Rest the Brain to Learn New Gait Patterns after Stroke

Abstract

Background: The ability to relearn a lost skill is critical to motor recovery after a stroke. Previous studies indicate that stroke typically affects the processes underlying motor control and execution but not the learning of those skills. However, these prior studies could have been confounded by the presence of significant motor impairments and/or have not focused on motor acuity tasks (i.e., tasks focusing on the quality of executed actions) that have direct functional relevance to rehabilitation.

Methods: Twenty-five participants (10 stroke; 15 controls) were recruited for this prospective, case-control study. Participants learned a novel foot-trajectory tracking task on two consecutive days while walking on a treadmill. On day 1, participants learned a new gait pattern by performing a task that necessitated greater hip and knee flexion during the swing phase of the gait. On day 2, participants repeated the task with their training leg to test retention. An average tracking error was computed to determine online and offline learning and was compared between stroke survivors and uninjured controls.

Results: Stroke survivors were able to improve their tracking performance on the first day (p=0.033); however, the amount of learning in stroke survivors was lower in comparison with the control group on both days (p≤0.05). Interestingly, the offline gains in motor learning were higher in stroke survivors when compared with uninjured controls (p=0.011).

Conclusions: The results suggest that even high-functioning stroke survivors may have difficulty acquiring new motor skills related to walking, which may be related to the underlying neural damage caused at the time of stroke. Furthermore, it is likely that stroke survivors may require longer training with adequate rest to acquire new motor skills, and rehabilitation programs should target motor skill learning to improve outcomes after stroke.

Keywords: consolidation; error-based learning; hemiparesis; motor task; skill acquisition.

Figure 1:

A schematic of the (A) experimental set-up and foot-trajectory tracking during treadmill walking, (B) experimental protocol, (C) participant’s baseline trajectory and their scaled (30%) target trajectory, and (D) computation of tracking error represented by the non-overlapping area (shaded in grey)

Figure 2:

A representative example of participants’ tracking error in each group on Day 1 (left) and Day 2 (right)

Figure 3:

(A) The average trajectory tracking error in each group on Day 1 (left) and Day 2 (right). For comparison purposes, we provide data (power-fit curve of the mean data) from young, uninjured adults taken from a previous publication. (B) Bar plots showing onlinae differences in learning between the stroke and the control group. (C) Bar plots showing differences in the amount of retention and offline gains between the stroke and the control group. Data for online learning and retention are shown as marginal mean changes (Δ) in tracking error. The error bars denote the standard error of the mean and asterisks (*) denotes statistical significance (p < 0.05). Positive values indicate improvements in performance.

Figure 4:

Raincloud plots showing distributions of normalized tracking error before (Pre) and after (Post) training in stroke survivors [top panel, (A) and (B)] and controls [bottom panel, (C) and (D)] on both days

Figure 5:

Raincloud plots showing distributions of (A) retention (computed as changes in normalized tracking error from Pre block on Day 1 to Pre block on Day 2) and (B) offline gains (computed as changes in normalized tracking error from Post block on Day 1 to Pre block on Day 2) in stroke survivors and controls