Complementary cognitive roles for D2-MSNs and D1-MSNs during interval timing

Abstract

The role of striatal pathways in cognitive processing is unclear. We studied dorsomedial striatal cognitive processing during interval timing, an elementary cognitive task that requires mice to estimate intervals of several seconds and involves working memory for temporal rules as well as attention to the passage of time. We harnessed optogenetic tagging to record from striatal D2-dopamine receptor-expressing medium spiny neurons (D2-MSNs) in the indirect pathway and from D1-dopamine receptor-expressing MSNs (D1-MSNs) in the direct pathway. We found that D2-MSNs and D1-MSNs exhibited distinct dynamics over temporal intervals as quantified by principal component analyses and trial-by-trial generalized linear models. MSN recordings helped construct and constrain a four-parameter drift-diffusion computational model in which MSN ensemble activity represented the accumulation of temporal evidence. This model predicted that disrupting either D2-MSNs or D1-MSNs would increase interval timing response times and alter MSN firing. In line with this prediction, we found that optogenetic inhibition or pharmacological disruption of either D2-MSNs or D1-MSNs increased interval timing response times. Pharmacologically disrupting D2-MSNs or D1-MSNs also changed MSN dynamics and degraded trial-by-trial temporal decoding. Together, our findings demonstrate that D2-MSNs and D1-MSNs had opposing dynamics yet played complementary cognitive roles, implying that striatal direct and indirect pathways work together to shape temporal control of action. These data provide novel insight into basal ganglia cognitive operations beyond movement and have implications for human striatal diseases and therapies targeting striatal pathways.

Keywords: dopamine; mouse; neuroscience; striatum; timing.

Figure 1.. Mouse-optimized interval timing.

(A) We trained mice to perform an interval timing task in which they had to respond by switching nosepokes after a 6-s interval (in gray shade in all panels). Mice start trials by making a back nosepoke, which triggers an auditory and visual cue. On 50% of trials, mice were rewarded for a nosepoke after 6 s at the designated ‘first’ front nosepoke; these trials were not analyzed. On the remaining 50% of trials, mice were rewarded for switching to the ‘second’ nosepoke; initial nosepokes at the second nosepoke after 18 s triggered reward when preceded by a first nosepoke. Switch response time was defined as the moment mice depart the first nosepoke prior to second nosepoke arrival. Because cues are identical and on for the full trial on all trials, switch responses are a time-based decision guided explicitly by temporal control of action. Indeed, mice switch nosepokes only if they do not receive a reward at the first nosepokes after the 6-s interval. Top row – screen captures from the operant chambers during a trial with switch response. (B) Response probability distribution from 30 mice for first nosepokes (purple), switch responses (green), and second nosepokes (orange). Responses at the first nosepoke peaked at 6 s, and switch responses peaked after 6 s. Because nosepoking at the second nosepoke was only rewarded after 18 s, second nosepokes tended to be highly skewed. Shaded area is standard error. (C) Cumulative switch response density for each of 30 mice. (D) Average cumulative switch response density; shaded area is standard error. (E) DeepLabCut tracking of position during interval timing from a single mouse behavioral session revealed increased velocity after trial start and then constant velocity throughout the trial. Shaded area is standard error. In (A–E), the 6-s interval is indicated in gray.

Figure 2.. D2-MSNs and D1-MSNs have opposing dynamics during interval timing.

(A) D2-MSNs in the indirect pathway, which project from the striatum to the globus pallidus external segment (GPe; sagittal section) and internal segment (GPi) and (B) D1-MSNs, which project from the striatum to the GPe, GPi, and substantia nigra (SNr; sagittal section). Peri-event raster (C) from an optogenetically tagged putative D2-MSN (red) and (D) from an optogenetically tagged putative D1-MSN (blue). Shaded area is the bootstrapped 95% confidence interval. (E) Peri-event time histograms (PETHs) from all D2-MSNs and (F) from all D1-MSNs were binned at 0.2 s, smoothed using kernel-density estimates using a bandwidth of 1, and z-scored. Average activity from PETHs revealed that (G) D2-MSNs (red) tended to ramp up, whereas (H) D1-MSNs (blue) tended to ramp down. Shaded area is standard error. Data from 32 tagged D2-MSNs in 4 D2-Cre mice and 41 tagged D1-MSNs in 5 D1-Cre mice.

Figure 2—figure supplement 1.. Striatal MSN recording.

