Opponent and bidirectional control of movement velocity in the basal ganglia

Abstract

For goal-directed behaviour it is critical that we can both select the appropriate action and learn to modify the underlying movements (for example, the pitch of a note or velocity of a reach) to improve outcomes. The basal ganglia are a critical nexus where circuits necessary for the production of behaviour, such as the neocortex and thalamus, are integrated with reward signalling to reinforce successful, purposive actions. The dorsal striatum, a major input structure of basal ganglia, is composed of two opponent pathways, direct and indirect, thought to select actions that elicit positive outcomes and suppress actions that do not, respectively. Activity-dependent plasticity modulated by reward is thought to be sufficient for selecting actions in the striatum. Although perturbations of basal ganglia function produce profound changes in movement, it remains unknown whether activity-dependent plasticity is sufficient to produce learned changes in movement kinematics, such as velocity. Here we use cell-type-specific stimulation in mice delivered in closed loop during movement to demonstrate that activity in either the direct or indirect pathway is sufficient to produce specific and sustained increases or decreases in velocity, without affecting action selection or motivation. These behavioural changes were a form of learning that accumulated over trials, persisted after the cessation of stimulation, and were abolished in the presence of dopamine antagonists. Our results reveal that the direct and indirect pathways can each bidirectionally control movement velocity, demonstrating unprecedented specificity and flexibility in the control of volition by the basal ganglia.

Extended Data Figure 1. Anatomical localization of stimulated neurons and their corticostriatal inputs

a) Schematic showing the histological reconstruction of optic fiber endpoints in dMSN (cyan) and iMSN (red) mice. Hemispheric segregation for display purposes only; actual implants were randomized left and right. b) Location of retrobead tracer injection just below the coordinates of fiber implant terminus. c) Location of the labeled projection neurons innervating the retrobead injection site corresponding to the rostral forelimb area (RFA, orange) and caudal forelimb area (CFA, yellow). d) Results of simulation of light intensity and spread through the brain based upon the model of Stujenske et al . Light intensity drops below 10% peak intensity 285 μm below the fiber. At this depth the lateral fall-off is a drop to 1% peak intensity 250 μm. Contour lines for 10% intensity and 1% intensity are overlaid in white and gray, respectively. e) Average photostimulation-evoked activity of individual neurons (each row = 1 neuron) sorted by electrode shank. We observed minimal fidelity in the light-evoked responses of neurons on the farthest shank consistent with the predicted fall off in light intensity from simulations. Blue dashes at top identify timing of laser pulses. Robust entrainment of spiking to photostimulation was also observed in vitro (Extended Data Fig. 7)

Extended Data Figure 2. Selective stimulation of MSNs produces changes in peak velocity

A) Density plot of movement trajectories showing the percentage of movements that passed through a given amplitude throughout time, aligned to movement onset. Particularly in the zoomed-in plot on the right, we can see that very few movement trajectories passed near the central 0.2 cm within the first 100ms. B) Peak velocity distributions for sham and stimulation datasets for dMSN stimulation (blue, left column), and iMSN stimulation (red, middle column). Top row shows complete distributions across animals - note that no particular part of the distribution is pronounced following stimulation. Bottom row shows means for all experimental sessions by animal. Animal number is for indexing purposes only. Most experiments were carried out concurrently across animals. The right column shows the contrast ratio (difference divided by sum) for dMSN and iMSN stimulation effects (blue and red respectively). These data show a steady mean shift in the data; for instance, iMSN stimulation (red) is positive for velocities slower than the sham (control) mean (an increase in frequency), and is negative for velocity values greater than the sham mean (a decrease in frequency). We have curtailed the contrast ratio plot where too few values existed to achieve reliable estimates (55cm/s). C) Autocorrelation of movement velocities for dMSN stim (blue), iMSN (red), and sham (black) data. D-E exhibit the same analyses as B-C, but for those sessions in which stimulation occurred on the slower, lower 1/3 of movement velocities.

Extended Data Figure 3. Trained animals can adjust amplitude to changing task requirements

A) Reach amplitudes from a sample of 7 sessions in 2 mice in which the eccentricity of the threshold to receive reward was suddenly increased at random. Green field identifies reaches performed with the increased amplitude threshold. Shaded area represents standard error of the mean. B) Success rate before (black) and after (green) the jump in amplitude threshold. C) Distribution of reach amplitudes across sessions for pre- (green) and post- (black) amplitude threshold jump. These data indicate that the mean amplitude was not saturated and suggest that behavior remains outcome dependent (i.e. goal-directed).

