The Efficiency, Efficacy, and Retention of Task Practice in Chronic Stroke

NSF DARE-Transforming modeling in neurorehabilitation: Four threads for catalyzing progress

We present an overview of the Conference on Transformative Opportunities for Modeling in Neurorehabilitation held in March 2023. It was supported by the Disability and Rehabilitation Engineering (DARE) program from the National Science Foundation's Engineering Biology and Health Cluster. The conference brought together experts and trainees from around the world to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation to understand, optimize, and improve clinical translation of neurorehabilitation. We organized the conference around four key, relevant, and promising Focus Areas for modeling: Adaptation & Plasticity, Personalization, Human-Device Interactions, and Modeling 'In-the-Wild'. We identified four common threads across the Focus Areas that, if addressed, can catalyze progress in the short, medium, and long terms. These were: (i) the need to capture and curate appropriate and useful data necessary to develop, validate, and deploy useful computational models (ii) the need to create multi-scale models that span the personalization spectrum from individuals to populations, and from cellular to behavioral levels (iii) the need for algorithms that extract as much information from available data, while requiring as little data as possible from each client (iv) the insistence on leveraging readily available sensors and data systems to push model-driven treatments from the lab, and into the clinic, home, workplace, and community. The conference archive can be found at (dare2023.usc.edu). These topics are also extended by three perspective papers prepared by trainees and junior faculty, clinician researchers, and federal funding agency representatives who attended the conference.

Dyad motor learning in a wrist-robotic environment: Learning together is better than learning alone

OBJECTIVE

Dyad motor practice is characterized by two learners alternating between physical and observational practice, which can lead to better motor outcomes and reduce practice time compared to physical practice alone. Robot-assisted therapy has become an established neurorehabilitation tool but is limited by high therapy cost and access. Implementing dyad practice in robot-assisted rehabilitation has the potential to improve therapeutic outcomes and/or to achieve them faster. This study aims to determine the effects of dyad practice on motor performance in a wrist-robotic environment to evaluate its potential use in robotic rehabilitation settings.

METHODS

Forty-two healthy participants (18-35 years) were randomized into three groups (n = 14): Dyad practice, physical practice with rest and physical practice without rest. Participants practiced a 2 degree-of-freedom gamified wrist movement task for 20 trials using a custom-made wrist robotic device. A motor performance score (MPS) that captured temporal and spatial time-series kinematics was computed at baseline, the end of training and 24 h later to assess retention.

RESULTS

MPS did not differ between groups at baseline. All groups revealed significant performance gains by the end of training. However, dyads outperformed the other groups at the end of training (p < 0.001) and showed higher retention after 24-h (p = 0.02). Median MPS improved by 46.5% in dyads, 25.3% in physical practice-rest, and 33.6% in physical practice-no rest at the end of training compared to baseline.

CONCLUSION

Compared to physical practice alone, dyad practice leads to superior motor outcomes in a robot-assisted motor learning task. Dyads still outperformed their counterparts 24-h after practice.

IMPACT STATEMENT

Improving motor function in complex motor tasks without increasing required practice time, dyad practice can optimize therapeutic resources. This is particularly impactful in robot-assisted rehabilitation regimens as it would help to improve patients' outcomes and increase care efficiency.

One-year retention of gait speed improvement in stroke survivors after treatment with a wearable home-use gait device

Background

Gait impairments after stroke are associated with numerous physical and psychological consequences. Treatment with the iStride® gait device has been shown to facilitate improvements to gait function, including gait speed, for chronic stroke survivors with hemiparesis. This study examines the long-term gait speed changes up to 12 months after treatment with the gait device.

Methods

Eighteen individuals at least one-year post-stroke completed a target of 12, 30-minute treatment sessions with the gait device in their home environment. Gait speed was measured at baseline and five follow-up sessions after the treatment period: one  week, one  month, three months, six months, and 12 months. Gait speed changes were analyzed using repeated-measures ANOVA from baseline to each follow-up time frame. Additional analysis included comparison to the minimal clinically important difference (MCID), evaluation of gait speed classification changes, and review of subjective questionnaires.

