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Temporal structure reveals the potential adaptive strategies employed during upper extremity movements. The authors compared the temporal structure of upper extremity joints under 3 different reaching conditions: preferred speed, fast speed, and reaching with rhythmic auditory cues in 10 individuals poststroke. They also investigated the temporal structure of these 3 reaching conditions in 8 healthy controls to aid in the interpretation of the observed patterns in the poststroke cohort. Approximate entropy (ApEn) was used to measure the temporal structure of the upper extremity joints. ApEn was similar between conditions in controls. After stroke, ApEn was significantly higher for shoulder, elbow, and wrist both at fast speed and with rhythmic cues compared with preferred speed. ApEn at index finger was significantly higher only with rhythmic cues compared with preferred speed. The authors propose that practice reaching at faster speed and with rhythmic cues as a component of rehabilitation interventions may enhance adaptability after stroke.

Evidence indicates that experience-dependent cortical plasticity underlies post-stroke motor recovery of the impaired upper extremity. Motor skill learning in neurologically intact individuals is thought to involve the primary motor cortex, and the majority of studies in the animal literature have studied changes in the primary sensorimotor cortex with motor rehabilitation. Whether changes in engagement in the sensorimotor cortex occur in humans after stroke currently is an area of much interest. The present study conducted a meta-analysis on stroke studies examining changes in neural representations following therapy specifically targeting the upper extremity to determine if rehabilitation-related motor recovery is associated with neural plasticity in the sensorimotor cortex of the lesioned hemisphere. Twenty-eight studies investigating upper extremity neural representations (e.g., TMS, fMRI, PET, or SPECT) were identified, and 13 met inclusion criteria as upper extremity intervention training studies. Common outcome variables representing changes in the primary motor and sensorimotor cortices were used in calculating standardized effect sizes for each study. The primary fixed effects model meta-analysis revealed a large overall effect size (ES = 0.84, S.D. = 0.15, 95% CI = 0.76-0.93). Moreover, a fail-safe analysis indicated that 42 null effect studies would be necessary to lower the overall effect size to an insignificant level. These results indicate that neural changes in the sensorimotor cortex of the lesioned hemisphere accompany functional paretic upper extremity motor gains achieved with targeted rehabilitation interventions.

The study aims to evaluate the efficacy of robot-assisted therapy for the treatment of long-term upper-limb impairment after stroke. The results indicate that there was no significant improvement following therapy at 12 weeks; however, in secondary analyses, such therapy improved outcomes over 36 weeks.

Objective. Bilateral arm training with rhythmic auditory cueing (BATRAC) has been reported to be efficacious in promoting upper-extremity (UE) recovery in chronic stroke. We tested a modified form of BATRAC [...]

Although stroke affects cognitive functioning as well as motor functioning, research on cognitive consequences has lagged behind that focused on motor function. The evidence that is accruing suggests that cognitive function is importantly related to successful rehabilitation. The present study examined two aspects of attentional functioning (divided attention and switching attention) in older adult stroke survivors and healthy older adults. In addition, the authors investigated the relation between attention and functional outcomes following stroke. Results revealed stroke-related deficits in both of the types of attention as well as significant associations between attentional functioning and both physical and social outcome measures. Poorer attentional performance was associated with a more negative impact of stroke on daily functioning. These findings suggest an important role for attention in post-stroke function and provide information that can contribute to improving outcomes following stroke.

Background. The ability to take a step quickly is important for balance maintenance during activities of daily living. The purpose of this study was to investigate the effects of age, reaction condition, and step direction on the ability to take a volitional step as fast as possible. Methods. The performance of a voluntary step task was measured in young adult (mean age 20, SD 0.9 years), young-old adult (mean age 67, SD 3.7 years), and old adult (mean age 78, SD 2.3 years) healthy female participants. Each participant stepped as fast as possible in eight directions in response to a visual cue in a simple or choice reaction time condition. The effects of age, reaction condition, and step direction and their interactions on the primary outcome variables of response time, step liftoff, and step landing time were examined. Results. The normal aging process progressively increased the response, liftoff, and landing times. The choice reaction time condition, compared to the simple, had significantly increased response, liftoff, and landing times. Step direction significantly affected the liftoff and landing times, with lateral, diagonal, and anterior and posterioir (A-P) times increasing, respectively. Conclusions. We found substantial declines in the ability to step rapidly in healthy adults as age increased. When a decision was required regarding the step direction, the step performance also declined. Step direction also significantly affected step performance. The assessment of voluntary step performance, which may be an indicator of balance ability, should include dimensions of both direction and the choice condition.

This study investigated whether a lifestyle modification program that encouraged a ketogenic diet (KD) for participants with lymphedema and obesity would reduce weight and limb volume and improve quality of life. A total of 12 participants with lymphedema and obesity (mean body mass index = 38.38; SD = 7.02) were enrolled in a lifestyle modification group. The timespan from baseline data collection to 30-day follow-up was 18 weeks. Retention rate was 83.3%. Data were analyzed with repeated-measures ANOVA and Pearson correlation. Participants demonstrated statistically significant improvement in most outcome measures. Mean weight loss was 5.18 kg—F(4, 36) = 11.17; P <.001—or 4.8% of mean baseline weight. The average limb volume reduction was 698.9 ml—F(4, 36) = 9.4; P <.001—and was positively correlated with weight loss (r = 0.8; P =.005). There appeared to be a tendency for participants who used a KD (n = 6) to demonstrate superior results in most outcome measures compared with those who did not use the diet (n = 4), although the sample size of the 2 groups was too small to report definitive results. This lifestyle modification program provided insight into the possible value of a KD for obesity and lymphedema management. [ABSTRACT FROM AUTHOR]