(A) Recording locations in the dorsomedial striatum (targeting AP +0.4, ML −1.4, DV −2.7). Electrode reconstructions for D2-Cre (red), D1-Cre (blue), and wild-type mice (green). Only the left striatum was implanted with electrodes in all animals. (B) Medium spiny neuron (MSN) classification by waveform criteria for sessions with optogenetic tagging. (C) Example of an optogenetically tagged MSN. This neuron expresses ChR2 and fired action potentials within 5 ms of 473 nm laser pulses (red line). Spikes from laser trials shown as red ticks; trials without laser shown as blue ticks. Inset on bottom right – waveforms from laser trials (red) and trials without laser (blue). Across 73 tagged neurons, waveform correlation coefficients for laser trials vs trials without laser was r = 0.97 (0.92–0.99), indicating that optogenetically triggered spikes were similar to non-optogenetically triggered spikes.

Figure 2—figure supplement 2.. D2- and D1-MSN activity over a longer epoch from 10 s prior to trial start, when mice initiated trials at the back nosepoke, to the end of 18 s, after which making a second nosepoke led to reward.

(A) Tagged D2-MSN from Figure 2C shown over a longer interval, and (B) tagged D1-MSN from Figure 2D shown over a longer interval. (C) Peri-event time histograms from D2-MSNs and (D) from D1-MSNs over a longer interval. (E) We noticed that on average, D2-MSNs and D1-MSNs had the biggest differences in dynamics during the 6-s interval after trial start, where they tended to have distinct slopes (Figure 3C, D); slope analyses were less reliable for other epochs. Data from 32 tagged D2-MSNs in 4 D2-Cre mice and 41 tagged D1-MSNs in 5 D1-Cre mice as in Figure 2.

Figure 3.. Quantification of opposing D2-MSN and D1-MSN dynamics.

(A) Principal component analysis revealed that the first component (PC1) exhibited time-dependent ramping. (B) The first principal component explained ~54% of variance across tagged medium spiny neuron (MSN) ensembles. (C) Differences between D2-MSNs (red) and D1-MSNs (blue) were captured by PC1 which exhibited time-dependent ramping. (D) These differences were also apparent in the linear slope of firing rate vs time in the interval, with D1-MSNs (blue) having a more negative slope than D2-MSNs (red). In C and D, each point represents data from a tagged MSN. *p < 0.05 via linear mixed effects models accounting for variance between mice. Data from 32 tagged D2-MSNs in 4 D2-Cre mice and 41 tagged D1-MSNs in 5 D1-Cre mice.

Figure 3—figure supplement 1.. PC1 and slopes from individual mice.

Effects in individual mice from optogenetic tagging experiments for (A) PC1 and (B) trial-by-trial generalized linear modeling (GLM) slope of firing rate over the interval; red = D2-MSN, and blue = D1-MSN. All statistics analyzing these data used linear mixed-effects models as incorporating a random effect for each mouse into the model allows to account for inherent between-mouse variability.

Figure 4.. Four-parameter drift-diffusion computational model of striatal activity during interval timing.

(A) We modeled interval timing with a low parameter diffusion process with a drift rate D, noise $\xi \left(t\right),$ and a baseline firing rate b that drifts toward a threshold T indicated by dotted lines. With D2-MSNs disrupted (solid red curves), this drift process decreases and takes longer to reach the threshold. (B) The same model also accounted for D1-MSNs with an opposite drift. With D1-MSNs disrupted (solid blue curves), the drift process again takes longer to reach the threshold. Because both D2-MSNs and D1-MSNs contribute to the accumulation of temporal evidence, this model predicted that (C) disrupting D2-MSNs would increase response times during interval timing (dotted red line) and (D) disrupting D1-MSNs would also increase response times (dotted blue line). Threshold T depends on b and target firing F. For details on the selection of parameter values in drift-diffusion model (DDM), see Methods and Figures 1—3.

Figure 4—figure supplement 1.. Trial-by-trial predictions of switch response time from D2-MSN and D1-MSN ensemble dynamics.

