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The role of motor cortex in motor sequence execution depends on demands for flexibility

Nature Neuroscience volume 27, pages 2466–2475 (2024)Cite this article

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Abstract

The role of the motor cortex in executing motor sequences is widely debated, with studies supporting disparate views. Here we probe the degree to which the motor cortex’s engagement depends on task demands, specifically whether its role differs for highly practiced, or ‘automatic’, sequences versus flexible sequences informed by external cues. To test this, we trained rats to generate three-element motor sequences either by overtraining them on a single sequence or by having them follow instructive visual cues. Lesioning motor cortex showed that it is necessary for flexible cue-driven motor sequences but dispensable for single automatic behaviors trained in isolation. However, when an automatic motor sequence was practiced alongside the flexible task, it became motor cortex dependent, suggesting that an automatic motor sequence fails to consolidate subcortically when the same sequence is produced also in a flexible context. A simple neural network model recapitulated these results and offered a circuit-level explanation. Our results critically delineate the role of the motor cortex in motor sequence execution, describing the conditions under which it is engaged and the functions it fulfills, thus reconciling seemingly conflicting views about motor cortex’s role in motor sequence generation.

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Fig. 1: Addressing the role of motor cortex in the execution of automatic and flexible motor sequences.
Fig. 2: Automatic motor sequence execution is robust to motor cortex lesion.
Fig. 3: Flexible motor sequence execution depends on motor cortex.
Fig. 4: Interference across tasks renders automatic motor sequences motor cortex-dependent.
Fig. 5: A neural network model reproduces the experimental results and predicts interference between automatic and flexible task conditions.

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Data availability

The raw behavioral data and processed kinematic data used in this manuscript can be found online at https://github.com/kmizes/MC-paper. Raw kinematic data are available upon reasonable request. For databases/datasets used in tracking, see https://pose.mpi-inf.mpg.de/#related.

Code availability

The example code used in this manuscript is available online at https://github.com/kmizes/MC-paper. DeeperCut Implementation: https://github.com/eldar/pose-tensorflow.

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Acknowledgements

We thank K. Hardcastle, N. K. Harpaz, K. Laboy-Juarez, C. Bhatia, D. Aldarondo and P. Zmarz for discussions and comments on the manuscript. We also thank S. Iuleu, M. Shah and G. Pho for technical support. We also thank 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. This work was supported by the National Institutes of Health (grants R01-NS099323-01 and R01-NS105349 to B.P.Ö.) J.L. was also supported by the DOE CSGF (DE-SC0020347). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

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

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

    G. Sean Escola

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  1. Kevin G. C. Mizes
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  2. Jack Lindsey
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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 input from J.L. and G.S.E.

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Correspondence to Kevin G. C. Mizes, G. Sean Escola or Bence P. Ölveczky.

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Nature Neuroscience thanks Jesse Goldberg, Pavel Rueda-Orozco and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Histology from cohorts of MC lesioned rats.

a,b, Outlines of MC lesion boundaries of rats imaged with micro-CT. White lines denote AP and ML from bregma, and dashed lines are spaced every 1 mm. a, MC lesions of a cohort of rats trained on the combined task (CUE, WM and AUTO). Shown are outlines from n = 5/7 rats; two rats were imaged via Nissl stain. b, MC lesions of a cohort of rats trained only on the automatic sessions (n = 6).

Extended Data Fig. 2 CUE and WM performance following contralateral lesion.

Fraction of successful trials pre- and post-unilateral lesion to the hemisphere contralateral to the lever-pressing forelimb, in the CUE and WM task. Lines denote individual rats (n = 7). **P < 0.01, ***P < 0.001, two-tailed t-test.

Extended Data Fig. 3 Individual lever presses are species-typical and unaffected by the MC lesion.

a, Average forelimb movement trajectories (scaled) for the left (L), center (C) and right (R) lever presses for all animals (n = 7) in the flexible task context. Each line denotes a different rat. Top row is the horizontal (left) and vertical (right) trajectories pre-lesion; bottom row is the trajectories post-lesion. b, Mean (left) and max (right) forelimb speed over single lever presses, before and after the lesion. Lines indicate individual rats (n = 7). P > 0.05, two-sided t-test. c, Correlation of the mean forelimb trajectory (horizontal and vertical) during a single lever-press, across levers (L, C or R) and rats (n = 7), giving us n = 3 × 7 samples. Each dot indicates a correlation between individual samples. Comparisons are made across mean forelimb trajectories pre-lesion (n = 210), post-lesion (n = 210) and between pre-lesion and post-lesion trajectories (n = 441). For all subpanels, *P < 0.05, two-sided paired t-test. n.s. signifies P > 0.05.

