Robotic Assessment of Motor, Sensory, and Cognitive Function in Acute Sport-Related Concussion and Recovery

Age-related differences in behavioral outcomes of bimanual functional motor tasks in children and adolescents with cerebral palsy: a scoping review

AIM

The objective of this review is to determine age-related differences in behavioral outcomes of bimanual motor tasks in children and adolescents with cerebral palsy (CP).

METHOD

This review followed the 6-stage Joanna Briggs Institute methodology. The Embase, EBSCO CINAHL, and PubMed databases were searched on May 2024. We included studies that employed instrumented measures to assess bimanual tasks in individuals with CP aged between 3 and 19 years.

RESULTS

Twenty-eight studies were included after full-text screening. This review reported on 544 individuals with CP. Bimanual tasks were grouped in seven categories and their varying complexities were listed and analyzed. There are numerous methods for assessing bimanual performance. The listed methods have shown that the gap between children with CP and healthy peers widens as task complexity increases. The data suggest that age-related outcomes result from a complex interaction between atypical development, the severity of deficits, and the context-dependent nature of the protocols.

CONCLUSION

The lack of standardized reporting on age-related results limits our understanding of bimanual developmental functions in CP. Standardizing these measures will enhance our understanding of bimanual function and better define the principles guiding therapeutic interventions, ultimately improving outcomes for individuals with CP.

Robotic assessment of sensorimotor and cognitive deficits in patients with temporal lobe epilepsy

OBJECTIVE

Individuals with temporal lobe epilepsy (TLE) frequently demonstrate impairments in executive function, working memory, and/or declarative memory. It is recommended that screening for cognitive impairment is undertaken in all people newly diagnosed with epilepsy. However, standard neuropsychological assessments are a limited resource and thus not available to all. Our study investigated the use of robotic technology (the Kinarm robot) for cognitive screening.

METHODS

27 participants with TLE (17 left) underwent both a brief neuropsychological screening and a robotic (Kinarm) assessment. The degree of impairments and correlations between standardized scores from both approaches to assessments were analysed across different neurocognitive domains. Performance was compared between people with left and right TLE to look for laterality effects. Finally, the association between the duration of epilepsy and performance was assessed.

RESULTS

Across the 6 neurocognitive domains (attention, executive function, language, memory, motor and visuospatial) assessed by our neuropsychological screening, all showed scores that significantly correlated with Kinarm tasks assessing the same cognitive domains except language and memory that were not adequately assessed with Kinarm. Participants with right TLE performed worse on most tasks than those with left TLE, including both visuospatial (typically considered right hemisphere), and verbal memory and language tasks (typically considered left hemisphere). No correlations were found between the duration of epilepsy and either the neuropsychological screening or Kinarm assessment.

SIGNIFICANCE

Our findings suggest that Kinarm may be a useful tool in screening for neurocognitive impairment in people with TLE. Further development may facilitate an easier and more rapid screening of cognition in people with epilepsy and distinguishing patterns of cognitive impairment.

Role of biomarkers and emerging technologies in defining and assessing neurobiological recovery after sport-related concussion: a systematic review

OBJECTIVE

Determine the role of fluid-based biomarkers, advanced neuroimaging, genetic testing and emerging technologies in defining and assessing neurobiological recovery after sport-related concussion (SRC).

DESIGN

Systematic review.

DATA SOURCES

Searches of seven databases from 1 January 2001 through 24 March 2022 using keywords and index terms relevant to concussion, sports and neurobiological recovery. Separate reviews were conducted for studies involving neuroimaging, fluid biomarkers, genetic testing and emerging technologies. A standardised method and data extraction tool was used to document the study design, population, methodology and results. Reviewers also rated the risk of bias and quality of each study.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Studies were included if they: (1) were published in English; (2) represented original research; (3) involved human research; (4) pertained only to SRC; (5) included data involving neuroimaging (including electrophysiological testing), fluid biomarkers or genetic testing or other advanced technologies used to assess neurobiological recovery after SRC; (6) had a minimum of one data collection point within 6 months post-SRC; and (7) contained a minimum sample size of 10 participants.

