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.

NSF DARE-transforming modeling in neurorehabilitation: a patient-in-the-loop framework.

In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation.

Domain-Specific Outcome Measures in Clinical Trials of Therapies Promoting Stroke Recovery: A Suggested Blueprint.

Different deficits recover to different degrees and with different time courses after stroke, indicating that plasticity differs across the brain's neural systems after stroke. To capture these differences, domain-specific outcome measures have received increased attention. Such measures have potential advantages over global outcome scales, which combine recovery across many domains into a single score and so blur the ability to capture individual measures of stroke recovery. Use of a global end point to rate disability can overlook substantial recovery in specific domains, such as motor or language, and may not differentiate between good and poor recovery for specific neurological domains. In light of these points, a blueprint is proposed for using domain-specific outcome measures in stroke recovery trials. Key steps include selecting a domain in the context of preclinical data, picking a domain-specific clinical trial end point, anchoring inclusion criteria to this end point, scoring this end point both before and after treatment, and then pursuing regulatory approval on the basis of the domain-specific results. This blueprint is intended to foster clinical trials that, by using domain-specific end points, are able to demonstrate favorable results in clinical trials of therapies that promote stroke recovery.

Predicting the Onset of Ischemic Stroke With Fast High-Resolution 3D MR Spectroscopic Imaging.

BACKGROUND: Neurometabolite concentrations provide a direct index of infarction progression in stroke. However, their relationship with stroke onset time remains unclear. PURPOSE: To assess the temporal dynamics of N-acetylaspartate (NAA), creatine, choline, and lactate and estimate their value in predicting early (<6 hours) vs. late (6-24 hours) hyperacute stroke groups. STUDY TYPE: Cross-sectional cohort. POPULATION: A total of 73 ischemic stroke patients scanned at 1.8-302.5 hours after symptom onset, including 25 patients with follow-up scans. FIELD STRENGTH/SEQUENCE: A 3 T/magnetization-prepared rapid acquisition gradient echo sequence for anatomical imaging, diffusion-weighted imaging and fluid-attenuated inversion recovery imaging for lesion delineation, and 3D MR spectroscopic imaging (MRSI) for neurometabolic mapping. ASSESSMENT: Patients were divided into hyperacute (0-24 hours), acute (24 hours to 1 week), and subacute (1-2 weeks) groups, and into early (<6 hours) and late (6-24 hours) hyperacute groups. Bayesian logistic regression was used to compare classification performance between early and late hyperacute groups by using different combinations of neurometabolites as inputs. STATISTICAL TESTS: Linear mixed effects modeling was applied for group-wise comparisons between NAA, creatine, choline, and lactate. Pearson's correlation analysis was used for neurometabolites vs. time. P < 0.05 was considered statistically significant. RESULTS: Lesional NAA and creatine were significantly lower in subacute than in acute stroke. The main effects of time were shown on NAA (F = 14.321) and creatine (F = 12.261). NAA was significantly lower in late than early hyperacute patients, and was inversely related to time from symptom onset across both groups (r = -0.440). The decrease of NAA and increase of lactate were correlated with lesion volume (NAA: r = -0.472; lactate: r = 0.366) in hyperacute stroke. Discrimination was improved by combining NAA, creatine, and choline signals (area under the curve [AUC] = 0.90). DATA CONCLUSION: High-resolution 3D MRSI effectively assessed the neurometabolite changes and discriminated early and late hyperacute stroke lesions. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 2.

Prognostication in Prolonged and Chronic Disorders of Consciousness.

Patients with prolonged disorders of consciousness (DOCs) longer than 28 days may continue to make significant gains and achieve functional recovery. Occasionally, this recovery trajectory may extend past 3 (for nontraumatic etiologies) and 12 months (for traumatic etiologies) into the chronic period. Prognosis is influenced by several factors including state of DOC, etiology, and demographics. There are several testing modalities that may aid prognostication under active investigation including electroencephalography, functional and anatomic magnetic resonance imaging, and event-related potentials. At this time, only one treatment (amantadine) has been routinely recommended to improve functional recovery in prolonged DOC. Given that some patients with prolonged or chronic DOC have the potential to recover both consciousness and functional status, it is important for neurologists experienced in prognostication to remain involved in their care.

