Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The basal ganglia control the detailed kinematics of learned motor skills

Abstract

The basal ganglia are known to influence action selection and modulation of movement vigor, but whether and how they contribute to specifying the kinematics of learned motor skills is not understood. Here, we probe this question by recording and manipulating basal ganglia activity in rats trained to generate complex task-specific movement patterns with rich kinematic structure. We find that the sensorimotor arm of the basal ganglia circuit is crucial for generating the detailed movement patterns underlying the acquired motor skills. Furthermore, the neural representations in the striatum, and the control function they subserve, do not depend on inputs from the motor cortex. Taken together, these results extend our understanding of the basal ganglia by showing that they can specify and control the fine-grained details of learned motor skills through their interactions with lower-level motor circuits.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: A hypothesized function for the BG in specifying the detailed kinematics of learned motor skills.
Fig. 2: Units in the DLS, but not the DMS, are strongly modulated throughout the execution of a learned motor skill.
Fig. 3: Ensemble unit activity in the DLS reflects continuous kinematics of the learned movement patterns.
Fig. 4: The DLS, but not the DMS, encodes detailed task-related movement kinematics.
Fig. 5: Kinematic encoding in the DLS is independent of the motor cortex.
Fig. 6: Lesions to the DLS, but not the DMS, degrade performance in the timed lever-pressing task.
Fig. 7: Lesions to the DLS, but not the DMS, lead to loss of idiosyncratic learned movement patterns and regression to lever-pressing behaviors common across animals.
Fig. 8: DLS lesions cause loss of idiosyncratic learned lever-press movements and regression to movements common across animals that are similar to presses early in learning.

Data availability

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

Code availability

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

References

  1. 1.

    Krakauer, J. W., Hadjiosif, A. M., Xu, J., Wong, A. L. & Haith, A. M. Motor learning. Compr. Physiol. 9, 613–663 (2019).

    PubMed  Article  Google Scholar 

  2. 2.

    Stephenson-Jones, M., Samuelsson, E., Ericsson, J., Robertson, B. & Grillner, S. Evolutionary conservation of the basal ganglia as a common vertebrate mechanism for action selection. Curr. Biol. 21, 1081–1091 (2011).

    CAS  PubMed  Article  Google Scholar 

  3. 3.

    Dudman, J. T. & Krakauer, J. W. The basal ganglia: from motor commands to the control of vigor. Curr. Opin. Neurobiol. 37, 158–166 (2016).

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Graybiel, A. M. & Grafton, S. T. The striatum: where skills and habits meet. Cold Spring Harb. Perspect. Biol. 7, a021691 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Barnes, T. D., Kubota, Y., Hu, D., Jin, D. Z. & Graybiel, A. M. Activity of striatal neurons reflects dynamic encoding and recoding of procedural memories. Nature 437, 1158–1161 (2005).

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Desmurget, M. & Turner, R. S. Motor sequences and the basal ganglia: kinematics, not habits. J. Neurosci. 30, 7685–7690 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Jin, X. & Costa, R. M. Start/stop signals emerge in nigrostriatal circuits during sequence learning. Nature 466, 457–462 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Lauwereyns, J., Watanabe, K., Coe, B. & Hikosaka, O. A neural correlate of response bias in monkey caudate nucleus. Nature 418, 413–417 (2002).

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Panigrahi, B. et al. Dopamine is required for the neural representation and control of movement vigor. Cell 162, 1418–1430 (2015).

    CAS  PubMed  Article  Google Scholar 

  10. 10.

    Rueda-Orozco, P. E. & Robbe, D. The striatum multiplexes contextual and kinematic information to constrain motor habits execution. Nat. Neurosci. 18, 453–460 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Samejima, K., Ueda, Y., Doya, K. & Kimura, M. Representation of action-specific reward values in the striatum. Science 310, 1337–1340 (2005).

    CAS  PubMed  Article  Google Scholar 

  12. 12.

    Ericsson, K. A., Krampe, R. T. & Tesch-Römer, C. The role of deliberate practice in the acquisition of expert performance. Psychol. Rev. 100, 363–406 (1993).

    Article  Google Scholar 

  13. 13.

    Kawai, R. et al. Motor cortex is required for learning but not for executing a motor skill. Neuron 86, 800–812 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction (Bradford Books, 2018).

  15. 15.

    Daw, N., Niv, Y. & Dayan, P. in Recent Breakthroughs in Basal Ganglia Research (ed. Bezard, E.) 91–106 (Nova Science, 2006).

  16. 16.

