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Action suppression reveals opponent parallel control via striatal circuits

Nature volume 607, pages 521–526 (2022)Cite this article

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Abstract

The direct and indirect pathways of the basal ganglia are classically thought to promote and suppress action, respectively1. However, the observed co-activation of striatal direct and indirect medium spiny neurons2 (dMSNs and iMSNs, respectively) has challenged this view. Here we study these circuits in mice performing an interval categorization task that requires a series of self-initiated and cued actions and, critically, a sustained period of dynamic action suppression. Although movement produced the co-activation of iMSNs and dMSNs in the sensorimotor, dorsolateral striatum (DLS), fibre photometry and photo-identified electrophysiological recordings revealed signatures of functional opponency between the two pathways during action suppression. Notably, optogenetic inhibition showed that DLS circuits were largely engaged to suppress—and not promote—action. Specifically, iMSNs on a given hemisphere were dynamically engaged to suppress tempting contralateral action. To understand how such regionally specific circuit function arose, we constructed a computational reinforcement learning model that reproduced key features of behaviour, neural activity and optogenetic inhibition. The model predicted that parallel striatal circuits outside the DLS learned the action-promoting functions, generating the temptation to act. Consistent with this, optogenetic inhibition experiments revealed that dMSNs in the associative, dorsomedial striatum, in contrast to those in the DLS, promote contralateral actions. These data highlight how opponent interactions between multiple circuit- and region-specific basal ganglia processes can lead to behavioural control, and establish a critical role for the sensorimotor indirect pathway in the proactive suppression of tempting actions.

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Fig. 1: Mice learned to dynamically suppress lateralized actions.
Fig. 2: Opposite modulation of DLS iMSNs and dMSNs during action suppression.
Fig. 3: Optogenetic inhibition of DLS iMSNs, but not dMSNs, disrupted action suppression and selection, whereas inhibition of dMSNs, but not iMSNs, slowed movement.
Fig. 4: A simplified dual agent model reproduces behaviour, neural activity and effects of optogenetic inhibition.

Data availability

The data and analysis code that support the findings of this study are available from the corresponding author upon reasonable request. Source data are provided with this paper.

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Acknowledgements

We thank B. Lau, J. Krakauer, E. DeWitt, B. Atallah, A. Klaus and T. Monteiro for comments on different versions of the manuscript and the entire J.J.P. laboratory for feedback during the course of this project. We also thank the ABBE Facility and the Scientific Hardware, Histopathology and Rodent Champalimaud Research Platforms for technical assistance, and B. Zarov and D. Domingues for helping with the training of some of the mice in this study. This work was developed with the support from the research infrastructure Congento, co-financed by Lisboa Regional Operational Programme (Lisboa2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF) and Fundação para a Ciência e Tecnologia (Portugal) under the project LISBOA-01-0145-FEDER-022170. The work was funded by an HHMI International Research Scholar Award to J.J.P. (55008745); a European Research Council Consolidator grant (DYCOCIRC - REP-772339-1) to J.J.P.; a Bial bursary for scientific research to J.J.P. (193/2016); internal support from the Champalimaud Foundation; and PhD fellowships (PD/BD/105945/2014 and PD/BD/128301/2017) from Fundação para a Ciência e Tecnologia to B.F.C. and G.G. respectively.

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Author notes

  1. Sofia Soares

    Present address: Department of Neurobiology, Harvard Medical School, Boston, MA, USA

  2. Asma Motiwala

    Present address: Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

Authors and Affiliations

  1. Champalimaud Neuroscience Programme, Champalimaud Research, Champalimaud Foundation, Lisbon, PT, Portugal

    Bruno F. Cruz, Gonçalo Guiomar, Sofia Soares, Asma Motiwala, Christian K. Machens & Joseph J. Paton

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Contributions

J.J.P. and B.F.C. conceived of the experiments and wrote the manuscript. S.S. assisted in analysis of the fibre photometry data and helped revise the manuscript. B.F.C. performed all experiments and analysed the data. G.G., B.F.C. and J.J.P. conceived of the model with assistance from A.M. G.G. built the model and performed all simulations. C.K.M. provided constructive feedback about the model and helped revise the manuscript. J.J.P. supervised all aspects of the project.

