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Separating cognitive and motor processes in the behaving mouse

Nature Neuroscience volume 28, pages 640–653 (2025)Cite this article

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Abstract

The cognitive processes supporting complex animal behavior are closely associated with movements responsible for critical processes, such as facial expressions or the active sampling of our environments. These movements are strongly related to neural activity across much of the brain and are often highly correlated with ongoing cognitive processes. A fundamental issue for understanding the neural signatures of cognition and movements is whether cognitive processes are separable from related movements or if they are driven by common neural mechanisms. Here we demonstrate how the separability of cognitive and motor processes can be assessed and, when separable, how the neural dynamics associated with each component can be isolated. We designed a behavioral task in mice that involves multiple cognitive processes, and we show that dynamics commonly taken to support cognitive processes are strongly contaminated by movements. When cognitive and motor components are isolated using a novel approach for subspace decomposition, we find that they exhibit distinct dynamical trajectories and are encoded by largely separate populations of cells. Accurately isolating dynamics associated with particular cognitive and motor processes will be essential for developing conceptual and computational models of neural circuit function.

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Fig. 1: Cortical dependence and uninstructed movements during a two-context task.
Fig. 2: Single-cell activity and population dynamics encode task-relevant cognitive and motor processes.
Fig. 3: Uninstructed movements are tightly linked to putative planning dynamics.
Fig. 4: Uninstructed movements are closely related to the neural encoding of context.
Fig. 5: Subspace decomposition of neural activity using trial-averaged and single-trial data.
Fig. 6: Schematic of potential relationships between internal and movement-related dynamics.
Fig. 7: Subspace decomposition allows for the re-examination of population measures of motor planning.
Fig. 8: A persistent, cognitive representation of context in the movement-null subspace.

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Data availability

The data described in this study are available on Zenodo at https://doi.org/10.5281/zenodo.13941414 (ref. 69).

Code availability

MATLAB and Python code for subspace identification is available at https://github.com/economolab/subspaceID. Custom MATLAB code used for analyses is available on Zenodo at https://doi.org/10.5281/zenodo.13941414 (ref. 69).

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Acknowledgements

We thank K. Svoboda, B. DePasquale, S. Druckmann and B. Scott for helpful discussions; T. Wang for helpful comments on the manuscript; and J. Jiang for help with crystal skull surgeries. This work was supported by the Whitehall Foundation, the Klingenstein Fund, the Simons Foundation and National Institutes of Health R01NS121409 and U19NS137920.

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Author notes

  1. These authors contributed equally: Munib A. Hasnain, Jaclyn E. Birnbaum.

Authors and Affiliations

  1. Department of Biomedical Engineering, Boston University, Boston, MA, USA

    Munib A. Hasnain, Juan Luis Ugarte Nunez, Emma K. Hartman & Michael N. Economo

  2. Center for Neurophotonics, Boston University, Boston, MA, USA

    Munib A. Hasnain, Jaclyn E. Birnbaum & Michael N. Economo

  3. Graduate Program for Neuroscience, Boston University, Boston, MA, USA

    Jaclyn E. Birnbaum

  4. Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA

    Chandramouli Chandrasekaran

  5. Department of Neurobiology & Anatomy, Boston University, Boston, MA, USA

    Chandramouli Chandrasekaran

  6. Center for Systems Neuroscience, Boston University, Boston, MA, USA

    Chandramouli Chandrasekaran & Michael N. Economo

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  1. Munib A. Hasnain
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Contributions

M.A.H., J.E.B. and M.N.E. conceived of the project. M.N.E. and C.C. supervised research. M.A.H., J.E.B. and M.N.E. designed experiments. M.A.H., J.E.B., J.L.U.N. and E.K.H. performed experiments. M.A.H., J.E.B. and M.N.E. analyzed data. M.A.H., J.E.B. and M.N.E. wrote the manuscript.

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Correspondence to Michael N. Economo.

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Extended data

Extended Data Fig. 1 Region-specific photoinactivation of ALM and tjM1.

