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Since cognitive status often declines with age, it is plausible that responsiveness to motor therapy can, at least in part, be predicted by cognitive factors.Įmpirically, there is a longstanding line of experimental motor learning studies that have shown that visuospatial function (i.e., of or relating to visual perception and spatial relationships between objects) is positively correlated with motor learning in both young and older adults.
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Furthermore, a number of studies have shown that advancing age is associated with less improvement in motor therapy following stroke and other musculoskeletal conditions. For example, attention, executive function, and visuospatial memory underlie crucial stages of motor learning and are also among the most common cognitive deficits reported following stroke. To our knowledge, no predictive models of therapeutic responsiveness include cognitive variables, despite growing evidence that they may explain significant amounts of variance in motor learning. However, when attempting to predict changes in post-stroke upper-extremity impairment following therapy (i.e., responsiveness to motor therapy), recent work in machine learning has shown that the inclusion of sophisticated neuroimaging measures does not improve prediction accuracy beyond basic clinical measures (i.e., baseline Fugl-Meyer score). There are already several models that have been developed to predict biological motor recovery post-stroke (e.g., the Predicting REcovery Potential algorithm ) that include personalized variables such as baseline motor function, age, severity of stroke, and white matter integrity. Because the effects of stroke can vary greatly between individuals, responsiveness to motor therapy can be difficult to predict. In other words, the benefits of motor therapy are theoretically predicated upon an individual’s capacity for skill reacquisition and long-term retention.
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Motor learning processes are fundamental to clinical motor rehabilitation. This proof-of-concept suggests that the relationship between delayed visuospatial memory and 1-month motor skill performance generalizes to individuals with chronic stroke, and supports the idea that visuospatial testing may provide prognostic insight into clinical motor rehabilitation outcomes. Results indicated that in both stroke and older adult datasets, inclusion of Delayed Recall scores explained significantly more variance of 1-month skill performance than models that included age, education, and baseline motor performance alone. To test if this predictive relationship generalized to individuals with chronic ischemic stroke, we then tested each trained model on an independent stroke dataset. To determine the extent to which Delayed Recall test scores impacted prediction accuracy of 1-month skill learning in older adults, we used leave-one-out cross-validation to evaluate the prediction error between models. Two regression models (one including Delayed Recall scores and one without) were trained using data from non-stroke older adults. The purpose of this short report was to validate previous findings using Rey–Osterrieth Complex Figure Delayed Recall test scores to predict motor learning and determine if this relationship generalized to a set of individuals post-stroke. Recent work has demonstrated that a clinical test of visuospatial memory (Rey–Osterrieth Complex Figure Delayed Recall) may predict 1-month skill learning in older adults however, whether this relationship persists in individuals with chronic stroke remains unknown. There is growing evidence from non-neurological populations supporting the role of visuospatial memory function in motor learning, but current predictive models of motor recovery of individuals with stroke generally exclude cognitive measures, thereby overlooking the potential link between motor learning and visuospatial memory. Motor learning is fundamental to motor rehabilitation outcomes.