Cortical pattern generation during dexterous movement is input-driven

Abstract

The motor cortex controls skilled arm movement by sending temporal patterns of activity to lower motor centres¹. Local cortical dynamics are thought to shape these patterns throughout movement execution^2-4. External inputs have been implicated in setting the initial state of the motor cortex^5,6, but they may also have a pattern-generating role. Here we dissect the contribution of local dynamics and inputs to cortical pattern generation during a prehension task in mice. Perturbing cortex to an aberrant state prevented movement initiation, but after the perturbation was released, cortex either bypassed the normal initial state and immediately generated the pattern that controls reaching or failed to generate this pattern. The difference in these two outcomes was probably a result of external inputs. We directly investigated the role of inputs by inactivating the thalamus; this perturbed cortical activity and disrupted limb kinematics at any stage of the movement. Activation of thalamocortical axon terminals at different frequencies disrupted cortical activity and arm movement in a graded manner. Simultaneous recordings revealed that both thalamic activity and the current state of cortex predicted changes in cortical activity. Thus, the pattern generator for dexterous arm movement is distributed across multiple, strongly interacting brain regions.

Extended data figure 1:

Summary of effects of optogenetic perturbations of motor cortex. Each of the three columns shows data from one mouse type: VGAT-ChR2-EYFP (n = 5 animals, n = 7 sessions, left column), Tlx3-Cre X Ai32 (n = 3 animals, n = 7 sessions, center column), and Sim1-Cre X Ai32 (n = 3 animals, n = 5 sessions, right column). a, Average z-scored firing rates of motor cortical neurons before, during and after optogenetic activation of inhibitory interneurons (left; VGAT-ChR2-EYFP mice), intratelencephalic neurons (center; Tlx3-Cre X Ai32 mice), and pyramidal tract neurons (right; Sim1-Cre X Ai32 mice). The blue bars under the x-axes represent laser-on epochs. In the left panel, the black bands at the bottom are putative inhibitory interneurons. b, Firing rates before and during laser stimulation for each mouse type. Firing rates less outside the range of 0.1 to 100 were plotted at these values, due to the log-log scale. c, Distribution of lift times on control (yellow), laser + cue (blue), and laser-only (magenta) trials for each mouse type. The histograms show data only for trials where a lift occurred. d, Probability of a lift in each time bin (binomial maximum likelihood estimate) for control (yellow), laser-only (magenta), and laser + cue (blue) trials. Error bars show 95% binomial confidence intervals. e, Distribution of lift times for trials in which a lift occurred within 500 ms of either the cue (for control trials, yellow), or following the end of the laser (for laser + cue trials, blue, and laser only trials, magenta). f, Average hand trajectories on control (yellow) and post-laser (blue) reaches. g, Neural population activity from lift −100 ms to lift +425 ms on control (yellow) and post-laser (blue) reaches, obtained using trial-averaged principal component analysis. For f and g, one session was removed for VGAT (n = 4 animals, n = 6 sessions), and one was removed for Sim1 (n = 2 animals, n = 4 sessions), due to the absence of post-laser reaches for alignment.

Extended data figure 2:

Comparison of the direction of neural trajectories for post-laser reaches with the direction of control trajectories, and with the direction to the initial cortical state on control trials. a, Explanation of the analysis method. We represent the population trajectory on control trials, r_c(t), and laser trials, r_l(t), using the first six principal component scores, which account for 98%, 99%, and 97% of the variance on control trials for VGAT, Tlx3, and Sim1, respectively. For each time point along the peri-lift neural trajectory r_l(t) for post-laser reaches, we obtain the direction of the neural trajectory by computing the derivative and dividing by the norm of the derivative (blue). We perform the same calculation for the control trajectory r_c(t) (yellow), and also compute the direction from the neural state in the laser trajectory to the initial control state (red). We then compare the direction of the laser trajectory with the control direction and the direction to the initial control state by taking the inner product with each. b, Left: neural population trajectories (first two principal components) for control (yellow) and post-laser (blue) reaches in VGAT-ChR2-EYFP mice (n = 4 mice, n = 6 sessions). The direction of the trajectories for control (yellow arrows) and laser (blue arrows) trajectories along the first two principal components are shown, along with the direction from the laser trajectory to the control initial state (red arrows). Right: similarity (inner product) between the direction of the laser trajectory and the direction of the control trajectory (yellow curve), and similarity between the direction of the laser trajectory and the control initial state (red curve). c, As in b, but for Tlx3-Cre X Ai32 mice (n = 3 mice, n = 7 sessions). d, As in b, but for Sim1-Cre X Ai32 mice (n = 2 mice, n = 4 sessions).

