Integrator dynamics in the cortico-basal ganglia loop for flexible motor timing
Experimental model and participant details
Mice
This study is based on both adult male and female mice (aged > P60). We used five mouse lines: C57BL/6 J (JAX# 000664), VGAT-ChR2-eYFP62 (JAX #14548), Drd1–cre FK150 (ref. 63), Adora2–cre KG126 (ref. 63) and R26-LNL-GtACR1-Fred-Kv2.1 (ref. 47) (JAX #33089). See Supplementary Table 1 for mice used in each experiment.
All procedures were in accordance with protocols approved by the MPFI IACUC committee. We followed the published water restriction protocol64. Mice were housed in a 12–12 reverse light–dark cycle and behaviourally tested during the dark phase. Ambient temperature was 74 °F and humidity ranged between 35% and 60%. A typical behavioural session lasts between 1 h and 2 h. Mice obtained all of their water in the behaviour apparatus (approximately 0.6 ml per day). Mice were implanted with a titanium headpost for head fixation64 and single housed. For cortical photoinhibition, mice were implanted with a clear skull cap37. For bilateral D1/D2-SPN silencing, tapered fibre optics65 (1.0-mm taper, NA 0.37 and core diameter of 200 µm, Doric lenses) were bilaterally implanted during the headpost surgery around the following target coordinates (Bregma): anteroposterior −0.3 mm, mediolateral ±3 mm and dorsoventral 3.5 mm for the VLS; and anteroposterior 0.6 mm, mediolateral ±1.5 mm and dorsoventral 3 mm for the dorsal medial striatum.
Craniotomies for recording were made after behavioural training.
Viral injection
To virally express stGtACR1 (ref. 66) in the striatum, we followed published protocols67 for virus injection. AAV2/5 CamKII-stGtACR1-FusionRed (titre: 9.5 × 1012) was injected into anteroposterior −0.3 mm, mediolateral 3 mm, dorsoventral 2.75 and 3.5 mm, 100 nl each depth. The same tapered fibre optics described above were bilaterally implanted at dorsoventral 3.5 mm.
Behaviour
At the beginning of each trial, an auditory cue was presented, which consisted of three repeats of pure tones (3 kHz, 150-ms duration with 100-ms inter-tone intervals, 74 dB). A delay epoch started from the onset of the cue presentation. Licking during the delay epoch aborted the trial without a water reward, followed by a 1.5-s timeout epoch. Licking during the 10-s answer epoch following the delay was considered a ‘correct lick’, and a water reward (approximately 2 µl per drop) was delivered immediately, followed by a 1.5-s consumption epoch. If mice did not lick during the 10-s answer period, the trial would end without a reward. Trials were separated by an ITI randomly sampled from an exponential distribution with a mean of 3 s, with 1-s offset (with a maximum ITI of 7 s). This prevented mice from predicting the trial onset without a cue.
Animals had to withhold licking during the full ITI epoch for the next trial to begin (otherwise, the ITI epoch repeated). In approximately 10% of randomly interleaved trials, the auditory cue was omitted to assess spontaneous lick rate (‘no-cue’ trials). No water reward was delivered in no-cue trials.
We followed the protocol described in Majumder et al.68 for training. In brief, the delay duration increased from 0.1 s to 1.8 s gradually based on the performance of the animal68. Once mice reached 1.8-s delay, we started either the switching delay, the random delay or the constant delay conditions (see Supplementary Fig. 3 for example sessions). In the switching delay condition, we switched the delay between 1 s versus 3 s or 1 versus 1.8 s every 30–70 trials (the number of trials was randomly selected from 30 to 70 and not contingent upon behaviour). Similarly, in the random delay condition, we randomly switched the delay among 0.5, 1.0, 1.5, 2.0, 3.0 or 5.0 s every 30–70 trials. For the constant delay condition, mice were trained with a constant delay of 1.5 s across sessions for at least 2 weeks. For the cue-intensity experiments (Extended Data Fig. 5), we changed the cue intensity (3-kHz auditory cue, ±15 dB, lasting 0.6 s) in randomly interleaved test trials (approximately 20%).
Except for this modification, the task structure was identical. Cue intensity stayed constant (74 dB) before the cue-intensity experiments. Otherwise, the task design and reward contingency remained the same. ALM and striatal perturbation experiments (Figs. 4 and 5) were performed under the switching delay condition. To avoid human bias, the behaviour was automatically controlled by Bpod (Sanworks) and custom MATLAB codes.
Optogenetics
Photostimulation was deployed on less than 25% in randomly selected trials. To prevent mice from distinguishing photostimulation trials from control trials using visual cues, a ‘masking flash’ (1-ms pulses at 10 Hz) was delivered using 470-nm LEDs (Luxeon Star) throughout the trial. For both ChR2 and stGtACR1, we used a 488-nm laser (OBIS 488–150C, Coherent).
The ChR2-assisted photoinhibition of the dorsal cortices was performed through clear-skull cap37 (Fig. 2e) or craniotomy (in case of simultaneous recording; Fig. 4). We scanned the 488-nm laser light using Galvo mirrors. We stimulated GABAergic interneurons in Vgat-ChR2-eYFP mice starting at 0.6 s after the cue, lasting for 1.2 s (including 0.2-s ramping down; Fig. 2e) or 0.6-s duration (including 0.3-s ramping down; Fig. 4). Time-averaged laser power was 1.5 mW per spot (or 0.3 mW per spot for Extended Data Fig. 12; 8 spots in total: 4 spots in each hemisphere centred around the target coordinates with 1-mm intervals; we photoinhibited each spot sequentially at the rate of 5 ms per step). For Fig. 2e, the targeted brain area was randomly selected for each photostimulation trial.