    Fee, M. S. & Goldberg, J. H. A hypothesis for basal ganglia-dependent reinforcement learning in the songbird. Neuroscience 198, 152–170 (2011).

    CAS  PubMed  Article  Google Scholar 

  17. 17.

    Frank, M. J. Computational models of motivated action selection in corticostriatal circuits. Curr. Opin. Neurobiol. 21, 381–386 (2011).

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Joel, D., Niv, Y. & Ruppin, E. Actor–critic models of the basal ganglia: new anatomical and computational perspectives. Neural Netw. 15, 535–547 (2002).

    PubMed  Article  Google Scholar 

  19. 19.

    Hikosaka, O. in Progress in Brain Research Vol. 160 (eds Tepper, J. M. et al.) 209–226 (Elsevier, 2007).

  20. 20.

    McHaffie, J. G., Stanford, T. R., Stein, B. E., Coizet, V. & Redgrave, P. Subcortical loops through the basal ganglia. Trends Neurosci. 28, 401–407 (2005).

    CAS  PubMed  Article  Google Scholar 

  21. 21.

    Balleine, B. W., Delgado, M. R. & Hikosaka, O. The role of the dorsal striatum in reward and decision-making. J. Neurosci. 27, 8161–8165 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Yin, H. H. & Knowlton, B. J. The role of the basal ganglia in habit formation. Nat. Rev. Neurosci. 7, 464–476 (2006).

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Hikosaka, O. & Wurtz, R. H. Modification of saccadic eye movements by GABA-related substances. II. Effects of muscimol in monkey substantia nigra pars reticulata. J. Neurophysiol. 53, 292–308 (1985).

    CAS  PubMed  Article  Google Scholar 

  24. 24.

    Jurado-Parras, M.-T. et al. The dorsal striatum energizes motor routines. Curr. Biol. 4362–4372.e6 (2020).

  25. 25.

    Vandaele, Y. et al. Distinct recruitment of dorsomedial and dorsolateral striatum erodes with extended training. eLife 8, e49536 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Houk, J. C. & Wise, S. P. Feature article: distributed modular architectures linking basal ganglia, cerebellum, and cerebral cortex: their role in planning and controlling action. Cereb. Cortex 5, 95–110 (1995).

    CAS  PubMed  Article  Google Scholar 

  27. 27.

    Redgrave, P., Prescott, T. J. & Gurney, K. The basal ganglia: a vertebrate solution to the selection problem? Neuroscience 89, 1009–1023 (1999).

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Park, J., Coddington, L. T. & Dudman, J. T. Basal ganglia circuits for action specification. Annu. Rev. Neurosci. 43, 485–507 (2020).

    CAS  PubMed  Article  Google Scholar 

  29. 29.

    Ali, F. et al. The basal ganglia is necessary for learning spectral, but not temporal, features of birdsong. Neuron 80, 494–506 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Andalman, A. S. & Fee, M. S. A basal ganglia–forebrain circuit in the songbird biases motor output to avoid vocal errors. Proc. Natl Acad. Sci. USA 106, 12518–12523 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Aronov, D., Andalman, A. S. & Fee, M. S. A specialized forebrain circuit for vocal babbling in the juvenile songbird. Science 320, 630–634 (2008).

    CAS  PubMed  Article  Google Scholar 

  32. 32.

    Turner, R. S. & Anderson, M. E. Pallidal discharge related to the kinematics of reaching movements in two dimensions. J. Neurophysiol. 77, 1051–1074 (1997).

    CAS  PubMed  Article  Google Scholar 

  33. 33.

    Kupferschmidt, D. A., Juczewski, K., Cui, G., Johnson, K. A. & Lovinger, D. M. Parallel, but dissociable, processing in discrete corticostriatal inputs encodes skill learning. Neuron 96, 476–489.e5 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Dhawale, A. K. et al. Automated long-term recording and analysis of neural activity in behaving animals. eLife 6, e27702 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Alexander, G. E., DeLong, M. R. & Strick, P. L. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu. Rev. Neurosci. 9, 357–381 (1986).

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    Hunnicutt, B. J. et al. A comprehensive excitatory input map of the striatum reveals novel functional organization. eLife 5, e19103 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Miyachi, S., Hikosaka, O. & Lu, X. Differential activation of monkey striatal neurons in the early and late stages of procedural learning. Exp. Brain Res. 146, 122–126 (2002).

    PubMed  Article  Google Scholar 

  38. 38.

