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Emergent modular neural control drives coordinated motor actions

Nature Neuroscience volume 22, pages 1122–1131 (2019)Cite this article

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Abstract

A remarkable feature of motor control is the ability to coordinate movements across distinct body parts into a consistent, skilled action. To reach and grasp an object, ‘gross’ arm and ‘fine’ dexterous movements must be coordinated as a single action. How the nervous system achieves this coordination is currently unknown. One possibility is that, with training, gross and fine movements are co-optimized to produce a coordinated action; alternatively, gross and fine movements may be modularly refined to function together. To address this question, we recorded neural activity in the primary motor cortex and dorsolateral striatum during reach-to-grasp skill learning in rats. During learning, the refinement of fine and gross movements was behaviorally and neurally dissociable. Furthermore, inactivation of the primary motor cortex and dorsolateral striatum had distinct effects on skilled fine and gross movements. Our results indicate that skilled movement coordination is achieved through emergent modular neural control.

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Fig. 1: Refinement of skilled fine and gross movements is dissociable during reach-to-grasp skill learning.
Fig. 2: Coordinated movement-related activity emerges across M1 and DLS during skill learning.
Fig. 3: Coordinated spiking activity emerges across M1 and DLS during skill learning.
Fig. 4: Coordinated M1 and DLS activity is specifically linked to skilled gross movements.
Fig. 5: M1 and DLS inactivation have differential effects on skilled fine and gross movements.
Fig. 6: DLS inactivation decreases movement-related low-frequency M1 activity.
Fig. 7: Persistent disruption of skilled fine movements after M1 lesion.
Fig. 8: Skilled fine movement representation in M1.

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Data availability

The data used for the analyses that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

The code used for the analyses that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

Research was supported by the Department of Veterans Affairs, Veterans Health Administration (VA Merit no. 1I01RX001640 to K.G.; Career Development Award no. 7IK2BX003308 to D.S.R.), the National Institute of Neurological Disorders and Stroke (grant nos. 5K02NS093014 and R01MH111871 to K.G.), the American Heart & Stroke Association (predoctoral fellowship no. 17PRE33410530/2016 to S.M.L.), A*STAR Singapore (fellowship to L.G.) and a Career Award for Medical Scientists from the Burroughs Wellcome Fund to D.S.R. (no. 1015644). and to K.G. (no 1009855).

Author information

Author notes

  1. Dhakshin S. Ramanathan

    Present address: Mental Health Service, San Diego Veterans Affairs Medical Center, San Diego, CA, USA

  2. Dhakshin S. Ramanathan

    Present address: Department of Psychiatry, University of California San Diego, San Diego, CA, USA

Authors and Affiliations

  1. Neuroscience Graduate Program, University of California San Francisco, San Francisco, CA, USA

    Stefan M. Lemke & Ling Guo

  2. Department of Neurology, University of California San Francisco, San Francisco, CA, USA

    Stefan M. Lemke, Dhakshin S. Ramanathan, Ling Guo, Seok Joon Won & Karunesh Ganguly

  3. Neurology Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA

    Stefan M. Lemke, Dhakshin S. Ramanathan, Ling Guo, Seok Joon Won & Karunesh Ganguly

  4. Mental Health Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA

    Dhakshin S. Ramanathan

  5. Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA

    Dhakshin S. Ramanathan

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Contributions

S.M.L., D.S.R. and K.G. designed the experiments. S.M.L. and D.S.R. carried out the electrophysiology experiments. S.M.L. carried out the acute inactivation experiments. S.M.L. and L.G. carried out the chronic lesion experiments. S.M.L. and S.J.W. performed the histology. S.M.L. carried out the analyses. S.M.L. and K.G. wrote the manuscript.

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Correspondence to Karunesh Ganguly.

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The authors declare no competing interests.

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Journal peer review information: Nature Neuroscience thanks Xin Jin, David Robbe, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Integrated supplementary information

Supplementary Fig. 1 Localization of electrodes.

a. Illustration of M1 and DLS recording sites. b. Quantification of electrolytic lesion sites marking electrode locations for four learning animals.

Supplementary Fig. 2 Corticostriatal projections.

a. Anterograde labeling of projections from M1 showing projections to the DLS. Representative image from four tracing experiments (n = 4 animals). b. Retrograde labeling of projections to the DLS showing strong inputs from cortex, including M1. Representative image from four tracing experiments (n = 4 animals).

Supplementary Fig. 3 Gross movement metrics and success rate do not covary on days five through eight of reach-to-grasp skill learning.

a. Scatterplots of mean movement duration, sub-movement timing variability and success rate for non-overlapping ten trial bins between days five and eight of training across animals (n = 123 bins across 4 animals; values are z-scored within each animal; Pearson’s r).