a. Percentage of time spent moving (see Methods) in a session determined by video recordings using the side view, bottom view, and both views. Points indicate each of the 12 randomly selected sessions used for this analysis. Error bars denote standard deviation across sessions (n = 12). b. Effect of delay epoch photoinactivation on behavioral performance (middle column) and uninstructed movements (right column) when photoinactivation was directed to the MC (ALM + tjM1; top row; same data as Fig. 1g–in = 14 sessions, 2 mice), the ALM (middle row, n = 15 sessions, 2 mice), and the tjM1 (bottom, n = 9 sessions, 2 mice). Photoinactivation of ALM and tjM1 led to similar behavioral impairment and reduction in uninstructed movements, with larger effects observed with MC (ALM + tjM1) photoinactivation. c. Tongue length during control and go cue/water drop photoinactivation trials for delayed-response (left) and water-cued (right) contexts. Blue traces indicate right lickport contacts, red traces indicate left contacts, and black traces indicate no contact. Vertical dashed line indicates go cue or water drop onset. Blue shaded region indicates photoinactivation period. d. Percentage of time with tongue visible during photoinactivation period for DR trials (left) and WC trials (right). Each colored point indicates mean value for an animal (n = 4 animals), individual animals are connected by black lines. Light gray lines denote individual sessions (n = 10 sessions). Bars are the mean across all sessions. Asterisks denote significant differences (p < 0.05) between control and photoinactivation trials (Percent reduction on all DR trials: 19 ± 7%, mean ± s.d., p = 1.6e-05; DR left trials: 20 ± 8%, p = 3.0e-05; DR right trials: 20 ± 7%, p = 1.6e-05; All WC trials: 4 ± 8%, p = 0.154; WC left trials: 1% ± 9%, p = 0.702; WC right trials: 7% ± 5%, p = 0.002; paired two-sided t-test, n = 10 sessions). Error bars indicate standard deviation across sessions. In WC trials, tongue protrusion was only significantly impaired on one trial type, while ability to successfully contact the lickport was impaired in all conditions (see Fig. 1c).

Extended Data Fig. 2 Session-by-session statistics.

a. Number of recorded single- and multi-units per session for the fixed delay task (left) or the randomized delay task (right). Left, Purple shaded region indicates sessions in which animals were only presented with DR trials. Green and blue bars underneath plots indicate the probe type used for a given session. b. Variance explained of trial-averaged neural activity by each coding direction. The coding directions were calculated using neural activity from individual sessions (n = 25). Bar height represents the mean across sessions and error bars indicate standard deviation across sessions. c. Receiver operating curves (ROC) demonstrating choice decoding accuracy from delay epoch CDchoice projections across all individual sessions (see Methods). Inset: area under the ROC curve (AUC). Bar height represents the mean across sessions and points indicate sessions.

Extended Data Fig. 3 Session-by-session variability in the relationship between kinematic features and putative cognitive dynamics.

a. Regression weights for each group of kinematic predictors of CDchoice projections, as a fraction of all predictor coefficients (see Methods). Sessions are sorted in descending order by motion energy fraction. Outlined bars indicate example sessions shown in (b) and (c). b. Example session where motion energy made up the largest fraction of regression weights for predicting CDchoice projections. Top, overlayed jaw/nose speed or motion energy for a subset trials. Bottom, two example trials of kinematic feature trajectories. c. Same as (b) but for an example session where jaw and nose features made up a larger fraction of regression weights. d. Same as (a) but for predicting CDcontext projections. e, f. Same as (b) and (c) but for two example sessions with different regression weight fractions for predicting CDcontext projections.

Extended Data Fig. 4 Control analyses for subspace decomposition.

a, b. Movement-null and movement-potent subspaces estimated as in Fig. 5g–l using DR and WC trials. a. Variance explained (R2) of motion energy by the sum squared magnitude of activity in the movement-null and movement-potent subspaces on single trials. Each point is the mean across trials for a session. b. Left, motion energy on single trials for an example session. Middle, sum-squared magnitude of activity in the movement-potent subspace. Right, sum-squared magnitude of activity in the movement-null subspace. Trials sorted by average delay epoch motion energy. c. Selectivity (left vs. right) of the neural population during WC trials. Mean and 95% CI across sessions shown. d, e. Same as (a,b) but estimating movement-null and movement-potent subspaces using WC trials only. f. Normalized magnitude of activity in the movement-null subspace (left) movement-potent subspace (right) when estimated using DR and WC trials as in (a,b), versus when estimated using WC trials only as in (d,e). Circles are average activity per trial for an example session. g, h. Same as (a,b), but estimating movement-null and movement-potent subspaces using data restricted to the response epoch of DR and WC trials. i. Magnitude of activity in the movement-null subspace (left) or movement-potent subspace (right) when estimated using DR and WC trials as in (a,b) versus when estimated using data from only the response epoch of DR and WC trials as in (g,h). Circles are average activity per trial for an example session. j, k. Same as (a,b), but estimating the movement-null and movement-potent subspaces using a two-stage PCA approach (see Methods). This approach is conservative in avoiding the mis-assignment of cognitive dynamics that correlate in time with movement to the movement-potent subspace. l. Magnitude of activity in the movement-null subspace (left) or movement-potent subspace (right) when estimated using DR and WC trials as in (a,b) versus when estimated using the two-stage PCA approach as in (j,k). Circles are average activity per trial for an example session.

Extended Data Fig. 5 Alignment of single-units to random subspaces.

