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Dissociating the contributions of sensorimotor striatum to automatic and visually guided motor sequences

Nature Neuroscience volume 26, pages 1791–1804 (2023)Cite this article

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Abstract

The ability to sequence movements in response to new task demands enables rich and adaptive behavior. However, such flexibility is computationally costly and can result in halting performances. Practicing the same motor sequence repeatedly can render its execution precise, fast and effortless, that is, ‘automatic’. The basal ganglia are thought to underlie both types of sequence execution, yet whether and how their contributions differ is unclear. We parse this in rats trained to perform the same motor sequence instructed by cues and in a self-initiated overtrained, or ‘automatic,’ condition. Neural recordings in the sensorimotor striatum revealed a kinematic code independent of the execution mode. Although lesions reduced the movement speed and affected detailed kinematics similarly, they disrupted high-level sequence structure for automatic, but not visually guided, behaviors. These results suggest that the basal ganglia are essential for ‘automatic’ motor skills that are defined in terms of continuous kinematics, but can be dispensable for discrete motor sequences guided by sensory cues.

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Fig. 1: Conceptual schematic showing differences in how a motor sequence can be specified and produced.
Fig. 2: Paradigm for training rats to produce visually guided, working memory-guided and automatic motor sequences.
Fig. 3: Single overtrained motor sequences show signatures of automaticity.
Fig. 4: DLS represents motor sequences similarly across task conditions.
Fig. 5: DLS encodes low-level kinematics, not high-level attributes, of discrete motor sequences.
Fig. 6: DLS is required for generating task-specific movement kinematics across all task conditions but it is not required for ordering basic movements in the prescribed sequence when visually cued.
Fig. 7: DMS lesions have no long-term effect on either flexible or automatic sequence execution.
Fig. 8: Experimental results emerge naturally during task learning in a dual module neural network model.

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Data availability

The generated datasets are available from the corresponding author upon reasonable request.

For databases/datasets used in tracking, see https://pose.mpi-inf.mpg.de/#related.

Code availability

All MATLAB analysis scripts will be made available upon reasonable request.

Movement smoothness implementations: https://github.com/siva82kb/smoothness/tree/master/matlab

DeeperCut Implementation: https://github.com/eldar/pose-tensorflow

Spike sorting (FAST) implementation: https://github.com/Olveczky-Lab/FAST-ChainViewer

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Acknowledgements

We thank K. Hardcastle, N. K. Harpaz, K. Laboy-Juarez, D. Aldarondo and P. Zmarz for their helpful discussions and comments on the manuscript. We also thank S. Iuleu, M. Shah and G. Pho for technical support, D. Aldarondo for help with 3D tracking, in addition to S. Turney and the Harvard Center for Biological Imaging, as well as G. Lin and the Harvard Center for Nanoscale Systems for infrastructure and support. S. Wolff, A. Dhawale and J. Marshall provided experimental advice and helped in analyzing and interpreting the data. This work was supported by National Institutes of Health (NIH) (grants R01-NS099323 (B.P.Ö.) and R01-NS105349 (B.P.Ö. and G.S.E.)). J.L. was also supported by the Department of Energy’s Computational Science Graduate Fellowship (DOE CSGF) (DE-SC0020347). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Program in Biophysics, Harvard University, Cambridge, MA, USA

    Kevin G. C. Mizes

  2. Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA

    Kevin G. C. Mizes & Bence P. Ölveczky

  3. Zuckerman Mind Brain and Behavior Institute, Columbia University, New York City, NY, USA

    Jack Lindsey & G. Sean Escola

  4. Department of Psychiatry, Columbia University, New York City, NY, USA

    G. Sean Escola

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Contributions

K.G.C.M. and B.P.Ö. conceived and designed the study. K.G.C.M. conducted the experiments and analyzed the data. J.L. and G.S.E. designed and analyzed the model. K.G.C.M. and B.P.Ö. wrote the manuscript with critical input from J.L. and G.S.E.

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Correspondence to Bence P. Ölveczky.

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Extended data

Extended Data Fig. 1 Rats express flexible and automatic behaviors in a goal-directed way.

a. Average number of taps performed per day. Homecage trained rats engage with the levers, which are always present, only during session times (n = 12 rats). b. Average number of trials per session, plotted for flexible (orange) and automatic (automatic) sessions, for typical and devalued sessions (see Methods, n = 10 rats). Bars indicate the grand average across rats, and lines are individual rats. **P < 0.01, ***P < 0.001, two-sided t-test.

Extended Data Fig. 2 Rat’s movements increase stereotypy along other axes and joints.

a. View of ‘piano task’ from a side (right) and top camera. Axes are defined as +x – towards lever, +y – towards top of box, and +z – towards right lever along the piano. b. Replotted from Fig. 1e is 8 example forelimb trajectories in the x and y dimension for each task condition from early and late in learning. Orange – CUE, blue – WM, green – AUTO. c. Same as B., but for the nose position in the x and z dimension. d. The average, trial-to-trial correlation of forelimb (in x and y dimensions) and nose (in x and z dimensions) trajectories increases with training. Bars represent grand averages over rats, and lines are averages within individual rats (n = 8 rats). *P < 0.05, **P < 0.01, Wilcoxon two-sided sign-rank test.