Extended data figure 3:

Decoding of hand velocity from motor cortical activity on control and post-perturbation reaches. a, Left: scatterplots of decoded vs observed hand velocity in the forward, right, and upward directions on control reaches in an example session from a VGAT-ChR2-EYFP mouse. Only testing trials not used for training the decoder were used. Right: R² values for the regression of observed on decoded velocities for control reaches in each VGAT-ChR2-EYFP dataset (n = 4 mice; n = 6 sessions). b, Left: scatterplots of decoded vs observed hand velocity for post-laser reaches in the dataset from a. Right: R² values for the regression of observed on decoded velocities for post-laser reaches in each VGAT-ChR2-EYFP dataset. c, Comparison of the decoder performance in control vs post-laser reaches for the dataset from a-b, assessed using the R² computed after pooling across directions. d, Decoded position trajectories obtained by upsampling and numerically integrating (Simpson’s rule) the decoded velocity for control trials (left) and laser trials (right) for the dataset in a-b. e-h, Decoder performance for Tlx3-Cre X Ai32 mice (n=3 mice; n=7 sessions), organized as a-d. i-l, Decoder performance for Sim1-Cre X Ai32 mice (n=2 mice; n=3 sessions), organized as a-d. m, Decoding performance for control testing trials on all sessions, by decoding method used (n=9 mice; n=16 sessions; all perturbation types aggregated). For each method, the number of neural dimensions used for decoding was cross-validated (see Methods). Boxplot shows the median and the 25th and 75th percentiles. n, Decoding performance for PCA, with coefficients extracted on lift-aligned trial averages, by the standard deviation of the Gaussian kernel used to extract firing rates (n=9 mice; n=16 sessions).

Extended data figure 4:

Variability of firing rates during optogenetic perturbations to the cortical state. a, Standard deviation of firing rates across trials during laser stimulation in VGAT-ChR2-EYFP mice. The black curve is the standard deviation (over trials), averaged over all neurons (n = 5 animals, n = 7 sessions, n = 155 neurons). Error bars show standard error of the mean. Identified inhibitory neurons, which exhibited a firing rate increase during the laser, were excluded. Smoothing was applied with a 50 ms Gaussian kernel for each trial. b, Standard deviation of firing rates across trials during laser stimulation in Tlx3-Cre X Ai32 mice, as in a (n = 3 animals, n = 7 sessions, n = 100 neurons). c, Standard deviation of firing rates across trials during laser stimulation in Sim1-Cre X Ai32 mice (n = 3 animals, n = 5 sessions, n = 115 neurons). Because it wasn’t possible to identify inhibitory neurons when excitatory neurons were stimulated, all cells were included in b and c.

Extended data figure 5:

Effect of different spike train smoothing methods. a, Gaussian smoothing with a kernel width of σ = 25 ms for the reach / no reach experiment, as shown in Fig. 2b. Note that the activity appears to change from the constant perturbed state slightly before the end of the laser. This is because the kernel smooths forward into the post-laser epoch. b, Gaussian smoothing with σ = 50 ms. The divergence from the perturbed state begins earlier, due to a higher level of smoothing. c, Causal smoothing with a half-Gaussian kernel, truncated to use samples only from the past. Neural activity diverges from the perturbed state only after the end of the laser. d, Acausal smoothing with a half-Gaussian kernel, truncated to use samples only from the future. e, Gaussian smoothing in the sequential inactivation experiment with a kernel width of σ = 25 ms, as shown in Fig. 3f. Note that the activity appears to change from the constant perturbed state slightly before the end of the cortical inactivation. There is also a delay from the the start of the cortical inactivation to the arrival of the neural state at the constant value. f, Gaussian smoothing with σ = 50 ms. g, Causal smoothing with a half-Gaussian kernel, truncated to use samples only from the past. Neural activity diverges from the perturbed state only after the end of the laser. However, there is still a lag from the start of cortical inactivation to the arrival of neural activity at the constant perturbed state. h, Acausal smoothing with a half-Gaussian kernel, truncated to use samples only from the future. Neural activity again diverges from the perturbed state before the end of the cortical inactivation. At the start of the cortical inactivation, neural activity has already arrived at the perturbed state.

Extended data figure 6:

Effect of mid-reach thalamic perturbation on hand trajectory in VGAT-ChR2-EYFP mice (c.f. Fig. 3c; n = 4 animals, n = 6 sessions). a, Average difference in hand elevation between mid-reach perturbation trials and control trials for each dataset. The example dataset shown in Fig. 3c is marked with the blue arrow. b, P-values from two-sided rank sum tests at each time point comparing the upward hand position on control and mid-reach thalamic inactivation trials.

