Somatosensory Cortex Plays an Essential Role in Forelimb Motor Adaptation in Mice

Abstract

Our motor outputs are constantly re-calibrated to adapt to systematic perturbations. This motor adaptation is thought to depend on the ability to form a memory of a systematic perturbation, often called an internal model. However, the mechanisms underlying the formation, storage, and expression of such models remain unknown. Here, we developed a mouse model to study forelimb adaptation to force field perturbations. We found that temporally precise photoinhibition of somatosensory cortex (S1) applied concurrently with the force field abolished the ability to update subsequent motor commands needed to reduce motor errors. This S1 photoinhibition did not impair basic motor patterns, post-perturbation completion of the action, or their performance in a reward-based learning task. Moreover, S1 photoinhibition after partial adaptation blocked further adaptation, but did not affect the expression of already-adapted motor commands. Thus, S1 is critically involved in updating the memory about the perturbation that is essential for forelimb motor adaptation.

Keywords: force field adaptation; motor adaptation; mouse; optogenetics; primary somatosensory cortex; sensorimotor learning; sensory prediction error.

Figure 1. Force Field-Based Motor Adaptation Task for Mice

(A) Diagram of the joystick platform with electromagnet, top-down view with physical dimensions indicating position 1 and 2, start and reward box, and the adaptation task structure: 75 baseline trials, 100 perturbation trials, and 75 “washout” (i.e., null field) trials. (B) Left: an example session of one mouse showing the average baseline trajectory (black), the first perturbation trial (sea green), average of first ten (green), and the last ten perturbation trials (purple), the first ten washout trials (orange) and the first washout trial (red). The numbers indicate trials. Right: average trajectories for all sessions (n = 27 sessions, from n = 7 mice) with temporally aligned averaging (see STAR Methods). Dashed sea green line is the average first perturbation, and the dashed red line is the average of the first five washout trials. Solid lines are average paw path ± SEM in the direction of the perpendicular deviation, with the same color scheme as the left panel; solid green is the first 25 trials during the perturbation epoch, solid purple is the last 25 trials of the perturbation epoch, and solid red line is the average of the first 25 washout trials. (C) Average perpendicular displacement in position 2 displayed as mean ± SEM versus trials; n = 27 sessions, from n = 7 mice. (D) Average perpendicular displacement within indicated trials. Adaptation was measured as a significant change from early to late perturbation trials, and as a significant after-effect during washout (compared to baseline, all washout bins are significant). RM ANOVA p < 0.0001. Post hoc Dunnett’s Multiple Comparison, *p < 0.01. Mean ± SEM. (E) As in (C) for position 1 (before force onset), showing predictive steering. Light gray shows change in perpendicular displacement inside the start box. (F) As in (D) for position 1; RM ANOVA p < 0.0001. Post hoc Dunnett’s Multiple Comparison, *p < 0.01. Mean ± SEM.

Figure 2. Model-Based Analysis Shows that the Adaptation Can Be Explained by Learning from Sensory Prediction Errors

(A) Left: average perpendicular displacement (black) and average reward rate (cyan) across the perturbation block (n = 27 sessions, from n = 7 mice). Right: reward rate during indicated trials, mean ± SEM. *p = 0.01 Wilcoxon signed rank test (first ten versus last ten perturbation block). (B) Best model fit using an actor-critic reinforcement-learning model, demonstrating a poor fit. Data from perpendicular displacement (at position 2) is shown in black (as in Figure 1C). Average best model fit is shown in red. As this model has several sources of noise (see STAR Methods), ten realizations of the model with best parameters are shown in soft lines and the solid line shows the average. Only the perturbation trials are used for fitting. (C) Schematic of the state estimator model: the efference copy of a motor command is used to generate predicted sensory feedback (internal model), which is contrasted with the actual feedback (SPE), then weighted by a Kalman gain, to then update the state estimate to drive future actions (see STAR Methods). (D) As in (B) but for the optimal state estimator model. (E) As in (B) but for hybrid model of an actor-critic reinforcement-learning model and a state estimator model. (F) The hybrid model’s motor command consists of three components derived from the optimal state estimator (Kalman filter and SPE), the optimal policy estimated by reinforcement-learning (RPE), and exploratory search noise. Left: the magnitude of the first two components corresponding to command signal (orange, RPE-based; purple, SPE-based). Right: quantification of the relative power ratio of contribution of those control signals from the best model fit.

Figure 3. Photoinhibition of Forelimb Somatosensory Cortex Abolishes Adaptation but Not Reward-Based Learning

(A) Task design: ChR2 was expressed in GABA neurons. The applied neuronal perturbation was achieved by photo-activating GABA neurons through an optical fiber. The adaptation task was performed in three vGAT-ChR2 mice. During perturbation trials, the laser was on concurrently with the force field (force field, 100 ms duration; laser at 50 Hz, 100 ms duration). (B) Temporally averaged trajectories across all control sessions (n = 12 sessions from 3 mice), coloring as in Figure 1B. (C) Temporally averaged trajectories across all photoinhibition sessions (n = 17 sessions, across the same 3 mice), coloring as in Figure 1B. (D) Averaged perpendicular displacement at position 2 across sessions, with (blue) and without (black) photoinhibition (smoothed mean [5 trials] ± SEM shown for visualization), **p = 0.001, (difference in the last ten perturbation trials, t test), ***p < 0.0001, (difference in the first ten washout trials, t test). (E) Quantification of all the control sessions, *p < 0.05, **p < 0.01, (t test). Mean ± SEM. (F) Quantification of all the photoinhibition sessions. Mean ± SEM. (G) Behavioral performance across all sessions in the target-shift task with (blue) or without (black) photoinhibition. All sessions averaged at position 2; there was no significant difference with or without photoinhibition, p = 0.57 (two-way ANOVA). Mean ± SEM. (H) Quantification of all the control sessions. *p < 0.05, **p < 0.01, ***p < 0.001 (t test). Mean ± SEM. (I) Quantification of all the photoinhibition sessions, **p < 0.001 (t test). Mean ± SEM.

Figure 4. Somatosensory Cortex Is Required for Updating Motor Commands during Adaptation but Not for the Expression of the Adapted Motor Command

(A) Task structure. During the perturbation block (trials 76–175), photoinhibition was applied only in the second half of trials (126–175). (B) Model prediction. The best-fit parameters from the control data (Figure 3D) were used. At trial 126, SPE was clamped to zero (cyan) or the perturbation estimate, ( $\widehat{p_k}$) was set to zero (dark blue). (C) Session average from position 2, showing adaptation up to trial 125 (Mann-Whitney U test, trials 76–85 versus trials 116–125, p = 0.01). (D) Top: average perpendicular displacement plotted per session of trials 120–125 versus 126–131 (Mann-Whitney U test, p = 0.24). Bottom: by session displacement value for trials early perturbation (P_e, trials 75–80), 124, 125, 126, 127, and late perturbation (P_l, trials 170–175) showing there is no systematic drop from trial 125 to trial 126 (paired t test p = 0.82). (E) Perpendicular displacement across blocks in position bin 1 (spatially above force onset). (F) Top: average perpendicular displacement of trials 120–125 versus 126–131. Mann-Whitney U test, p = 0.05. Bottom: same as in (D), showing there is no systematic drop from trial 125 to trial 126 (paired t test, p = 0.73).