Extended Data Fig. 3 Histology of DLS implants, DLS lesions, and DMS lesions.

a. Location of recording electrode implantation sites in DLS marked with a colored arrowhead for each of the 4 rats. For some individuals multiple sites are marked, due to individual tetrode bundles spreading during implantation. Coronal slices are labeled from distance relative to bregma. b. The extents of DLS lesions from 6 rats (11 hemispheres) are marked for the DLS lesion along the anterior-posterior axis of the striatum, and shaded in green. Lesion extent was calibrated to target the motor cortex-recipient region of dorsolateral striatum, as determined from virally-mediated fluorescent labeling in24. c. Same as B, but 12 across 6 rats hemispheres are labeled for DMS lesions. Targeting is based on the prefrontal cortex recipient region of dorsomedial striatum, also from work in24.

Extended Data Fig. 4 MSNs and FSIs represent AUTO, CUE, and WM sequences similarly.

a. Z-scored average activity of 415 putative medium spiny neurons (MSNs) recorded in the DLS for the same sequence during the AUTO, CUE, and WM task condition (from n = 4 rats). The trials were linearly time-warped to each lever press (red vertical lines). Units were sorted by the time of their peak activity. The sorting index was calculated from half the available trials for each unit, taken from the AUTO task, and then applied to the remaining trials and tasks. b. (Left) Histogram of the average firing rate of putative MSNs during the trial period for each task condition. (Right) Average firing rates across all rats are not significantly different (p > 0.05, two-tailed t-test). Lines represent individual rats. c. (Left) Histogram of correlation coefficients of trial-averaged neural activity across the task conditions (CUE x WM - purple, WM x AUTO - pink, CUE x AUTO - yellow). (Right) Average correlation coefficient across all units, for each rat (n = 4). Average correlations are not significantly different across each task comparison (p > 0.05, two-tailed t-test). d–f. Same as A-C, but for 164 putative FSIs. Note n = 3 only, as one rat had no putative FSIs recorded that met our criteria (see Methods).

Extended Data Fig. 5 Neural and kinematic similarity for all orientation movements.

a. Comparing kinematic similarity across different orientation movements. Plotted is the average trial-to-trial correlation between kinematic traces of the forelimb (side view, x and y) and nose (top view, x and z) from different orientation movements (for example, L- > C and C- > R). Orientation movements are cropped 0.2 seconds after and before the lever presses. Bars are averages across rats, and lines represent averages in individual rats (n = 4). Colors denote whether orientation movements match in length (that is short vs. long) or orientation direction (that is left- vs. right-wards). b. Comparing neural similarity across different orientation movements. Bars indicate average similarity across all rats, lines denote individual rats (n = 4). Population activity is averaged during the orientation movement (defined as 0.2 seconds after and before the presses) for each different orientation movement, and correlation coefficients are computed between population vectors.

Extended Data Fig. 6 DLS encodes 3d nose and forelimb kinematic trajectories.

a. Views from our three cameras (right, left, and top) are shown, along with a set of static features in the box that were used to calibrate multiple views to the world for triangulation151. To triangulate the forelimb, the left and right view were calibrated using the blue points. To triangulate the nose, the top and either left or right view were calibrated using the yellow or red points. Some points are shared across calibrations. b. An example trajectory of the forelimb (left) and nose (right) plotted in 3 dimensions, during performance of the sequence C- > R- > C. Forelimb coordinates are relative to the top-left blue point in A, and nose coordinates are relative to the top-left yellow point in A. c–e. Decoding analysis, performed the same as in Fig. 4f–h. c. Schematic of the decoding analysis. A feed-forward neural network is trained to predict the velocity components (x, y, and z) of the nose and forelimb in 3 dimensions. d. (Top) Heatmap of normalized forelimb (left) and nose (right) velocities in each dimension, observed in an example flexible session. (Bottom) Heatmap of the predicted forelimb and nose velocities output by our model. e. Decoding performance, measured in pseudo-R2 of the model on a held-out set of test trials (see Methods). Dots indicate model performance on individual rats, and bar is average over rats (n = 4).

Extended Data Fig. 7 Performance on 3-lever task is unaffected by a 7-day mock break.

a–f. Performance metrics before and after the mock break, in expert animals. Gray lines represent individual rats (n = 7), bars are averages across rats. a. Normalized success rate, b. Trial time, c. Variance in the trial time, d. Entropy, or randomness, of errors, e. Average speed during the trial, f. Average trial-to-trial correlation. *P < 0.05 Wilcoxon two-sided signed rank test. Orange – CUE, Blue – WM, Green – AUTO.

