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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.

Up to two-thirds of stroke survivors experience persistent sensorimotor impairments. Recovery relies on the integrity of spared brain areas to compensate for damaged tissue. Deep grey matter structures play a critical role in the control and regulation of sensorimotor circuits. The goal of this work is to identify associations between volumes of spared subcortical nuclei and sensorimotor behaviour at different timepoints after stroke. We pooled high-resolution T1-weighted MRI brain scans and behavioural data in 828 individuals with unilateral stroke from 28 cohorts worldwide. Cross-sectional analyses using linear mixed-effects models related post-stroke sensorimotor behaviour to non-lesioned subcortical volumes (Bonferroni-corrected, P

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling - regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity.

BackgroundWalking-related disability is the most frequent reason for inpatient stroke rehabilitation. Task-related practice is a critical component for improving patient outcomes.ObjectiveTo test the feasibility of providing quantitative feedback about daily walking performance and motivating greater skills practice via remote sensing.MethodsIn this phase III randomized, single blind clinical trial, patients participated in conventional therapies while wearing wireless sensors (triaxial accelerometers) at both ankles. Activity-recognition algorithms calculated the speed, distance, and duration of walking bouts. Three times a week, therapists provided either feedback about performance on a 10-meter walk (speed only) or walking speed feedback plus a review of walking activity recorded by the sensors (augmented). Primary outcomes at discharge included total daily walking time, derived from the sensors, and a timed 15-meter walk.ResultsSixteen rehabilitation centers in 11 countries enrolled 135 participants over 15 months. Sensors recorded more than 1800 days of therapy, 37 000 individual walking bouts, and 2.5 million steps. No significant differences were found between the 2 feedback groups in daily walking time (15.1 ± 13.1 vs 16.6 ± 14.3 minutes, P = .54) or 15-meter walking speed (0.93 ± 0.47 vs 0.91 ± 0.53 m/s, P = .96). Remarkably, 30% of participants decreased their total daily walking time over their rehabilitation stay.ConclusionsIn this first trial of remote monitoring of inpatient stroke rehabilitation, augmented feedback beyond speed alone did not increase the time spent practicing or improve walking outcomes. Remarkably modest time was spent walking. Wireless sensing, however, allowed clinicians to audit skills practice and provided ground truth regarding changes in clinically important, mobility-related activities.

Control comparator selection is a critical trial design issue. Preclinical and clinical investigators who are doing trials of stroke recovery and rehabilitation interventions must carefully consider the appropriateness and relevance of their chosen control comparator as the benefit of an experimental intervention is established relative to a comparator. Establishing a strong rationale for a selected comparator improves the integrity of the trial and validity of its findings. This Stroke Recovery and Rehabilitation Roundtable (SRRR) taskforce used a graph theory voting system to rank the importance and ease of addressing challenges during control comparator design. "Identifying appropriate type of control" was ranked easy to address and very important, "variability in usual care" was ranked hard to address and of low importance, and "understanding the content of the control and how it differs from the experimental intervention" was ranked very important but not easy to address. The CONtrol DeSIGN (CONSIGN) decision support tool was developed to address the identified challenges and enhance comparator selection, description, and reporting. CONSIGN is a web-based tool inclusive of seven steps that guide the user through control comparator design. The tool was refined through multiple rounds of pilot testing that included more than 130 people working in neurorehabilitation research. Four hypothetical exemplar trials, which span preclinical, mood, aphasia, and motor recovery, demonstrate how the tool can be applied in practice. Six consensus recommendations are defined that span research domains, professional disciplines, and international borders. [ABSTRACT FROM AUTHOR]

Background and Purpose: We addressed questions about the potential discrepancy between improvements in activity capacity and improvements in activity performance in daily life. We asked whether this discrepancy is: Common in routine, outpatient care, or an artifact of intervention studies? Unique to upper limb (UL) rehabilitation, or is it seen in walking rehabilitation too? Only seen in persons with stroke, or a broader neurorehabilitation problem? Methods: A longitudinal, observational cohort of 156 participants with stroke or Parkinson disease (PD) receiving outpatient rehabilitation at 5 clinics was assessed around admission and monthly during their episode of care. Individual, longitudinal capacity (Action Research Arm Test or walking speed) and performance (wearable sensor measurements of use ratio or steps/day) data were modeled to extract predicted change scores. Simulation methods were used to determine whether an individual's change was greater than 1 standard error. Participants were classified into categories based on whether or not they improved in capacity (C+ or C−) and/or performance (P+ or P−). Results: A majority (59%) were classified as C+P−. Smaller portions of the sample were classified as C+P+ (20%) and C−P− (21%), with 1 participant (<1%) classified as C−P+. The proportions in the C+P− category were significantly larger in the stroke (χ2 = 48.7, P < 0.0001) and PD (χ2 = 24.3, P < 0.0001) walking subgroups than in the stroke UL subgroup. Discussion and Conclusions: The discrepancy between improvements in capacity and performance is a problem in routine, outpatient neurorehabilitation. If performance information were available, patients and clinicians could act to address it. Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A396). [ABSTRACT FROM AUTHOR]