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Stroke is a leading cause of disability worldwide which can cause significant and persistent upper limb (UL) impairment. It is difficult to predict UL motor recovery after stroke and to forecast the expected outcomes of rehabilitation interventions during the acute and subacute phases when using clinical data alone. Accurate prediction of response to treatment could allow for more timely and targeted interventions, thereby improving recovery, resource allocation, and reducing the economic impact of post-stroke disability. Initial motor impairment is currently the strongest predictor of post-stroke motor recovery. Despite significant progress, current prediction models could be refined with additional predictors, and an emphasis on the time dependency of patient-specific predictions of UL recovery profiles. In the current paper a panel of experts provide their opinion on additional predictors and aspects of the literature that can help advance stroke outcome prediction models. Potential strategies include close attention to post-stroke data collection timeframes and adoption of individual-computerized modeling methods connected to a patient’s health record. These models should account for the non-linear and the variable recovery pattern of spontaneous neurological recovery. Additionally, input data should be extended to include cognitive, genomic, sensory, neural injury, and function measures as additional predictors of recovery. The accuracy of prediction models may be further improved by including standardized measures of outcome. Finally, we consider the potential impact of refined prediction models on healthcare costs.

Transcranial magnetic stimulation (TMS) has been widely applied in both basic and clinical neuroscience since its introduction in 1985 . . .

Objective: Converging evidence supporting an effect of transcranial direct current stimulation (tDCS) on postural control and human verticality perception highlights this strategy as promising for post-stroke rehabilitation. We have previously demonstrated polarity-dependent effects of high-definition tDCS (HD-tDCS) on weight-bearing asymmetry. However, there is no investigation regarding the time-course of effects on postural control induced by HD-tDCS protocols. Thus, we performed a nonlinear time series analysis focusing on the entropy of the ground reaction force as a secondary investigation of our randomized, double-blind, placebo-controlled, crossover clinical trial. Materials and Methods: Twenty healthy right-handed young adults received the following conditions (random order, separate days); anode center HD-tDCS, cathode center HD-tDCS or sham HD-tDCS at 1, 2, and 3 mA over the right temporo-parietal junction (TPJ). Using summarized time series of transfer entropy, we evaluated the exchanging information (causal direction) between both force plates and compared the dose-response across the healthy subjects with a Generalized Linear Hierarchical/Mixed Model (GLMM). Results: We found significant variation during the dynamic information flow (p < 0.001) among the dominant bodyside (and across time). A greater force transfer entropy was observed from the right to the left side during the cathode-center HD-tDCS up to 2 mA, with a causal relationship in the information flow (equilibrium force transfer) from right to left that decreased over time. Conclusions: HD-tDCS intervention induced a dynamic influence over time on postural control entropy. Right hemisphere TPJ stimulation using cathode-center HD-tDCS can induce an asymmetry of body weight distribution towards the ipsilateral side of stimulation. These results support the clinical potential of HD-tDCS for post-stroke rehabilitation.

Background Effectiveness of robotic therapy and transcranial direct current stimulation is conventionally assessed with clinical measures. Robotic metrics may be more objective and sensitive for measuring the efficacy of interventions on stroke survivor’s motor recovery. This study investigated if robotic metrics detect a difference in outcomes, not seen in clinical measures, in a study of transcranial direct current stimulation (tDCS) preceding robotic therapy. Impact of impairment severity on intervention response was also analyzed to explore optimization of outcomes by targeting patient sub-groups. Methods This 2020 study analyzed data from a double-blind, sham-controlled, randomized multi-center trial conducted from 2012 to 2016, including a six-month follow-up. 82 volunteers with single chronic ischemic stroke and right hemiparesis received anodal tDCS or sham stimulation, prior to robotic therapy. Robotic therapy involved 1024 repetitions, alternating shoulder-elbow and wrist robots, for a total of 36 sessions. Shoulder-elbow and wrist kinematic and kinetic metrics were collected at admission, discharge, and follow-up. Results No difference was detected between the tDCS or sham stimulation groups in the analysis of robotic shoulder-elbow or wrist metrics. Significant improvements in all metrics were found for the combined group analysis. Novel wrist data showed smoothness significantly improved (P < ·001) while submovement number trended down, overlap increased, and interpeak interval decreased. Post-hoc analysis showed only patients with severe impairment demonstrated a significant difference in kinematics, greater for patients receiving sham stimulation. Conclusions Robotic data confirmed results of clinical measures, showing intensive robotic therapy is beneficial, but no additional gain from tDCS. Patients with severe impairment did not benefit from the combined intervention. Wrist submovement characteristics showed a delayed pattern of motor reco