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Correspondence to Joseph J. Paton.

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Extended data figures and tables

Extended Data Fig. 1 Histological reconstruction of sites of fibre implantation for photometry, electrophysiology and optogenetic experiments in the DLS and the DMS.

a–d, Photometry (a), electrophysiology (b) and optogenetic experiments in the DLS (c) and the DMS (d). Mice are coloured by their genotype according to the legend. For electrophysiology and optogenetics experiments, the DV coordinate is shown as the deepest position of the shanks or tapered fibre, respectively, was observed in histological slices. All coordinates were projected to the same coronal slice (AP = +0.5 from bregma) adapted from70. Behaviour metrics across genotypes (e-n). A2a-Cre and D1-Cre single mice, included in the photometry experiments, are shown in red and blue, respectively. Single-mouse psychometric curve (e) fits and respective parameters (see Methods for further details, two-tailed t-test, f, p = 0.935, t12 = 0.083, g, P = 0.17, t12 = −1.459, h, P = 0.823, t12 = 0.228, i, P = 0.826, t12 = −0.225). j, Overall probability of breaking fixation (all trials included) (P = 0.665, t12 = 0.445) k, Percentage of trials in which mice attempted to make a choice after breaking fixation (all trials included) (P = 0.703, t12 = 0.391). l, Probability of reporting at the “long choice” port after breaking fixation contingent on whether the mouse aborted before (<−1.5 s) or after (>1.5 s) the decision boundary (P = 0.872, t12 = −0.165). m, Left, Hazard of breaking fixation in time for single mice (thin curves) and overall averages within genotype (thick lines). Right, differences between the hazard of breaking fixation after and before the decision boundary(P = 0.165, t12  = 1.48). n, Mean velocity during the delay period from correct trials of the longest interval (2.4 s, Data from Fig. 1c, P = 0.892, t12 = −0.139). Error bars represent s.e.m. n.s. P > 0.05.

Source data

Extended Data Fig. 2 Photometry activity during the period of active immobility and movement.

a) Average activity across both hemispheres during the immobility period for each hemisphere (data points) relative to baseline, and across mice. Activity for iMSNs and dMSNs is shown in red and blue, respectively (n = 16 hemispheres, from 8 A2a-Cre mice, n = 10 hemispheres from 6 D1-Cre mice, only correct trials were included). b,c, Difference between average activity after the 1.5-s decision boundary, and before the 1.5-s decision boundary (>1.5 s - <1.5 s) for activity recorded in the hemisphere contralateral to the side of the long choice port (CL, contra-long, b, N = 8 A2a-Cre and 5 D1-Cre mice)) and for activity recorded in the hemisphere contralateral to the short choice port (CS, contra-short, c, N = 8 A2a-Cre and 5 D1-Cre mice)). White lines and boxes represent mean and s.e.m., respectively. d-e) Both DLS direct and indirect pathways are more active during contralateral movements. Photometry signal recorded from from A2a-Cre (d) and D1-Cre (e), aligned to leaving the centre port during a near boundary interval (1.74s) where the same stimulus results in different choices (ipsilateral and contralateral to the recorded hemisphere, depicted as cyan and orange, respectively). Same trial selection as Fig. 2e. All boxes or shaded regions represent mean ± s.e.m.