The target coordinates were anteroposterior 2.5 mm and mediolateral ±1.5 mm for the ALM; anteroposterior 0.5 mm and mediolateral ±1.5 mm for M1B; anteroposterior 0.5 mm and mediolateral ±2.5 mm for S1TJ; anteroposterior −1.0 mm and mediolateral ±1.5 mm for S1TR; anteroposterior −1.0 mm and mediolateral ±3.0 mm for S1B; anteroposterior −2 mm and mediolateral ±1.5 mm for PPC; and anteroposterior −2.5 mm and mediolateral ±3.5 mm for V1, respectively (Bregma).
To silence D1-SPNs using stGtACR1 (Fig. 5), we delivered photostimuli (0.25 mW or 0.5 mW, 488 nm) bilaterally (Fig. 5l–o) or unilaterally (in case of optrode; Fig. 5h–k) in the striatum starting 0.6 s after the cue and lasting for 0.6 s (including 0.3-s ramping down). In precue inhibition trials, photostimuli were delivered 0.81 s, 0.6 s before the cue for the ALM, D1-SPN perturbation, respectively, both lasting for 0.6 s. The light was delivered through implanted fibre optics, and intensity was measured at the fibre tip.
Extracellular electrophysiology
A small craniotomy (diameter of 0.5–1 mm) was made over the recording sites 1 day before the first recording session. Extracellular spikes were recorded acutely using 64-channel two-shank silicon probes (H-2, Cambridge Neurotech) for the ALM and Neuropixels probe 1.0 (ref. 69) for the striatum. For the H-2 probes, voltage signals were multiplexed, recorded on a PCI6133 board (National Instruments) and digitized at 400 kHz (14-bit). All recordings were made with the open-source software SpikeGLX (http://billkarsh.github.io/SpikeGLX/). During recordings, the craniotomy was immersed in a cortex buffer (125 mM NaCl, 5 mM KCl, 10 mM glucose, 10 mM HEPES, 2 mM MgSO4 and 2 mM CaCl2; adjusted pH to 7.4). Brain tissue was allowed to settle for at least 5 min before recordings.
For the optrode recordings (Fig. 5h–k), we used 64-channel two-shank silicon optrodes with a 1.0-mm taper fibre optic attached adjacently (NA 0.22, core diameter of 200 µm; Cambridge Neurotech). Optrode was acutely inserted in each session and the light delivery protocol was identical to that used for behavioural experiments described in the section ‘Optogenetics’. Neuropixels probe and optrode tracks labelled with CM-DiI were used to determine recording locations70.
Histology
Mice were perfused transcardially with PBS, followed by 4% paraformaldehyde/0.1 M PBS. To reconstruct recording tracks, we either generated coronal sections followed by conventional imaging (protocol described in Inagaki et al.71) or cleared the brain followed by light-sheet microscopy. To clear the brain, we used the EZ Clear method72. We followed the previous protocol to map the recording tracks to the Allen Common Coordinate Framework70,73.
Quantification and statistical analysis
Behavioural analysis
We analysed the time of the first lick after the cue onset in each trial. Lick time was measured by detecting the contact of the tongue with the lick port using an electrical lick detector. For optogenetic experiments, we analysed trials with the first lick occurring after the onset time of photostimulation (0.6 s after the cue) in both control and photostimulated trials to compare the effect of photostimulation on behaviour. The no-lick rate was calculated as the probability of mice not responding within 5 s after the cue. The shift in lick time (Δlick time) was based on the median lick time. The post-stimulation lick rate (Extended Data Fig. 6) was calculated as the probability of mice licking within 0.6 s after the photostimulation offset time in no-cue trials.
To analyse behaviour while the mice were engaged in the task, we analysed all trials between the first occurrence of five consecutive cue trials with licks and 20 trials before the last occurrence of three consecutive no-lick trials without photostimulation.
Owing to the attenuation of behavioral effects of optogenetic manipulation (Extended Data Fig. 6), we restricted analyses of both behavioural and physiological data to the first (for striatal manipulation) or the first two (for ALM manipulation) manipulation sessions per mouse. All analyses, including the calculation of confidence intervals and P values, were performed using a hierarchical bootstrap, unless stated otherwise. First, we randomly selected animals with replacements. Second, we randomly selected sessions for each animal with replacement. Third, we randomly selected trials for each session with replacements. Then, we calculated the behavioural metrics described above. This procedure was repeated 1,000 times to estimate the mean, confidence intervals and statistics.
Timer model and hazard rate analyses
To interpret the effects of optogenetic manipulations, we numerically simulated how different operations influence a timer, an accumulator that infers passage of time by integrating a constant input or periodic event, such as a water clock, hourglass, pendulum clock and quartz watch (Fig. 6). We modelled time as a scalar variable representing the temporal integration of a constant inflow signal. Specifically, the internal representation of time T(t) evolves according to the equation:
