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The striatum multiplexes contextual and kinematic information to constrain motor habits execution

Nature Neuroscience volume 18, pages 453–460 (2015)Cite this article

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Abstract

The striatum is required for the acquisition of procedural memories, but its contribution to motor control once learning has occurred is unclear. We created a task in which rats learned a difficult motor sequence characterized by fine-tuned changes in running speed adjusted to spatial and temporal constraints. After training and extensive practice, we found that the behavior was habitual, yet tetrode recordings in the dorsolateral striatum (DLS) revealed continuous integrative representations of running speed, position and time. These representations were weak in naive rats that were hand-guided to perform the same sequence and developed slowly after learning. Finally, DLS inactivation in well-trained animals preserved the structure of the sequence while increasing its trial-by-trial variability. We conclude that, after learning, the DLS continuously integrates task-relevant information to constrain the execution of motor habits. Our results provide a straightforward mechanism by which the basal ganglia may contribute to habit formation and motor control.

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Figure 1: A fixed interval estimation task adapted to treadmill locomotion favored the learning of a stereotyped running sequence.
Figure 2: Evidence for habitual performance of the running sequence.
Figure 3: Continuous sequential modulation of striatal spiking activity during sequence execution.
Figure 4: Linear and integrative representation of running speed, position and time after learning.
Figure 5: Speed-related spiking activity changes more slowly than running speed.
Figure 6: Position and running speed representations in the DLS are specific of motor habits.
Figure 7: Inactivation of the DLS increases trial-by-trial variability of running sequence execution.

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Acknowledgements

We thank J. Krakauer, K. Diba, J. Epsztein, O. Manzoni and H. Martin for excellent discussions and critical reading of this and/or earlier version of the manuscript, I. Cordon Morillas, C. Sales-Carbonell and L. Lalla for help with training of the animals, J. Perez-Ortega for help with LabView Vision, A. Brovelli for advice with multiple regression analysis, and R. Martinez for technical help. This work was supported by the Spanish Ministerio de Ciencia e Innovación (Ramon-Y-Cajal program, D.R.) EU-fp7 (International Reintegration Grant, IRG230976, D.R.; Marie Curie International Incoming Fellowship, IIF253873, P.E.R.-O.), INSERM (Avenir Program, D.R.), European Research Council (ERC-2013-CoG – 615699_NeuroKinematics, D.R.) and the Mexican Consejo Nacional de Ciencia y Tecnología (P.E.R.-O.).

Author information

Authors and Affiliations

  1. INSERM, U901, Marseille, France

    Pavel E Rueda-Orozco & David Robbe

  2. Aix-Marseille University, UMR 901, Marseille, France

    Pavel E Rueda-Orozco & David Robbe

  3. INMED, Marseille, France

    Pavel E Rueda-Orozco & David Robbe

  4. Department of Systems Neuroscience, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain

    Pavel E Rueda-Orozco & David Robbe

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  1. Pavel E Rueda-Orozco
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Contributions

P.E.R.-O. performed the experiments. D.R. and P.E.R.-O. designed the experiments, analyzed the data and wrote the manuscript.

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Correspondence to David Robbe.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Additional illustrations of the front-rear-front running sequence in several rats.

(a–c) Trajectories of 6 animals during single sessions after extensive training (at least 2 months of daily training). The goal time was fixed at 5 s (a, Rat 4 and 9), 6 s (b, Rat 6) or 7 s (c, Rat 2, 7, 17). Trajectories of Rat 2, 2 months after shortening the treadmill, are shown in the bottom panel in c.

Supplementary Figure 2 High level of stereotypy in trained animals.

(a) Trajectory of the left forelimb of a well-trained animal during a single correct trial. The trough of each oscillation (yellow dots) corresponds to the time of the step cycle at which the left forelimb is stretched forward. The last step of the sequence is indicated in red. (b) Forelimb stretched times during a single session. Trials are aligned relative to the last step of the running sequence (red arrow) and sorted according to the time of the last but one step. (c) Filtered trajectories (same session than b), realigned to the end of the sequence (left) and speed profiles (right) derived from the trajectories. Dashed red lined indicates treadmill's speed.

Supplementary Figure 3 Electrophysiological procedures.

(a) Microdrive (NLX-9) loaded with 8 tetrodes (arrow). (b) Example of a tetrode track (arrow) on a cresyl violet-stained coronal section of the striatum. We did not systematically perform electrolytic lesions. (c) Schematic localization of the tip of all tetrodes (n = 22, 5 rats) used in this study. (d) Quality of isolation of single-units recorded with tetrodes. Upper left: projection of two first principal components extracted from the spikes waveforms recorded from a single tetrode. 3 well-isolated clusters are clearly visible. Lower left: spikes from isolated units in the wide-band striatal LFP. Upper right: filtered spike waveforms. Lower right: auto- and cross-correlograms (± 30 ms) computed from the spike trains of the 3 units.