Neurorecovery after Critical COVID-19 Illness.

With the hundreds of millions of people worldwide who have been, and continue to be, affected by pandemic coronavirus disease (COVID-19) and its chronic sequelae, strategies to improve recovery and rehabilitation from COVID-19 are critical global public health priorities. Neurologic complications have been associated with acute COVID-19 infection, usually in the setting of critical COVID-19 illness. Neurologic complications are also a core feature of the symptom constellation of long COVID and portend poor outcomes. In this article, we review neurologic complications and their mechanisms in critical COVID-19 illness and long COVID. We focus on parallels with neurologic disease associated with non-COVID critical systemic illness. We conclude with a discussion of how recent findings can guide both neurologists working in post-acute neurologic rehabilitation facilities and policy makers who influence neurologic resource allocation.

Recovery after stroke: the severely impaired are a distinct group.

INTRODUCTION: Stroke causes different levels of impairment and the degree of recovery varies greatly between patients. The majority of recovery studies are biased towards patients with mild-to-moderate impairments, challenging a unified recovery process framework. Our aim was to develop a statistical framework to analyse recovery patterns in patients with severe and non-severe initial impairment and concurrently investigate whether they recovered differently. METHODS: We designed a Bayesian hierarchical model to estimate 3-6 months upper limb Fugl-Meyer (FM) scores after stroke. When focusing on the explanation of recovery patterns, we addressed confounds affecting previous recovery studies and considered patients with FM-initial scores <45 only. We systematically explored different FM-breakpoints between severe/non-severe patients (FM-initial=5-30). In model comparisons, we evaluated whether impairment-level-specific recovery patterns indeed existed. Finally, we estimated the out-of-sample prediction performance for patients across the entire initial impairment range. RESULTS: Recovery data was assembled from eight patient cohorts (n=489). Data were best modelled by incorporating two subgroups (breakpoint: FM-initial=10). Both subgroups recovered a comparable constant amount, but with different proportional components: severely affected patients recovered more the smaller their impairment, while non-severely affected patients recovered more the larger their initial impairment. Prediction of 3-6 months outcomes could be done with an R2=63.5% (95% CI=51.4% to 75.5%). CONCLUSIONS: Our work highlights the benefit of simultaneously modelling recovery of severely-to-non-severely impaired patients and demonstrates both shared and distinct recovery patterns. Our findings provide evidence that the severe/non-severe subdivision in recovery modelling is not an artefact of previous confounds. The presented out-of-sample prediction performance may serve as benchmark to evaluate promising biomarkers of stroke recovery.

Principles of Neural Repair and Their Application to Stroke Recovery Trials.

Neural repair is the underlying therapeutic strategy for many treatments currently under investigation to improve recovery after stroke. Repair-based therapies are distinct from acute stroke strategies: instead of salvaging threatened brain tissue, the goal is to improve behavioral outcomes on the basis of experience-dependent brain plasticity. Furthermore, timing, concomitant behavioral experiences, modality specific outcome measures, and careful patient selection are fundamental concepts for stroke recovery trials that can be deduced from principles of neural repair. Here we discuss core principles of neural repair and their implications for stroke recovery trials, highlighting related issues from key studies in humans. Research suggests a future in which neural repair therapies are personalized based on measures of brain structure and function, genetics, and lifestyle factors.

Brain-Computer Interfaces in Neurorecovery and Neurorehabilitation.

Recent advances in brain-computer interface technology to restore and rehabilitate neurologic function aim to enable persons with disabling neurologic conditions to communicate, interact with the environment, and achieve other key activities of daily living and personal goals. Here we evaluate the principles, benefits, challenges, and future directions of brain-computer interfaces in the context of neurorehabilitation. We then explore the clinical translation of these technologies and propose an approach to facilitate implementation of brain-computer interfaces for persons with neurologic disease.

