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Flexible neural control of motor units

Nature Neuroscience volume 25, pages 1492–1504 (2022)Cite this article

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Abstract

Voluntary movement requires communication from cortex to the spinal cord, where a dedicated pool of motor units (MUs) activates each muscle. The canonical description of MU function rests upon two foundational tenets. First, cortex cannot control MUs independently but supplies each pool with a common drive. Second, MUs are recruited in a rigid fashion that largely accords with Henneman’s size principle. Although this paradigm has considerable empirical support, a direct test requires simultaneous observations of many MUs across diverse force profiles. In this study, we developed an isometric task that allowed stable MU recordings, in a rhesus macaque, even during rapidly changing forces. Patterns of MU activity were surprisingly behavior-dependent and could be accurately described only by assuming multiple drives. Consistent with flexible descending control, microstimulation of neighboring cortical sites recruited different MUs. Furthermore, the cortical population response displayed sufficient degrees of freedom to potentially exert fine-grained control. Thus, MU activity is flexibly controlled to meet task demands, and cortex may contribute to this ability.

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Fig. 1: Rigid versus flexible MU control and experimental setup.
Fig. 2: Stimulation experiments.
Fig. 3: Quantifications of flexible control.
Fig. 4: Optimal MU recruitment.
Fig. 5: Dynamic experiments.
Fig. 6: Muscle length experiments.
Fig. 7: Latent factor model.
Fig. 8: Quantifying neural degrees of freedom.

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Data availability

Sample data can be accessed via Dropbox.

Code availability

Code can be accessed via GitHub.

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Acknowledgements

We thank Y. Pavlova for excellent animal care. This work was supported by the Grossman Charitable Trust, the Simons Foundation (M.M.C., J.P.C. and L.F.A.), the McKnight Foundation (M.M.C. and J.P.C.), NIH Director’s DP2 NS083037 (M.M.C.), NIH CRCNS R01NS100066 (M.M.C. and J.P.C.), NIH 1U19NS104649 (M.M.C., L.F.A. and J.P.C.), NIH F31 NS110201 (N.J.M.), NIH K99 NS119787 (J.I.G.), National Science Foundation (NSF) GRFP (N.J.M.), NSF NeuroNex (J.I.G. and L.F.A.), the Kavli Foundation (M.M.C. and L.F.A.), the Brain & Behavior Research Foundation NARSAD Young Investigator Grant (E.M.T), the Howard Hughes Medical Institute (M.N.S.) and the Gatsby Charitable Foundation (J.I.G., L.F.A. and J.P.C.). 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. Department of Neuroscience, Columbia University Medical Center, New York, NY, USA

    Najja J. Marshall, Eric M. Trautmann, Elom A. Amematsro, Michael N. Shadlen, L. F. Abbott & Mark M. Churchland

  2. Zuckerman Institute, Columbia University, New York, NY, USA

    Najja J. Marshall, Joshua I. Glaser, Eric M. Trautmann, Elom A. Amematsro, Sean M. Perkins, Michael N. Shadlen, L. F. Abbott, John P. Cunningham & Mark M. Churchland

  3. Department of Statistics, Columbia University, New York, NY, USA

    Joshua I. Glaser & John P. Cunningham

  4. Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA

    Joshua I. Glaser, Eric M. Trautmann, John P. Cunningham & Mark M. Churchland

  5. Center for Theoretical Neuroscience, Columbia University Medical Center, New York, NY, USA

    Joshua I. Glaser, L. F. Abbott & John P. Cunningham

  6. Department of Biomedical Engineering, Columbia University, New York, NY, USA

    Sean M. Perkins

  7. Kavli Institute for Brain Science, Columbia University Medical Center, New York, NY, USA

    Michael N. Shadlen, L. F. Abbott & Mark M. Churchland

  8. Howard Hughes Medical Institute, Columbia University, New York, NY, USA

    Michael N. Shadlen

  9. Department of Physiology and Cellular Biophysics, Columbia University Medical Center, New York, NY, USA

    L. F. Abbott

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Contributions

M.M.C. conceived the study. N.J.M., M.M.C. and S.M.P. designed experiments. N.J.M. collected and analyzed EMG datasets. N.J.M. and E.M.T. collected neural datasets, supervised by M.M.C. and M.N.S. J.P.C and J.I.G. developed and trained latent factor models, with input from M.M.C and N.J.M. E.A.A. and N.J.M. analyzed neural datasets. N.J.M. developed the optimal recruitment model, supervised by L.F.A. and M.M.C. N.J.M. and M.M.C. wrote the paper. All authors contributed to editing.

