Robotic exoskeleton assessment of transient ischemic attack

Simmatis, Leif; Krett, Jonathan D.; Scott, Stephen H.; Jin, Albert

doi:10.1371/journal.pone.0188786

Cited by 37 publications

(57 citation statements)

References 37 publications

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“…Proprioception (an aspect of sensory behavior) was assessed using the Arm Position Matching (APM) task [26]. Cognition (including response inhibition, spatial working memory, and set-switching) was assessed using the Reverse Visually Guided Reaching (RVGR) [27], Spatial Span (SPS) [19,28], and Trailmaking (TMT) [29] tasks. Detailed descriptions of these tasks are provided in Table 1.…”

Section: Robotic Assessmentmentioning

confidence: 99%

“…The Kinarm robotic assessment system (Kinarm, Kingston, ON, Canada) is a tool that quantifies performance on a collection of behavioral tasks that test motor, cognitive, and sensory functions, referred to as Kinarm Standard Tests™ (KSTs). It has been used to characterize deficits in a variety of clinical populations such as stroke ( [14][15][16]), ALS [17], Parkinson's disease [18], TIA [19], and concussion [20]. Furthermore, previous work has highlighted the test-retest reliability of Kinarm assessment results [21].…”

Section: Introductionmentioning

confidence: 99%

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Assessing various sensorimotor and cognitive functions in people with epilepsy is feasible with robotics

Simmatis

Jin

Keiski

et al. 2020

Epilepsy & Behavior

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Background: Epilepsy is a common neurological disorder characterized by recurrent seizures, along with comorbid cognitive and psychosocial impairment. Current gold standards of assessment can quantify cognitive and motor performance, but may not capture all subtleties of behavior. Here, we study the feasibility of assessing various upper limb sensorimotor and cognition functions in people with epilepsy using the Kinarm robotic assessment system. We quantify performance across multiple behavioral domains and additionally consider the possible effects of epilepsy subtype and medication. Methods: We recruited individuals with a variety of epilepsy subtypes. Participants performed 8 behavioral tasks that tested motor, cognitive, and sensory domains. We collected data on the same tasks from a group of control participants that had no known neurological impairments. We quantified performance using Task Scores, which provide a composite measure of overall performance on a given task and are adjusted for age, sex, and handedness. Results: We collected data from 46 individuals with epilepsy and 92 control participants. The assessment was well-tolerated, with no adverse events recorded. Cognitive tasks testing spatial working memory, executive function, and motor response inhibition were the most frequently impaired in the epilepsy cohort, with 33/46 (72%) being outside the normal range on at least one of these tasks. Additionally, 29/46 (63%) were impaired on at least one task testing primarily motor skill, and 14/46 (30%) were impaired on a proprioceptive sensory task. People with either focal epilepsy or generalized epilepsy performed significantly worse on both motor and cognitive tasks than control participants after correcting for multiple comparisons. There were no statistical differences between generalized and focal epilepsy groups on Task Scores. Finally, individuals taking topiramate trended toward having worse performance on a spatial working memory task than other individuals with epilepsy who were not taking topiramate. Conclusions: Kinarm robotic assessment is feasible in individuals with epilepsy and is well-tolerated. Our robotic paradigm can detect impairments in various sensorimotor and cognitive functions across the population with epilepsy. Future studies will explore the role of epilepsy subtype and medications.

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Section: Robotic Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing various sensorimotor and cognitive functions in people with epilepsy is feasible with robotics

Simmatis

Jin

Keiski

et al. 2020

Epilepsy & Behavior

Self Cite

View full text Add to dashboard Cite

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“…Each parameter is normalized to a z‐score based on normative data that account for age effects when necessary 33. Normative data are based on 178 subjects for VGR, 494 subjects for PM, 433 subjects for OH, and 293 subjects for OHA.…”

Section: Methodsmentioning

confidence: 99%

“…For the stroke survivors in this article, z‐scores close to zero indicate performance consistent with controls, with higher magnitudes of z‐scores indicating performance further from controls. A single composite task score is also calculated for each task by calculating the root‐mean‐square distance of all the parameter z‐scores for that task and re‐normalizing compared with performance of healthy controls 33, 35, 36…”