    Yin, H. H. et al. Dynamic reorganization of striatal circuits during the acquisition and consolidation of a skill. Nat. Neurosci. 12, 333–341 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Miyachi, S., Hikosaka, O., Miyashita, K., Kárádi, Z. & Rand, M. K. Differential roles of monkey striatum in learning of sequential hand movement. Exp. Brain Res. 115, 1–5 (1997).

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Thorn, C. A., Atallah, H., Howe, M. & Graybiel, A. M. Differential dynamics of activity changes in dorsolateral and dorsomedial striatal loops during learning. Neuron 66, 781–795 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Insafutdinov, E., Pishchulin, L., Andres, B., Andriluka, M. & Schiele, B. in Computer VisionECCV 2016. Lecture Notes in Computer Science Vol. 9910 (eds Leibe, B. et al.) 34–50 (Springer International, 2016).

  42. 42.

    Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018).

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Diedrichsen, J. & Kornysheva, K. Motor skill learning between selection and execution. Trends Cogn. Sci. 227–233 (2015).

  44. 44.

    Graybiel, A. M. The basal ganglia and chunking of action repertoires. Neurobiol. Learn. Mem. 70, 119–136 (1998).

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    Jin, X., Tecuapetla, F. & Costa, R. M. Basal ganglia subcircuits distinctively encode the parsing and concatenation of action sequences. Nat. Neurosci. 17, 423–430 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Sternad, D. It’s not (only) the mean that matters: variability, noise and exploration in skill learning. Curr. Opin. Behav. Sci. 20, 183–195 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Markowitz, J. E. et al. The striatum organizes 3D behavior via moment-to-moment action selection. Cell 174, 44–58.e17 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Sjöbom, J., Tamtè, M., Halje, P., Brys, I. & Petersson, P. Cortical and striatal circuits together encode transitions in natural behavior. Sci. Adv. 6, eabc1173 (2020).

    PubMed  Article  Google Scholar 

  49. 49.

    Geddes, C. E., Li, H. & Jin, X. Optogenetic editing reveals the hierarchical organization of learned action sequences. Cell 174, 32–43.e15 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Mink, J. W. The basal ganglia: focused selection and inhibition of competing motor programs. Prog. Neurobiol. 50, 381–425 (1996).

    CAS  PubMed  Article  Google Scholar 

  51. 51.

    Turner, R. S. & Desmurget, M. Basal ganglia contributions to motor control: a vigorous tutor. Curr. Opin. Neurobiol. 20, 704–716 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Grillner, S. & Robertson, B. The basal ganglia downstream control of brainstem motor centres—an evolutionarily conserved strategy. Curr. Opin. Neurobiol. 33, 47–52 (2015).

    CAS  PubMed  Article  Google Scholar 

  53. 53.

    Redgrave, P. & Coizet, V. Brainstem interactions with the basal ganglia. Parkinsonism Relat. Disord. 13, S301–S305 (2007).

    PubMed  Article  Google Scholar 

  54. 54.

    Ruder, L. & Arber, S. Brainstem circuits controlling action diversification. Annu. Rev. Neurosci. 42, 485–504 (2019).

    CAS  PubMed  Article  Google Scholar 

  55. 55.

    Yin, H. H. The sensorimotor striatum is necessary for serial order learning. J. Neurosci. 30, 14719–14723 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    Shmuelof, L. & Krakauer, J. W. Are we ready for a natural history of motor learning? Neuron 72, 469–476 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Wolff, S. B. E., Ko, R. & Ölveczky, B. P. Distinct roles for motor cortical and thalamic inputs to striatum during motor learning and execution. Preprint at bioRxiv https://doi.org/10.1101/825810 (2019).

  58. 58.

    Berridge, K. C. & Whishaw, I. Q. Cortex, striatum and cerebellum: control of serial order in a grooming sequence. Exp. Brain Res. 90, 275–290 (1992).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  59. 59.

    Cromwell, H. C. & Berridge, K. C. Implementation of action sequences by a neostriatal site: a lesion mapping study of grooming syntax. J. Neurosci. 16, 3444–3458 (1996).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Grillner, S. & Wallén, P. Innate versus learned movements—a false dichotomy? Prog. Brain Res. 143, 3–12 (2004).

    PubMed  Google Scholar 

  61. 61.

    Poddar, R., Kawai, R. & Ölveczky, B. P. A fully automated high-throughput training system for rodents. PLoS ONE 8, e83171 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  62. 62.

    Paxinos, G. & Watson, C. The Rat Brain in Stereotaxic Coordinates (Academic Press, 1998).

  63. 63.

    Katz, L. C. & Iarovici, D. M. Green fluorescent latex microspheres: a new retrograde tracer. Neuroscience 34, 511–520 (1990).

    CAS  PubMed  Article  Google Scholar 

  64. 64.

    Katz, L. C., Burkhalter, A. & Dreyer, W. J. Fluorescent latex microspheres as a retrograde neuronal marker for in vivo and in vitro studies of visual cortex. Nature 310, 498–500 (1984).

    CAS  PubMed  Article  Google Scholar 

  65. 65.

    Berke, J. D., Okatan, M., Skurski, J. & Eichenbaum, H. B. Oscillatory entrainment of striatal neurons in freely moving rats. Neuron 43, 883–896 (2004).

    CAS  Article  Google Scholar 

  66. 66.

    Gage, G. J., Stoetzner, C. R., Wiltschko, A. B. & Berke, J. D. Selective activation of striatal fast spiking interneurons during choice execution. Neuron 67, 466–479 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. 67.

    Leonardo, A. & Fee, M. S. Ensemble coding of vocal control in birdsong. J. Neurosci. 25, 652–661 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. 68.

    Ölveczky, B. P., Otchy, T. M., Goldberg, J. H., Aronov, D. & Fee, M. S. Changes in the neural control of a complex motor sequence during learning. J. Neurophysiol. 106, 386–397 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  69. 69.

    Lehky, S. R., Sejnowski, T. J. & Desimone, R. Selectivity and sparseness in the responses of striate complex cells. Vis. Res. 45, 57–73 (2005).

    PubMed  Article  Google Scholar 

  70. 70.

    Martiros, N., Burgess, A. A. & Graybiel, A. M. Inversely active striatal projection neurons and interneurons selectively delimit useful behavioral sequences. Curr. Biol. 28, 560–573.e5 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. 71.

    Friedman, J. H., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  72. 72.

    Colin Cameron, A. & Windmeijer, F. A. G. An R-squared measure of goodness of fit for some common nonlinear regression models. J. Econom. 77, 329–342 (1997).

    Article  Google Scholar 

  73. 73.

    Glaser, J. I. et al. Machine learning for neural decoding. eNeuro https://doi.org/10.1523/ENEURO.0506-19.2020 (2020).

  74. 74.

    Maaten, Lvander & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  75. 75.

    Rodriguez, A. & Laio, A. Clustering by fast search and find of density peaks. Science 344, 1492–1496 (2014).

    CAS  PubMed  Article  Google Scholar 

  76. 76.

    Ramkumar, P. et al. Chunking as the result of an efficiency computation trade-off. Nat. Commun. 7, 12176 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  77. 77.

    Tchernichovski, O., Nottebohm, F., Ho, C. E., Pesaran, B. & Mitra, P. P. A procedure for an automated measurement of song similarity. Anim. Behav. 59, 1167–1176 (2000).

    CAS  PubMed  Article  Google Scholar 

  78. 78.

    Wiltschko, A. B. et al. Mapping sub-second structure in mouse behavior. Neuron 88, 1121–1135 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

We thank S. Escola, J. Murray and members of the Ölveczky Lab for advice on data analyses and for discussions and comments on the manuscript. We thank S. Turney and the Harvard Center for Biological Imaging for infrastructure and support. We thank A. Mathis and M. Mathis for help with setting up markerless tracking. This work was supported by NIH grants R01-NS099323-01 and R01-NS105349 to B.P.Ö., by a Life Sciences Research Foundation and Charles A. King Foundation postdoctoral fellowship to A.K.D., and by EMBO (ALTF1561-2013) and HFSP (LT 000514/2014) postdoctoral fellowships to S.B.E.W. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Affiliations

Authors

Contributions

A.K.D., S.B.E.W., R.K. and B.P.Ö. designed the study. A.K.D. and S.B.E.W. contributed equally and authorship order was determined by a coin toss. A.K.D. performed electrophysiological recordings and analyzed the electrophysiology data. S.B.E.W. performed electrophysiological recordings, helped with the analysis, performed lesion experiments and analyzed the behavioral data. R.K. performed pilot lesion experiments and described the initial phenomenology. A.K.D., S.B.E.W. and B.P.Ö. wrote the manuscript.

