Explicit and implicit locomotor learning in individuals with chronic hemiparetic stroke

Abstract

Motor learning involves both explicit and implicit processes that are fundamental for acquiring and adapting complex motor skills. However, stroke may damage the neural substrates underlying explicit and/or implicit learning, leading to deficits in overall motor performance. Although both learning processes are typically used in concert in daily life and rehabilitation, no gait studies have determined how these processes function together after stroke when tested during a task that elicits dissociable contributions from both. Here, we compared explicit and implicit locomotor learning in individuals with chronic stroke to age- and sex-matched neurologically intact controls. We assessed implicit learning using split-belt adaptation (where two treadmill belts move at different speeds). We assessed explicit learning (i.e., strategy-use) using visual feedback during split-belt walking to help individuals explicitly correct for step length errors created by the split-belts. After the first 40 strides of split-belt walking, we removed the visual feedback and instructed individuals to walk comfortably, a manipulation intended to minimize contributions from explicit learning. We used a multirate state-space model to characterize individual explicit and implicit process contributions to overall behavioral change. The computational and behavioral analyses revealed that, compared with controls, individuals with chronic stroke demonstrated deficits in both explicit and implicit contributions to locomotor learning, a result that runs counter to prior work testing each process individually during gait. Since poststroke locomotor rehabilitation involves interventions that rely on both explicit and implicit motor learning, future work should determine how locomotor rehabilitation interventions can be structured to optimize overall motor learning. NEW & NOTEWORTHY Motor learning involves both implicit and explicit processes, the underlying neural substrates of which could be damaged after stroke. Although both learning processes are typically used in concert in daily life and rehabilitation, no gait studies have determined how these processes function together after stroke. Using a locomotor task that elicits dissociable contributions from both processes and computational modeling, we found evidence that chronic stroke causes deficits in both explicit and implicit locomotor learning.

Keywords: explicit aiming; hemiparesis; motor learning; sensorimotor adaptation; split-belt locomotion.

Graphical abstract

Figure 1.

Experimental design. A: individuals walked on a split-belt treadmill with a vertically mounted television screen in front of them. The visual feedback was a grid of 12 different step lengths, each 10 cm in height. The step length feedback was represented on the screen as blue (left) and red (right) feet that appeared on the screen as soon as heel strike was detected and disappeared once toe off was detected. B: all participants completed four walking phases: 1) a Baseline (Bsl) phase of normal walking where no feedback was on the screen; 2) a Practice phase where individuals were introduced to the visual feedback while walking (purple shading); 3) an Adaptation phase where the slow belt (dotted black line) moved at half the speed of the fast belt (solid black line), with feedback activated during only the first 40 strides (purple shading); 4) a De-adaptation phase where the belts returned to the same speed. The length of each phase (in minutes) is displayed in the gray shading at the bottom of the figure.

Figure 2.

Step length asymmetry. Mean baseline-corrected step length asymmetry for each group for the Adaptation and De-adaptation phases. Purple shading is the time when the feedback was on. The vertical dashed line separates the Adaptation and De-adaptation phases. Each phase was truncated to the participant with the shortest phase for visualization purposes. Shading represents standard error of the mean.

Figure 3.

Adaptation index. A: group averaged Adaptation Index data for the Adaptation and De-adaptation phases. The dashed line represents the walking period when the belts were split (i.e., the perturbation). Purple shading represents the time when the feedback was turned on. For visualization purposes, data for each phase were truncated to the individual with least number of strides. Solid lines represent group means, and shading represents standard error of the mean. B: group and individual data for the Feedback On and Feedback Off timepoints. Thick lines represent the group average slopes. C: group and individual data for the Feedback On timepoint. D: group and individual data for the Implicit Aftereffect timepoint. For C and D, bars represent group means, error bars represent 1 SD and smaller dots represent individuals. The insets display a histogram of the posterior distribution for the between-group differences. The black vertical line in the histogram is there to aid visualization of the credibility of a between-group difference (i.e., how much of the posterior probability distribution is on one side of zero). We report the 95% high-density interval (HDI) regarding the range of credible effect sizes above the insets of the posterior distributions.

Figure 4.

Computational model results. Mean model fits to bootstrapped samples plotted against the bootstrapped stride-by-stride data for the stroke group (A) and control group (B). See supplemental Figs. S1 and S2 for the model fits for each individual participant. Purple shading represents the time when the feedback was turned on. For visualization purposes, data for each phase were truncated to the individual with least number of strides. Shading represents 1 SD of the bootstrapped samples (i.e., the standard deviation of sample means or standard error of the mean). C–G: model parameter values for each group. Bars represent group means and error bars represent 1 SD and smaller dots represent individuals. The insets are histograms of the posterior of the between groups difference (contrast) in parameter values. We report the 95% high-density interval (HDI) regarding the range of credible effect sizes above the insets of the posterior distributions. Note the scale of the x-axis varies for these inset plots.