RESULTS

A total of 205 studies met inclusion criteria, including 81 neuroimaging, 50 fluid biomarkers, 5 genetic testing, 73 advanced technologies studies (4 studies overlapped two separate domains). Numerous studies have demonstrated the ability of neuroimaging and fluid-based biomarkers to detect the acute effects of concussion and to track neurobiological recovery after injury. Recent studies have also reported on the diagnostic and prognostic performance of emerging technologies in the assessment of SRC. In sum, the available evidence reinforces the theory that physiological recovery may persist beyond clinical recovery after SRC. The potential role of genetic testing remains unclear based on limited research.

CONCLUSIONS

Advanced neuroimaging, fluid-based biomarkers, genetic testing and emerging technologies are valuable research tools for the study of SRC, but there is not sufficient evidence to recommend their use in clinical practice.

PROSPERO REGISTRATION NUMBER

CRD42020164558.

Exploring biomarkers of processing speed and executive function: The role of the anterior thalamic radiations

INTRODUCTION

Processing speed and executive function are often impaired after stroke and in typical aging. However, there are no reliable neurological markers of these cognitive impairments. The trail making test (TMT) is a common index of processing speed and executive function. Here, we tested candidate MRI markers of TMT performance in a cohort of older adults and individuals with chronic stroke.

METHODS

In 61 older adults and 32 individuals with chronic stroke, we indexed white matter structure with region-specific lesion load (of white matter hyperintensities (WMHs) and stroke lesions) and diffusion tensor imaging (DTI) from four regions related to TMT performance: the anterior thalamic radiations (ATR), superior longitudinal fasciculus (SLF), forceps minor, and cholinergic pathways. Regression modelling was used to identify the marker(s) that explained the most variance in TMT performance.

RESULTS

DTI metrics of the ATR related to processing speed in both the older adult (TMT A: β = -3.431, p < 0.001) and chronic stroke (TMT A: β = 11.282, p < 0.001) groups. In the chronic stroke group executive function was best predicted by a combination of ATR and forceps minor DTI metrics (TMT B: _adjustedR² = 0.438, p < 0.001); no significant predictors of executive function (TMT B) emerged in the older adult group. No imaging metrics related to set shifting (TMT B-A). Regional DTI metrics predicted TMT performance above and beyond whole-brain stroke and WMH volumes and removing whole-brain lesion volumes improved model fits.

CONCLUSIONS

In this comprehensive assessment of candidate imaging markers, we demonstrate an association between ATR microstructure and processing speed and executive function performance. Regional DTI metrics provided better predictors of cognitive performance than whole-brain lesion volumes or regional lesion load, emphasizing the importance of lesion location in understanding cognition. We propose ATR DTI metrics as novel candidate imaging biomarker of post-stroke cognitive impairment.

Phybrata Sensors and Machine Learning for Enhanced Neurophysiological Diagnosis and Treatment