Extended Data Figure 4. Variance of movement velocity does not change throughout a session

Each session was z-scored and the standard deviation for each movement number is plotted for dMSN (cyan) and iMSN (red) stimulation

Extended Data Figure 5. Non-selective stimulation does not affect motor control or initiation

A) “All-stim” (15 stimulation and 17 sham sessions from 4 dMSN mice; 17 stimulation and 20 sham sessions from 4 iMSN mice) and B) “random stim” (11 stimulation and 15 sham dMSN sessions from 3 dMSN mice, 8 stimulation and 12 sham sessions from 3 iMSN mice) summary data showing (top) the mean velocity as a function of movement number for dMSN (cyan) and iMSN (red) stimulation sessions and (bottom) histograms of the inter-move interval (IMI; left) and lick rate (LR; right). Shaded area indicates standard error of the mean. We found no differences between sham (black lines) and stimulation (colored lines) sessions for either dMSN stimulation (cyan) or iMSN stimulation (red). C) Plot of average trajectory position aligned to stimulation onset for random dMSN (top) and iMSN stimulation (middle) for stimulation (colored) and sham (black) sessions (In sham sessions, timing was randomly chosen, but no stimulation was given). Stimulation did not systematically induce forelimb movement. For reference, the bottom trace displays closed-loop dMSN aligned to stimulation onset. Gray field represents the 450 ms stimulation period.

Extended Data Figure 6. A corticostriatal circuit model that implements the MeSH learning rule

a) In the left panel, we present a schematic of the corticostriatal pathway consistent with known anatomical data . Descending cortical outputs, largely from Layer 5 of the neocortex project subcortically and intracortically elaborating axon collaterals onto direct (blue) and indirect (red) MSNs in the dorsal striatum. By typical convention we assume that dMSN have a net positive effect on behavior (increase in velocity in this case) and iMSN have a net inhibitory effect (decrease in velocity). These pathways are combined at the basal ganglia output nucleus (substantia nigra pars reticulata; not shown) and then combined with cortical drive to produce the net movement velocity. The model assumes that both dMSN and iMSN are positively correlated with cortical activity and with movement velocity. We assume a monotonic relationship between cortical activity and movement velocity. The model is initialized at a presumptive steady-state in which weights between cortical inputs and dMSN and iMSN units are noisy, but distributed around 0.5 and bounded [0,1]. Most simulations were performed with 100 cortical units and 250 dMSN and iMSN each. Under all conditions weights are subject to updating according to a balanced plasticity rule (inset) in which inactive units are subject to depression and active units are subject to potentiation. All synapses drift back towards a mean of 0.5 thereby implementing a homeostatic set point to the weight distribution. Finally, photostimulation is assumed to enhance (90% increase) the magnitude of both depression and potentiation on stimulated trials in the stimulated population. Random sets of cortical inputs are assumed to be active on any given reach and are drawn from a Gamma (shape parameters: 8, 63) distribution that gives a distribution of velocities similar to that observed experimentally. Further details of the model are provided in the Methods. b) Example simulations of 100 trials (first 50 receive stimulation according to conditions described in legend followed by 50 un-stimulated recovery trials). Curves reflect averages and standard errors of 100 repetitions of the simulated condition. Other conventions as in main figures. c) Schematic of dendrite of MSN containing synapses active during arm movements. Synaptic plasticity enhanced by stimulation (inset, a) produces a net bias in synaptic weights when delivered in closed-loop. This bias can become uniform by permuting the active synapses on each simulated trial.

Extended Data Figure 7. D1+ and D2+ MSNs can follow repetitive stimulus trains of ~20 Hz photostimulation in vitro

Example MSNs recorded in vitro in brain slices containing the DMS. Upper row are two example D1+ positive dMSN recorded from DMS of a Drd1a-cre::Ai32 mouse. Lower row are two example D2+ positive dMSN recorded from DMS of a Drd2a-cre::Ai32 mouse. 3 example traces shown from each. Spiking is evoked by increasing current injection (traces selected for approximately similar evoked spiking rate) and ~500 ms later by a train of 5 pulses of blue light of increasing duty cycle. All cells recorded were able to follow rapid phasic stimulation and action potentials were reliably evoked on every stimulus of these 20 Hz trains (approximately similar to the stimulus trains used in stimulation experiments described in the text). Examples were selected from 7 neurons from 3 D1+ animals, and 4 neurons, from 3 D2+ animals that were recorded for this particular stimulation design.