Extended data figure 7:

Sequential inactivation of cortex and thalamus (c.f. Fig. 3d–f). a, Fraction of trials with lifts in each epoch for control trials (yellow), cortical inactivation only (blue), and sequential inactivation of cortex and thalamus (green) (n = 3 animals, n = 4 sessions). The cortical inactivation ends at 2000 ms from trial start, and the thalamic inactivation ends at 4000 ms. Bars show maximum likelihood estimates of the binomial probability, with 95% confidence intervals. b, Lift-locked neural population activity from lift −100ms to lift +350 ms for control (yellow), post-cortex-inactivation (blue), and post-sequential-inactivation reaches (green), obtained using trial-averaged principal component analysis; n = 3 animals, n = 4 sessions, n = 127 neurons. Circles indicate lift −100 ms, lift, and grab times. c, Firing rates and spike rasters for an example cortical neuron on control trials (yellow), cortical inactivation (blue), and sequential inactivation of cortex and thalamus (green).

Extended data figure 8:

Effects of stimulation of thalamocortical terminals on cortical firing rates and behavior (c.f. Fig. 4). a, Firing rates and spike rasters for two example neurons at each stimulation frequency. b, Firing rates in the 2 s before stimulation vs the 2 s during stimulation at each stimulation frequency. Each point is a single neuron (n = 288 cells). c, Left: single-trial hand position and neural activity (first two principal components) for control trials in the dataset shown in Fig. 4b. Right: Hand position and neural activity in the same session under optogenetic stimulation of thalamocortical terminals at 4 Hz, 10 Hz, and 40 Hz. d, Probability that a lift is initiated within the first 500 ms of the cue on control trials and at each stimulation frequency. Each curve shows a single session (n = 6 sessions, n = 3 mice).

Extended data figure 9:

Hand kinematics and neural activity during thalamocortical stimulation for each dataset (c.f. Fig. 4; n = 3 mice, n = 6 sessions). a, Trial-averaged hand position aligned to the cue under 4 Hz, 10 Hz, and 40 Hz stimulation. The control position is shown in gray. Vertical lines indicate the times of laser pulses. Each row corresponds to a single experimental session. b, Hand trajectories for control and laser trials for each dataset in a. Time limits are cue −250 ms to cue +1000 ms, and the dot marks the end of the trajectory. c, Neural trajectories for each dataset in a.

Extended data figure 10:

Simultaneous recording in thalamus and motor cortex. a, Raw data from the thalamic Neuropixels probe aligned to motor cortex stimulation (cyan). A band of channels (red dotted line) exhibited activity locked to motor cortical stimulation, indicating projections to motor cortex. b, Histological section showing targeting of probe to motor thalamus. The bright region in the thalamus indicates ChR2 expression (EYFP) in an Ai32 mouse with an injection of AAV-2/9-Syn-Cre. The Neuropixels probe in the thalamus was coated with a green dye (DiO). The red dotted line corresponds approximately to the red dotted line in a. c, Spike rasters from cortical and thalamic neurons on a single reaching trial. d, Peri-lift firing rates for thalamic neurons (n = 3 mice, n = 3 sessions).

Figure 1:

Motor cortex as a dynamical system controlling the arm. a, The dynamical systems model for motor cortical control of reaching (see Methods). b, Left: generation of firing rate patterns if motor cortex were driven by strong recurrent dynamics, h(r(t)), with external inputs, u(t), exerting a limited influence and not necessary for pattern generation. Right: generation of firing rate patterns if motor cortex were dependent on strong temporally-patterned external inputs, u(t). c, Experimental setup. Head-fixed mice reached for a pellet of food following an acoustic cue during recording and optogenetic perturbation of cortical activity. d, Raw video, electrophysiological recording, and mouse behavior on a single trial. Three-dimensional hand trajectories and the timing of each waypoint in the behavioral sequence were extracted from video using computer vision methods. e, Spike raster plots and peri-event time histograms for four example neurons recorded in d, centered on lift. Numbers indicate the maximum value of the y-axis, in spikes per second. f, Average z-scored firing rates and mean firing rates for all motor cortical neurons (n = 19 mice, n = 39 sessions, n = 843 neurons). During prehension, most neurons exhibited increases (39%) or decreases (37%) in spike counts around lift (two-sided rank sum test with Benjamini-Hochberg correction, q < .05). g, Distribution of lift times on control (yellow), laser + cue (blue), and laser-only (magenta) trials for VGAT-ChR2-EYFP mice (n = 5 animals, n = 7 sessions). h, Neural population activity from lift −100 ms to lift +425 ms on control (yellow) and post-laser (blue) reaches in VGAT-ChR2-EYFP mice, obtained using trial-averaged principal component analysis (n = 4 animals, n = 6 sessions, n = 144 neurons).