Supplementary Figure 4 Light on-responsive units.

Each row corresponds to an example unit recorded in a different animal. (a) Spike rasters (top) and average firing rate (bottom) aligned to treadmill onset. Red lines indicate light on, green lines treadmill onset, blue lines goal time. (b) Spike rasters (top) and average firing rate (bottom) aligned relative to the end of each running sequence, when animals crossed for the fist time the photodetector (black line). (c) Auto-correlograms of spiking activity during the entire recording session. (d) Spike rasters aligned relative to the time at which the left forelimb is stretched forward (red line, see Supplementary Fig. 2).

Supplementary Figure 5 Light off-responsive units.

Same panel structure than Supplementary Figure 4, but for units showing strong firing rate increases at the end of correct trials. Note that we make no assumption on the meaning of these modulations (reward consumption, sensorimotor activity associated with drinking-related movements,...). The trials in which there is no apparent change in firing rate correspond to incorrect trials.

Supplementary Figure 6 Forelimb-responsive units.

Same panel structure than Supplementary Figures 4 and 5 but for units that show strong rhythmical modulation of their firing rate coordinated with the oscillatory dynamics of forelimb movements during treadmill locomotion.

Supplementary Figure 7 Trial-by-trial correlations between firing rate and speed or position.

(a) Trial-by-trial color-coded instantaneous running speeds (top) and firing rates (bottom) for the unit shown in Figure 4b. Trials were sorted according to the time of maximum running speed. (b) Two error trials taken from a. Animal's position (top), running speed (middle) and firing rate of the unit (bottom). (c) Trial-by-trial color-coded positions (top) and instantaneous firing rates (bottom) during a single session for the unit shown in Figure 4d. Trials were sorted according to the time of termination of the front-rear-front sequence. (d), Two errors trials taken from c. Position (top) and firing rate of the unit (bottom) are shown. In the illustrative trials, the animal entered too early in the stop area. The gray area shows the portion of the trial corresponding to the performance of an archetypical front-rear-front sequence.

Supplementary Figure 8 Correlation between firing rate and running speed cannot be explained by gait transition.

(a) Scatter plot showing average firing rate versus speed (mean ± s.d.) for a speed-modulated unit during a session in which the animal galloped in a few trials. The partial correlation coefficient between running speed and firing rate was computed either with or without including galloping state (1 animal is galloping, 0 animal is not galloping) in our correlation matrix. (b) Example trials where speed (blue) and firing rate (black) are compared. To facilitate visual inspection, speed and firing rate were normalized to their maximum values in each trial. Frames in which the animal galloped are marked with yellow dots on top of the plot.

Supplementary Figure 9 The DLS of well-trained animals multiplex time, position, speed and acceleration.

(a) Median of the multiple correlation coefficients (all units) computed with regression models taking into account a single predictive variable (same color code than Fig. 4a), two (pink), three (orange) or four predictive variables (statistical differences against the model with 4 predictors are indicated as follows: * P < 0.05; ** P < 0.01; ***P < 0.001). (b) Cumulative distributions of the multiple correlation coefficients computed with regression models taking into account a single predictive variable and 4 predictive variables. (c) Median of Akaike information criterion (AIC) values computed for all recorded units, using the same regression models than in a Red dotted line indicates the AIC median value of the best model. (d) Differences in AIC values between the best model (4 predictors) and all the other models. (e) Statistical comparisons of the differences shown in d. Statistical differences in a and e were determined with Kruskall-Wallis and Tukey's HSD test.

Supplementary Figure 10 Predominance of linear relations between firing rate and kinematics variables in the DLS of well-trained animals.

(a,b) Mean firing rate vs. normalized position (blue), speed (gray) and acceleration (black) for two representative cells. Doted red lines indicate shuffled-derived global acceptance limits. (c) Distributions, for all the units analyzed, of the parts of the tuning curves that crossed the significance limits (red dots in a and b). (d) Fraction of units modulated by position, speed and acceleration. e, Correlation coefficients between firing rate and kinematics variables for all modulated units (median ± first and third quartiles). (f) Fraction of modulated units that display significant linear relationship between firing rate and the kinematics variables. (g,h) same cells than a and b. Surrogate tuning curves were pseudo-randomly constructed to test, for instance, (g, left panel, gray dashed lines) how much the interaction between firing rate and position was expected from the relation of these two variables with speed. The significant relation between acceleration and firing rate (g, right panel, black line) is entirely explained by the relationship of these two variables with position (blue). (i–l) Same than c–f after correcting for the relations between the different variables.