Corresponding author

Correspondence to Bence P. Ölveczky.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks the anonymous reviewers for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Striatal subdivisions, recording sites and extent of lesions.

a, Virally-mediated fluorescent labeling of axons originating in either motor cortex (MC) or prefrontal cortex (PFC) to determine the outlines of the MC-recipient dorsolateral striatum (DLS) and of the PFC-recipient dorsomedial striatum (DMS), respectively. Based on the distinct projection patterns we estimated the extent of the DLS and DMS, respectively, along the anterior-posterior axis of the striatum. b, DLS/DMS outlines, recording sites and lesion extents. The outlines of the DLS and DMS determined in a along the anterior-posterior axis are indicated by red and green lines, respectively. Locations of recording electrode implantation sites in DLS and DMS are marked with arrowheads. Numbers indicate individual animals. For some animals several recording locations were determined, due to individual tetrode bundles of our recording arrays spreading apart during implantation. The extents of MC lesions in three recorded animals are marked in different shades of grey for individual animals and the dotted lines indicate the area in MC targeted for lesions. The extents of the DLS and DMS lesions are marked as shaded red and green areas, respectively. Lighter areas indicate the extent of the largest lesion across animals at a given anterior-posterior position, darker areas indicate the extent of the smallest lesion. Blue dotted lines indicate the target area for PFC tracing injections.

Extended Data Fig. 2 Classification of striatal units and statistics of task-aligned FSI activity in DLS and DMS.

a, Classification of single units recorded in striatum into putative spiny projection neurons (SPNs, maroon) and fast spiking interneurons (FSIs, blue). (Left) Spike waveform features such as peak-width and peak-to-valley interval, as well as average firing rates were used in combination to classify units as SPNs or FSIs. Grey dots indicate unclassified units (8.5%) that were excluded from further analysis. (Right) Population averaged spike waveforms for putative SPNs (top) and FSIs (bottom). Data presented as the mean ± s.d. across units. All waveforms were rescaled to unit amplitude prior to averaging. b, Average firing rate during the trial-period (P = 3 × 10−3), maximum modulation of Z-scored firing rate during the trial-period (P = 0.01), sparseness index (P = 0.13) and average trial-to-trial correlation of task-aligned spiking (P = 1 × 10−3) in putative FSIs recorded in the DLS (red, n = 171) and DMS (green, n = 138). Bars and error-bars represent mean and s.e.m., respectively, across units. P values measure the two-sided probability that two datasets have the same mean and are computed by bootstrapping difference in the means (n = 1 × 104 resamples).

Extended Data Fig. 3 Population-averaged activity in the DLS at the beginning and end of the skilled behavior.

a, Average Z-scored activity of putative SPN (top) or FSI (bottom) populations recorded in the DLS around the time of the 1st (solid line) and 2nd (dashed lined) lever-presses (n = 3 rats). Grey shading represents 95% confidence interval, corrected for multiple comparisons, of the distribution of Z-scored activity expected by chance if lever-presses occurred at random times (n = 1 × 104 randomizations). b, Trial-to-trial variability of an example rat’s task-aligned movement trajectories. (Top) Trajectories of the rat’s forelimb (vertical component) in an example session corresponding to a specific sequence mode (see Extended Data Fig. 4). Each line denotes a trial. (Bottom) Normalized trial-by-trial variability (see Methods) of movement trajectories of the rat’s forelimbs and head. Times at which this measure exceeds a value of 2 (dashed lines) are designated the start or stop time of the motor sequence. On average, starts occurred 0.42 ± 0.46 s prior to the first lever-press and stops occurred at 0.35 ± 0.02 s after the second lever-press (mean ± s.d., n = 3 rats). c, Average Z-scored activity for populations of SPNs (top) or FSIs (bottom) recorded in the DLS around the start (solid line) and stop (dashed lined) of the skilled behavior (n = 3 rats). Grey shading represents 95% confidence interval, corrected for multiple comparisons, of the distribution of Z-scored activity expected by chance if start/stop occurred at random times (n = 1 × 104 randomizations). d, Average Z-scored activity for populations of SPNs recorded in the DLS of three example rats during execution of a representative sequence mode (red lines). Superimposed are trial-averaged forelimb speed profiles (black, averaged over both contra- and ipsi-lateral forelimbs), from the same individuals. e, Non-uniformity of the average Z-scored activity profiles of DLS SPNs, measured by their s.d., was averaged across sequence modes and then across rats (red line, n = 3 rats). Grey histogram shows distribution expected by chance if SPNs showed independent activity (generated by randomly jittering the Z-scored PETHs of individual units prior to averaging, n = 1 × 104 randomizations), and P value quantifies the two-sided probability that this explains the data. h, Correlation coefficient between average speed profiles and average activity of DLS SPN populations (both shown in panel d), averaged across sequences modes and then across rats (red line, n = 3 rats). Grey histogram shows the statistic distribution under the null hypothesis (no relationship between the variables) computed by randomization (n = 1 × 104 permutations), and P value quantifies the two-sided probability that this explains the data.

