Locomotor adaptation

Abstract

Motor learning is an essential part of human behavior, but poorly understood in the context of walking control. Here, we discuss our recent work on locomotor adaptation, which is an error driven motor learning process used to alter spatiotemporal elements of walking. Locomotor adaptation can be induced using a split-belt treadmill that controls the speed of each leg independently. Practicing split-belt walking changes the coordination between the legs, resulting in storage of a new walking pattern. Here, we review findings from this experimental paradigm regarding the learning and generalization of locomotor adaptation. First, we discuss how split-belt walking adaptation develops slowly throughout childhood and adolescence. Second, we demonstrate that conscious effort to change the walking pattern during split-belt training can speed up adaptation but worsens retention. In contrast, distraction (i.e., performing a dual task) during training slows adaptation but improves retention. Finally, we show the walking pattern acquired on the split-belt treadmill generalizes to natural walking when vision is removed. This suggests that treadmill learning can be generalized to different contexts if visual cues specific to the treadmill are removed. These findings allow us to highlight the many future questions that will need to be answered in order to develop more rational methods of rehabilitation for walking deficits.

Fig. 1

(a) Diagram of marker locations and an example of the paradigm structure. Limb angle convention is shown on the stick figure (left panel). Panel on the right shows an example experimental paradigm indicating the periods of split and tied-belt walking. The walking pattern is first recorded during a baseline period in which both treadmill belts move at the same speed. Then, changes to the walking pattern are recorded during an adaptation period in which one belt moves two to four times faster than the other. Finally, stored changes to the walking pattern are assessed during a deadaptation period in which the treadmill belts move at the same speed as in the baseline period. (b) An example of kinematic data of two consecutive steps is shown. Kinematic data for every two steps were used to calculate step symmetry, defined as the difference in step lengths normalized by the step lengths sum. (c) Figure adapted from Malone and Bastian (2010). Limb angle trajectories plotted as a function of time in late split-belt adaptation—two cycles are shown. Gray trajectory represents the movement in the slow limb in early adaptation. Positive limb angles are when the limb is in front of the trunk (flexion). Two time points are marked—slow heel strike (HS) in black and fast HS in gray. The spread between the limb angles is directly proportional to the step lengths shown in the bottom. Step lengths can be equalized by changing the position of the foot at landing (i.e., the “spatial” placement of the foot). This spatial strategy is known as a shift in the center of oscillation difference since subjects change midpoint angle around which each leg oscillates, with respect to the other leg. (d) Step lengths can also be equalized by changing the timing of foot landing, as shown by the change in phasing of the slow limb from the gray trajectory (early adaptation) to the black trajectory. This purely temporal strategy is known as phase shift since subjects equalize step lengths by changing the timing of foot landings with respect to each other.

Fig. 2

Rates of adaptation (left column) and deadaptation (right column) in 3- to 5-year olds (red; n=10), 12- to 14-year olds (blue; n=10), and adults (black; n=10). Step symmetry data are shown in the top row, center of oscillation difference in the middle and phasing on the bottom. Shaded regions indicate standard error. Data were fit with linear, single-exponential, or double-exponential functions depending on which fit resulted in the highest r² values. For 3- to 5-year-old step symmetry and center of oscillation difference, linear fits were best; double-exponential fits were best for the phasing data. A single exponential fit was used for 12- to 14-year-old center of oscillation difference adaptation data and all remaining 12- to 14-year-old data were best fit by double-exponential functions. All adult data were fit by double-exponential functions.

Fig. 3

(a) Experimental paradigm showing the periods of split-belt walking and conditions. In baseline, tied walking all groups were given no specific instructions. Subjects were divided into three groups for adaptation (split belts). The conscious correction group (N=11) was instructed on how to step more symmetrically and given intermittent visual feedback of their stepping during adaptation. The distraction group (N=11) was given an auditory and visual dual-task they were asked to focus on. The control group (N=11) was given no specific instructions. In deadaptation (tied belts), all groups walked under “Control” conditions, where the visual feedback and distracter were removed. (b) Adaptation and deadaptation curves for step symmetry. Average adaptation curves for the three groups, with standard errors indicated by the shaded area. Baseline values are subtracted out from curves (i.e., symmetry is indicated by a value of 0). Average deadaptation curves for the three groups. Recall that all groups deadapted under the same condition (no feedback or distraction). Curves are shown individually to more clearly illustrate the plateau level. Bar graphs represent group averages for adaptation and deadaptation rate, assessed by the number of strides until plateau is reached (i.e., behavior is level and stable). Note that with step symmetry, the conscious correction group adapted faster, and the Distraction group adapted slower. However, retention was improved with the Distraction group because they took longer to deadapt, despite removal of the distracter. (c) Adaptation and deadaptation curves for the center of oscillation difference. Average adaptation curves for the three groups plotted as in (b). Trends seen in the center of oscillation difference are comparable to those seen in step symmetry. (d) Average adaptation and deadaptation curves for phasing, plotted as similar to (b). Note that our interventions did not significantly affect the rate of adaptation or deadaptation of phasing.

Fig. 4

(a) Overall paradigm. In all groups, baseline behavior was recorded overground (OG) and subsequently on the treadmill with the two belts moving at 0.7 m/s. Then subjects were adapted for a total of 15 min, during which one belt was moving at 0.5 m/s and the other belt at 1 m/s. After 10 min of adaptation, a 10-s catch trial was introduced, in which both belts moved at 0.7 m/s. Subjects were readapted (i.e., belts’ ratio at 2:1) for five more minutes before they were asked to walk OG, where we tested the transfer of treadmill adaptation to natural walking. Subjects were transported on a wheelchair to a 6-m walkway where they walked back-and-forward 15 times. All steps on the walkway were recorded except for those when subjects were turning to return to the initial position. Finally, subjects returned to the treadmill where they walked for 5–10 min at 0.7 m/s to determine form the remaining aftereffects the extent to which walking without the device washed out the learning specific to the treadmill. (b) Spatial symmetry (i.e., symmetry in step lengths of the two legs) of sample subjects of the vision and no-vision group when walking on the treadmill (TM) and OG during baseline, catch, and deadaptation periods. Behavior of two sample subjects is shown: one walking with vision (gray trace) and one walking without vision (black trace). Lines represent the running average using a three-step window±SD (shaded area). No differences in step symmetry were observed preadaptation when subjects walked with and without vision on the treadmill or OG. However, the subject that walked without vision had larger aftereffects on the treadmill during the catch trial (i.e., more learning), more transfer of treadmill learning to OG walking, and more washout of learning specific to the treadmill than subject that walked with vision. (c) Aftereffects on treadmill during catch trial for vision and no-vision groups. Subjects that trained without vision had significantly larger aftereffects—greater learning, than subjects that trained with vision. Bars’ height indicates the averaged aftereffects of the first three steps during the catch trial across subjects±SE. (d) Transfer of adaptation effects to OG walking. (e) Washout of treadmill spatial aftereffects following OG walking. Removing vision during training had a significant effect on the washout of step symmetry aftereffects specific to the treadmill. Step symmetry transfer and washout are expressed as a percentage of the aftereffects on the treadmill during catch. Bars’ height indicates the average across subjects±SE of % transfer and % washout for the first three steps OG or when returning to the treadmill. Figures in all panels were adapted from Torres-Oviedo and Bastian (2010). *p<0.01.