Figure 2:

Divergence of neural trajectories from the same initial state. a, Average hand trajectory for trials with (blue) and without (red) post-laser reaches in VGAT-ChR2-EYFP mice (n = 4 animals, n = 6 sessions). b, Neural population activity aligned to the end of the laser for trials with (blue) and without (red) a post-laser reach (n = 128 neurons). Time limits are 250 ms before the end of the laser to 250 ms after the end of the laser. Black dot indicates baseline activity before the start of the trial. c, Spatial (left) and neural (right) distance to target, centered on the end of the laser, for trials with (blue) and without (red) post-laser reaches. d, Schematic illustrating that a divergence in neural trajectories from the same initial state implies a difference in external inputs. e, Estimated difference in external inputs between reach and no reach conditions (see Methods). The divergence of the traces shortly before the end of the laser is due to smoothing (Extended Data Fig. 5a–d).

Figure 3:

External inputs are required for the motor cortical pattern during reaching. a, Experimental schematic: placement of fibers over motor cortex and thalamus. b, Distribution of lift times on control (yellow) and thalamic inactivation (green) trials; n = 3 animals (VGAT-ChR2-EYFP), n = 7 sessions. Right inset shows the probability of a lift within the first 500 ms following the cue for control and thalamus inactivation trials. c, Hand position in the upper direction centered on lift on control trials (light yellow) and mid-reach thalamic inactivation trials (black; green indicates laser on) for a single dataset. Dots indicate the start of the laser. Data from all animals (n = 4 mice, n = 6 sessions) shown in Extended Data Fig. 6. d, Lift times for control trials (yellow), cortical inactivation (blue), and sequential inactivation of cortex and thalamus (green); n = 3 mice (VGAT-ChR2-EYFP), n = 4 sessions. e, Average firing rate Z-scores for all recorded neurons under inactivation of cortex alone (left) and sequential inactivation of cortex and thalamus (right); n = 3 mice, n = 4 sessions, n = 127 neurons. f, Population activity following the end of cortical inactivation for trials with cortical inactivation only (blue) and inactivation of thalamus after cortex (green). Plotting limits start 500 ms before the end of cortical inactivation and finish 500 ms after the cortical inactivation (blue trace) and 500 ms after the thalamic inactivation (green trace). The divergence of the trajectories shortly before the end of cortical inactivation is due to smoothing (Extended Data Fig. 5e–h), and inhibitory interneurons were excluded. g, Schematic illustrating that the divergence from the cortex-inactivated state reveals differences in input. h, Estimated difference in external inputs following the end of cortical inactivation between thalamus inactivated and not inactivated conditions.

Figure 4:

Modification of the temporal pattern of inputs perturbs cortical activity and movement. a, Experimental design. Mice expressing ChR2 in thalamic neurons performed the task during recording of cortical activity and optogenetic stimulation of thalamocortical terminals. b, Left: example average hand trajectory on control trials and stimulation trials with stimulation frequencies of 4, 10, and 40 Hz from a single experimental session. Data from all animals (n = 3 mice, n = 6 sessions) shown in Extended Data Fig. 9. Time limits are cue −250 ms to cue +1000 ms. Right: average neural activity (first two principal components) in the same session. Dots indicate the end of the trajectory at cue +1000 ms. c, Left: average Euclidean distance from the hand position in each stimulation condition to the control hand position at the same time point. Each curve shows a single experimental session (n = 3 animals; n = 6 sessions). Center: Euclidean distance from the neural state in the stimulated conditions to the neural state in the control condition. Right: distance from hand trajectory to control vs distance from the neural trajectory to control.

Figure 5:

Relationship between population activity in motor thalamus and motor cortex. a, Experimental setup. Spiking activity was simultaneously recorded in motor cortex with a four-shank, 64-channel probe and in thalamus with a 384-channel Neuropixels probe (n = 3 animals, n = 3 sessions). The thalamic region projecting to motor cortex was identified by optogenetic stimulation of thalamocortical terminals. b, Population trajectories for thalamus (left, green) and cortex (right, magenta) obtained with trial-averaged PCA. c, Single-trial population activity in thalamus (top) and cortex (middle), along with hand position (bottom). d, Goodness-of-fit of regression models (coefficient of determination, R²). The dependent variable was the derivative of the cortical population state for the first three principal components. The independent variable was either the cortical state (magenta), the thalamic state (green), or both (gray) for the first 1 … N principal components, where N was varied between 1 and 10. The top row shows the goodness-of-fit for trial-averaged data, and the bottom for single-trial data.