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Correspondence to Mark M. Churchland.

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Extended data

Extended Data Fig. 1 Example MU spikes and sorting, including a challenging moment with spike overlap.

Behavior and MU responses during one dynamic-experiment trial. The target force profile was a chirp. Top: generated force. Middle: eight-channel EMG signals recorded from the lateral triceps. 20 MUs were isolated across the full session; 13 MUs were active during the displayed trial. MU spike times are plotted as circles (one row and color per MU) below the force trace. EMG traces are colored by the inferred contribution from each MU (since spikes could overlap, more than one MU could contribute at a time). Bottom left: waveform template for each MU (columns) and channel (rows). Templates are 5 ms long. As shown on an expanded scale (bottom right), EMG signals were decomposed into superpositions of individual-MU waveform templates. The use of multiple channels was critical to sorting during challenging moments such as the one illustrated in the expanded scale. For example, MU2, MU5, and MU10 had very different across-channel profiles. This allowed them to be identified when, near the end of the record, their spikes coincided just before the final spike of MU12. The ability to decompose voltages into a sum of waveforms also allowed sorting of two spikes that overlapped on the same channel (e.g., when the first spike of MU6 overlaps with that of MU10, or when the first spike of MU9 overlaps with that of MU5). The fact that sorting focused on waveform shape across time and channels (rather than primarily on amplitude) guarded against mistakenly sorting one unit as two if the waveform scaled modestly across repeated spikes (as occurred for a modest subset of MUs).

Extended Data Fig. 2 Basic properties of MU responses.

(a) Comparison, for all MUs recorded during the dynamic experiments, of maximum rates during the four-second increasing ramp and during the chirp. Each point plots the maximum trial-averaged firing rate and its standard error for one MU (N = number of trials for that MU and that condition; 28 on average). Labels highlight four MUs that have similar maximum firing rates during the ramp but different maximum firing rates during the chirp. There were also many MUs that were nearly silent during the ramp but achieved high rates during the chirp (points clustered near the vertical axis). Inset: distribution of recruitment thresholds, estimated as the force at which the MU’s firing rate, during the four-second ramp condition, exceeded 10% of its maximum rate during that condition. (b) Analysis of the possible impact of fatigue across the course of each session. Top: total MU spike counts, over the full recorded population, after dividing the session into thirds. Each line corresponds to one session. Bottom: Mean and standard error (across 14 sessions) of the normalized total MU spike counts (counts for each session were normalized by maximum across trial epochs). There is little overall change in MU activity over the course of a session.

Extended Data Fig. 3 Additional documentation of single-trial responses during a dynamic experiment session.

Presentation is similar to Fig. 5a-c but voltage traces are shown for six total trials. Furthermore, spike rasters are shown for all active MUs and all trials for two conditions: the four-second ramp and the 3 Hz sinusoid. (a) Same as Fig. 5a but force traces are highlighted for six trials (three trials per condition) rather than two. (b) MU spike templates, repeated from Fig. 5b. (c) EMG voltage traces for the highlighted trials and times (slightly greater time ranges are used relative to Fig. 5c). Data are shown for six trials, three in each column. Four recording channels are shown per trial. Left column: three trials for the ramp. Right column: three trials for the sinusoid. In each column, the second trial repeats that shown in Fig. 5c. Vertical and horizontal scales are shared with panel b. (d) Responses of all MUs that were active for these two conditions during this session. Data is shown for the four-second ramp (left column) and the 3 Hz sinusoid (right column). Data is color-coded by MU. Labels give both the session-specific MU identity (1–7), and the overall ID. Trial-averaged rates are shown with flanking standard errors. Spike rasters contain one row per trial, ordered from the first trial for that condition at the bottom to the last at top. Trials for all conditions were interleaved during the experiment. Vertical scale: 20 spikes/s. Horizontal scale: 500 ms.

Extended Data Fig. 4 Example MU responses and waveforms across muscle lengths.