Extended Data Fig. 8 Post-lesion kinematics are more similar to early in learning.

a. Trials presses per session for both CUE, WM, and AUTO sequences decrease on average following the lesion. The average number of trials per session was not significantly different between the flexible (CUE and WM) and automatic (AUTO) session types before (p = 0.9375) or after (p = 0.8125, Wilcoxon sign-rank test) the lesion (n = 7 rats). b. Forelimb kinematics from 8 example trials of the same sequence, from one rat, sampled early in learning, late in learning, and following the bilateral DLS lesion (also see Fig. 1e, Fig. 5e, and see Methods for timing). c. Average trial-to-trial correlation for forelimb trajectories of the active paw (both horizontal (x) and vertical (y)) from early in training, compared to late (pre-lesion), and post-lesion, for all task conditions (orange=CUE, blue=WM, green=AUTO). Gray lines are average within rats (n = 7 late and lesion, n = 6 early, 1 rat was not recorded early in learning) and bars represent average across rats. d. Trial time from 1st to 3rd lever press early, late (or pre-lesion), and post-lesion (n = 7 rats). e. Average forelimb speed during the trial (n = 7 rats late and lesion, n = 6 rats early). f. Variability in errors, measured through the Shannon entropy of the error distribution (see Methods, n = 7 rats). *P < 0.05, Wilcoxon two-sided sign-rank test.

Extended Data Fig. 9 The effect of DLS lesion on different types of orientating movements, and on vigor and kinematics.

a–d. The effect of DLS lesions on short (for example, L- > C) and long (for example, L- > R) orientation movements. a. Plotted is the average inter-lever interval, split by short and long orientation movements, before (darker shade) and after (lighter shade) the lesion, for each task condition (CUE – orange, WM – blue, AUTO – green). Note that only 4 of 7 rats had long orientation movements in their prescribed AUTO sequence. In all plots, lines represent averages within individual rats, and bars are grand averages over all rats (n = 7 except where noted). b. The factor increase in trial time (post lesion time/pre lesion time) is similar for short and long movements (n = 7 rats, or n = 4 for AUTO). c–d. Similar to A-B, but for the average forelimb speed during the orientation movements (submovements). e–g. The effect of DLS lesion on the vigor of successful and unsuccessful orienting movements. e. Average forelimb speed of successful and unsuccessful trials, for CUE (orange), WM (blue), and AUTO (green) trial types, plotted pre (darker bars) and post (lighter bars) DLS lesion (n = 7 rats). f–g. Average inter-lever interval (submovement time) for actions performed in successful (Hit) and unsuccessful (Miss) trials (n = 7 rats). f. For short (for example, L- > C) submovements, and g. long (for example, L- > R) submovements. Only 4 of 7 rats had a long orientation movement in the AUTO sequence. h. The effect of DLS lesion on vigor compared to the effect of DLS lesion on kinematics (from Fig. 6e–i). Plotted is the change in vigor (that is, trial time or forelimb speed, plotted on the x-axis) against the change in kinematics (that is, movement smoothness or forelimb correlation, plotted on the y-axis). Each graph is a different kinematic vs. vigor metric comparison, and each dot indicates one rat, and the color indicates the task condition. Correlations between vigor and kinematics are calculated within each task condition for all rats (plot insets). *p < 0.05, Wilcoxon two-sided sign rank test.

Extended Data Fig. 10 Alternative network models fail to reproduce experimental results.

a–g: A neural network model with scalar DLS outputs fails to learn task-invariant DLS activity a. Schematic illustrating architecture of a model variant in which DLS outputs to downstream motor circuits are constrained to be scalar-valued. b–g: Replication of analyses in Fig. 8d, f–i, for this model variant (n = 10 runs). The neural representations are much less similar across task conditions than in the original model (panels E and F here versus Fig. 8d, f). h–m: A neural network model with action selection signals fails to learn strong kinematic representations. h. Schematic illustrating architecture of a model variant in which DLS outputs to downstream motor circuits are suppressed except at trial initiation and transitions between lever presses. i–m: Replication of analyses in Fig. 8d, f–i, for this model variant (n = 10 runs). The neural representations show much less egocentricity than in the original model (panel M here vs. Figure 8f). n–s: A neural network model without pre-trained circuits is not robust to DLS lesions in the flexible task. n. Schematic illustrating architecture of a model variant in which the entire model is trained on the cued and automatic tasks from scratch, rather than using the strategy of pretraining downstream motor circuits on cued trials first. o–s: Replication of analyses in Fig. 8d, f–i, for this model variant (n = 10 runs). The resilience of flexible task performance seen in the original model is lost (panel O here versus Fig. 8g).

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2.

Reporting Summary

Supplementary Video 1

Three trials of the same motor sequence were performed in the CUE, WM and AUTO task conditions. Videos are shown first from a top camera and then from a side camera. In the side videos, the kinematics of the active forelimb is tracked and plotted.

Supplementary Video 2

Trials from the AUTO task from an example rat are shown to demonstrate the types of errors we observe. Example trials include five consecutive successful trials, two motor errors that follow a successful trial and finally a run of sequence errors following motor errors.

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Mizes, K.G.C., Lindsey, J., Escola, G.S. et al. Dissociating the contributions of sensorimotor striatum to automatic and visually guided motor sequences. Nat Neurosci 26, 1791–1804 (2023). https://doi.org/10.1038/s41593-023-01431-3

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