Arm Motor Recovery After Ischemic Stroke: A Focus on Clinically Distinct Trajectory Groups.

BACKGROUND AND PURPOSE: Recovery of arm function poststroke is highly variable with some people experiencing rapid recovery but many experiencing slower or limited functional improvement. Current stroke prediction models provide some guidance for clinicians regarding expected motor outcomes poststroke but do not address recovery rates, complicating discharge planning. This study developed a novel approach to defining recovery groups based on arm motor recovery trajectories poststroke. In addition, between-group differences in baseline characteristics and therapy hours were explored. METHODS: A retrospective cohort analysis was conducted where 40 participants with arm weakness were assessed 1 week, 6 weeks, 3 months, and 6 months after an ischemic stroke. Arm recovery trajectory groups were defined on the basis of timing of changes in the Fugl-Meyer Assessment Upper Extremity (FMA-UE), at least the minimal clinically important difference (MCID), 1 week to 6 weeks or 6 weeks to 6 months. Three recovery trajectory groups were defined: Fast (n = 19), Extended (n = 12), and Limited (n = 9). Between-group differences in baseline characteristics and therapy hours were assessed. Associations between baseline characteristics and group membership were also determined. RESULTS: Three baseline characteristics were associated with trajectory group membership: FMA-UE, NIH Stroke Scale, and Barthel Index. The Fast Recovery group received the least therapy hours 6 weeks to 6 months. No differences in therapy hours were observed between Extended and Limited Recovery groups at any time points. DISCUSSION AND CONCLUSIONS: Three clinically relevant recovery trajectory groups were defined using the FMA-UE MCID. Baseline impairment, overall stroke severity, and dependence in activities of daily living were associated with group membership and therapy hours differed between groups. Stratifying individuals by recovery trajectory early poststroke could offer additional guidance to clinicians in discharge planning.(See Supplemental Digital Content 1 for Video Abstract, available at: http://links.lww.com/JNPT/A337.).

Reimagining Stroke Rehabilitation and Recovery Across the Care Continuum: Results From a Design-Thinking Workshop to Identify Challenges and Propose Solutions.

Systems for stroke rehabilitation and recovery are variable and fragmented; stroke survivors often experience gaps in care with detrimental effects on their recovery. We designed and hosted a multidisciplinary and interactive workshop to discuss challenges facing patients recovering from stroke and to brainstorm solutions. Forty-one participants including clinicians, researchers, and stroke survivors attended the workshop. Participants were surveyed beforehand about challenges facing stroke recovery and results were tabulated as a word cloud. An interactive, design-thinking exercise was conducted that involved completing workbooks, hands-on prototype designing, and presentations, which were then analyzed through qualitative content analysis using an inductive approach. High frequency words in the word cloud of survey responses included access, fragmented, and uncertainty. Qualitative analysis revealed 6 major challenge themes including poor (1) transitions in and (2) access to care; (3) barriers to health insurance; (4) lack of patient support; (5) knowledge gaps; and (6) lack of standardized outcomes. Eleven unique solutions were proposed that centered around new technologies, health care system changes, and the creation of new support roles. Analysis of the alignment between the challenges and solutions revealed that the single proposed solution that solved the most identified challenges was a "comprehensive stroke clinic with follow-up programs, cutting edge treatments, patient advocation and research." Through our interactive design-thinking workshop process and inductive thematic analysis, we identified major challenges facing patients recovering from stroke, collaboratively proposed solutions, and analyzed their alignment. This process offers an innovative approach to reaching consensus among interdisciplinary stakeholders.

Corticospinal Tract Injury Estimated From Acute Stroke Imaging Predicts Upper Extremity Motor Recovery After Stroke.