Results

Participants retained an average gait speed improvement >0.21 m/s compared to baseline at all post-treatment time frames. Additionally, 94% of participants improved their gait speed beyond the MCID during one or more post-treatment measurements, and 88% subjectively reported a gait speed improvement.

Conclusion

Treatment with the gait device may result in meaningful, long-term gait speed improvement for chronic stroke survivors with hemiparetic gait impairments.

Clinical trial registration

https://clinicaltrials.gov/ct2/show/NCT03649217, identifier NCT03649217.

Long-term forecasting of a motor outcome following rehabilitation in chronic stroke via a hierarchical bayesian dynamic model

BACKGROUND

Given the heterogeneity of stroke, it is important to determine the best course of motor therapy for each patient, i.e., to personalize rehabilitation based on predictions of long-term outcomes. Here, we propose a hierarchical Bayesian dynamic (i.e., state-space) model (HBDM) to forecast long-term changes in a motor outcome due to rehabilitation in the chronic phase post-stroke.

METHODS

The model incorporates the effects of clinician-supervised training, self-training, and forgetting. In addition, to improve forecasting early in rehabilitation, when data are sparse or unavailable, we use the Bayesian hierarchical modeling technique to incorporate prior information from similar patients. We use HBDM to re-analyze the Motor Activity Log (MAL) data of participants with chronic stroke included in two clinical trials: (1) the DOSE trial, in which participants were assigned to a 0, 15, 30, or 60-h dose condition (data of 40 participants analyzed), and (2) the EXCITE trial, in which participants were assigned a 60-h dose, in either an immediate or a delayed condition (95 participants analyzed).

RESULTS

For both datasets, HBDM accounts well for individual dynamics in the MAL during and outside of training: mean RMSE = 0.28 for all 40 DOSE participants (participant-level RMSE 0.26 ± 0.19-95% CI) and mean RMSE = 0.325 for all 95 EXCITE participants (participant-level RMSE 0.32 ± 0.31), which are small compared to the 0-5 range of the MAL. Bayesian leave-one-out cross-validation shows that the model has better predictive accuracy than static regression models and simpler dynamic models that do not account for the effect of supervised training, self-training, or forgetting. We then showcase model's ability to forecast the MAL of "new" participants up to 8 months ahead. The mean RMSE at 6 months post-training was 1.36 using only the baseline MAL and then decreased to 0.91, 0.79, and 0.69 (respectively) with the MAL following the 1st, 2nd, and 3rd bouts of training. In addition, hierarchical modeling improves prediction for a patient early in training. Finally, we verify that this model, despite its simplicity, can reproduce previous findings of the DOSE trial on the efficiency, efficacy, and retention of motor therapy.

CONCLUSIONS

In future work, such forecasting models can be used to simulate different stages of recovery, dosages, and training schedules to optimize rehabilitation for each person. Trial registration This study contains a re-analysis of data from the DOSE clinical trial ID NCT01749358 and the EXCITE clinical trial ID NCT00057018.

The Role of Extra-motor Networks in Upper Limb Motor Performance Post-stroke

BACKGROUND

Motor improvement post-stroke may happen even if resting state functional connectivity between the ipsilesional and contralesional components of the sensorimotor network is not fully recovered. Therefore, we investigated which extra-motor networks might support upper limb motor gains in response to treatment post-stroke.

METHODS

Both resting state functional connectivity and upper limb capacity were measured prior to and after an 8-week intervention of task-specific training in 29 human participants [59.24 ± (SD) 10.40 yrs., 12 females and 17 males] with chronic stroke. The sensorimotor and five extra-motor networks were defined: default mode, frontoparietal, cingulo-opercular, dorsal attention network, and salience networks. The Network Level Analysis toolbox was used to identify network pairs whose connectivities were enriched in connectome-behavior relationships.