Background A detailed sensorimotor evaluation is essential in planning effective, individualized therapy post-stroke. Robotic kinematic assay may offer better accuracy and resolution to understand stroke recovery. Here we investigate the added value of distal wrist measurement to a proximal robotic kinematic assay to improve its correlation with clinical upper extremity measures in chronic stroke. Secondly, we compare linear and nonlinear regression models. Methods Data was sourced from a multicenter randomized controlled trial conducted from 2012 to 2016, investigating the combined effect of robotic therapy and transcranial direct current stimulation (tDCS). 24 kinematic metrics were derived from 4 shoulder-elbow tasks and 35 metrics from 3 wrist and forearm evaluation tasks. A correlation-based feature selection was performed, keeping only features substantially correlated with the target attribute (R > 0.5.) Nonlinear models took the form of a multilayer perceptron neural network: one hidden layer and one linear output. Results Shoulder-elbow metrics showed a significant correlation with the Fugl Meyer Assessment (upper extremity, FMA-UE), with a R = 0.82 (P < 0.001) for the linear model and R = 0.88 (P < 0.001) for the nonlinear model. Similarly, a high correlation was found for wrist kinematics and the FMA-UE (R = 0.91 (P < 0.001) and R = 0.92 (P < 0.001) for the linear and nonlinear model respectively). The combined analysis produced a correlation of R = 0.91 (P < 0.001) for the linear model and R = 0.91 (P < 0.001) for the nonlinear model. Conclusions Distal wrist kinematics were highly correlated to clinical outcomes, warranting future investigation to explore our nonlinear wrist model with acute or subacute stroke populations. Trial registration http://www.clinicaltrials.gov . Actual study start date September 2012. First registered on 15 November 2012. Retrospectively registered. Unique identifiers: NCT01726673 and NCT03562663 .

In people with hemiparesis after stroke, intensive upper limb motor practice, such as robot-assisted training, can lead to clinically meaningful improvement, yet the benefit typically falls far short of full functional recovery. Supplemental therapies have the potential to enhance training effects, and non-invasive brain stimulation as a candidate add-on therapy has previously been reported; this literature was systematically reviewed by Reis and colleagues. The review found that there is no beneficial effect of non-invasive brain stimulation as a supplement to robot-assisted training. The eight reviewed trials used a variety of existing best non-invasive brain stimulation practices and available robotic technology.

© Copyright © 2020 Nascimento, Pinto-Orellana, Leite, Edwards, Louzada and Santos. Sparse time series models have shown promise in estimating contemporaneous and ongoing brain connectivity. This paper was motivated by a neuroscience experiment using EEG signals as the outcome of our established interventional protocol, a new method in neurorehabilitation toward developing a treatment for visual verticality disorder in post-stroke patients. To analyze the [complex outcome measure (EEG)] that reflects neural-network functioning and processing in more specific ways regarding traditional analyses, we make a comparison among sparse time series models (classic VAR, GLASSO, TSCGM, and TSCGM-modified with non-linear and iterative optimizations) combined with a graphical approach, such as a Dynamic Chain Graph Model (DCGM). These dynamic graphical models were useful in assessing the role of estimating the brain network structure and describing its causal relationship. In addition, the class of DCGM was able to visualize and compare experimental conditions and brain frequency domains [using finite impulse response (FIR) filter]. Moreover, using multilayer networks, the results corroborate with the susceptibility of sparse dynamic models, bypassing the false positives problem in estimation algorithms. We conclude that applying sparse dynamic models to EEG data may be useful for describing intervention-relocated changes in brain connectivity.

© European Stroke Organisation 2020. Introduction: Non-invasive brain stimulation (NIBS) with speech therapy might improve recovery from post-stroke aphasia. This three-armed sham-controlled blinded prospective proof-of-concept study tested 1 Hz subthreshold repetitive transcranial magnetic stimulation (rTMS) and 2-mA cathodal transcranial direct current stimulation (ctDCS) on the right pars triangularis in subacute post-stroke aphasia. Patients and methods: Sixty-three patients with left middle cerebral artery infarcts were recruited in five hospitals (Canada/United States/Germany, 01–2014/03–2018) and randomized to receive rTMS (N = 20), ctDCS (N = 24) or sham stimulation (N = 19) with ST for 10 days. Primary outcome variables were Z-score changes in naming, semantic fluency and comprehension tests and adverse event frequency. Secondary outcome variable was the percent change in the Unified Aphasia Score. Intention-to-treat analyses tested between-group effects at days 1 and 30 post-treatment with a pre-planned subgroup analysis for lesion location (affecting Broca’s area or not). Results: Naming was significantly improved by rTMS (median = 1.91/interquartile range = 0.77/p =.01) at 30 days versus ctDCS (median = 1.11/interquartile range = 1.51) and sham stimulation (median = 1.02/interquartile range = 1.71). All other primary results were non-significant. The rTMS effect was driven by the patient subgroup with intact Broca’s area where NIBS tended to improve UnAS (median = 33.2%/interquartile range = 46.7%/p =.062) versus sham stimulation (median = 12.5%/interquartile range = 7.9%) at day 30. Conversely, in patients with infarcted Broca’s area, UnAS tended to improve more with sham stimulation (median = 75.0%/interquartile range = 86.9%/p =.053) versus NIBS (median = 12.7%/interquartile range = 31.7). Conclusion: We found a delayed positive effect of low-frequency rTMS targeting the right pars triangularis on the recovery of naming performance in subacute post-st