PASS. (E) A coronal view of a participant with a lesion that overlaps with the left caudate creates ambiguous boundaries. In this case, FreeSurfer overestimated the left caudate, mistaking the lesion as a gray matter boundary (FAIL)

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