RESULTS

Mean upper limb capacity score increased 5.45 ± (SD) 5.55 following treatment. Baseline connectivity of some motor but mostly extra-motor network interactions of cingulo-opercular and default-mode networks were predictive of upper limb capacity following treatment. Also, changes in connectivity for extra-motor interactions of salience with default mode, cingulo-opercular, and dorsal attention networks were correlated with gains in upper limb capacity.

CONCLUSIONS

These connectome-behavior patterns suggest larger involvement of cingulo-opercular networks in prediction of treatment response and of salience networks in maintenance of improved skilled behavior. These results support our hypothesis that cognitive networks may contribute to recovery of motor performance after stroke and provide additional insights into the neural correlates of intensive training.

User-centered development process of an operating interface to couple a robotic glove with a virtual environment to optimize hand rehabilitation following a stroke

Introduction

Task-specific neurorehabilitation is crucial to optimize hand recovery shortly after a stroke, but intensive neurorehabilitation remains limited in resource-constrained healthcare systems. This has led to a growing interest in the use of robotic gloves as an adjunct intervention to intensify hand-specific neurorehabilitation. This study aims to develop and assess the usability of an operating interface supporting such a technology coupled with a virtual environment through a user-centered design approach.

Methods

Fourteen participants with hand hemiparesis following a stroke were invited to don the robotic glove before browsing through the operating interface and its functionalities, and perform two mobility exercises in a virtual environment. Feedback was collected for improving technology usability. Participants completed the System Usability Scale and ABILHAND questionnaires and their recommendations were gathered and prioritized in a Pugh Matrix.

Results

The System Usability Scale (SUS) score for the operating interface was excellent (M = 87.0 SD = 11.6). A total of 74 recommendations to improve the user interface, calibration process, and exercise usability were identified.

Conclusion

The application of a full cycle of user-centred design approach confirms the high level of usability of the system which is perceived by end users as acceptable and useful for intensifying neurorehabilitation.

Accounting for the valley of recovery during post-stroke rehabilitation training via a model-based analysis of macaque manual dexterity

Background

True recovery, in which a stroke patient regains the same precise motor skills observed in prestroke conditions, is the fundamental goal of rehabilitation training. However, a transient drop in task performance during rehabilitation training after stroke, observed in human clinical outcome as well as in both macaque and squirrel monkey retrieval data, might prevent smooth transitions during recovery. This drop, i.e., recovery valley, often occurs during the transition from compensatory skill to precision skill. Here, we sought computational mechanisms behind such transitions and recovery. Analogous to motor skill learning, we considered that the motor recovery process is composed of spontaneous recovery and training-induced recovery. Specifically, we hypothesized that the interaction of these multiple skill update processes might determine profiles of the recovery valley.

Methods

A computational model of motor recovery was developed based on a state-space model of motor learning that incorporates a retention factor and interaction terms for training-induced recovery and spontaneous recovery. The model was fit to previously reported macaque motor recovery data where the monkey practiced precision grip skills after a lesion in the sensorimotor area in the cortex. Multiple computational models and the effects of each parameter were examined by model comparisons based on information criteria and sensitivity analyses of each parameter.

Result

Both training-induced and spontaneous recoveries were necessary to explain the behavioral data. Since these two factors contributed following logarithmic function, the training-induced recovery were effective only after spontaneous biological recovery had developed. In the training-induced recovery component, the practice of the compensation also contributed to recovery of the precision grip skill as if there is a significant generalization effect of learning between these two skills. In addition, a retention factor was critical to explain the recovery profiles.

Conclusions

We found that spontaneous recovery, training-induced recovery, retention factors, and interaction terms are crucial to explain recovery and recovery valley profiles. This simulation-based examination of the model parameters provides suggestions for effective rehabilitation methods to prevent the recovery valley, such as plasticity-promoting medications, brain stimulation, and robotic rehabilitation technologies.