Source data

Extended Data Fig. 3 The photo-identified iMSN population is enriched in cells with a higher firing rate during the delay period.

a, Activity profile of photo-identified indirect-pathway MSNs (photo-Ided iMSNs) and non-photo-identified putative MSNs (see methods for details). Each row represents a unit’s z-scored activity aligned to trial initiation that results from averaging the activity for all intervals cropped at second tone. Units are ordered by the angular position formed by the first two principal component projections. PCs were computed using a period of −2 to 2.4 s from trial initiation. b, Cumulative distribution of changes in firing rate during the delay period of photo-Ided iMSN (red) and all other putative MSNs (blue). Average ΔFR is significantly larger for iMSNs when compared to the distribution of non-identified cells (two-tailed t-test, P = 0.0196, t286 = 2.35). c, Proportion of up, down, and not modulated cells during the delay period (see methods for details). Proportions are significantly different between the two groups (Chi-squared test, P = 0.0115, χ22 = 8.939). d, Example of putative MSNs (pMSNs) classified as photo-identified. Top) Raster plot with single spikes aligned to laser pulse onset (2-ms duration). Bottom-Left) Distribution of latencies of the first spike observed in a 1–10ms after laser pulse onset. Bottom-right) mean waveform (black) and mean laser-triggered waveform (red). e,h, Distribution of: e, probability of observing a spike between 1 and 10ms after laser onset pulse, f, differences in firing rate between the baseline and 1–10ms post-pulse window, g, correlation coefficient (ρ) between mean waveform and mean laser-triggered waveform, h, median latency of the first spike in a window 1–50 ms after pulse onset (trials wherein no spike was observed in this window were not included).

Source data

Extended Data Fig. 4 Changes of activity in the indirect pathway preceding broken fixations.

a, Differences on the rate (derivative) of activity change (d(ΔF/FBrokenFixation)/dt- d(ΔF/FValid)/dt) aligned on broken fixations calculated from data recorded from A2a-Cre mice (16 hemispheres from 8 mice). b, Difference of mean activity (FRBrokenFixation- FRValid, Hz) of all photo-identified units not modulated (left, N = 46 units), positively modulated (centre, N = 12 units) and negatively modulated (right, N = 31 units) during the delay period. Blue lines depict periods during which there are significant differences between the average activity average activity on broken fixations and valid trials, across cells (two-tailed, paired t-test, p \(\leqslant \) 0.05). c, Schematic depicting the analysis performed in a) and b) in order to compared activity aligned to broken fixation trials. In brief, we took valid (black), or broken fixation (green), trials aligned to trial initiation (first-tone onset) cropped them at second tone or at the broken fixation event, respectively. We used the valid trials to compute a reference “valid trial” that reflected the average activity of all valid trials, cropped at second tone (mid, orange). Averaging available data (i.e. up until second tone) guarantees that only data from the fixation period is used, without incurring into contamination due to movement onset after the cue is delivered. We subsequently align each broken fixation trial to its occurrence (right) and take, from the reference valid trace, a time-matched fragment which we align to the same time since first tone. To compare traces aligned on broken fixation events to valid trials, we then average all broken fixation trials and the corresponding time-matched valid reference traces (see methods). d, Same analysis as in Fig. 3l but for an epoch [−0.5:−0.4]s relative to broken fixation events (Linear regression, slope = −0.042, P = 0.132, t87 = −1.522). Error bars represent s.e.m. n.s. P > 0.05.

Source data

Extended Data Fig. 5 Electrophysiological identification of putative MSNs and summary effect of opsin activation.

a, Identification of putative MSNs based on firing statistics and waveform duration (see methods). Green data points indicate units ultimately identified as putative MSNs (pMSNs). b, Changes in firing rate during light delivery and baseline period versus the baseline firing rate of all recorded isolated units (Including non- putative MSN units) from A2a-Cre (red) and D1-Cre (blue) mice infected with ArchT. Significantly negatively or positively modulated cells are shown as closed and open circles, respectively. Maximum theoretical inhibition is plotted as a grey dashed line (−ΔFR = Baseline FR). c, Distribution of changes in firing rate (Hz) during the period of light delivery, versus baseline, for putatively labelled MSN units recorded from A2a (Red) and D1-Cre (Blue) mice expressing ArchT, outside the context of the task. Grey depicts non-significantly modulated cells, closed and open shapes depict significantly down- and upmodulated cells, respectively. d, Overall average peristimulus time histogram of all negatively light-modulated cells, putatively labelled as MSNs, recorded from A2a-Cre and D1-Cre mice during the ArchT acute experiment. All units were z-scored (see methods). Shaded coloured area depicts the time of laser illumination. e–h, Same as b–d but for mice expressing ChR2 in MSNs. i, Summary of overall modulation effects of ArchT versus ChR2 activation in pMSNs.

