The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke.
Sook-Lei LiewArtemis Zavaliangos-PetropuluNeda JahanshadCatherine E LangKathryn S HaywardKeith R LohseJulia M JulianoFrancesca AssognaLee A BaughAnup K BhattacharyaBavrina BigjahanMichael R BorichLara A BoydAmy BrodtmannCathrin M BuetefischWinston D ByblowJessica M CassidyAdriana B ConfortoR Cameron CraddockMichael A DimyanAdrienne N DulaElsa ErmerMark R EthertonKelene A FerchoChris M GregoryShahram HadidchiJess A HolguinDarryl H HwangSimon JungSteven A KautzMohamed Salah KhlifNima KhoshabBokkyu KimHosung KimAmy F KuceyeskiMartin LotzeBradley J MacIntoshJohn L MargetisFeroze B MohamedFabrizio PirasAnder Ramos-MurguialdayGenevieve RichardPamela RobertsAndrew D RobertsonJane M RondinaNatalia S RostNerses SanossianNicolas SchweighoferNa Jin SeoMark S ShiroishiSurjo R SoekadarGianfranco SpallettaCathy M StinearAnisha SuriWai Kwong W TangGregory T ThielmanDaniela VecchioArno VillringerNick S WardEmilio WerdenLars Tjelta WestlyeCarolee WinsteinGeorge F WittenbergKristin A WongChunshui YuSteven C CramerPaul M ThompsonPublished in: Human brain mapping (2020)
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.
Keyphrases
- big data
- atrial fibrillation
- electronic health record
- artificial intelligence
- machine learning
- magnetic resonance imaging
- systematic review
- cerebral ischemia
- randomized controlled trial
- computed tomography
- brain injury
- quality improvement
- blood brain barrier
- cross sectional
- subarachnoid hemorrhage
- weight gain
- meta analyses
- diffusion weighted imaging