Section: Methodsmentioning

confidence: 99%

Differential Impact of Acute Lesions Versus White Matter Hyperintensities on Stroke Recovery

Hawe

Findlater

Kenzie

et al. 2018

JAHA

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BackgroundUnderstanding how the size of acute lesions and white matter hyperintensities (WMH) impact stroke recovery can improve our ability to predict outcomes and tailor treatments. The aim of this exploratory study was to investigate the role of acute lesion volume and WMH volume on longitudinal recovery of specific sensory, motor, and cognitive impairments after stroke using robotic and clinical measures.Methods and ResultsEighty‐two individuals were assessed at 1, 6, 12, and 26 weeks poststroke with robotic tasks and commonly used clinical measures. The volumes of acute lesions and WMH were measured on fluid‐attenuated inversion recovery images. Linear mixed models were used to investigate the role of acute lesions and WMH on parameters derived from the robotic tasks and clinical measures. Regression analysis determined the added value of acute lesion and WMH volumes along with measures of initial performance to predict outcomes at 6 months. Acute lesion volume has widespread effects on sensory, motor, and overall functional recovery poststroke. The impact of WMH was specific to cognitive impairments. Apart from the robotic position sense task, neither lesion volume nor WMH measure had significant ability to predict outcomes at 6 months over using initial impairment as measured by robotic assessments alone.ConclusionsWhile acute lesion volume and WMH may impact different impairments poststroke, their clinical utility in predicting outcomes at 6 months poststroke is limited.

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“…Control data sets for each task parameter were transformed to a normal distribution using a Box–Cox transformation (Box & Cox, ). Outliers beyond ±3.29 standard deviations ( SD s) were removed from the sample (on average ~1% of control subjects were removed per parameter; BKIN Technologies, ; Simmatis, Krett, Scott, & Jin, ). Z ‐scores represent the distance from the mean performance of healthy control subjects in SD s. The overall task scores were calculated from the root‐mean‐square of all parameter z‐scores for each task and renormalized based on the performance of healthy controls (BKIN Technologies, ; Kenzie et al, ; Simmatis et al, ).…”

Section: Methodsmentioning

confidence: 99%

Proprioception and motor performance after stroke: An examination of diffusion properties in sensory and motor pathways

Findlater

Mazerolle

Pike

et al. 2019

Human Brain Mapping

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Proprioceptive and motor impairments commonly occur after stroke. Relationships between corticospinal tract (CST) fractional anisotropy (FA) and motor recovery have been identified. However, the relationship between sensory tract microstructure and proprioceptive recovery remains unexplored. Using probabilistic tractography, we examined the relationship between diffusion metrics in three tracts known to contain proprioceptive information (a) dorsal‐column medial‐lemniscal (DCML), (b) postcentral gyrus to supramarginal gyrus (POCG‐SMG), (c) postcentral gyrus to Heschl's gyrus (POCG‐HG) and proprioception at 1 (n = 26) and 6 months (n = 19) poststroke. Proprioception was assessed using two robotic tasks. Motor performance was also assessed robotically and compared to CST diffusion metrics. At 1‐month poststroke, a nonsignificant relationship (r = −0.43, p = 0.05) was observed between DCML‐FA and proprioceptive impairment. A moderate relationship was identified between POCG‐SMG FA and POCG‐HG FA and proprioceptive impairment (r = −0.47, p = 0.001 and r = −0.51, p = 0.008, respectively). No relationships were significant at 6 months poststroke. Similar to previous studies, lower CST‐FA correlated with motor impairment at 1 month poststroke (r = −0.58, p = 0.002). While CST‐FA is considered a predictor of motor impairment, our findings suggest that the relationship between FA and tracts containing proprioceptive information is not as straightforward and highlights the importance of sensory association areas in proprioception.

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Robotic exoskeleton assessment of transient ischemic attack

Cited by 37 publications

References 37 publications

Assessing various sensorimotor and cognitive functions in people with epilepsy is feasible with robotics

Assessing various sensorimotor and cognitive functions in people with epilepsy is feasible with robotics

Differential Impact of Acute Lesions Versus White Matter Hyperintensities on Stroke Recovery

Proprioception and motor performance after stroke: An examination of diffusion properties in sensory and motor pathways

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