Extended Data Fig. 4 Sensory- and working-memory-guided performance kinematics resembles performance early in training.

a,b, Average performance (n = 7) over 1000 trials at the start of training, immediately pre-lesion and for the first training session post-bilateral lesion for (a) trial duration and (b) horizontal movement speed. c, Kinematic traces from one example rat early in learning and before and after the lesion. d, Average trial-to-trial correlation of forelimb trajectories for a single sequence, averaged across all rats (n = 7). One of seven rats had no videos captured during early learning and was excluded from the ‘early’ analysis. *P < 0.05, **P < 0.01, ***P < 0.001, two-sided paired t-test.

Extended Data Fig. 5 Lever presses occur in discrete positions and error mode distributions.

a, Spatial distributions of an example rat’s nose for rewarded/unrewarded sequences, sampled pre- and post-lesion. b, Same as a, but the nose location is sampled only during the lever press. c, The variability of the nose position, quantified by computing the entropy of the spatial distribution across 2000 trials, for rewarded and unrewarded presses (dark/light shades) and pre-lesion/post-lesion (red/blue), averaged across rats (n = 5). Two of the seven ‘full task’ rats did not have videos recorded from the top view and were excluded from this analysis. Shaded area indicates s.e.m. d, Proportion of error trials classified as ‘motor errors’ for both CUE and WM (n = 7), and the AUTO-only tasks (n = 6), across lesion conditions. P > 0.05, two-tailed t-test.

Extended Data Fig. 6 Automatic task performance in combined cohort does not recover after one month of retraining.

a, Performance in the AUTO task from the 1st week of training (early), 7 days before the lesion (pre-lesion), 7 days after the lesion (post-lesion) and 1 month following lesion (late) in the combined task (green, n = 7) and AUTO-only (purple, n = 6) cohorts. Error bars denote s.e.m. b, Average performance, measured as the fraction of successful trials, from time conditions (pre, post and late) across rats (n = 6 for AUTO-only and n = 7 for combined cohorts), represented as individual lines. c–e, Kinematic metrics plotted in the week before lesion (pre), the week after lesion (post) and a month following lesion (late). c, Trial time. d, Trial speed. e, Forelimb trajectory correlation. Lines denote individual rats (n = 6 for AUTO-only and n = 7 for combined cohorts). *P < 0.05, **P < 0.01, ***P < 0.001, two-sided paired (within cohort) or unpaired (across cohorts) t-test.

Extended Data Fig. 7 Pre-lesion training metrics do not differ across the combined task and AUTO-only cohorts.

a, Rats across both cohorts (combined task (n = 7)—green; and AUTO-only (n = 6)—purple) perform a similar number of trials per session before the MC lesion. Dots represent individual rat averages, and bars are grand averages. b, Both combined (n = 7) and AUTO-only (n = 6) cohorts reach expert AUTO performance (Methods) in a similar number of training trials. c,d, Both cohorts (n = 7 for combined and n = 6 for AUTO-only) train for a similar number of total trials (c) and sessions (d) on the AUTO sequence before the lesion. P > 0.05 in all subpanels, two-sided unpaired t-test.

Supplementary information

Supplementary Information

Supplementary Note (statistical data for Figs. 2–4 and Extended Data Figs. 2–7).

Reporting Summary

Supplementary Video 1

Effects of MC lesion on a representative rat trained only on the automatic task. Shown are two example trials from before and after the bilateral MC lesion side-by-side.

Supplementary Video 2

Video of the one ‘outlier’ rat that showed a performance deficit on the automatic task after MC lesion.

Supplementary Video 3

Effects of MC lesion on automatic task performance in a rat trained on the combined (flexible and automatic) task.

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Mizes, K.G.C., Lindsey, J., Escola, G.S. et al. The role of motor cortex in motor sequence execution depends on demands for flexibility. Nat Neurosci 27, 2466–2475 (2024). https://doi.org/10.1038/s41593-024-01792-3

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