Extended Data Fig. 4 Identification of sequence modes and population-averaged activity in the DLS at choice points between modes.

a, Two-dimensional t-distributed stochastic neighborhood embedding (tSNE) of task-aligned kinematic trajectories for a subset of trials from an example rat. Each point represents a trial and colors represent distinct modes identified by a semi-automated unsupervised clustering algorithm (see Methods). On average we identified 5 ± 2 modes (mean ± s.d.) per rat (n = 9 rats). b, Task-aligned horizontal and vertical components of the position and velocity of a single forelimb averaged across trials within each sequence mode shown in panel a. Shading represents s.d. across trials. c, Task-aligned kinematic variables including forelimb position (left) and velocity (right) for a random subset of 20,000 trials performed by the example rat, sorted by sequence mode (indicated by colors, as in panels ab). Kinematics have been time-warped to account for trial-by-trial variability in the interval between the 1st and 2nd lever-presses. d, Pairwise correlations between the kinematics of the trials shown in c, sorted by sequence mode. e, Average Z-scored activity for populations of SPNs (top) or FSIs (bottom) recorded in the DLS around the time of choice-points (left) or at peak discriminability between the trajectories corresponding to pairs of modes (right). Z-scored activity is averaged across units and modes in a mode pair, then across all mode-pairs in each rat and then across rats (n = 3 rats). Grey shading represents 95% confidence interval, corrected for multiple comparisons, of the distribution of Z-scored activity expected by chance if these events occurred at random times (n = 1 × 104 randomizations).

Extended Data Fig. 5 Comparison between encoding of different kinematic features by striatal neurons.

a, Scatter plots comparing the goodness of fit, measured using the pseudo-R2 (see Methods), between encoding models that use a combination of all kinematic variables (position, velocity and acceleration) versus those that use only position (left), velocity (middle) or acceleration (right) variables to predict the activity of DLS SPNs (top) and FSIs (bottom). P < 1 × 10−4 for all SPN kinematic comparisons and P = 4 × 10−3, < 1 × 10−4, < 1 × 10−4 for FSI kinematic encoding comparisons to position, velocity and acceleration variables, respectively. P values are computed by bootstrapping the paired difference in means (n = 1 × 104 resamples) and quantify the likelihood that two distributions have the same mean. b, Goodness of fit, measured by pseudo-R2 (see Methods), for encoding models that use detailed kinematics (position, velocity and acceleration) of all tracked effectors and those that only use kinematics of the contralateral forelimb, ipsilateral forelimb, both forelimbs or the head to predict spiking activity of putative SPNs (left) and FSIs (right) in the DLS (red, n = 492 SPNs and 164 FSIs from 3 rats) and DMS (green, n = 213 SPNs and 123 FSIs from 3 rats). Boxes denote 1st, 2nd (median) and 3rd quartiles, while whiskers show the 5th and 95th percentile of the distribution. P < 1 × 10−4 for all encoding comparisons between SPNs in DLS and DMS, and P = 1 × 10−4, 6 × 10−3, 0.49, 4 × 10−3, 1 × 10−4 for comparisons between FSI encoding in the DLS and DMS of all effectors, contra-, ipsi-, both forelimbs and head, respectively. P values measure the probability that the two datasets have the same mean and are estimated by bootstrapping difference in means (n = 1 × 104 resamples).

Extended Data Fig. 6 Characterization of task performance and DLS representations after motor cortex lesion.