(A) Exemplar integral of network activity ${\displaystyle x\left(t\right)=\sum {\beta }_j{x}_j\left(t\right)}$ generated from an ensemble of 13 D2-MSNs for a single animal on a single trial. The coefficients ${\beta }_j$ were computed from the logistic regression fit to the switch time $t^{*}$ using the neurons firing rates ${\left(x_j\right(t\left)\right)}_j$ as the predictor matrix (see Methods). On this trial, this integral drifted toward the response threshold 0.5, and logistic regression accurately predicted the switch response time. (B) Exemplar integral network activity ${\displaystyle x\left(t\right)}$ generated from an ensemble of 15 D1-MSNs in a single animal on a single trial. (C) Across all D2-MSN and D1-MSN ensembles per individual mouse, we found that the trial-by-trial accuracy increased with ensemble size, exceeding >90% when D2-MSN and D1-MSN ensembles were >11 neurons. Neuronal data from 5 D1-Cre (blue dots) and 4 D2-Cre (red dots) mice in Figures 2 and 3. Corresponding light blue/light red dots show accuracy values computed in 100 simulations of respective D2-MSN/D1-MSN ensembles with Poisson spikes matched to MSN firing rates.

Figure 4—figure supplement 2.. Drift-diffusion model (DDM) parameter exploration.

The relative error of simulated mean (μ_s) to the behavioral gamma-fit mean ${\displaystyle {\mu }_M}$, or $E^{\mu }=\left|\right({\mu }_S-{\mu }_M)/{\mu }_M|,$ for (A) D2-Cre mice with Laser Off and for (B) D2-MSN inhibition. The absolute error of the DDM computed coefficient of variation ${CV}_S$ relative to the behavioral gamma-fit ${CV}_M$ , or ($E^{cv}=\left|{CV}_S-{CV}_M\right|$), for (C) D2-Cre mice with Laser Off and for (D) D2-MSN inhibition. The relative error of simulated mean (μ_s) to the behavioral gamma-fit mean (${\displaystyle {\mu }_M}$), for (E) D1-Cre mice with Laser Off and for (F) D1-MSN inhibition. The absolute error of the DDM computed coefficient of variation ${CV}_S$ relative to the behavioral gamma-fit ${CV}_M$ for (G) D1-Cre mice with Laser Off and for (H) D1-MSN inhibition. Black squares and triangles represent parameters $D$ and $\sigma$ for Figure 4.

Figure 4—figure supplement 3.. DDM details.

Histograms of behavioral data from D2-Cre mice with (A) Laser Off and (B) D2-MSN inhibition (red). Data from D1-Cre mice with (C) Laser Off and with (D) D1-MSN inhibition (blue). (E–H) model predictions. (I–L) Comparisons of empirical data vs model. All panels: fits for the gamma distribution with dotted circles; see Supplementary file 1 for the parameter values defining each gamma distribution. Behavioral data from 10 D2-Cre mice and 6 D1-mice from Figure 5A–D. Model data from numerical simulations of the drift-diffusion model (DDM) model shown in Figure 4.

Figure 5.. Disrupting D2- or D1-MSNs increases response times.

(A) As predicted by our drift-diffusion model (DDM) in Figure 4, optogenetic inhibition of D2-MSNs (red) shifted cumulative distributions of response times to the right, and (B) increased response times; data from 10 D2-Cre mice expressing halorhodopsin (Halo). Also as predicted by our DDM, (C) optogenetic inhibition of D1-MSNs shifted cumulative distribution functions to the right, and (D) increased response times; data from 6 D1-Cre mice expressing Halo. Similarly, (E) pharmacologically disrupting D2-dopamine receptors (red) with the D2 antagonist sulpiride shifted cumulative distribution functions to the right, and (F) increased response times; data from 10 wild-type mice. Also, (G) pharmacologically disrupting D1-dopamine receptors (blue) with the D1 antagonist SCH23390 shifted cumulative distribution functions to the right, and (H) increased response times; data from the same 10 wild-type mice as in (E, F). In (B, D, F, and H) connected points represent the mean response time from each animal in each session, and horizontal black lines represent group medians. *p = <0.05, signed rank test. See Figure 5—figure supplement 2 for data from opsin-negative controls.

Figure 5—figure supplement 1.. Dorsomedial striatal optogenetics.

Fiber optic locations from (A) an opsin-expressing mouse with mCherry-tagged halorhodopsin and bilateral fiber optics, and (B) across 10 D2-Cre mice (red) and 6 D1-cre mice (blue) with fiber optics (targeting AP +0.9, ML +/–1.3, DV –2.5).

Figure 5—figure supplement 2.. Optogenetic control data.

To control for heating and nonspecific effects of optogenetics, we conducted control experiments with identical laser exposures except a virus without opsin was used. Experiments in D2-Cre mice injected with virus without opsins did not reliably affect (A) cumulative density functions (CDFs) or (B) switch response times (signed rank p = 0.44). Experiments in D1-Cre mice expressing virus without opsins did not reliably affect (C) CDFs or (D) switch response times (signed rank p = 0.81). Laser parameters (589 nm laser, 12 mW, 18-s duration) were identical to experimental animals in Figure 5.