Background and Purpose- Injury to the corticospinal tract (CST) has been shown to have a major effect on upper extremity motor recovery after stroke. This study aimed to examine how well CST injury, measured from neuroimaging acquired during the acute stroke workup, predicts upper extremity motor recovery. Methods- Patients with upper extremity weakness after ischemic stroke were assessed using the upper extremity Fugl-Meyer during the acute stroke hospitalization and again at 3-month follow-up. CST injury was quantified and compared, using 4 different methods, from images obtained as part of the stroke standard-of-care workup. Logistic and linear regression were performed using CST injury to predict ΔFugl-Meyer. Injury to primary motor and premotor cortices were included as potential modifiers of the effect of CST injury on recovery. Results- N=48 patients were enrolled 4.2±2.7 days poststroke and completed 3-month follow-up (median 90-day modified Rankin Scale score, 3; interquartile range, 1.5). CST injury distinguished patients who reached their recovery potential (as predicted from initial impairment) from those who did not, with area under the curve values ranging from 0.70 to 0.8. In addition, CST injury explained ≈20% of the variance in the magnitude of upper extremity recovery, even after controlling for the severity of initial impairment. Results were consistent when comparing 4 different methods of measuring CST injury. Extent of injury to primary motor and premotor cortices did not significantly influence the predictive value that CST injury had for recovery. Conclusions- Structural injury to the CST, as estimated from standard-of-care imaging available during the acute stroke hospitalization, is a robust way to distinguish patients who achieve their predicted recovery potential and explains a significant amount of the variance in poststroke upper extremity motor recovery.

The Diagnosis and Natural History of Multiple System Atrophy, Cerebellar Type.

The objective of this study was to identify key features differentiating multiple system atrophy cerebellar type (MSA-C) from idiopathic late-onset cerebellar ataxia (ILOCA). We reviewed records of patients seen in the Massachusetts General Hospital Ataxia Unit between 1992 and 2013 with consensus criteria diagnoses of MSA-C or ILOCA. Twelve patients had definite MSA-C, 53 had possible/probable MSA-C, and 12 had ILOCA. Autonomic features, specifically urinary urgency, frequency, and incontinence with erectile dysfunction in males, differentiated MSA-C from ILOCA throughout the disease course (p = 0.005). Orthostatic hypotension developed later and differentiated MSA-C from ILOCA (p < 0.01). REM sleep behavior disorder (RBD) occurred early in possible/probable MSA-C (p < 0.01). Late MSA-C included pathologic laughing and crying (PLC, p < 0.01), bradykinesia (p = 0.01), and corticospinal findings (p = 0.01). MRI distinguished MSA-C from ILOCA by atrophy of the brainstem (p < 0.01) and middle cerebellar peduncles (MCP, p = 0.02). MSA-C progressed faster than ILOCA: by 6 years, MSA-C walker dependency was 100 % and ILOCA 33 %. MSA-C survival was 8.4 ± 2.5 years. Mean length of ILOCA illness to date is 15.9 ± 6.4 years. A sporadic onset, insidiously developing cerebellar syndrome in midlife, with autonomic features of otherwise unexplained bladder dysfunction with or without erectile dysfunction in males, and atrophy of the cerebellum, brainstem, and MCP points strongly to MSA-C. RBD and postural hypotension confirm the diagnosis. Extrapyramidal findings, corticospinal tract signs, and PLC are helpful but not necessary for diagnosis. Clarity in early MSA-C diagnosis can prevent unnecessary investigations and facilitate therapeutic trials.

Two-year-olds with autism orient to non-social contingencies rather than biological motion.