Stroke recovery phenotyping through network trajectory approaches and graph neural networks

Stroke is a leading cause of neurological injury characterized by impairments in multiple neurological domains including cognition, language, sensory and motor functions. Clinical recovery in these domains is tracked using a wide range of measures that may be continuous, ordinal, interval or categorical in nature, which can present challenges for multivariate regression approaches. This has hindered stroke researchers’ ability to achieve an integrated picture of the complex time-evolving interactions among symptoms. Here, we use tools from network science and machine learning that are particularly well-suited to extracting underlying patterns in such data, and may assist in prediction of recovery patterns. To demonstrate the utility of this approach, we analyzed data from the NINDS tPA trial using the Trajectory Profile Clustering (TPC) method to identify distinct stroke recovery patterns for 11 different neurological domains at 5 discrete time points. Our analysis identified 3 distinct stroke trajectory profiles that align with clinically relevant stroke syndromes, characterized both by distinct clusters of symptoms, as well as differing degrees of symptom severity. We then validated our approach using graph neural networks to determine how well our model performed predictively for stratifying patients into these trajectory profiles at early vs. later time points post-stroke. We demonstrate that trajectory profile clustering is an effective method for identifying clinically relevant recovery subtypes in multidimensional longitudinal datasets, and for early prediction of symptom progression subtypes in individual patients. This paper is the first work introducing network trajectory approaches for stroke recovery phenotyping, and is aimed at enhancing the translation of such novel computational approaches for practical clinical application.

Virtuous and Vicious Cycles of Arm Use and Function Post-stroke

Large doses of movement practice have been shown to restore upper extremities' motor function in a significant subset of individuals post-stroke. However, such large doses are both difficult to implement in the clinic and highly inefficient. In addition, an important reduction in upper extremity function and use is commonly seen following rehabilitation-induced gains, resulting in "rehabilitation in vain". For those with mild to moderate sensorimotor impairment, the limited spontaneous use of the more affected limb during activities of daily living has been previously proposed to cause a decline of motor function, initiating a vicious cycle of recovery, in which non-use and poor performance reinforce each other. Here, we review computational, experimental, and clinical studies that support the view that if arm use is raised above an effective threshold, one enters a virtuous cycle in which arm use and function can reinforce each other via self-practice in the wild. If not, one enters a vicious cycle of declining arm use and function. In turn, and in line with best practice therapy recommendations, this virtuous/vicious cycle model advocates for a paradigm shift in neurorehabilitation whereby rehabilitation be embedded in activities of daily living such that self-practice with the aid of wearable technology that reminds and motivates can enhance paretic limb use of those who possess adequate residual sensorimotor capacity. Altogether, this model points to a user-centered approach to recovery post-stroke that is tailored to the participant's level of arm use and designed to motivate and engage in self-practice through progressive success in accomplishing meaningful activities in the wild.

Pilot Test of Dosage Effects in HEXORR II for Robotic Hand Movement Therapy in Individuals With Chronic Stroke

Impaired use of the hand in functional tasks remains difficult to overcome in many individuals after a stroke. This often leads to compensation strategies using the less-affected limb, which allows for independence in some aspects of daily activities. However, recovery of hand function remains an important therapeutic goal of many individuals, and is often resistant to conventional therapies. In prior work, we developed HEXORR I, a robotic device that allows practice of finger and thumb movements with robotic assistance. In this study, we describe modifications to the device, now called HEXORR II, and a clinical trial in individuals with chronic stroke. Fifteen individuals with a diagnosis of chronic stroke were randomized to 12 or 24 sessions of robotic therapy. The sessions involved playing several video games using thumb and finger movement. The robot applied assistance to extension movement that was adapted based on task performance. Clinical and motion capture evaluations were performed before and after training and again at a 6-month followup. Fourteen individuals completed the protocol. Fugl-Meyer scores improved significantly at the 6 month time point compared to baseline, indicating reductions in upper extremity impairment. Flexor hypertonia (Modified Ashworth Scale) also decreased significantly due to the intervention. Motion capture found increased finger range of motion and extension ability after the intervention that continued to improve during the followup period. However, there was no change in a functional measure (Action Research Arm Test). At the followup, the high dose group had significant gains in hand displacement during a forward reach task. There were no other significant differences between groups. Future work with HEXORR II should focus on integrating it with functional task practice and incorporating grip and squeezing tasks.