Figure 5—figure supplement 3.. Optogenetically inhibiting D2-MSNs or D1-MSNs does not affect task-specific motor control.

We measured nosepoke duration (time of nosepoke entry to exit) on switch responses. During interval timing there was no effect of optogenetic inhibition (red) of dorsomedial striatal D2-MSNs on (A, B) nosepoke duration (p = 0.63) or (C) the traversal time between the first and second nosepokes (p = 0.49); traversal time is distinct from the switch response, which is the moment animals depart the first nosepoke prior to arriving at the second nosepoke. There was also no effect of optogenetic inhibition (blue) of dorsomedial striatal D1-MSNs on nosepoke duration (D, E; p = 0.31) or (F) traversal time (p = 0.22). Data from the same 10 D2-Cre mice and 6 D1-Cre mice, as in Figure 5. Horizontal black lines in B, C, E, and F represent group medians.

Figure 5—figure supplement 4.. Switch response variance and reward rate.

(A) Standard deviation of switch response times for D2-MSN inhibition sessions (signed rank test, p = 0.19) and (B) D1-MSN inhibition sessions (p = 0.84), and the number of total rewards for (C) D2-MSN inhibition sessions (p = 0.07) and (D) D1-MSN inhibition sessions (p = 0.25). Data from 10 D2-Cre mice and 6 D1-Cre mice as in Figure 5.

Figure 6.. D2 and D1 blockade shift temporal dynamics.

(A) We recorded dorsomedial striatal medium spiny neuron (MSN) ensembles during interval timing in sessions with saline, D2 blockade with sulpiride, or D1 blockade with SCH23390. (B–D) Example peri-event raster from MSNs in sessions with saline (black), D2-dopamine blockade (red), or D1-dopamine blockade (blue). Shaded area is the bootstrapped 95% confidence interval. (E) MSNs from 99 neurons in 11 mice from saline, D2 blockade, or D1 blockade session; MSNs were matched across sessions based on waveforms and interspike interval. Each row represents a peri-event time histogram (PETH) binned at 0.2 s, smoothed using kernel-density estimates using a bandwidth of 1, and z-scored. Colors indicate z-scored firing rate. See Figure 6—figure supplement 1 for analyses that assume statistical independence. (F) Principal component analysis (PCA) identified MSN ensemble patterns of activity. The first principal component (PC1) exhibited time-dependent ramping. (G) PC1 explained 54% of population variance among MSN ensembles; higher components were not analyzed. (H) PC1 scores were closer to zero and significantly different with D2 or D1 blockade; *p < 0.05 via linear mixed effects; data from 99 MSNs in 11 mice.

Figure 6—figure supplement 1.. We analyzed medium spiny neuron (MSN) ensembles in sessions with saline (158 neurons), D2 blockade (167 neurons), or D1 blockade (144 neurons) – unlike Figure 6; all sessions were sorted independently and assumed to be fully statistically independent.

(A) Principal component analysis (PCA) identified MSN ensemble patterns of activity. The first principal component (PC1) exhibited time-dependent ramping. (B) PC1 explained 55% of population variance among MSN ensembles. (C) PC1 scores were shifted and significantly different with D2 or D1 blockade as when all sessions were sorted together in Figure 6 (D2 blockade vs saline: F = 5.5, p = 0.02; D1 blockade vs saline: F = 4.9, p = 0.03; all analyses accounting for variance between mice). PC1 scores inverted to match Figure 6F, H. *p < 0.05 via linear mixed effects models; all analyses assumed statistical independence.

Figure 6—figure supplement 2.. PC1 scores from individual mice in Figure 6H.

Figure 6—figure supplement 3.. MSN classification by waveform criteria for pharmacology sessions.

Figure 7.. D2 and D1 blockades degrade medium spiny neuron (MSN) temporal decoding.

We used naive Bayesian classifiers to decode time from MSN ensembles in (A) saline sessions, (B) D2 blockade sessions, and (C) D1 blockade sessions. Color represents the temporal prediction across 20 trials with red representing stronger predictions. (D) Temporal decoding was strong early in the interval, and D2 or D1 blockade degraded classification accuracy. Temporal decoding was decreased later in the interval. Each point represents the R² for each trial of behavior for MSN ensembles from 11 mice. *p < 0.05 vs saline from 0 to 6 s. Horizontal black lines in (D) represent group medians.