Supplementary Figure 11 Temporal profile and magnitude of the firing rate modulations of striatal units recorded during hand-guided execution of the running sequence.

(a) Averaged modulation of the firing rate of all recorded units during hand-guided execution of the task, for the three naive animals (similar than Fig. 3). (b) Distribution of the maximum Z-scores for all the units recorded in each animal (left) or grouped per task condition (hand-guided versus well- trained, right). The maximum values of the Z-scores are slightly higher in the hand-guided group compared to the well-trained animals (Wilcoxon rank-sum test).

Supplementary Figure 12 Reduced correlations between firing rate and task variables cannot be explained by differences in running speed.

(a) Distribution (mean ± std) of running speeds for all the animals in which we computed correlations between firing rate and task variables. Hand-guided animals spent more time running at the treadmill speed (35–40 cm s−1) and less time running between 50 and 70 cm s−1 than well-trained animals. (b–d) In Rat 17, we recorded neuronal activity during hand-guided sessions and after training. Data were randomly removed (rectangles in a) to match the speed distribution in the two conditions. Speed distribution for Rat 17 in the hand-guided condition before (red) and after (gray) removing data (b). Speed distribution for Rat 17 after extensive training, before (blue) and after (gray) removing data (c). After correction, the speed distributions for Rat 17 are identical in both conditions (d). (e) Speed corrections did not affect the distribution of multiple correlation coefficients between firing rate and task variables (red versus dark gray and blue versus light gray). When speed distributions were matched between hand-guided and well-trained conditions the multiple correlation coefficients were highly increased after training (dark versus light gray, Wilcoxon rank-sum test, P < 0.05).

Supplementary Figure 13 Effect of bilateral injections of muscimol in the DLS on treadmill-based locomotion in naive animals.

(a,b) Trajectories during single sessions performed 10 min after bilateral injections (1 μl each side) of saline (a, black) or of 50 ng of muscimol (b, blue). During behavioral testing the animal ran freely during 20 s long trials with the treadmill speed set at 35 cm s−1. The two injections/behavioral tests were separated by 30 min (saline injection was performed first). (c) Running speed distributions were similar after saline and 50 ng muscimol injection. (d) Red line shows the difference between speed distributions. Dashed lines indicate the 5 % global confidence interval determined using a shuffling bootstrap procedure. (e,f) Similar than c,d but for the position of the animal on the treadmill. No significant differences were found. g–l, similar than a–f but for injections of 500 ng of muscimol. The animal spent more time at low speeds and spend less time in the front of the treadmill after muscimol injection.

Supplementary Figure 14 Spread of muscimol injections, histology and behavioral effects.

(a), Injection cannula tracks and sites of injection in three rats. (b) Visualization of the spread of diffusion using a fluorescent dye. (c) Distributions of running speeds from a well-trained animal before and after muscimol injections. (d) Red line shows the difference between speed distributions before and after muscimol. Dashed lines indicate the 5 % global confidence interval determined using a shuffling/bootstrap procedure.

Supplementary Figure 15 Effect of bilateral injections of muscimol in the DLS of well-trained animals on running trajectories.

(a) Group effect of muscimol injections on percentage of correct trials. (b,c) Trajectories in trials that started similarly (red rectangle), one day before (a) and 10 min after (b) muscimol injection. Note the increased variability in entrance times (red dots) after muscimol injection. Trajectories were selected from the muscimol and pre-muscimol sessions shown in Figure 7a. (d–f) For each muscimol injection experiment, average trajectories (thick lines) and standard deviations (thin lines) in the session before (black), during (blue) and after (gray) muscimol injections. Experiments performed with goal times set at 4, 5 and 7 s are presented in columns d, e, and f respectively.

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Supplementary Figures 1–15 (PDF 5621 kb)

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3 consecutive trials performed by Rat 9 during the first training session.

The bottom plot depicts the real time change in position of the rat while running on the treadmill. Trials 2 and 3 are cut at 8 s. The upper plot displays the complete trajectories for the 3 trials. Notice that the animal spent most of the trial in the front of the treadmill. (MP4 1378 kb)

7 consecutive trials performed by Rat 1 after 100 sessions of training.

The bottom plot depicts the positions of the fluorescent marker on the front left limb. The upper plot displays the smoothed trajectories (animal's position). Notice how regular is the running routine of the animal across trials. Last trial is incorrect. (MP4 2624 kb)

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Rueda-Orozco, P., Robbe, D. The striatum multiplexes contextual and kinematic information to constrain motor habits execution. Nat Neurosci 18, 453–460 (2015). https://doi.org/10.1038/nn.3924

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