Source data

Extended Data Fig. 6 Manipulation-induced changes in vigour and action selection depend on MSN type and striatum sub-location.

a, Cartoon depicts the three different manipulated trial types:Choice Time (Delay), laser was ramped off as the second tone is played. BrokenFix Choice Time (Delay), laser was ramped off as the mouse leaves the centre port causing a broken fixation. MovementTime (Decision) laser was turned on as the second tone is played until the mouse either performs its choice or 400ms elapse, whichever occurs first. b, Differences in single mouse’s median choice time between inhibited and non-inhibited trials (ΔChoiceTime = ChoiceTimeManipulation - ChoiceTimeControl). For each mouse, we concatenated all sessions and split trials in manipulated versus non-manipulated. From top to bottom: two-tailed t-test, P = 0.198, t5 = 1.482; P = 0.198, t5 = 1.482; P = 0.005, t5 = 4.834; P = 0.128, t2 = 2.523; P = 0.718, t4 = 0.388; P = 0.46, t4 = −0.817; P = 0.617, t5 = −0.533; P = 0.774, t5 = −0.304; P = 0.032, t5 = 2.959; P = 0.005, t5 = 4.876; P = 0.247, t5 = 1.309; P = 0.306, t5 = 1.141. c, Differences in probability of reporting a contralateral choice, relative to inhibition side (ΔP = P_ContraManipulation - P_ContraControl). For each mouse, we concatenated sessions from sessions with unilateral perturbation and normalized choices to the side contralateral to inhibition site. From top to bottom:P = 0.597, t11 = −0.545; P = 0.427, t11 = −0.824; P = 0.259, t5 = −1.274; P = 0.149, t7 = 1.623; P << 0.001, t7 = 6.605; P = 0.365, t11 = 1.623; P = 0.028, t11 = −2.528; P = 0.94, t11 = 0.077; P = 0.933, t11 = −0.086; P = 0.985, t11 = −0.02; P = 0.143, t11 = 1.577. All boxes represent mean ± s.e.m. *P ≤ 0.05, **P ≤ 0.01, **P ≤ 0.001.

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Extended Data Fig. 7 Further quantification of the effects of selective unilateral inhibition of DLS MSNs on broken fixation trials (see Fig. 3).

a,b, Same as Fig. 3m,q, respectively, but expressed as the hazard rate of breaking fixation (see Methods). c, Quantification of the effect shown in Fig. 3m. We calculated the hazard of breaking fixation during the period where the choice contralateral to the site of inhibition would be correct or incorrect (before/after 1.5 s and after/before 1.5 s for CS and CL, respectively). Data shown are the differences between session matched controls and manipulations. Each pair of points depicts data from the same hemisphere and the colour the site of manipulation. (contralateral_correct: two-tailed t-test, P = 0.005, t7 = 4.055, contralateral_incorrect: P = 0.215, t7 = −1.363, N = 8 Hemispheres). d, Same as a) but referent to Fig. 3q. (contralateral_correct: two-tailed t-test, P = 0.711, t11 = 0.38, contralateral_incorrect: P = 0.211, t11 = 1.329, N = 12 Hemispheres) e, Bias to report a contralateral choice after inhibition of iMSNs is not explained by the tendency of mice to make particular choices after breaking fixation early or late in the delay. Each panel, one for manipulations performed in each hemisphere, depicts the data shown in Fig. 3l further split by whether fixation was broken before or after the 1.5-s decision boundary. All Error bars or boxes represent mean ± s.e.m. n.s. P > 0.05, **P ≤ 0.01.

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Extended Data Fig. 8 Unilateral DLS indirect-pathway inhibition did not systematically affect movement trajectories across subject mice.