Extended Data Figure 8. Characterization of movement onset and reach initiation threshold crossing time

A sample reach from a sham block of 50 is shown, with eccentricity in black and velocity in blue (in right panel). The reach start is identified with the greed dot, the threshold crossing, 9 ms later when the reach eccentricity surpassed 0.1 cm, is identified with the magenta dot. The beginning of the reach (green) was assessed offline for each reach and was determined to be the first time point, sampled at 1kHz, constituting the increasing velocity associated with that reach.

Figure 1. Paradigm for closed-loop stimulation in dorsomedial striatum

a) Mice were head-fixed in front of a side-mounted joystick and a water port. Optical fibers were chronically implanted. Tips were positioned in the dorsomedial striatum and coupled to a 473 nm laser. Insert shows fiber position. Fluorescenct image is from iMSN neurons expressing ChR2-YFP. b) To receive liquid reward, mice made forelimb movements with the joystick (either a pull or push) past the criterion distance. Reward delivered 1 second after threshold crossing. Inter-trial intervals were 3 seconds (uncued). c) Instantaneous velocity and position of joystick for 7 trials (green triangle indicates trial start). Velocity threshold for closed-loop optical stimulation and time of stimulation onset indicated by the blue dashed line and diamonds, respectively. Yellow squares indicate reward. d) Histograms of movement amplitude, peak velocity, and duration for all 8 mice (45 sham sessions). e) Average response (Z-scored change from baseline firing rate) of striatal units aligned to movement onset from a single session. Population average shown above. f) Raster plot of population activity during photostimulation from a single session.

Figure 2. Closed-loop stimulation produces opponent, bidirectional control of movement velocity

a) Difference in peak velocity between stimulation and sham session (‘ΔVelocity’) for sessions in which dMSN (upper, blue throughout) or iMSN (lower, red throughout) were stimulated on the fastest third of 50 trials during stimulation and no stimulus was delivered during recovery. Example session shown. b) Histograms of inter-movement-interval (left) and lick rate during reward consumption (right) for sham (black; 25 sessions in 4 dMSN mice, 20 sessions in 4 iMSN mice) and stimulation (colored; 22 sessions in dMSN mice, 26 sessions in iMSN mice) sessions. c) Population average of change in movement parameters when fastest third of reaches were stimulated. d) Population average ΔVelocity as a function of movement (trial) number when fastest third of reaches were stimulated. e-f) Same as c-d but for sessions in which stimulation occurred on the slowest third of movements. *, p < 0.05; **, p < 0.005, two tailed t-test. Shaded area indicates standard error of the mean. Data come from 16 stimulation and 18 sham sessions in the same 4 dMSN mice, 20 stimulation, 16 sham sessions in the same 4 iMSN mice.

Figure 3. Changes in velocity are consistent with dopamine-dependent reinforcement learning

a) Simulation of MeSH learning rule (see text for details). Change in average peak velocity (arbitrary units) as a function of trial number for dMSN-stimulation (blue) and iMSN-stimulation (red) simulations. b) ΔVelocity as a function of trial for stimulation of dMSN (blue) and iMSN (red) on the fastest third of 50 stimulation trials in the presence of dopamine receptor antagonists. Data from 14 stimulation and 11 sham dMSN sessions; 8 stimulation and 9 iMSN sham sessions. c) Movement parameter distributions for control sessions (black) and sessions following dopamine antagonist administration (colored). d) Summary of the changes in velocity for experiments as indicated for dMSN (blue) and iMSN (red) stimulation sessions as defined in text. Shaded area and error bars indicate standard error of the mean. **, p < 0.005, two tailed t-test.

Figure 4. Corticostriatal circuit model implements MeSH rule and experimental validation

a) Change in movement velocity (au) as a function of number of simulated trials (shaded area indicates standard deviation; N=100) for simulations in which dMSN (blue) or iMSN (red) were stimulated during fastest ~30% of reaches. b) Histogram of the change in the average slope between dMSN (blue) or iMSN (red) activity and movement velocity for simulations with dMSN stimulation. Triangles indicate mean. Circles indicate mean change in weight of corticostriatal synapse (see text for details). c) Mean firing rate during movement (0-505ms after onset) is plotted as a function of peak movement velocity with overlaid linear fit (‘tuning slope’) for 3 of 35 example striatal units. d) Stimulation Response (percent change in baseline firing rate during photostimulation) as a function of the change in the tuning slope (‘ΔSlope’) for unstimulated trials (subthreshold velocity) of sessions in which dMSN were stimulated on the fastest third of movements. Linear fit overlaid (r = 0.47).