Concussion injuries remain a significant public health challenge. A significant unmet clinical need remains for tools that allow related physiological impairments and longer-term health risks to be identified earlier, better quantified, and more easily monitored over time. We address this challenge by combining a head-mounted wearable inertial motion unit (IMU)-based physiological vibration acceleration (“phybrata”) sensor and several candidate machine learning (ML) models. The performance of this solution is assessed for both binary classification of concussion patients and multiclass predictions of specific concussion-related neurophysiological impairments. Results are compared with previously reported approaches to ML-based concussion diagnostics. Using phybrata data from a previously reported concussion study population, four different machine learning models (Support Vector Machine, Random Forest Classifier, Extreme Gradient Boost, and Convolutional Neural Network) are first investigated for binary classification of the test population as healthy vs. concussion (Use Case 1). Results are compared for two different data preprocessing pipelines, Time-Series Averaging (TSA) and Non-Time-Series Feature Extraction (NTS). Next, the three best-performing NTS models are compared in terms of their multiclass prediction performance for specific concussion-related impairments: vestibular, neurological, both (Use Case 2). For Use Case 1, the NTS model approach outperformed the TSA approach, with the two best algorithms achieving an F1 score of 0.94. For Use Case 2, the NTS Random Forest model achieved the best performance in the testing set, with an F1 score of 0.90, and identified a wider range of relevant phybrata signal features that contributed to impairment classification compared with manual feature inspection and statistical data analysis. The overall classification performance achieved in the present work exceeds previously reported approaches to ML-based concussion diagnostics using other data sources and ML models. This study also demonstrates the first combination of a wearable IMU-based sensor and ML model that enables both binary classification of concussion patients and multiclass predictions of specific concussion-related neurophysiological impairments.

Characterization of Cognitive-Motor Function in Women Who Have Experienced Intimate Partner Violence-Related Brain Injury

Intimate partner violence (IPV) affects at least one in three women worldwide, and up to 92% report symptoms consistent with brain injury (BI). Although a handful of studies have examined different aspects of brain structure and function in this population, none has characterized potential deficits in cognitive-motor function. This knowledge gap was addressed in the current study by having participants who had experienced IPV complete the bimanual Object Hit & Avoid (OHA) task in a Kinesiological Instrument for Normal and Altered Reaching Movement (KINARM) End-Point Laboratory. BI load, post-traumatic stress disorder (PTSD), anxiety, depression, substance use, and history of abuse were also assessed. A stepwise multiple regression was undertaken to explore the relationship between BI load and task performance while accounting for comorbid psychopathologies. Results demonstrated that BI load accounted for a significant amount of variability in the number of targets hit and the average hand speed. PTSD, anxiety, and depression also contributed significantly to the variability in these measures as well as to the number and proportion of distractor hits, and the object processing rate. Taken together, these findings suggest that IPV-related BI, as well as comorbid PTSD, anxiety, and depression, disrupt the processing required to quickly and accurately hit targets while avoiding distractors. This pattern of results reflects the complex interaction between the physical injuries induced by the episodes of IPV and the resulting impacts that these experiences have on mental health.

Evidence of altered interhemispheric communication after pediatric concussion

OBJECTIVES: To investigate neurophysiological alterations within the typical symptomatic period after concussion (1-month) and throughout recovery (6-months) in adolescents; and (2) to examine relationships between neurophysiological and upper limb kinematic outcomes.METHODS: 18 adolescents with concussion were compared to 17 healthy controls. Transcranial magnetic stimulation (TMS) was used to assess neurophysiological differences between groups including: short- and long-interval intracortical inhibition, intracortical facilitation, short- and long-latency afferent inhibition, afferent facilitation, and transcallosal inhibition (TCI). Behavioral measures of upper limb kinematics were assessed with a robotic device.RESULTS: Mixed model analysis of neurophysiological data identified two key findings. First, participants with concussion demonstrated delayed onset of interhemispheric inhibition, as indexed by TCI, compared to healthy controls. Second, our exploratory analysis indicated that the magnitude of TCI onset delay in adolescents with concussion was related to upper limb kinematics.CONCLUSIONS: Our findings indicate that concussion in adolescence alters interhemispheric communication. We note relationships between neurophysiological and kinematic data, suggesting an affinity for individuals with less concussion-related physiological change to improve their motor behavior over time. These data serve as an important step in future development of assessments (neurobiological and clinical) and interventions for concussion.

Robot enhanced stroke therapy optimizes rehabilitation (RESTORE): a pilot study

BACKGROUND

Robotic rehabilitation after stroke provides the potential to increase and carefully control dosage of therapy. Only a small number of studies, however, have examined robotic therapy in the first few weeks post-stroke. In this study we designed robotic upper extremity therapy tasks for the bilateral Kinarm Exoskeleton Lab and piloted them in individuals with subacute stroke. Pilot testing was focused mainly on the feasibility of implementing these new tasks, although we recorded a number of standardized outcome measures before and after training.

METHODS

Our team developed 9 robotic therapy tasks to incorporate feedback, intensity, challenge, and subject engagement as well as addressing both unimanual and bimanual arm activities. Subacute stroke participants were assigned to a robotic therapy (N = 9) or control group (N = 10) in a matched-group manner. The robotic therapy group completed 1-h of robotic therapy per day for 10 days in addition to standard therapy. The control group participated only in standard of care therapy. Clinical and robotic assessments were completed prior to and following the intervention. Clinical assessments included the Fugl-Meyer Assessment of Upper Extremity (FMA UE), Action Research Arm Test (ARAT) and Functional Independence Measure (FIM). Robotic assessments of upper limb sensorimotor function included a Visually Guided Reaching task and an Arm Position Matching task, among others. Paired sample t-tests were used to compare initial and final robotic therapy scores as well as pre- and post-clinical and robotic assessments.

RESULTS

Participants with subacute stroke (39.8 days post-stroke) completed the pilot study. Minimal adverse events occurred during the intervention and adding 1 h of robotic therapy was feasible. Clinical and robotic scores did not significantly differ between groups at baseline. Scores on the FMA UE, ARAT, FIM, and Visually Guided Reaching improved significantly in the robotic therapy group following completion of the robotic intervention. However, only FIM and Arm Position Match improved over the same time in the control group.

CONCLUSIONS

The Kinarm therapy tasks have the potential to improve outcomes in subacute stroke. Future studies are necessary to quantify the benefits of this robot-based therapy in a larger cohort.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT04201613, Registered 17 December 2019-Retrospectively Registered, https://clinicaltrials.gov/ct2/show/NCT04201613 .

Physiological Vibration Acceleration (Phybrata) Sensor Assessment of Multi-System Physiological Impairments and Sensory Reweighting Following Concussion

Objective

To assess the utility of a head-mounted wearable inertial motion unit (IMU)-based physiological vibration acceleration (“phybrata”) sensor to support the clinical diagnosis of concussion, classify and quantify specific concussion-induced physiological system impairments and sensory reweighting, and track individual patient recovery trajectories.

Methods

Data were analyzed from 175 patients over a 12-month period at three clinical sites. Comprehensive clinical concussion assessments were first completed for all patients, followed by testing with the phybrata sensor. Phybrata time series data and spatial scatter plots, eyes open (Eo) and eyes closed (Ec) phybrata powers, average power (Eo+Ec)/2, Ec/Eo phybrata power ratio, time-resolved phybrata spectral density (TRPSD) distributions, and receiver operating characteristic (ROC) curves are compared for individuals with no objective impairments and those clinically diagnosed with concussions and accompanying vestibular impairment, other neurological impairment, or both vestibular and neurological impairments. Finally, pre- and post-injury phybrata case report results are presented for a participant who was diagnosed with a concussion and subsequently monitored during treatment, rehabilitation, and return-to-activity clearance.

Results

Phybrata data demonstrate distinct features and patterns for individuals with no discernable clinical impairments, diagnosed vestibular pathology, and diagnosed neurological pathology. ROC curves indicate that the average power (Eo+Ec)/2 may be utilized to support clinical diagnosis of concussion, while Eo and Ec/Eo may be utilized as independent measures to confirm accompanying neurological and vestibular impairments, respectively. All 3 measures demonstrate area under the curve (AUC), sensitivity, and specificity above 90% for their respective diagnoses. Phybrata spectral analyses demonstrate utility for quantifying the severity of concussion-induced physiological impairments, sensory reweighting, and subsequent monitoring of improvements throughout treatment and rehabilitation.

Conclusion

Phybrata testing assists with objective concussion diagnosis and provides an important adjunct to standard concussion assessment tools by objectively ascertaining neurological and vestibular impairments, guiding targeted rehabilitation strategies, monitoring recovery, and assisting with return-to-sport/work/learn decision-making.

Assessment of Postural Stability During an Upper Extremity Rapid, Bimanual Motor Task After Sport-Related Concussion

CONTEXT

Sport-related concussion (SRC) often presents with multidimensional and subtle neurologic deficits that are difficult to detect with standard clinical tests. New assessment approaches that efficiently quantify deficits across multiple neurologic domains are needed.

OBJECTIVE

To quantify impairments in postural movements during an assessment of rapid, bimanual motor ability in athletes within 10 days of experiencing an SRC and evaluate relationships between impairments in upper extremity and postural performance.

DESIGN

Cohort study.

SETTING

Sports medicine clinic.

PATIENTS OR OTHER PARTICIPANTS

Initial baseline assessments were completed for 711 athletes. Seventy-five athletes (age = 15.8 ± 3.3 years at baseline) sustained SRCs and were reassessed within 10 days. Seventy-eight athletes (age = 15.5 ± 2.0 years) completed 2 assessments in a healthy state.

MAIN OUTCOME MEASURE(S)

Athletes stood on force plates and performed a rapid, bimanual motor task, termed the object-hit task, delivered using a Kinesiological Instrument for Normal and Altered Reaching Movements endpoint robot. Measures of postural stability that quantified center-of-pressure movements and measures of upper extremity performance were used to characterize task performance.

RESULTS

Performance changes across assessments were converted to reliable change indices. We observed a difference in reliable change indices values between athletes with SRC and healthy control athletes on the combined postural measures (P = .01). Using measures to evaluate the change in postural movements from the early, easier portion of the task to the later, more difficult portion, we identified the highest levels of impairment (19%-25% of the sample impaired). We also noted a difference between individuals with concussion and healthy individuals on the combined upper extremity measures (P = .003), but these impairments were largely unrelated to those identified in the postural movements.

CONCLUSIONS

Measurement of postural movements during the object-hit task revealed impairments in postural stability that were not related to impairments in upper extremity performance. The findings demonstrated the benefits of using assessments that simultaneously evaluate multiple domains of neurologic function (eg, upper extremity and postural control) after SRC.

Statistical measures of motor, sensory and cognitive performance across repeated robot-based testing

BACKGROUND

Traditional clinical assessments are used extensively in neurology; however, they can be coarse, which can also make them insensitive to change. Kinarm is a robotic assessment system that has been used for precise assessment of individuals with neurological impairments. However, this precision also leads to the challenge of identifying whether a given change in performance reflects a significant change in an individual's ability or is simply natural variation. Our objective here is to derive confidence intervals and thresholds of significant change for Kinarm Standard Tests™ (KST).

METHODS

We assessed participants twice within 15 days on all tasks presently available in KST. We determined the 5-95% confidence intervals for each task parameter, and derived thresholds for significant change. We tested for learning effects and corrected for the false discovery rate (FDR) to identify task parameters with significant learning effects. Finally, we calculated intraclass correlation of type ICC [1, 2] (ICC-C) to quantify consistency across assessments.

RESULTS

We recruited an average of 56 participants per task. Confidence intervals for Z-Task Scores ranged between 0.61 and 1.55, and the threshold for significant change ranged between 0.87 and 2.19. We determined that 4/11 tasks displayed learning effects that were significant after FDR correction; these 4 tasks primarily tested cognition or cognitive-motor integration. ICC-C values for Z-Task Scores ranged from 0.26 to 0.76.

CONCLUSIONS

The present results provide statistical bounds on individual performance for KST as well as significant changes across repeated testing. Most measures of performance had good inter-rater reliability. Tasks with a higher cognitive burden seemed to be more susceptible to learning effects, which should be taken into account when interpreting longitudinal assessments of these tasks.