. a, Example trajectories for a single mouse aligned to centre-out for choices to the “Long port” (red) or “Short pot” (blue) for completed trials (Left) and Broken fixation trials (Right). Trials are further broken down by whether the indirect pathway was inhibited (bottom) or not (top) in mice implanted in the DLS. b, same as a) but for trajectories measured during the delay period (up until second tone or broken fixation events). c) Quantitative differences between trajectory distributions among different conditions. In brief, we computed a mean reference trajectory from “Valid & Non-inhibited” condition and computed, for each trial from each condition, the average Euclidean distance to this reference trajectory. Values shown in the heat maps correspond to the means of these distributions. Significance was accessed by computing a two-sample Kolmogorov–Smirnov test between the reference and testing condition (P ⩽ 0.05 is reported as a red dots).

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Extended Data Fig. 9 Inhibition of DMS dMSNs has a mild effect on the probability of reporting a contralateral action, in the absence of changes in broken fixation rates.

a, Overall probability of breaking fixation during dMSN inhibition experiments. Coloured and black dots represent data from laser-on and laser-off trials, respectively. “Bilateral” condition represents data from sessions wherein light was delivered bilaterally to the DMS (two-tailed t-test, P = 0.893, t5 = −0.141, N = 6 pairs of hemispheres) whereas “unilateral” represents data from sessions wherein light was delivered to a single hemisphere. (two-tailed t-test, P = 0.09, t11 = −1.861,N = 12 hemispheres. Green/Purple code for CS/CL manipulation sessions, respectively). b, Change in probability of registering a choice at the port contralateral to the hemisphere manipulated, after breaking fixation (ΔP = PManipulation - PControl, two-tailed t-test, P = 0.028, t11 = −2.528, N = 12 hemispheres). c, Quantification of the effect shown in d) (contralateral_correct: two-tailed t-test, P = 0.076, t11 = −1.961; contralateral_incorrect: P = 0.383, t11 = −0.909). We calculated the hazard of breaking fixation during the period where the choice contralateral to the site of inhibition would be correct or incorrect (before/after 1.5 s and after/before 1.5 s for CS and CL, respectively). d, Distribution of broken fixation times, expressed as the histogram of trial counts normalized over all included trials of a given condition. e, Change in hazard (ΔH = HManipulation - HControl, N = 12 Hemispheres) due to inhibition of the CS (green) or CL (pruple) relative to session matched control trials. f–j, Same as a-e) but for A2a-Cre mice implanted in the DMS. (f) Bilateral: two-tailed t-test, P = 0.463, t5 = 0.795, N = 6 pairs of hemispheres; Unilateral: two-tailed t-test, P = 0.233, t11 = 1.262, 12 hemispheres. g) two-tailed t-test, P = 0.985, t11 = −0.02, N = 12 hemispheres. h, contralateral_correct: two-tailed t-test, P = 0.149, t11 = 1.551; contralateral_incorrect: P = 0.207, t11 = 1.339, N = 12 hemispheres. All error bars or boxes represent mean ± s.e.m. n.s. P > 0.05, *P ≤ 0.05.

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Extended Data Fig. 10 The model’s pattern of broken fixations when inhibiting the DLS indirect pathway.

Same data as Fig. 4m but expressed as hazard rate (see Methods for details). a, Hazard of breaking fixation for the control (black outline) and inhibition conditions resulting from the selective reduction of action preference values for the Indirect pathway (AL,I(stL,at)) for the SHORT (green) or LONG (purple) actions. b, Change in hazard rate (ΔHR = HRManipulation- HRControl).

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This file contains Supplementary Table 1: Summary of statistical tests performed in the study and additional relevant information; and Supplementary Table 2: Summary of the training procedure for the Opponent Multi-Agent Actor-Critic algorithm.

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Cruz, B.F., Guiomar, G., Soares, S. et al. Action suppression reveals opponent parallel control via striatal circuits. Nature 607, 521–526 (2022). https://doi.org/10.1038/s41586-022-04894-9

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