These data address a potential artifact: an apparent change in recruitment across muscle lengths could occur if, due to tiny shifts in electrode location across muscle lengths, the spikes of an MU become undetectable. On the one hand, the stability of neighboring MUs largely rules out this concern. On the other hand, it is conceivable that most MUs could remain stable, while one (or more) MUs undergo a dramatic change in waveform that renders them unsortable. Addressing this concern thus requires confirming that changes in recruitment are observed concurrently with waveform stability. (a) State-space plots illustrating, for two MUs, that changes in muscle length create large departures from a 1D monotonic manifold. Departures occurred because the activity of MU158 was greatly reduced when muscle length was shortened. A natural concern is thus that the waveform of MU158 may have changed (or disappeared) across muscle lengths, causing an apparent drop in firing rate due to most spikes being missed. If one considered only the data for the 2 Hz sinusoid, this potential confound cannot be ruled out because there are no spikes from MU158 during that condition. Thus, one cannot distinguish between the possibility that MU158 is no longer detectable and the possibility that it is no longer active. However, other conditions did evoke activity from MU158. Thus, MU158 is still detectable, it is just much less active. (b) The above observations largely rule out the concern that the drop in firing rate of MU158, at the shorter muscle length, is due to it becoming undetectable. Yet perhaps its waveform changed considerably – enough to be detected only occasionally? Alternatively, perhaps MU158 did indeed become undetectable, and the spikes attributed to it were from some other MU? Both these possibilities are very unlikely: waveform shape, across multiple channels, provided a unique signature of this MU that was stable across muscle lengths. Left. Template of MU158 across the 5 EMG channels used during this session. Middle. Across all conditions when the muscle was long, we sorted 5147 spikes matching the template. The 20 with the best match are shown. Right. Across all conditions when the muscle was short, we sorted 1568 spikes that matched the template (~30% as many spikes as when the muscle was long). The 20 with the best match are shown and illustrate that this waveform was still very much present. This rules out the concern that the waveform of MU158 has changed dramatically. It also addresses the concern that the waveform of MU158 has become undetectable, and the apparent spikes of MU158 are due to missorted spikes from some other MU. This is extremely unlikely; the ‘other’ MU would have to produce spikes that matched the original template, across both time and channels.

Extended Data Fig. 5 Single-latent model fits to artificial data constructed to be consistent with rigid control, but with various types of noise.

We generated realistic simulated data from a 1-latent model by fitting our 1-latent model to the empirical MU response from one session. Then, using the learned latents, link functions, and lags, we generated simulated MU activity. We then fit a different model (with different initialization or parameters) to confirm that it could successfully fit the simulated responses. This acts as both a test of whether optimization succeeds in finding a perfect fit when one is possible and as a way of documenting the behavior of the cross-validated fit error. (a) ‘True’ (gray traces) and model fit (dashed red traces) responses for two example simulated MUs (columns) during four conditions (rows). Simulations involved no sampling error. R2 values of the fit were above 0.99 for all MUs. (b) Cross-validated error plots (as in Fig. 7) for simulated data for this example session, and after incorporating noise into the simulations. ‘Independent emission noise’ is Gaussian noise that is independently added at each time point, with standard deviation for each MU equal to the SEM of MU activity across trials (a typical value was found by averaging across time-points and MUs). ‘Realistic emission noise’ is T-dimensional gaussian noise (where T is the number of time points) generated from a MU’s temporal covariance structure computed across trials (i.e., the covariance matrix of a T × R matrix of activity, where R is the number of trials). This noise structure is calculated separately for each MU. ‘Realistic latent noise’ is T-dimensional gaussian noise that is added to the latent (prior to the link functions), rather than noise directly added to the MU activities. This noise was generated from the latents’ temporal covariance structure computed across trials. Independent noise was added to the latent for each MU, corresponding to each neuron receiving a noisy version of a single latent. Error bars show mean and 95% range of the cross-validation error across 10 partitionings of the data. Note that cross-validated error should be zero on average if sampling noise is the only impediment to a perfect fit and is thus expected (in that situation) to take on a range of positive and negative values centered near zero. Cross-validated error was indeed near zero for all simulations (and much lower than the fit error for the empirical data, orange) confirming that optimization was successful and cross-validated error behaved as expected. Note that this session had larger single-latent model violations for the empirical data than the average session, so the scale of the y-axis is larger than in Fig. 7.

Extended Data Fig. 6 Example M1 neural activity.

(a) Trial-averaged forces from one recording session. Each column corresponds to one condition (the intermediate static force condition is omitted for space). Vertical scale bar indicates 8 N. Horizontal scale bar indicates 1 s. (b–j) Responses of 9 M1 neurons. Each subpanel plots the trial-averaged firing rate with standard error (top) and single-trial spike rasters (bottom). Vertical scale bars indicate 20 spikes/s. Horizontal scale bars indicate 1 s.

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Supplementary Methods regarding spike sorting and the optimal recruitment model

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Marshall, N.J., Glaser, J.I., Trautmann, E.M. et al. Flexible neural control of motor units. Nat Neurosci 25, 1492–1504 (2022). https://doi.org/10.1038/s41593-022-01165-8

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