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Item Brain oscillatory activity as a biomarker of motor recovery in chronic stroke(2020-04-01) Ray, Andreas M.; Figueiredo, Thiago D. C.; López‐Larraz, Eduardo; Birbaumer, Niels; Ramos‐Murguialday, Ander; López-Larraz, Eduardo; Ramos-Murguialday, Ander; Medical TechnologiesIn the present work, we investigated the relationship of oscillatory sensorimotor brain activity to motor recovery. The neurophysiological data of 30 chronic stroke patients with severe upper‐limb paralysis are the basis of the observational study presented here. These patients underwent an intervention including movement training based on combined brain–machine interfaces and physiotherapy of several weeks recorded in a double‐blinded randomized clinical trial. We analyzed the alpha oscillations over the motor cortex of 22 of these patients employing multilevel linear predictive modeling. We identified a significant correlation between the evolution of the alpha desynchronization during rehabilitative intervention and clinical improvement. Moreover, we observed that the initial alpha desynchronization conditions its modulation during intervention: Patients showing a strong alpha desynchronization at the beginning of the training improved if they increased their alpha desynchronization. Patients showing a small alpha desynchronization at initial training stages improved if they decreased it further on both hemispheres. In all patients, a progressive shift of desynchronization toward the ipsilesional hemisphere correlates significantly with clinical improvement regardless of lesion location. The results indicate that initial alpha desynchronization might be key for stratification of patients undergoing BMI interventions and that its interhemispheric balance plays an important role in motor recovery.Item 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(2020) Liew, Sook‐Lei; Zavaliangos‐Petropulu, Artemis; Jahanshad, Neda; Lang, Catherine E.; Hayward, Kathryn S.; Lohse, Keith R.; Juliano, Julia M.; Assogna, Francesca; Baugh, Lee A.; Bhattacharya, Anup K.; Bigjahan, Bavrina; Borich, Michael R.; Boyd, Lara A.; Brodtmann, Amy; Buetefisch, Cathrin M.; Byblow, Winston D.; Cassidy, Jessica M.; Conforto, Adriana B.; Craddock, R. Cameron; Dimyan, Michael A.; Dula, Adrienne N.; Ermer, Elsa; Etherton, Mark R.; Fercho, Kelene A.; Gregory, Chris M.; Hadidchi, Shahram; Holguin, Jess A.; Hwang, Darryl H.; Jung, Simon; Kautz, Steven A.; Khlif, Mohamed Salah; Khoshab, Nima; Kim, Bokkyu; Kim, Hosung; Kuceyeski, Amy; Lotze, Martin; MacIntosh, Bradley J.; Margetis, John L.; Mohamed, Feroze B.; Piras, Fabrizio; Ramos‐Murguialday, Ander; Richard, Geneviève; Roberts, Pamela; Robertson, Andrew D.; Rondina, Jane M.; Rost, Natalia S.; Sanossian, Nerses; Schweighofer, Nicolas; Seo, Na Jin; Shiroishi, Mark S.; Soekadar, Surjo R.; Spalletta, Gianfranco; Stinear, Cathy M.; Suri, Anisha; Tang, Wai Kwong W.; Thielman, Gregory T.; Vecchio, Daniela; Villringer, Arno; Ward, Nick S.; Werden, Emilio; Westlye, Lars T.; Winstein, Carolee; Wittenberg, George F.; Wong, Kristin A.; Yu, Chunshui; Cramer, Steven C.; Thompson, Paul M.; Zavaliangos-Petropulu, Artemis; Ramos-Murguialday, Ander; Medical TechnologiesThe 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.