Modulation of internal model formation during force field-induced motor learning by anodal transcranial direct current stimulation of primary motor cortex

Abstract

Human subjects can quickly adapt and maintain performance of arm reaching when experiencing novel physical environments such as robot-induced velocity-dependent force fields. Using anodal transcranial direct current stimulation (tDCS) this study showed that the primary motor cortex may play a role in motor adaptation of this sort. Subjects performed arm reaching movement trials in three phases: in a null force field (baseline), in a velocity-dependent force field (adaptation; 25 N s m(-1)) and once again in a null force field (de-adaptation). Active or sham tDCS was directed to the motor cortex representation of biceps brachii muscle during the adaptation phase of the motor learning protocol. During the adaptation phase, the global error in arm reaching (summed error from an ideal trajectory) was similar in both tDCS conditions. However, active tDCS induced a significantly greater global reaching (overshoot) error during the early stage of de-adaptation compared to the sham tDCS condition. The overshoot error may be representative of the development of a greater predictive movement to overcome the expected imposed force. An estimate of the predictive, initial movement trajectory (signed error in the first 150 ms of movement) was significantly augmented during the adaptation phase with active tDCS compared to sham tDCS. Furthermore, this increase was linearly related to the change of the overshoot summed error in the de-adaptation process. Together the results suggest that anodal tDCS augments the development of an internal model of the novel adapted movement and suggests that the primary motor cortex is involved in adaptation of reaching movements of healthy human subjects.

Figure 1. Study design

Each subject undertook two sessions of motor adaptation involving target reaching in velocity-dependent force fields separated by 1 week. The order of active or sham tDCS during motor adaptation was randomized for each subject. The protocol included 96 trials of reaching with no force field (Null 1–4), then 96 trials of reaching with force fields (Force 1–4) followed by 48 trials of reaching with no force field (Null 5–6).

Figure 3

A, effect of anodal tDCS on summed trajectory error during motor adaptation. Main panel: trial-by-trial error during pre-adaptation (trials 1–96; Blocks N1–N4), motor adaptation (trials 97–192; Blocks F1–F4) and after adaptation (trials 193–240; Blocks N5–N6) periods. Black bar represents period of tDCS application. Black line is continual active tDCS condition (1 mA; 17 min) and grey line is sham tDCS condition (1 mA; 30 s). Dotted lines are s.e.m. values of average data from pre-adaptation null field trials in the sham tDCS condition (trials 73–96; Block N4). Trial values are means only (s.e.m. not included for clarity; n= 14 for each tDCS condition). Inset: mean ±s.e.m. trial-by-trial data (n= 14) during early de-adaptation period (trials 193–216; Block N5) after combined tDCS and motor adaptation. Half-lives (t_1/2; trials) were calculated using an exponential decay curve fit. B, differences in summed trajectory error before, during and after motor adaptation between active vs. sham tDCS. Summed error (values are mean ±s.e.m.; n= 14) was averaged from 24 trials in each block. Blocks N1–N4, pre-adaptation null force field. Blocks F1–F4, during motor adaptation to the velocity-dependent force field. Blocks N5–N6, null force fields after motor adaptation. Black columns are active tDCS and grey columns are sham tDCS conditions. NS, no significant difference between active and sham tDCS in that block. *Significant difference between active and sham tDCS conditions in that block (paired t test; P < 0.05). Main statistical analysis is restricted to Blocks N4, F1, F4 and N5 (see text for full results).

Figure 5. Comparisons of trajectory and movement errors after motor adaptation with anodal tDCS

Effects were analysed after motor adaptation (Block N5) compared to baseline (Block N4). There was a significantly greater summed error after motor adaptation with active tDCS compared to sham tDCS (Block N5, all comparisons). *Significant difference at P < 0.05 level. There were no significant differences in comparisons between active vs. sham tDCS after motor adaptation for signed error, movement time or reaction time.

Figure 4. Comparisons of trajectory and movement errors during motor adaptation with anodal tDCS

Effects were analysed during motor adaptation (Blocks F1 and F4) compared to baseline (Block N4). There were no significant differences in comparisons between active vs. sham tDCS during motor adaptation for summed error, movement time or reaction time. There was a significantly greater development of signed error during motor adaptation with active tDCS compared to sham tDCS (Block F4–Block F1). *Significant difference at P < 0.05 level.

Figure 2. Kinematics of arm reaching during motor adaptation to a velocity-dependent force field (clockwise force) with active tDCS

A, hand path trajectory from the start position (0, 0 m) towards target (0, −0.15 m) position in a single subject. Reaching in pre-adaptation null field trials (thick grey line) is close to ideal. Reaching in a novel force field (first trial, thick red line) shows a perturbed trajectory, which is gradually reduced with reaching repetition (thin red lines). After practice, the hand trajectory (trials in late adaptation; thin green lines) approaches that of pre-adaptation null trials (last trial; thick green vs. thick grey lines). The first trial after adaptation (thick blue line) had a trajectory which was in the opposite direction and greater: i.e. the typical after-effect is augmented by active tDCS. The trajectories of the subsequent 8 trials approached control paths (thin blue lines and thick blue vs. thick grey lines). B, movement velocity curves of individual trials shown in A. Last pre-adaptation null trial (trial 96; thick grey line) illustrates a bell-shaped profile which becomes less smooth and has more peaks when the subject reaches in a velocity-dependent force field (trials 97–102; red lines). After practice in the force field, the velocity curves assume a bell-shaped profile once more (trials 187–192; green lines). Withdrawal of the force field causes a loss of smoothness in the velocity profile which is rapidly recovered (trials 193–201; blue lines).

Figure 6. Correlation between development of signed error during motor adaptation with anodal tDCS and summed trajectory error after motor adaptation

A, correlation between signed and summed errors from both tDCS conditions using all data in regression analysis (n= 28 total; filled circles, active tDCS; open circles, sham tDCS). Thick dark line is mean with ± 95% confidence intervals for regression (grey lines). Values are mean ±s.e.m. for regression equation components. B, illustration of main effect of anodal tDCS on error relationships during and after motor adaptation. Mean values (±s.e.m.; n= 14) for signed error development during motor adaptation (X-axis) and global summed error after adaptation (Y-axis) for the two tDCS conditions. Open circle is sham tDCS and filled circle is active tDCS. C, correlation between signed and summed errors from active tDCS condition only (n= 14). Thick dark line is mean with ± 95% confidence intervals for regression (grey lines). D, correlation between signed and summed errors from sham tDCS condition only (n= 14). Thick dark line is mean with ± 95% confidence intervals for regression (grey lines). Values are mean ±s.e.m. for regression equation components in C and D.