Somatic and Reinforcement-Based Plasticity in the Initial Stages of Human Motor Learning

Abstract

As one learns to dance or play tennis, the desired somatosensory state is typically unknown. Trial and error is important as motor behavior is shaped by successful and unsuccessful movements. As an experimental model, we designed a task in which human participants make reaching movements to a hidden target and receive positive reinforcement when successful. We identified somatic and reinforcement-based sources of plasticity on the basis of changes in functional connectivity using resting-state fMRI before and after learning. The neuroimaging data revealed reinforcement-related changes in both motor and somatosensory brain areas in which a strengthening of connectivity was related to the amount of positive reinforcement during learning. Areas of prefrontal cortex were similarly altered in relation to reinforcement, with connectivity between sensorimotor areas of putamen and the reward-related ventromedial prefrontal cortex strengthened in relation to the amount of successful feedback received. In other analyses, we assessed connectivity related to changes in movement direction between trials, a type of variability that presumably reflects exploratory strategies during learning. We found that connectivity in a network linking motor and somatosensory cortices increased with trial-to-trial changes in direction. Connectivity varied as well with the change in movement direction following incorrect movements. Here the changes were observed in a somatic memory and decision making network involving ventrolateral prefrontal cortex and second somatosensory cortex. Our results point to the idea that the initial stages of motor learning are not wholly motor but rather involve plasticity in somatic and prefrontal networks related both to reward and exploration.

Significance statement: In the initial stages of motor learning, the placement of the limbs is learned primarily through trial and error. In an experimental analog, participants make reaching movements to a hidden target and receive positive feedback when successful. We identified sources of plasticity based on changes in functional connectivity using resting-state fMRI. The main finding is that there is a strengthening of connectivity between reward-related prefrontal areas and sensorimotor areas in the basal ganglia and frontal cortex. There is also a strengthening of connectivity related to movement exploration in sensorimotor circuits involved in somatic memory and decision making. The results indicate that initial stages of motor learning depend on plasticity in somatic and prefrontal networks related to reward and exploration.

Keywords: reinforcement; resting-state fMRI; sensorimotor learning; somatosensory.

Copyright © 2016 the authors 0270-6474/16/3611682-11$15.00/0.

Figures

Figure 1.

A, Schematic diagram showing what the subject sees during the experiment. The robotic manipulandum and the subject's own arm are situated underneath the display mirror. Participants are supposed to reach out 45° to the left toward the target stripe. Movement trajectories are not visible at any time during the experiment. B, Three different target zone widths (W) are used during the training blocks. If the movement ends inside the target zone, positive feedback is given. C, Schematic illustrating the lateral PD at the movement endpoint. Motor improvement of each subject was quantified as the reduction in the average magnitude of PD between the PRE and POST training blocks. D, Block diagram showing the overall experimental sequence.

Figure 2.

A, Behavioral performance (n = 22 participants) during baseline movements before training (PRE), training trials with feedback, and motor assessment following learning (POST). Zone I to Zone III refers to different sizes of the target zone. Positive feedback was provided following a successful trial when the reaching movement ended in the corresponding zone. y-axis indicates the average |PD| in millimeters. Inset, Percentage of successful trials over the course of training. Shaded colors represent SE. B, Linear dependency between the overall number of successful trials and the improvement in accuracy following training. C, The overall distribution of Δm, the absolute difference in PD between trial n and n + 1 when the current trial n is unsuccessful (S = 0, red) and successful (S = 1, orange). Bar plot represents the average Δm across subjects. D, The average change in direction as a function of number of consecutive successful (orange) and unsuccessful movements (red), fitted with a weighted least-squares regression.

Figure 3.

Left column represents seed regions (ROIs) within the sensorimotor cortices and putamen. Middle column represents cluster maps indicating statistically significant change in connectivity strength (ΔFC) with each ROI, which are reliably correlated with the number of successful trials as the behavioral predictor. Graphs on the right column are scatterplots illustrating the linear relationship between ΔFC and the behavioral predictor. Red to yellow color bar represents an increase in connectivity. Light to dark blue color bar represents a decrease in connectivity. Thresholded at Z = 2.40, corrected p < 0.05. r, Pearson's correlation coefficient.

Figure 4.

Changes in connectivity related to changes in movement direction (Δm) regardless of whether the previous movement was successful or not (top) and to changes in movement direction following unsuccessful trial (bottom). As in Figure 3: Left, Seed regions. Middle, Clusters whose connectivity with the seed regions varies with change in direction. Right, Relation between change in connectivity and movement direction change.