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Cortical pattern generation during dexterous movement is input-driven


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 execution2,3,4. External inputs have been implicated in setting the initial state of the motor cortex5,6, 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.

Data availability

The data generated in this study are available from the corresponding author on reasonable request.

Code availability

Code for automatic annotation of behaviour and behavioural waypoint estimation is available at Code for hand tracking is available at Code for spike sorting is available at,, and Other code is available from the corresponding author on reasonable request.


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We thank B. Yu and the Yu laboratory, S. Edgley, J. Dudman, A. K. Lee, M. Ahrens, A. Finkelstein, J. Fitzgerald and K. Shan for discussions and comments on an earlier version of the manuscript; A. Lee for tracking software; T. Harris, B. Barbarits, B. Karsh, S. Sawtelle, P. Polidoro, D. Flickinger and the Neuropixels Project for instrumentation development and support; W. Sun for probe sharpening and PEDOT application; A. Taylor for development of WaveSurfer; J. Jun and M. Pachitariu for spike-sorting software; S. Chung for assistance with video labelling; S. DiLiso for fibre implantation surgeries; K. Ritola and the Janelia Virus Tools facility for providing viruses; J. Kuhl for the mouse drawings; and the Janelia Vivarium, Histology and Scientific Computing facilities for support.

Author information

Author notes

  1. These authors contributed equally: Britton A. Sauerbrei, Jian-Zhong Guo, Jeremy D. Cohen


  1. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
    • Britton A. Sauerbrei
    • , Jian-Zhong Guo
    • , Jeremy D. Cohen
    • , Matteo Mischiati
    • , Wendy Guo
    • , Mayank Kabra
    • , Brett Mensh
    • , Kristin Branson
    •  & Adam W. Hantman
  2. Department of Computer Science, Columbia University, New York, NY, USA
    • Nakul Verma


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B.A.S., J.-Z.G., J.D.C. and A.W.H. designed the experiments. B.A.S. and J.-Z.G. performed electrophysiological recordings in motor cortex. J.-Z.G. performed recordings in cortex with thalamic inactivation. J.D.C. performed simultaneous recordings in cortex and thalamus and recordings in cortex during stimulation of thalamic terminals. J.-Z.G. performed behavioural experiments. B.A.S. analysed electrophysiology and behaviour data and generated the figures. J.-Z.G. and W.G. analysed behaviour data. M.M. developed and performed the neural decoding analyses. B.A.S., M.M. and A.W.H. interpreted the results. N.V. and K.B. developed preliminary analyses for decoding of behavioural waypoints. M.K. and K.B. developed computer vision algorithms and software. B.A.S., A.W.H., M.M., B.M. and K.B. wrote the paper with input from all authors. A.W.H. supervised the project.

Corresponding author

Correspondence to
Adam W. Hantman.

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The authors declare no competing interests.

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Peer review information Nature thanks Jesse Goldberg and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Summary of effects of optogenetic perturbations of motor cortex.

Each of the three columns shows data from one mouse type: left, VGAT-ChR2-eYFP (n = 5 mice, n = 7 sessions); middle, Tlx3-Cre x Ai32 (n = 3 mice, n = 7 sessions); right, Sim1-Cre x Ai32 (n = 3 mice, n = 5 sessions). a, Average z-scored firing rates of motor cortical neurons before, during and after optogenetic activation of inhibitory interneurons (left), intratelencephalic neurons (middle) and pyramidal tract neurons (right). The blue bars under the x axes represent laser-on epochs. Left, black bands at the bottom are putative inhibitory interneurons. b, Firing rates before and during laser stimulation for each mouse type. Firing rates outside the range 0.1–100 were plotted at these values owing 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. 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 (blue for laser + cue trials and magenta for laser only trials). 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 PCA. For f and g, one session was removed for VGAT (n = 4 mice, n = 6 sessions), and one was removed for Sim1 (n = 2 mice, n = 4 sessions), owing to the absence of post-laser reaches for alignment.

Extended Data Fig. 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, rc(t), and laser trials, rl(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 rl(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 rc(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 Fig. 3 Decoding of hand velocity from motor cortical activity on control and post-perturbation reaches.

a, Left, scatterplots of decoded versus 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, scatter plots of decoded versus 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 versus 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. eh, Decoder performance for Tlx3-Cre x Ai32 mice (n = 3 mice, n = 7 sessions), organized as in ad. il, Decoder performance for Sim1-Cre x Ai32 mice (n = 2 mice, n = 3 sessions), organized as in ad. m, Decoding performance for control testing trials on all sessions, by decoding method used (n = 9 mice, n = 16 sessions; all perturbation types aggregated). PCAavg refers to PCA coefficients extracted on lift-aligned trial averages with single trials projected onto these coefficients; PCAcat refers to PCA coefficients extracted on concatenated trial data; MU refers to multiunit activity; and SU refers to single units. For each method, the number of neural dimensions used for decoding was cross-validated (see Methods). Box plot shows the median and the 25th and 75th percentiles. n, Decoding performance for the PCAavg MU method, by the standard deviation of the Gaussian kernel used to extract firing rates (n = 9 mice, n = 16 sessions).

Extended Data Fig. 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 mice, n = 7 sessions, n = 155 neurons). Error bars show s.e.m. 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 mice, 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 mice, 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 Fig. 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 owing 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 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 Fig. 6 Effect of mid-reach thalamic perturbation on hand trajectory in VGAT-ChR2-eYFP mice.

a, Average difference in hand elevation between mid-reach perturbation trials and control trials for each dataset (n = 4 mice, n = 6 sessions). 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 Fig. 7 Sequential inactivation of cortex and thalamus.

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 mice, n = 4 sessions). The cortical inactivation ends at 2,000 ms from the start of the trial, and the thalamic inactivation ends at 4,000 ms. Bars show maximum likelihood estimates of the binomial probability, with 95% confidence intervals. Corresponding data in Fig. 3d–f. b, Lift-locked neural population activity from lift −100 ms to lift +350 ms for control (yellow), post-cortex-inactivation (blue) and post-sequential-inactivation reaches (green), obtained using trial-averaged PCA; n = 3 mice, 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 Fig. 8 Effects of stimulation of thalamocortical terminals on cortical firing rates and behaviour.

a, Firing rates and spike rasters for two example neurons at each stimulation frequency. b, Firing rates in the 2 s before stimulation versus 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). Corresponding data in Fig. 4.

Extended Data Fig. 9 Hand kinematics and neural activity during thalamocortical stimulation for each dataset.

a, Trial-averaged hand position aligned to the cue under 4 Hz, 10 Hz and 40 Hz stimulation. The control position is shown in grey. 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 +1,000 ms, and the dot marks the end of the trajectory. c, Neural trajectories for each dataset in a. Corresponding data in Fig. 4; n = 3 mice, n = 6 sessions.

Extended Data Fig. 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).

Supplementary information

Video 1

Head-fixed prehension behaviour and hand-tracking. The video shows raw images from two cameras capturing the movement sequence, along with the triangulated three-dimensional location of the hand.

Video 2

Single-trial motor cortex population activity and hand position during control and post-laser reaching in a VGAT-ChR2-EYFP mouse, centered on cue. Neural state, computed using GPFA, is shown in the left panel, and hand position is shown in the right panel. Each point corresponds to a single trial, with yellow indicating control and blue indicating post-laser reaches. Lift and grab times are green and magenta, respectively. Shadows show two-dimensional projections of the same data.

Video 3

Single-trial motor cortex population activity and hand position during control and post-laser reaching, centered on grab. As in video 2, but trajectories are aligned to the grab time.

Video 4

Motor cortex population activity following the end of cortical inactivation for trials with cortical inactivation only (blue) and inactivation of thalamus after cortex (green). Time limits for the blue trace are 500 ms before the end of cortical inactivation to 500 ms after the end of cortical inactivation. Time limits for the green trace are 500 ms before the end of cortical inactivation to 500 ms after the end of thalamic inactivation (3,000 ms total). Dots indicate laser end times, as in Fig. 3f.

Video 5

Motor cortex population activity (left) and hand trajectories (right) on control trials (white) and under stimulation of thalamocortical terminals at 4 Hz (pink), 10 Hz (purple), and 40 Hz (cyan), starting at cue onset. Data are from the session shown in the second row of Extended Data Fig. 9. Time limits are cue −250 ms to cue +1,000 ms.

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Sauerbrei, B.A., Guo, J., Cohen, J.D. et al. Cortical pattern generation during dexterous movement is input-driven.
Nature (2019) doi:10.1038/s41586-019-1869-9

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