a, Comparison of performance measures before and after MC lesion (n = 3). IPI: Inter-Press Interval, CV of IPI: Coefficient of Variation of the IPI, IPI close to target: Fraction of trials close to target IPI (700 ms ± 20%), ITI: Inter-Trial Interval. Pre-Lesion: last 2,000 trials before lesion, post-Lesion: first 2,000 trials after lesion. Dots indicate individual animals and bars show means ± s.e.m. For statistical details see Supplementary Table 7. b, (Top) Comparing task-aligned activity statistics, including average firing rate during the trial-period (P = 0.09), maximum modulation of Z-scored firing rate during the trial-period (P < 1 × 10−4), sparseness index (P = 0.02) and average trial-to-trial correlation of task-aligned spiking (P < 1 × 10−4), between putative SPNs recorded in the intact (red, n = 683, replotted from Fig. 2c) and MC-lesioned (blue, n = 379) DLS. (Bottom) Average firing rate during the trial-period (P = 0.01), maximum modulation of Z-scored firing rate during the trial-period (P = 5 × 10−4), sparseness index (P = 0.08) and average trial-to-trial correlation of task-aligned spiking (P = 0.05) in putative FSIs recorded in the intact (red, n = 171, replotted from Extended Data Fig. 2b) and MC-lesioned (blue, n = 153) DLS. Bars and error-bars represent mean and s.e.m., respectively, across units. P values measure the probability that two datasets have the same mean and are computed by bootstrapping difference in means (n = 1 × 104 resamples). c, Goodness of fit, measured by pseudo-R2, for encoding models that use kinematics of all tracked effectors and those that only use kinematics of the contralateral forelimb, ipsilateral forelimb, both forelimbs or the head to predict spiking activity of putative SPNs (left) and FSIs (right) in the DLS of intact (red, n = 492 SPNs and 164 FSIs from 3 rats, replotted from Extended Data Fig. 5b) and MC-lesioned (blue, n = 279 SPNs and 169 FSIs from 3 rats) animals. Boxes denote 1st, 2nd (median) and 3rd quartiles, while whiskers show the 5th and 95th percentile of the distribution. P < 1 × 10−4, = 4 × 10−3, < 1 × 10−4, < 1 × 10−4 for comparisons between SPN encoding in the intact and MC-lesioned DLS of all effectors, contra-, ipsi-, both forelimbs and head, respectively. P = 0.04, 0.66, 0.04, 0.04 for comparisons between FSI encoding in the intact and MC-lesioned DLS of all effectors, contra-, ipsi-, both forelimbs and head, respectively. P values measure the probability that the two datasets have the same mean and are estimated by bootstrapping the difference in means (n = 1 × 104 resamples).

Extended Data Fig. 7 Task performance after DLS, but not DMS, lesions is impaired, resembles performance early in training, and does not recover.

Comparison of performance measures at different stages before and after lesions of DLS (n = 7 rats), DMS (n = 5), and control injections (n = 5). IPI: Inter-Press Interval, CV of IPI: Coefficient of Variation of the IPI, IPI close to target: Fraction of trials close to target IPI (700 ms ± 20%), ITI: Inter-Trial Interval. Early: first 2,000 trials in training, pre-lesion: last 2,000 trials before lesion, post-lesion: first 2,000 trials after lesion, late: trials 10,000 to 12,000 after lesion. Presses/session: Average number of lever-presses per session. Early: first 10 sessions in training, pre-lesion: last 20 sessions before lesion, post-lesion: first 20 sessions after lesion, late: sessions 50 to 70 after lesion. Dots indicate individual animals and bars show mean ± s.e.m. For statistical details see Supplementary Table 8. *P < 0.05, **P < 0.01, ***P < 0.001.

Extended Data Fig. 8 Lesions of the GPi affect task performance similarly to DLS lesions.

a, Representative example of the effect of a GPi lesion on task performance. Left: Example histological image of a unilateral GPi lesion, showing the comparison between the lesioned and the intact GPi. Experimental animals underwent bilateral GPi lesions (see Methods). Right: IPIs and ITIs for an example animal early in training, before and after bilateral GPi lesion. Population data shown in panel b. b, GPi lesions (n = 5 rats) have long-lasting effects on various measures of performance (cf. Extended Data Fig. 7). DLS performance as shown in Extended Data Fig. 7, here shown for comparison. Dots indicate individual animals and bars show the mean ± s.e.m. c, Left: Example distributions of IPI and ITI interval lengths early in training, and before and after GPi lesion. d, JS Divergence of IPI and ITI distributions for all GPi-lesioned animals (n = 5 rats). Dots indicate individual animals and bars show the mean ± s.e.m.. For statistical details see Supplementary Table 9. *P < 0.05, **P < 0.01.

Extended Data Fig. 9 DLS lesions do not affect lever-press vigor, but lead to regression to a common lever-pressing behavior.