Supplementary Fig. 4 M1 and DLS LFP channel pairs with high coherence have non-zero phase lag.

a. Example mean movement onset-locked LFP signals from M1 and DLS (top) and extracted phase (bottom). b. Histogram of phase difference between M1 and DLS LFP signals from 250ms before movement onset to 500ms after movement onset for example M1-DLS LFP pair (grey shaded region in a). c. Peak of phase difference histogram (as in b) for all high coherence (coherence value >0.2) pairs of M1-DLS channels on day eight.

Supplementary Fig. 5 LFP power and coherence increase for behaviorally-matched trials early and late in reach-to-grasp skill learning.

a. Differences in M1 3-6Hz LFP power, DLS 3-6Hz LFP power, and M1-DLS 3-6Hz LFP coherence between behaviorally-matched ‘fast’ trials with a duration between 200-400ms on days one and two (Early Days) and days seven and eight (Late Days). Grey lines represent mean values from individual animals (n = 4 animals) and black lines represent mean ± s.e.m. across animals. P values from mixed-effects models. b. Same as a for behaviorally-matched high forelimb trajectory correlation trials (individual trial trajectory with correlation value >0.9 to mean session trajectory).

Supplementary Fig. 6 Peak frequency of M1-DLS LFP coherence covaries with movement duration on day eight.

a. Example mean M1 and DLS LFP signals for slowest third of trials (top) and fastest third of trials (bottom) on day eight for example animal. b. Difference in 3-6Hz M1-DLS LFP coherence between slowest third of trials (Slow Trials) and fastest third of trials (Fast Trials; normalized to mean coherence for slowest third of trials) on day one and day eight (n = 4 animals). Grey lines represent mean values from individual animals and black lines represent mean ± s.e.m. across animals. P values from mixed-effects models. c. Difference in peak M1-DLS frequency between 3-6Hz for slowest third of trials and fastest third of trials on day one and day eight (n = 4 animals). Grey lines represent mean values from individual animals and black lines represent mean ± s.e.m. across animals. P values from mixed-effects models.

Supplementary Fig. 7 Details of M1 and DLS spiking activity.

a. Example waveforms from array spanning M1 and DLS for one session. b. Total recorded units and total task-related units across days in M1. c. Total recorded units and total task-related units across days in DLS. d. Histogram of firing rates for all units across all sessions, vertical lines denote median for each region. e. PETHs of all M1 units time-locked to movement onset on day one (left) and day eight (right). f. PETHs of all DLS units time-locked to movement onset on day one (left) and day eight (right). g. M1 unit activity during reaching. h. DLS unit activity during reaching. i. PETHs of all phase-locked and not phase-locked M1 units on day eight. j. PETHs of all phase-locked and not phase-locked DLS units on day eight.

Supplementary Fig. 8 Body posture during reaching before and after DLS inactivation.

a. Example of top camera view and quantification of body axis/posture during reaching. b. Example body posture for all trials at the time of movement onset before DLS muscimol infusion (Baseline Session) and after DLS muscimol infusion (DLS Inactivation Session). Grey lines represent individual trials and red line represents mean position across session. c. Difference in posture variability and lateral bias for trials before DLS muscimol infusion and after DLS muscimol infusion (n = 3 sessions across 3 animals). Grey lines represent mean values from individual animals and black lines represent mean ± s.e.m. across animals. P values from mixed-effects models.

Supplementary Fig. 9 Localization and behavioral effects of excitotoxic DLS lesions.

a. DLS excitotoxic lesion localization (immunolabeling: DAPI in blue; NeuN in red). b. Differences in reach duration, sub-movement timing variability, and success rate between trials before DLS lesion (Pre Lesion) and trials two weeks after DLS excitotoxic lesion (Post Lesion). Grey lines represent mean values from individual animals (n = 3 animals) and black lines represent mean ± s.e.m. across animals. P values from mixed-effects models.

Supplementary Fig. 10 Localization of muscimol infusion.

a. Localization of muscimol infusion into DLS. Representative image from three localizations (n = 3 animals).

Supplementary Fig. 11 Localization of M1 photothrombotic lesion.

a. Example M1 photothrombotic lesion. Representative image from five localizations (n = 5 animals).

Supplementary Fig. 12 Increased GPFA neural trajectory consistency during reach-to-grasp skill learning.

a. GPFA neural trajectories for M1 (top) and DLS (bottom) on day one and day eight from example animal. b. Difference in consistency of GPFA trajectories between early days (days 1–4) and late days (days 5–8) in M1 (top) and DLS (bottom). Grey dots represent individual sessions (Early Days: n = 12 sessions across 4 animals; Late Days: n = 10 sessions across 4 animals) and black lines represent mean ± s.e.m. across sessions.

Supplementary information

Supplementary Figs. 1–12.

Reporting Summary

Supplementary Video 1

Video of fine movements involved in grasping the pellet during trials before DLS lesion and 2 weeks post-DLS lesion.

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Lemke, S.M., Ramanathan, D.S., Guo, L. et al. Emergent modular neural control drives coordinated motor actions. Nat Neurosci 22, 1122–1131 (2019). https://doi.org/10.1038/s41593-019-0407-2

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