Typically developing human infants preferentially attend to biological motion within the first days of life. This ability is highly conserved across species and is believed to be critical for filial attachment and for detection of predators. The neural underpinnings of biological motion perception are overlapping with brain regions involved in perception of basic social signals such as facial expression and gaze direction, and preferential attention to biological motion is seen as a precursor to the capacity for attributing intentions to others. However, in a serendipitous observation, we recently found that an infant with autism failed to recognize point-light displays of biological motion, but was instead highly sensitive to the presence of a non-social, physical contingency that occurred within the stimuli by chance. This observation raised the possibility that perception of biological motion may be altered in children with autism from a very early age, with cascading consequences for both social development and the lifelong impairments in social interaction that are a hallmark of autism spectrum disorders. Here we show that two-year-olds with autism fail to orient towards point-light displays of biological motion, and their viewing behaviour when watching these point-light displays can be explained instead as a response to non-social, physical contingencies--physical contingencies that are disregarded by control children. This observation has far-reaching implications for understanding the altered neurodevelopmental trajectory of brain specialization in autism.

Changes in input strength and number are driven by distinct mechanisms at the retinogeniculate synapse.

Recent studies have demonstrated that vision influences the functional remodeling of the mouse retinogeniculate synapse, the connection between retinal ganglion cells and thalamic relay neurons in the dorsal lateral geniculate nucleus (LGN). Initially, each relay neuron receives a large number of weak retinal inputs. Over a 2- to 3-wk developmental window, the majority of these inputs are eliminated, and the remaining inputs are strengthened. This period of refinement is followed by a critical period when visual experience changes the strength and connectivity of the retinogeniculate synapse. Visual deprivation of mice by dark rearing from postnatal day (P)20 results in a dramatic weakening of synaptic strength and recruitment of additional inputs. In the present study we asked whether experience-dependent plasticity at the retinogeniculate synapse represents a homeostatic response to changing visual environment. We found that visual experience starting at P20 following visual deprivation from birth results in weakening of existing retinal inputs onto relay neurons without significant changes in input number, consistent with homeostatic synaptic scaling of retinal inputs. On the other hand, the recruitment of new inputs to the retinogeniculate synapse requires previous visual experience prior to the critical period. Taken together, these findings suggest that diverse forms of homeostatic plasticity drive experience-dependent remodeling at the retinogeniculate synapse.

Multiple system atrophy of the cerebellar type: clinical state of the art.

Multiple system atrophy (MSA) is a late-onset, sporadic neurodegenerative disorder clinically characterized by autonomic failure and either poorly levodopa-responsive parkinsonism or cerebellar ataxia. It is neuropathologically defined by widespread and abundant central nervous system α-synuclein-positive glial cytoplasmic inclusions and striatonigral and/or olivopontocerebellar neurodegeneration. There are two clinical subtypes of MSA distinguished by the predominant motor features: the parkinsonian variant (MSA-P) and the cerebellar variant (MSA-C). Despite recent progress in understanding the pathobiology of MSA, investigations into the symptomatology and natural history of the cerebellar variant of the disease have been limited. MSA-C presents a unique challenge to both clinicians and researchers alike. A key question is how to distinguish early in the disease course between MSA-C and other causes of adult-onset cerebellar ataxia. This is a particularly difficult question, because the clinical framework for conceptualizing and studying sporadic adult-onset ataxias continues to undergo flux. To date, several investigations have attempted to identify clinical features, imaging, and other biomarkers that may be predictive of MSA-C. This review presents a clinically oriented overview of our current understanding of MSA-C with a focus on evidence for distinguishing MSA-C from other sporadic, adult-onset ataxias.

A Residual U-Net Neural Network for Seismocardiogram Denoising and Analysis During Physical Activity.