Random subspaces were constructed by independently and identically drawing from a normal distribution with zero mean and unit variance. Each random subspace was then biased towards the covariance structure of the actual data (see Methods). a. Null distributions of alignment indices for trial-averaged data. b. Null distributions of alignment indices for single-trial data. Null alignment distributions are skewed towards the movement-potent subspace due to the unbalanced variance between delay and response epochs (a) or between stationary and movement time points (b), reflecting the strong movement tuning of many units.

Extended Data Fig. 6 Varying dimensionality of subspaces.

Analyses were repeated while varying the dimensionality of movement-null and movement-potent subspaces. Each subspace was constrained to be 4 (left), 6 (middle left), 8 (middle right), or 13 dimensions (right). a. Upper bound estimate of dimensionality for trial-averaged (PSTH) data and single-trial data. Bar heights indicate mean across sessions, points indicate sessions, and error bars indicate standard deviation across sessions (n = 25 sessions). b. Cumulative variance explained of the neural activity by the activity in movement-null and movement-potent subspaces. Bold lines and points indicate mean across sessions. Thin lines represent single sessions c. Normalized variance explained of neural activity during the delay or response epoch by the activity in movement-null and movement-potent subspaces. Points indicate sessions, bar height indicates mean across sessions, and error bars indicate standard deviation across sessions (n = 25 sessions). d–f. Subspace (d), CDchoice (e), and CDramp (f) alignment distributions when varying dimensionalities of each subspace.

Extended Data Fig. 7 Projections along movement-null and movement-potent components of CDchoice.

a. Same data as in Fig. 7a except all time in trial shown to highlight activity during the response epoch. Selectivity (projections onto CDchoice on lick-right trials minus projections on lick-left trials) of movement-null (left) and movement-potent (right) subspace activity. Mean and 5–95% CI of the bootstrap distribution for correct (solid) and error (dashed) trials shown. b. Change in selectivity between the last 100 ms of the delay epoch and the last 100 ms of the sample epoch in movement-null and movement-potent components of projections along CDchoice (Movement-potent: 2.25 ± 1.57, mean ± s.d., Movement-null: 0.76 ± 0.8, p = 1 × 10−5, paired two-sided t-test, n = 25 sessions). Points indicate individual sessions, bar height indicates mean across sessions, and error bars indicate standard deviation across sessions. c. Three example sessions from three different mice depicting selectivity along CDchoice as in Fig. 7a. Solid lines denote the mean projection on correct trials and dashed lines denote the mean projection on error trials.

Extended Data Fig. 8 Within-subspace CD projections using variations on procedure to determine subspaces.

a. Projections of movement-null and movement-potent subspace activity along CDramp for each of three analytical variations. Movement-null and movement-potent subspaces were identified using both DR and WC trials (left), WC trials only (middle), and the response epoch of DR and WC trials (right). Mean and 95% CI of bootstrap distribution shown. b. Projections along movement-null (left) and movement-potent (right) components of CDramp when determined from activity within each subspace individually, rather than from the full neural population. Mean and 95% CI of bootstrap distribution shown.

Extended Data Fig. 9 Encoding of context in both the null and potent subspaces tracks block-wise task structure.

a. Heatmap of single-trial projections of null and potent subspace activity along CDcontext for an example session. The chronological DR or WC block within the session is denoted by differently shaded purple and orange rectangles, respectively, on the right of each plot. b. Same as (a) but for another example session, from a different animal.

Extended Data Fig. 10 Relationship between tongue angle and neural activity in the movement-null and movement-potent subspaces.

a. Projections along movement-potent (top) and movement-null (bottom) components of CDaction. Correct trials shown in solid lines and error trials shown in dashed lines. Shaded region depicts 95% CI of bootstrap distribution. b. Tongue angle for an example session for correct and error trials Black values indicate tongue not visible. c. Tongue angle on correct and error right and left trials. Tongue angle was linearly time warped to allow for averaging over trials and sessions. Mean and s.e.m. across sessions shown. d. Tongue angle (left) and predictions from the full population neural activity (middle left), null subspace activity (middle right), and potent subspace activity (right) for an example session. e. Variance explained (R2) of tongue angle by prediction from movement-null (green) and movement-potent (pink) subspaces. Asterisks denote significant differences between predictions from null and potent subspaces and error bars indicate standard deviation across sessions (p = 2 × 10−8, paired two-sided t-test, n = 25 sessions).

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2.

Reporting Summary

Supplementary Movie 1

Uninstructed movements vary in their identity and timing. Example trials in which uninstructed movements vary in their identity (across rows) and timing (across columns). Traces represent the y position of the feature within the video frame. All example trials are taken from the same mouse and session.

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Hasnain, M.A., Birnbaum, J.E., Ugarte Nunez, J.L. et al. Separating cognitive and motor processes in the behaving mouse. Nat Neurosci 28, 640–653 (2025). https://doi.org/10.1038/s41593-024-01859-1

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