a, Comparison of mean and peak lever-press speeds before and after DLS lesion (see Fig. 8). Speeds were averaged over 1st and 2nd lever-presses. Dots indicate individual animals and bars show mean ± s.e.m. No significant differences were detected. For statistical details see Methods. b, The comparison of 1st and 2nd lever-presses across animals early in training and after DLS lesion (see Fig. 8c) was extended to additional animals. The post-lesion lever-presses of the 2 DLS-lesioned animals which were not included in Fig. 8c (due to lack of trajectories for the early presses) were added. In addition, the trajectories of the early lever-presses of 2 of the DMS-lesioned animals (shown in Fig. 7) were added. The remaining animals were re-plotted from Fig. 8c. (Column 1) Forelimb movement trajectories for the 1st and 2nd presses early in training (green) and after DLS lesion (red), overlaid for all tracked animals (early in training: DLS-lesioned animals n = 4, DMS-lesioned animals n = 2; post-lesion: DLS-lesioned animals n = 6). (Column 2) Pairwise correlations between press trajectories of all animals early in training and after DLS lesion. Shown are average trial-to-trial correlations across individual presses (animal 1 press 1, animal 1 press 2, etc.). (Column 3) Averages of across animal correlations per condition. Shown are correlations between all presses early, all presses after DLS lesion and between all presses early and all presses after lesion (early-post). Mean ± s.e.m. For statistical details see Supplementary Table 10. ***P < 0.001.

Extended Data Fig. 10 Small lesions of the DLS affect performance and movement kinematics but do not, in contrast to large DLS lesions, cause animals to revert to species-typical lever-pressing behaviors.

a, Fraction of DLS lesioned. Red: Animals with large DLS lesions, included in Figs. 68. Yellow: Animals with small DLS lesions (excluded from prior analysis). b, Average performance across animals (large DLS lesions n = 7 rats; small DLS lesions n = 3, Control n = 5), normalized to pre-manipulation performance. Fraction of trials with IPIs close to target (700 ms ± 20%). Shading represents s.e.m.. Partially replotted from Fig. 6b. c, Comparison of average forelimb trajectories (vertical position) before and after small DLS lesions for all animals (from trials within a range of mean IPI ± 30 ms). The forelimb performing the 1st lever-press is regarded dominant (n = 3 rats). d, Forelimb vertical displacement in randomly selected trials (200 per animal) of all animals before and after small DLS lesions. Trials are sorted by IPI and normalized to minimum and maximum displacement for each animal. Black lines mark the 1st, grey lines the 2nd lever-press. e, Pairwise correlations between trials shown in d, averaged per animal. f, Averages of correlations shown in e by condition (averages of all pre-to-pre, post-to-post and pre-to-post correlations). Mean ± s.e.m. g, Distributions of correlation coefficients between individual forelimb trajectories before (blue) and after (black) small DLS lesion, and the animal’s pre-lesion modes (see Methods). Probability distributions were computed for each rat and then averaged (n = 3). Fraction of trials with correlations >0.85 (Mean ± s.e.m.): pre 0.53 ± 0.24, pre-post 0 ± 0. h, Comparison of forelimb trajectories associated with 1st and 2nd lever-presses across animals with large and small DLS lesions. Pairwise correlations between press trajectories of animals early in training, of animals after large (replotted from Extended Data Fig. 9b) and of animals after small DLS lesions. Shown are average trial-to-trial correlations across individual presses (animal 1 press 1, animal 1 press 2, etc.) (n = 6 rats early, n = 6 large DLS lesions (partially overlapping, see Extended Data Fig. 9b), n = 3 small DLS lesions). i, Averages of across animal correlations for selected conditions. Left: correlations between all presses early (dark green dotted square in h) and all presses after large DLS lesions (dark red dotted square in h) as in Extended Data Fig. 9b. Right: correlations between all presses early and all presses after small DLS lesions (small-early; light green dotted square in h) and between all presses after large DLS lesions and all presses after small DLS lesions (small-post; light red dotted square in h). All comparisons show statistically significant differences with P < 0.001, except the comparison small-early to small-post. Mean ± s.e.m. For statistical details see Supplementary Table 11.

Supplementary information

Supplementary Information

Supplementary Tables 1–11.

Reporting Summary

Supplementary Video 1

Comparison of movement trajectories and neural representations in the DLS during execution of two sequence modes (the same example modes shown in Fig. 3) by an example rat. High-speed videos (at 120 Hz) and the position of the forelimb contralateral to the recording site are shown for two representative trials from two distinct modes. The neural representation is computed from trial-averaged activity of an ensemble of neurons compiled over multiple recording days.

Supplementary Video 2

Effects of DLS lesions on a learned task-specific movement pattern. Example trials from two animals in our timed lever-pressing task early in training, before DLS lesion, and after DLS lesion. Shown are videos and (vertical) trajectories of both forelimbs (marked with yellow and magenta dots in the videos). The forelimb trajectories of the two animals are overlaid when the second animal is shown.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Dhawale, A.K., Wolff, S.B.E., Ko, R. et al. The basal ganglia control the detailed kinematics of learned motor skills. Nat Neurosci (2021). https://doi.org/10.1038/s41593-021-00889-3

Download citation

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing