Transforming modeling in neurorehabilitation: clinical insights for personalized rehabilitation.

Practicing clinicians in neurorehabilitation continue to lack a systematic evidence base to personalize rehabilitation therapies to individual patients and thereby maximize outcomes. Computational modeling- collecting, analyzing, and modeling neurorehabilitation data- holds great promise. A key question is how can computational modeling contribute to the evidence base for personalized rehabilitation? As representatives of the clinicians and clinician-scientists who attended the 2023 NSF DARE conference at USC, here we offer our perspectives and discussion on this topic. Our overarching thesis is that clinical insight should inform all steps of modeling, from construction to output, in neurorehabilitation and that this process requires close collaboration between researchers and the clinical community. We start with two clinical case examples focused on motor rehabilitation after stroke which provide context to the heterogeneity of neurologic injury, the complexity of post-acute neurologic care, the neuroscience of recovery, and the current state of outcome assessment in rehabilitation clinical care. Do we provide different therapies to these two different patients to maximize outcomes? Asking this question leads to a corollary: how do we build the evidence base to support the use of different therapies for individual patients? We discuss seven points critical to clinical translation of computational modeling research in neurorehabilitation- (i) clinical endpoints, (ii) hypothesis- versus data-driven models, (iii) biological processes, (iv) contextualizing outcome measures, (v) clinical collaboration for device translation, (vi) modeling in the real world and (vii) clinical touchpoints across all stages of research. We conclude with our views on key avenues for future investment (clinical-research collaboration, new educational pathways, interdisciplinary engagement) to enable maximal translational value of computational modeling research in neurorehabilitation.

Recovery of Function After Acquired Neurological Injury.

The articles in this Special Issue on Recovery of Function After Neurological Injury include an impressive range of clinical diagnoses, scientific approaches, and theoretical frameworks that demonstrate the breadth and depth of occupational therapy in the restoration of function after neurological injury. An emerging theme throughout is the need for the profession of occupational therapy to identify and use more efficient methods for diagnosing and treating people with neurological injuries to improve their quality of life and the impact of care.

Pre-Implementation Analysis of the Usability and Acceptability of a Poststroke Complex Telehealth Biofeedback Intervention.

IMPORTANCE: Complex telehealth interventions can facilitate remote occupational therapy services and improve access for people living with chronic neurological conditions. Understanding the factors that influence the uptake of these technologies is important. OBJECTIVE: To explore the fit between electromyography (EMG) biofeedback and telerehabilitation for stroke survivors, optimize EMG biofeedback interventions, and, more broadly, support other efforts to develop complex telerehabilitation interventions. DESIGN: Pre-implementation mixed-methods analysis of usability and acceptability data collected during a pilot and feasibility study. SETTING: Community. PARTICIPANTS: Adult stroke survivors with hemiparesis (N = 11; M age = 54 yr). INTERVENTION: Game-based EMG biofeedback system for arm sensorimotor rehabilitation, delivered via telehealth. OUTCOMES AND MEASURES: Post-Study System Usability Questionnaire, an extended Unified Theory of Acceptance and Use of Technology model questionnaire, and semistructured interview. We coded the interview data using questionnaire constructs. RESULTS: Participants used an EMG biofeedback intervention at home. Quantitative measures show high levels of perceived usability and acceptability, supported by qualitative findings describing specific facilitators and barriers. CONCLUSIONS AND RELEVANCE: Pre-implementation studies can improve the design and relevance of complex telehealth interventions. One major conclusion from this study is the influence of therapy providers on acceptability and usability of complex telehealth interventions. Plain-Language Summary: This study contributes to an emerging body of literature that examines the use of complex telehealth interventions with survivors of neurological injury. The findings highlight the value and support the development and use of complex telehealth interventions, which have the potential to improve remote access to occupational therapy for clients living with chronic neurological conditions. Complex telehealth interventions can open doors for survivors of neurological injury who face barriers to accessing occupational therapy and would benefit from technology-enabled therapy at home.

White Matter Integrity and Chronic Poststroke Upper Limb Function: An ENIGMA Stroke Recovery Analysis.

BACKGROUND: Integrity of the corticospinal tract (CST) is an important biomarker for upper limb motor function following stroke. However, when structurally compromised, other tracts may become relevant for compensation or recovery of function. METHODS: We used the ENIGMA Stroke Recovery data set, a multicenter, retrospective, and cross-sectional collection of patients with upper limb impairment during the chronic phase of stroke to test the relevance of tracts in individuals with less and more severe (laterality index of CST fractional anisotropy ≥0.25) CST damage in an observational study design. White matter integrity was quantified using fractional anisotropy for the CST, the superior longitudinal fascicle, and the callosal fibers interconnecting the primary motor cortices between hemispheres. Optic radiations served as a control tract as they have no a priori relevance for the motor system. Pearson correlation was used for testing correlation with upper limb motor function (Fugl-Meyer upper extremity). RESULTS: From 1235 available data sets, 166 were selected (by imaging, Fugl-Meyer upper extremity, covariates, stroke location, and stage) for analyses. Only individuals with severe CST damage showed a positive association of fractional anisotropy in both callosal fibers interconnecting the primary motor cortices (r[21]=0.49; P=0.025) and superior longitudinal fascicle (r[21]=0.51; P=0.018) with Fugl-Meyer upper extremity. CONCLUSIONS: Our data support the notion that individuals with more severe damage of the CST depend on residual pathways for achieving better upper limb outcome than those with less affected CST.

Testing a convolutional neural network-based hippocampal segmentation method in a stroke population.

As stroke mortality rates decrease, there has been a surge of effort to study poststroke dementia (PSD) to improve long-term quality of life for stroke survivors. Hippocampal volume may be an important neuroimaging biomarker in poststroke dementia, as it has been associated with many other forms of dementia. However, studying hippocampal volume using MRI requires hippocampal segmentation. Advances in automated segmentation methods have allowed for studying the hippocampus on a large scale, which is important for robust results in the heterogeneous stroke population. However, most of these automated methods use a single atlas-based approach and may fail in the presence of severe structural abnormalities common in stroke. Hippodeep, a new convolutional neural network-based hippocampal segmentation method, does not rely solely on a single atlas-based approach and thus may be better suited for stroke populations. Here, we compared quality control and the accuracy of segmentations generated by Hippodeep and two well-accepted hippocampal segmentation methods on stroke MRIs (FreeSurfer 6.0 whole hippocampus and FreeSurfer 6.0 sum of hippocampal subfields). Quality control was performed using a stringent protocol for visual inspection of the segmentations, and accuracy was measured as volumetric correlation with manual segmentations. Hippodeep performed significantly better than both FreeSurfer methods in terms of quality control. All three automated segmentation methods had good correlation with manual segmentations and no one method was significantly more correlated than the others. Overall, this study suggests that both Hippodeep and FreeSurfer may be useful for hippocampal segmentation in stroke rehabilitation research, but Hippodeep may be more robust to stroke lesion anatomy.

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke.

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.

Increased cognitive load in immersive virtual reality during visuomotor adaptation is associated with decreased long-term retention and context transfer.

BACKGROUND: Complex motor tasks in immersive virtual reality using a head-mounted display (HMD-VR) have been shown to increase cognitive load and decrease motor performance compared to conventional computer screens (CS). Separately, visuomotor adaptation in HMD-VR has been shown to recruit more explicit, cognitive strategies, resulting in decreased implicit mechanisms thought to contribute to motor memory formation. However, it is unclear whether visuomotor adaptation in HMD-VR increases cognitive load and whether cognitive load is related to explicit mechanisms and long-term motor memory formation. METHODS: We randomized 36 healthy participants into three equal groups. All groups completed an established visuomotor adaptation task measuring explicit and implicit mechanisms, combined with a dual-task probe measuring cognitive load. Then, all groups returned after 24-h to measure retention of the overall adaptation. One group completed both training and retention tasks in CS (measuring long-term retention in a CS environment), one group completed both training and retention tasks in HMD-VR (measuring long-term retention in an HMD-VR environment), and one group completed the training task in HMD-VR and the retention task in CS (measuring context transfer from an HMD-VR environment). A Generalized Linear Mixed-Effect Model (GLMM) was used to compare cognitive load between CS and HMD-VR during visuomotor adaptation, t-tests were used to compare overall adaptation and explicit and implicit mechanisms between CS and HMD-VR training environments, and ANOVAs were used to compare group differences in long-term retention and context transfer. RESULTS: Cognitive load was found to be greater in HMD-VR than in CS. This increased cognitive load was related to decreased use of explicit, cognitive mechanisms early in adaptation. Moreover, increased cognitive load was also related to decreased long-term motor memory formation. Finally, training in HMD-VR resulted in decreased long-term retention and context transfer. CONCLUSIONS: Our findings show that cognitive load increases in HMD-VR and relates to explicit learning and long-term motor memory formation during motor learning. Future studies should examine what factors cause increased cognitive load in HMD-VR motor learning and whether this impacts HMD-VR training and long-term retention in clinical populations.

Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis.

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.

Development of a Low-Cost, Modular Muscle-Computer Interface for At-Home Telerehabilitation for Chronic Stroke.

Stroke is a leading cause of long-term disability in the United States. Recent studies have shown that high doses of repeated task-specific practice can be effective at improving upper-limb function at the chronic stage. Providing at-home telerehabilitation services with therapist supervision may allow higher dose interventions targeted to this population. Additionally, muscle biofeedback to train patients to avoid unwanted simultaneous activation of antagonist muscles (co-contractions) may be incorporated into telerehabilitation technologies to improve motor control. Here, we present the development and feasibility of a low-cost, portable, telerehabilitation biofeedback system called Tele-REINVENT. We describe our modular electromyography acquisition, processing, and feedback algorithms to train differentiated muscle control during at-home therapist-guided sessions. Additionally, we evaluated the performance of low-cost sensors for our training task with two healthy individuals. Finally, we present the results of a case study with a stroke survivor who used the system for 40 sessions over 10 weeks of training. In line with our previous research, our results suggest that using low-cost sensors provides similar results to those using research-grade sensors for low forces during an isometric task. Our preliminary case study data with one patient with stroke also suggest that our system is feasible, safe, and enjoyable to use during 10 weeks of biofeedback training, and that improvements in differentiated muscle activity during volitional movement attempt may be induced during a 10-week period. Our data provide support for using low-cost technology for individuated muscle training to reduce unintended coactivation during supervised and unsupervised home-based telerehabilitation for clinical populations, and suggest this approach is safe and feasible. Future work with larger study populations may expand on the development of meaningful and personalized chronic stroke rehabilitation.

Virtual Reality for the Treatment of Anxiety Disorders: A Scoping Review.

IMPORTANCE: Virtual reality in head-mounted displays (HMD-VR) may be a valuable tool in occupational therapy to address anxiety. Findings from the virtual reality exposure therapy (VRET) literature may facilitate translation of HMD-VR to occupational therapy psychosocial practice. OBJECTIVE: To explore how HMD-VR has been used to treat anxiety through VRET and could be translated to occupational therapy. DATA SOURCES: We searched seven electronic databases for articles published between 2000 and 2020: CINAHL, Cochrane Library, Embase, ERIC, Ovid MEDLINE, PsycINFO, and Web of Science. Search terms included HMD-VR constructs, products, and therapy concepts. Study Selection and Data Collection: We used Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to report studies implementing VRET to treat anxiety. At least two reviewers assessed each citation, and a third resolved disagreements. Articles were included if they were in English, reported experimental data, and used HMD-VR. Letters, commentaries, book chapters, technical descriptions, theoretical papers, conference proceedings (≤4 pages), and reviews were excluded. FINDINGS: Twenty-eight studies used HMD-VR to treat posttraumatic stress disorder (n = 3), specific phobias (n = 19), and performance-based social anxiety (n = 6); protocols and levels of evidence varied (randomized controlled trials, n = 11; controlled trials without randomization, n = 6; case-control or cohort studies, n = 11). Qualitative examination indicates HMD-VR is an effective treatment tool. CONCLUSIONS AND RELEVANCE: HMD-VR can be a valuable tool for occupational therapy to simulate environments where clients with anxiety disorders participate. Eliciting presence through multisensory features and body representation may enhance outcomes. What This Article Adds: Drawing from the VRET literature, this scoping review suggests that HMD-VR can be used by occupational therapy practitioners to simulate ecologically valid environments, evaluate client responses to fearful stimuli, and remediate anxiety though immersion in virtual tasks when participation in natural contexts is unfeasible. Having ecologically valid environments is particularly important for people with anxiety disorders because they need support to cope when they encounter triggers in everyday life environments.

Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

Transfer of motor skill between virtual reality viewed using a head-mounted display and conventional screen environments.

BACKGROUND: Virtual reality viewed using a head-mounted display (HMD-VR) has the potential to be a useful tool for motor learning and rehabilitation. However, when developing tools for these purposes, it is important to design applications that will effectively transfer to the real world. Therefore, it is essential to understand whether motor skills transfer between HMD-VR and conventional screen-based environments and what factors predict transfer. METHODS: We randomized 70 healthy participants into two groups. Both groups trained on a well-established measure of motor skill acquisition, the Sequential Visual Isometric Pinch Task (SVIPT), either in HMD-VR or in a conventional environment (i.e., computer screen). We then tested whether the motor skills transferred from HMD-VR to the computer screen, and vice versa. After the completion of the experiment, participants responded to questions relating to their presence in their respective training environment, age, gender, video game use, and previous HMD-VR experience. Using multivariate and univariate linear regression, we then examined whether any personal factors from the questionnaires predicted individual differences in motor skill transfer between environments. RESULTS: Our results suggest that motor skill acquisition of this task occurs at the same rate in both HMD-VR and conventional screen environments. However, the motor skills acquired in HMD-VR did not transfer to the screen environment. While this decrease in motor skill performance when moving to the screen environment was not significantly predicted by self-reported factors, there were trends for correlations with presence and previous HMD-VR experience. Conversely, motor skills acquired in a conventional screen environment not only transferred but improved in HMD-VR, and this increase in motor skill performance could be predicted by self-reported factors of presence, gender, age and video game use. CONCLUSIONS: These findings suggest that personal factors may predict who is likely to have better transfer of motor skill to and from HMD-VR. Future work should examine whether these and other predictors (i.e., additional personal factors such as immersive tendencies and task-specific factors such as fidelity or feedback) also apply to motor skill transfer from HMD-VR to more dynamic physical environments.

A Virtual Reality Muscle-Computer Interface for Neurorehabilitation in Chronic Stroke: A Pilot Study.

Severe impairment of limb movement after stroke can be challenging to address in the chronic stage of stroke (e.g., greater than 6 months post stroke). Recent evidence suggests that physical therapy can still promote meaningful recovery after this stage, but the required high amount of therapy is difficult to deliver within the scope of standard clinical practice. Digital gaming technologies are now being combined with brain-computer interfaces to motivate engaging and frequent exercise and promote neural recovery. However, the complexity and expense of acquiring brain signals has held back widespread utilization of these rehabilitation systems. Furthermore, for people that have residual muscle activity, electromyography (EMG) might be a simpler and equally effective alternative. In this pilot study, we evaluate the feasibility and efficacy of an EMG-based variant of our REINVENT virtual reality (VR) neurofeedback rehabilitation system to increase volitional muscle activity while reducing unintended co-contractions. We recruited four participants in the chronic stage of stroke recovery, all with severely restricted active wrist movement. They completed seven 1-hour training sessions during which our head-mounted VR system reinforced activation of the wrist extensor muscles without flexor activation. Before and after training, participants underwent a battery of clinical and neuromuscular assessments. We found that training improved scores on standardized clinical assessments, equivalent to those previously reported for brain-computer interfaces. Additionally, training may have induced changes in corticospinal communication, as indexed by an increase in 12-30 Hz corticomuscular coherence and by an improved ability to maintain a constant level of wrist muscle activity. Our data support the feasibility of using muscle-computer interfaces in severe chronic stroke, as well as their potential to promote functional recovery and trigger neural plasticity.

Embodiment Is Related to Better Performance on a Brain-Computer Interface in Immersive Virtual Reality: A Pilot Study.

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) for motor rehabilitation aim to "close the loop" between attempted motor commands and sensory feedback by providing supplemental information when individuals successfully achieve specific brain patterns. Existing EEG-based BCIs use various displays to provide feedback, ranging from displays considered more immersive (e.g., head-mounted display virtual reality (HMD-VR)) to displays considered less immersive (e.g., computer screens). However, it is not clear whether more immersive displays improve neurofeedback performance and whether there are individual performance differences in HMD-VR versus screen-based neurofeedback. In this pilot study, we compared neurofeedback performance in HMD-VR versus a computer screen in 12 healthy individuals and examined whether individual differences on two measures (i.e., presence, embodiment) were related to neurofeedback performance in either environment. We found that, while participants' performance on the BCI was similar between display conditions, the participants' reported levels of embodiment were significantly different. Specifically, participants experienced higher levels of embodiment in HMD-VR compared to a computer screen. We further found that reported levels of embodiment positively correlated with neurofeedback performance only in HMD-VR. Overall, these preliminary results suggest that embodiment may relate to better performance on EEG-based BCIs and that HMD-VR may increase embodiment compared to computer screens.

Applications of Head-Mounted Displays for Virtual Reality in Adult Physical Rehabilitation: A Scoping Review.

IMPORTANCE: Head-mounted displays for virtual reality (HMD-VR) may be used as a therapeutic medium in physical rehabilitation because of their ability to immerse patients in safe, controlled, and engaging virtual worlds. OBJECTIVE: To explore how HMD-VR has been used in adult physical rehabilitation. DATA SOURCES: A systematic search of MEDLINE, Embase, Cochrane Library, CINAHL, Web of Science, PsycINFO, and ERIC produced 11,453 abstracts, of which 777 underwent full-text review. STUDY SELECTION AND DATA COLLECTION: This scoping review includes 21 experimental studies that reported an assessment or intervention using HMD-VR in a physical rehabilitation context and within the scope of occupational therapy practice. FINDINGS: HMD-VR was used for assessment and intervention for patients with a range of disorders, including stroke, multiple sclerosis, spinal cord injury, and Parkinson's disease. CONCLUSIONS AND RELEVANCE: HMD-VR is an emerging technology with many uses in adult physical rehabilitation. Higher quality clinical implementation studies are needed to examine effects on patient outcomes. WHAT THIS ARTICLE ADDS: We review existing research on how immersive virtual reality (e.g., using head-mounted displays) has been used for different clinical populations in adult physical rehabilitation and highlight emerging opportunities in this field for occupational therapists.

A comparison of automated lesion segmentation approaches for chronic stroke T1-weighted MRI data.

Accurate stroke lesion segmentation is a critical step in the neuroimaging processing pipeline for assessing the relationship between poststroke brain structure, function, and behavior. Many multimodal segmentation algorithms have been developed for acute stroke neuroimaging, yet few algorithms are effective with only a single T1-weighted (T1w) anatomical MRI. This is a critical gap because multimodal MRI is not commonly available due to time and cost constraints in the stroke rehabilitation setting. Although several attempts to automate the segmentation of chronic lesions on single-channel T1w MRI have been made, these approaches have not been systematically evaluated on a large dataset. We performed an exhaustive review of the literature and identified one semiautomated and three fully automated approaches for segmentation of chronic stroke lesions using T1w MRI within the last 10 years: Clusterize, automated lesion identification (ALI), Gaussian naïve Bayes lesion detection (lesionGnb), and lesion identification with neighborhood data analysis (LINDA). We evaluated each method on a large T1w stroke dataset (N = 181). LINDA was the most computationally expensive approach, but performed best across the three main evaluation metrics (median values: dice coefficient = 0.50, Hausdorff's distance = 36.34 mm, and average symmetric surface distance = 4.97 mm). lesionGnb had the highest recall/least false negatives (median = 0.80). However, across the automated methods, many lesions were either misclassified (ALI: 28, lesionGnb: 39, LINDA: 45) or not identified (ALI: 24, LINDA: 23, lesionGnb: 0). Segmentation accuracy in all automated methods were influenced by size (small: worst) and stroke territory (brainstem, cerebellum: worst) of the lesion. To facilitate reproducible science, our analysis files have been made publicly available online.

Laterality of Poststroke Cortical Motor Activity during Action Observation Is Related to Hemispheric Dominance.

Background: Increased activity in the lesioned hemisphere has been related to improved poststroke motor recovery. However, the role of the dominant hemisphere-and its relationship to activity in the lesioned hemisphere-has not been widely explored. Objective: Here, we examined whether the dominant hemisphere drives the lateralization of brain activity after stroke and whether this changes based on if the lesioned hemisphere is the dominant hemisphere or not. Methods: We used fMRI to compare cortical motor activity in the action observation network (AON), motor-related regions that are active both during the observation and execution of an action, in 36 left hemisphere dominant individuals. Twelve individuals had nondominant, right hemisphere stroke, twelve had dominant, left-hemisphere stroke, and twelve were healthy age-matched controls. We previously found that individuals with left dominant stroke show greater ipsilesional activity during action observation. Here, we examined if individuals with nondominant, right hemisphere stroke also showed greater lateralized activity in the ipsilesional, right hemisphere or in the dominant, left hemisphere and compared these results with those of individuals with dominant, left hemisphere stroke. Results: We found that individuals with right hemisphere stroke showed greater activity in the dominant, left hemisphere, rather than the ipsilesional, right hemisphere. This left-lateralized pattern matched that of individuals with left, dominant hemisphere stroke, and both stroke groups differed from the age-matched control group. Conclusions: These findings suggest that action observation is lateralized to the dominant, rather than ipsilesional, hemisphere, which may reflect an interaction between the lesioned hemisphere and the dominant hemisphere in driving lateralization of brain activity after stroke. Hemispheric dominance and laterality should be carefully considered when characterizing poststroke neural activity.

Improvements in motor control are associated with improved quality of life following an at-home muscle biofeedback program for chronic stroke.

Introduction: Chronic stroke survivors with severe arm impairment have limited options for effective rehabilitation. High intensity, repetitive task practice (RTP) is known to improve upper limb function among stroke survivors who have some volitional muscle activation. However, clients without volitional movement of their arm are ineligible for RTP-based interventions and require hands-on facilitation from a clinician or robotic therapy to simulate task practice. Such approaches can be expensive, burdensome, and have marginal effects. Alternatively, supervised at-home telerehabilitation using muscle biofeedback may provide a more accessible, affordable, and effective rehabilitation option for stroke survivors with severe arm impairment, and could potentially help people with severe stroke regain enough volitional activation to be eligible for RTP-types of therapies. Feedback of muscle activity via electromyography (EMG) has been previously used with clients who have minimal or no movement to improve functional performance. Specifically, training to reduce unintended co-contractions of the impaired hand using EMG biofeedback may modestly improve motor control in people with limited movement. Importantly, these modest and covert functional changes may influence the perceived impact of stroke-related disability in daily life. In this manuscript, we examine whether physical changes following use of a portable EMG biofeedback system (Tele-REINVENT) for severe upper limb hemiparesis also relate to perceived quality of life improvements. Secondarily, we examined the effects of Tele-REINVENT, which uses EMG to quantify antagonistic muscle activity during movement attempt trials and transform individuated action into computer game control, on several different domains of stroke recovery. Methods: For this pilot study, nine stroke survivors (age = 37-73 years) with chronic impairment (Fugl-Meyer = 14-40/66) completed 30 1-hour sessions of home-based training, consisting of six weeks of gaming that reinforced wrist extensor muscle activity while attenuating coactivation of flexor muscles. To assess motor control and performance, we measured changes in active wrist ranges of motion, the Fugl-Meyer Assessment, and Action Research Arm Test. We also collected an EMG-based test of muscle control to examine more subtle changes. To examine changes in perceived quality of life, we utilized the Stroke Impact Scale along with participant feedback. Results: Results from our pilot data suggest that 30 sessions of remote training can induce modest changes on clinical and functional assessments, showing a statistically significant improvement of active wrist ranges of motion at the group level, changes that could allow some people with severe stroke to be eligible for other therapeutic approaches, such as RTP. Additionally, changes in motor control were correlated with the perceived impact of stroke on participation and impairment after training. We also report changes in corticomuscular coherence, which showed a laterality change from the ipsilesional motor cortex towards the contralesional hemisphere during wrist extension attempts. Finally, all participants showed high adherence to the protocol and reported enjoying using the system. Conclusion: Overall, Tele-REINVENT represents a promising telerehabilitation intervention that might improve sensorimotor outcomes in severe chronic stroke, and that improving sensorimotor abilities even modestly may improve quality of life. We propose that Tele-REINVENT may be used as a precursor to help participants gain enough active movement to participate other occupational therapy interventions, such as RTP. Future work is needed to examine if home-based telerehabilitation to provide feedback of individuated muscle activity could increase meaningful rehabilitation accessibility and outcomes for underserved populations.

Modulation of the Association Between Corticospinal Tract Damage and Outcome After Stroke by White Matter Hyperintensities.

BACKGROUND AND OBJECTIVES: Motor outcomes after stroke relate to corticospinal tract (CST) damage. The brain leverages surviving neural pathways to compensate for CST damage and mediate motor recovery. Thus, concurrent age-related damage from white matter hyperintensities (WMHs) might affect neurologic capacity for recovery after CST injury. The role of WMHs in post-stroke motor outcomes is unclear. In this study, we evaluated whether WMHs modulate the relationship between CST damage and post-stroke motor outcomes. METHODS: We used data from the multisite ENIGMA Stroke Recovery Working Group with T1 and T2/fluid-attenuated inversion recovery imaging. CST damage was indexed with weighted CST lesion load (CST-LL). WMH volumes were extracted with Freesurfer's SAMSEG. Mixed-effects beta-regression models were fit to test the impact of CST-LL, WMH volume, and their interaction on motor impairment, controlling for age, days after stroke, and stroke volume. RESULTS: A total of 223 individuals were included. WMH volume related to motor impairment above and beyond CST-LL (ß = 0.178, 95% CI 0.025-0.331, p = 0.022). Relationships varied by WMH severity (mild vs moderate-severe). In individuals with mild WMHs, motor impairment related to CST-LL (ß = 0.888, 95% CI 0.604-1.172, p < 0.001) with a CST-LL × WMH interaction (ß = -0.211, 95% CI -0.340 to -0.026, p = 0.026). In individuals with moderate-severe WMHs, motor impairment related to WMH volume (ß = 0.299, 95% CI 0.008-0.590, p = 0.044), but did not significantly relate to CST-LL or a CST-LL × WMH interaction. DISCUSSION: WMHs relate to motor outcomes after stroke and modify relationships between motor impairment and CST damage. WMH-related damage may be under-recognized in stroke research as a factor contributing to variability in motor outcomes. Our findings emphasize the importance of brain structural reserve in motor outcomes after brain injury.

Modulating the motor system by action observation after stroke.

BACKGROUND AND PURPOSE: Much recent interest surrounds the use of action observation, which is observing another individual performing a motor task, in stroke rehabilitation, to promote motor recovery by engaging similar brain regions to action execution. This may be especially useful in individuals with limited mobility. Here, we assess how cortical motor activity during action observation is affected by stroke and by stroke-related motor deficits. METHODS: We used functional MRI to compare brain activity during right and left hand action observation in right-handed nondisabled participants and participants who were right-handed before left hemisphere stroke. All participants performed the same actions after their functional MRI. RESULTS: Nondisabled participants show greater bilateral cortical motor activity when observing actions made using the left hand, whereas participants with stroke show greater ipsilesional cortical motor activity when observing actions made using the right (paretic) hand (P<0.05; corrected). For both groups, action processing is modulated by motor capability: cortical motor activity is greater when observing the hand with lower motor scores (P<0.05; corrected). Furthermore, for stroke, the extent of ipsilesional activity correlates with lesion volume (P=0.049), in a pattern that suggests adaptive plasticity. CONCLUSIONS: We found that action observation activates specific motor plans in damaged motor circuits after stroke, and this activity is related to motor capability to perform the same actions. Cortical motor activity during action observation may be relevant to motor learning, and to motor relearning in stroke rehabilitation.