Purpose : To develop a robust multidimensional deep‐learning based method to simultaneously generate accurate neurite orientation dispersion and density imaging (NODDI) and generalized fractional anisotropy (GFA) parameter maps from undersampled q‐space datasets for use in stroke imaging. Methods : Traditional diffusion spectrum imaging (DSI) capable of producing accurate NODDI and GFA parameter maps requires hundreds of q‐space samples which renders the scan time clinically untenable. A convolutional neural network (CNN) was trained to generated NODDI and GFA parameter maps simultaneously from 10× undersampled q‐space data. A total of 48 DSI scans from 15 stroke patients and 14 normal subjects were acquired for training, validating, and testing this method. The proposed network was compared to previously proposed voxel‐wise machine learning based approaches for q‐space imaging. Network‐generated images were used to predict stroke functional outcome measures. Results : The proposed network achieves significant performance advantages compared to previously proposed machine learning approaches, showing significant improvements across image quality metrics. Generating these parameter maps using CNNs also comes with the computational benefits of only needing to generate and train a single network instead of multiple networks for each parameter type. Post‐stroke outcome prediction metrics do not appreciably change when using images generated from this proposed technique. Over three test participants, the predicted stroke functional outcome scores were within 1–6% of the clinical evaluations. Conclusions : Estimates of NODDI and GFA parameters estimated simultaneously with a deep learning network from highly undersampled q‐space data were improved compared to other state‐of‐the‐art methods providing a 10‐fold reduction scan time compared to conventional methods. [ABSTRACT FROM AUTHOR]

Artificial sensory feedback via electrocutaneous stimulation can be used to assist or rehabilitate stroke survivors with sensory deficits. Conveying the magnitude of tactile stimuli is an important aspect of artificial sensory feedback. Here, we explore how stroke-related sensory deficits impact the ability of electrocutaneous stimulation to convey the magnitude of tactile stimuli. Using classical psychophysical methods, we quantified the threshold of detection and the just-noticeable difference of electrocutaneous stimulation current in five stroke survivors with unilateral sensory deficits. We show significantly greater (40%) stimulation currents are needed for initial perception on the paretic hand compared to the non-paretic hand. We also show significantly greater percent changes in stimulation current (140%) are needed for reliable incremental perception on the paretic hand compared to the non-paretic hand. Lastly, we show little correlation between electrocutaneous discrimination performance and clinical sensory assessments of light-touch and spatial mechanoperception. These findings can help guide the implementation of artificial sensory feedback as an assistive or rehabilitative intervention for individuals experiencing sensory loss after a stroke.Clinical Relevance- Our results can help guide the implementation of electrical stimulation as an assistive or rehabilitative intervention for individuals with sensory loss after stroke.

Background and Purpose: The brain changes that underlie therapy-induced improvement in motor function after stroke remain obscure. This study sought to demonstrate the feasibility and utility of measuring motor system physiology in a clinical trial of intensive upper extremity rehabilitation in chronic stroke-related hemiparesis. Methods: This was a substudy of two multi-center clinical trials of intensive robotic and intensive conventional therapy arm therapy in chronic, significantly hemiparetic, stroke patients. Transcranial magnetic stimulation was used to measure motor cortical output to the biceps and extensor digitorum communus muscles. Magnetic resonance imaging (MRI) was used to determine the cortical anatomy, as well as to measure fractional anisotropy, and blood oxygenation (BOLD) during an eyes-closed rest state. Region-of-interest time-series correlation analysis was performed on the BOLD signal to determine interregional connectivity. Functional status was measured with the upper extremity Fugl-Meyer and Wolf Motor Function Test. Results: Motor evoked potential (MEP) presence was associated with better functional outcomes, but the effect was not significant when considering baseline impairment. Affected side internal capsule fractional anisotropy was associated with better function at baseline. Affected side primary motor cortex (M1) activity became more correlated with other frontal motor regions after treatment. Resting state connectivity between affected hemisphere M1 and dorsal premotor area (PMAd) predicted recovery. Conclusions: Presence of motor evoked potentials in the affected motor cortex and its functional connectivity with PMAd may be useful in predicting recovery. Functional connectivity in the motor network shows a trends towards increasing after intensive robotic or non-robotic arm therapy. Clinical Trial Registration URL: http://www.clinicaltrials.gov. Unique identifiers: NCT00372411 \& NCT00333983., Competing Interests: Competing interests: No competing interests were disclosed.

Diffusion weighted imaging (DWI) is widely used to study changes in white matter following stroke. In various studies employing diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI) modalities, it has been shown that fractional anisotropy (FA), mean diffusivity (MD), and generalized FA (GFA) can be used as measures of white matter tract integrity in stroke patients. However, these measures may be non-specific, as they do not directly delineate changes in tissue microstructure. Multi-compartment models overcome this limitation by modeling DWI data using a set of indices that are directly related to white matter microstructure. One of these models which is gaining popularity, is neurite orientation dispersion and density imaging (NODDI). This model uses conventional single or multi-shell HARDI data to describe fiber orientation dispersion as well as densities of different tissue types in the imaging voxel. In this paper, we apply for the first time the NODDI model to 4-shell HARDI stroke data. By computing NODDI indices over the entire brain in two stroke patients, and comparing tissue regions in ipsilesional and contralesional hemispheres, we demonstrate that NODDI modeling provides specific information on tissue microstructural changes. We also introduce an information theoretic analysis framework to investigate the non-local effects of stroke in the white matter. Our initial results suggest that the NODDI indices might be more specific markers of white matter reorganization following stroke than other measures previously used in studies of stroke recovery.