Seismocardiogram (SCG) signals are noninvasively obtained cardiomechanical signals containing important features for cardiovascular health monitoring. However, these signals are prone to contamination by motion noise, which can significantly impact accuracy and robustness of the measurements. A deep learning model based on the U-Net architecture is proposed to recover SCG signals contaminated by motion noise induced by walking. The model performance was evaluated through qualitative visualization, as well as quantitative analyses. Quantitative analyses included distance-based comparisons before and after applying our model. Analyses also included assessments of the model's efficacy in improving the performance of downstream tasks related to health parameter estimation during walking. Experimental findings revealed that the denoising model improved similarity to clean signals by approximately 90%. The performance of the model in enhancing heart rate estimation demonstrated a mean absolute error of 1.21 BPM and a root-mean-squared error (RMSE) of 1.97 BPM during walking after denoising with 9.16 BPM and 10.38 BPM improvements, respectively, compared to without denoising. Furthermore, the RMSEs of aortic opening and aortic closing time estimation after denoising for one dataset with catheter ground truth were 7.29 ms and 19.71 ms during walking, respectively, with 50.33 ms and 51.91 ms RMSE improvements compared to without denoising. And for another dataset with ICG-derived PEP ground truth, the RMSE of aortic opening time estimation after denoising was 10.21 ms during walking, with 38.74 ms RMSE improvement compared to without denoising. The proposed model attenuates motion noise from corrupted SCG signals while preserving cardiac information. This development paves the way for improved ambulatory cardiac health monitoring using wearable accelerometers during daily activities.

A Comparison of Normalization Techniques for Individual Baseline-Free Estimation of Absolute Hypovolemic Status Using a Porcine Model.

Hypovolemic shock is one of the leading causes of death in the military. The current methods of assessing hypovolemia in field settings rely on a clinician assessment of vital signs, which is an unreliable assessment of hypovolemia severity. These methods often detect hypovolemia when interventional methods are ineffective. Therefore, there is a need to develop real-time sensing methods for the early detection of hypovolemia. Previously, our group developed a random-forest model that successfully estimated absolute blood-volume status (ABVS) from noninvasive wearable sensor data for a porcine model (n = 6). However, this model required normalizing ABVS data using individual baseline data, which may not be present in crisis situations where a wearable sensor might be placed on a patient by the attending clinician. We address this barrier by examining seven individual baseline-free normalization techniques. Using a feature-specific global mean from the ABVS and an external dataset for normalization demonstrated similar performance metrics compared to no normalization (normalization: R2 = 0.82 ± 0.025|0.80 ± 0.032, AUC = 0.86 ± 5.5 × 10-3|0.86 ± 0.013, RMSE = 28.30 ± 0.63%|27.68 ± 0.80%; no normalization: R2 = 0.81 ± 0.045, AUC = 0.86 ± 8.9 × 10-3, RMSE = 28.89 ± 0.84%). This demonstrates that normalization may not be required and develops a foundation for individual baseline-free ABVS prediction.

Novel Platform for Quantitative Assessment of Functional Object Interactions After Stroke.

Many persons with stroke exhibit upper extremity motor impairments. These impairments often lead to dysfunction and affect performance in activities of daily living, where successful manipulation of objects is essential. Hence, understanding how upper extremity motor deficits manifest in functional interactions with objects is critical for rehabilitation. However, quantifying skill in these tasks has been a challenge. Traditional rehabilitation assessments require highly trained clinicians, are time-consuming, and yield subjective scores. This paper introduces a custom-designed device, the "MAGIC Table", that can record real-time kinematics of persons with stroke during interaction with objects, specifically a 'cup of coffee'. The task and its quantitative assessments were derived from previous basic-science studies. Six participants after stroke and six able-bodied participants moved a 3D-printed cup with a rolling ball inside, representing sloshing coffee, with 3 levels of difficulty. Movements were captured via a high-resolution camera above the table. Conventional kinematic metrics (movement time and smoothness) and novel kinematic metrics accounting for object interaction (risk and predictability) evaluated performance. Expectedly, persons with stroke moved more slowly and less smoothly than able-bodied participants, in both simple reaches and during transport of the cup-and-ball system. However, the more sensitive metric was mutual information, which captured the predictability of interactions, essential in cup transport as shown in previous theoretical research. Predictability sensitively measured differences in performance with increasing levels of difficulty. It also showed the best intraclass consistency, promising sensitive differentiation between different levels of impairment. This study highlights the feasibility of this new device and indicates that examining dynamic object interaction may provide valuable insights into upper extremity function after stroke useful for assessment and rehabilitation.