Trial Registration:ClinicalTrials.gov, NCT04536987. Registered 3 September 2020 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/record/NCT04536987.

Lost in Translation: Simple Steps in Experimental Design of Neurorehabilitation-Based Research Interventions to Promote Motor Recovery Post-Stroke

Stroke continues to be a leading cause of disability. Basic neurorehabilitation research is necessary to inform the neuropathophysiology of impaired motor control, and to develop targeted interventions with potential to remediate disability post-stroke. Despite knowledge gained from basic research studies, the effectiveness of research-based interventions for reducing motor impairment has been no greater than standard of practice interventions. In this perspective, we offer suggestions for overcoming translational barriers integral to experimental design, to augment traditional protocols, and re-route the rehabilitation trajectory toward recovery and away from compensation. First, we suggest that researchers consider modifying task practice schedules to focus on key aspects of movement quality, while minimizing the appearance of compensatory behaviors. Second, we suggest that researchers supplement primary outcome measures with secondary measures that capture emerging maladaptive compensations at other segments or joints. Third, we offer suggestions about how to maximize participant engagement, self-direction, and motivation, by embedding the task into a meaningful context, a strategy more likely to enable goal-action coupling, associated with improved neuro-motor control and learning. Finally, we remind the reader that motor impairment post-stroke is a multidimensional problem that involves central and peripheral sensorimotor systems, likely influenced by chronicity of stroke. Thus, stroke chronicity should be given special consideration for both participant recruitment and subsequent data analyses. We hope that future research endeavors will consider these suggestions in the design of the next generation of intervention studies in neurorehabilitation, to improve translation of research advances to improved participation and quality of life for stroke survivors.

Neural Correlates of Motor Skill Learning Are Dependent on Both Age and Task Difficulty

Although a general age-related decline in neural plasticity is evident, the effects of age on neural plasticity after motor practice are inconclusive. Inconsistencies in the literature may be related to between-study differences in task difficulty. Therefore, we aimed to determine the effects of age and task difficulty on motor learning and associated brain activity. We used task-related electroencephalography (EEG) power in the alpha (8–12 Hz) and beta (13–30 Hz) frequency bands to assess neural plasticity before, immediately after, and 24-h after practice of a mirror star tracing task at one of three difficulty levels in healthy younger (19–24 yr) and older (65–86 yr) adults. Results showed an age-related deterioration in motor performance that was more pronounced with increasing task difficulty and was accompanied by a more bilateral activity pattern for older vs. younger adults. Task difficulty affected motor skill retention and neural plasticity specifically in older adults. Older adults that practiced at the low or medium, but not the high, difficulty levels were able to maintain improvements in accuracy at retention and showed modulation of alpha TR-Power after practice. Together, these data indicate that both age and task difficulty affect motor learning, as well as the associated neural plasticity.

Five Features to Look for in Early-Phase Clinical Intervention Studies

Neurorehabilitation relies on core principles of neuroplasticity to activate and engage latent neural connections, promote detour circuits, and reverse impairments. Clinical interventions incorporating these principles have been shown to promote recovery and demote compensation. However, many clinicians struggle to find interventions centered on these principles in our nascent, rapidly growing body of literature. Not to mention the immense pressure from regulatory bodies and organizational balance sheets that further discourage time-intensive recovery-promoting interventions, incentivizing clinicians to prioritize practical constraints over sound clinical decision making. Modern neurorehabilitation practices that result from these pressures favor strategies that encourage compensation over those that promote recovery. To narrow the gap between the busy clinician and the cutting-edge motor recovery literature, we distilled 5 features found in early-phase clinical intervention studies-ones that value the more enduring biological recovery processes over the more immediate compensatory remedies. Filtering emerging literature through this lens and routinely integrating promising research into daily practice can break down practical barriers for effective clinical translation and ultimately promote durable long-term outcomes. This perspective is meant to serve a new generation of mechanistically minded and caring clinicians, students, activists, and research trainees, who are poised to not only advance rehabilitation science, but also erect evidence-based policy changes to accelerate recovery-based stroke care.