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 studies could be confounded by the presence of significant motor impairments. Furthermore, prior research involving the upper extremity indicates that stroke survivors have an advantage in offline motor learning when compared with controls. However, this has not been examined using motor acuity tasks (i.e., tasks focusing on the quality of executed actions) that have direct functional relevance to rehabilitation.

Objective: Investigate how stroke affects leg motor skill learning during walking in stroke survivors.

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. The task necessitated greater hip and knee flexion during the swing phase of the gait. Online learning was measured by comparing tracking error at the beginning and end of each practice session, offline (rest-driven) learning was measured by comparing the end of the first practice session to the beginning of the second, and retention was measured by comparing the beginning of the first practice session to the beginning of the second. Online learning, offline learning, and retention were compared between the stroke survivors and uninjured controls.

Results: Stroke survivors improved 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, stroke survivors showed higher offline learning gains when compared with uninjured controls (p = 0.011).

Conclusions: Even stroke survivors with no perceivable motor impairments 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, stroke survivors may require longer training with adequate rest to acquire new motor skills.

Keywords: Consolidation; Error-based learning; Hemiparesis; Motor task; Skill acquisition.

Fig. 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)

Fig. 2

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

Fig. 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 [31]. (B) Bar plots showing online 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

Fig. 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

Fig. 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

Fig. 6

(A) Average predicted tracking error in each block on Day 1 (left) and Day 2 (right) using exponential functions fit to participants’ tracking error. Here, the shaded regions represent the standard error of each mean curve. (B) Bar plots showing online differences in predicted learning between the stroke and the control group. (C) Bar plots showing differences in the amount of predicted 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

Fig. 7

Average micro-offline learning between each block on Day 1 (left) and Day 2 (right). Here, micro-offline learning is defined as the difference in tracking error at the beginning of a block and the error at the end of the previous block. Error bars represent standard error of the mean