Trajectory formation principles are the same after mild or moderate stroke

Abstract

When we make rapid reaching movements, we have to trade speed for accuracy. To do so, the trajectory of our hand is the result of an optimal balance between feed-forward and feed-back control in the face of signal-dependant noise in the sensorimotor system. How far do these principles of trajectory formation still apply after a stroke, for persons with mild to moderate sensorimotor deficits who recovered some reaching ability? Here, we examine the accuracy of fast hand reaching movements with a focus on the information capacity of the sensorimotor system and its relation to trajectory formation in young adults, in persons who had a stroke and in age-matched control participants. We find that persons with stroke follow the same trajectory formation principles, albeit parameterized differently in the face of higher sensorimotor uncertainty. Higher directional errors after a stroke result in less feed-forward control, hence more feed-back loops responsible for segmented movements. As a consequence, movements are globally slower to reach the imposed accuracy, and the information throughput of the sensorimotor system is lower after a stroke. The fact that the most abstract principles of motor control remain after a stroke suggests that clinicians can capitalize on existing theories of motor control and learning to derive principled rehabilitation strategies.

Pattern of improvement in upper limb pointing task kinematics after a 3-month training program with robotic assistance in stroke

“…the gradual decrease in the number of movement arrests, resulting in gradually reduced number of sub-movements and thus of new risks of error along the ideal trajectory [ 25 , 26 , 61 ]. These results may also fit the well-known speed-accuracy trade-off that governs voluntary movements (Fitts’ law), whereby it would be difficult to improve both parameters simultaneouly, including in stroke-induced hemiparesis [ 62 , 63 ]. These findings may finally support the model that submovements may blend as a mechanism of recovery from stroke [ 25 , 26 , 64 ].…”

“…In such cases of subacute stroke-induced hemiparesis, it is not surprising to observe markedly faster and smoother reaching movements especially as “spontaneous” recovery (lesion-induced plasticity) and rehabilitation-related recovery (behavior-induced plasticity) are intertwined - and might even potentiate each other - in the first six months post stroke. The combination of these four kinematic measures thus seems sensitive enough to detect small changes on motor performance and comforts the idea of a training-induced motor learning process in which progress over time does not necessarily have to plateau out [ 61 , 62 ].…”

A data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments

“…We used the path length ratio (i.e., shortest possible distance divided by the actually covered distance) to represent inefficient movements 86 . Additionally, the throughput (ratio of target distance and target width divided by movement time) was used as an information theory-driven descriptor of movement efficiency 54,89 . The metrics were extracted from the start of the transport phase until the current peg was released and from the start of the return phase until the next peg was taken, as not only ballistic movements but also the endpoint error is of interest when describing the efficiency of movements.…”

“…The initial movement angle was defined as the angular deviation between the actual and optimal trajectory 88 . As this metric requires the definition of a specific timepoint in the trajectory to measure the deviation, and as multiple approaches were used in literature 63,[88][89][90] , we explored three different ways to define the timepoint. This included the time at which 20% of the shortest distance between peg and hole was covered (initial movement angle θ 1 ), the time at which 20% of the actually covered distance between peg and hole was reached initial movement angle θ 2 , and the time at which peak velocity was achieved (initial movement angle θ 3 ).…”

Examining the effect of state anxiety on compensatory and strategic adjustments in the planning of goal-directed aiming

“…To elucidate, prior knowledge of visual feedback for goal-directed movements typically elicits a larger magnitude force and shorter proportional time at peak velocity (i.e., positive skew in the time-course of movement velocity) (Hansen, Glazebrook, Anson, Weeks, & Elliott, 2006;Khan, Elliott, Coull, Chua, & Lyons, 2002;see Causer, Hayes, Hooper, & Bennett, 2017 for an example of oculomotor control in golf-putting). What's more, a suspected decline in the ability to control can cause an increasingly shorter proportion of time to peak velocity (Mottet, van Dokkum, Froger, Gouïach, & Laffont, 2017;Timmis & Pardhan, 2012;Welsh, Higgins, & Elliott, 2007). That is, a further and faster reach within the early portions of the trajectory is presumably prepared to accommodate the late online control phase.…”