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Different contributions of preparatory activity in the basal ganglia and cerebellum for self-timing

  1. Jun Kunimatsu  Is a corresponding author
  2. Tomoki W Suzuki
  3. Shogo Ohmae
  4. Masaki Tanaka  Is a corresponding author
  1. Hokkaido University School of Medicine, Japan
  2. National Institutes of Health, United States
  3. Baylor College of Medicine, United States
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Cite this article as: eLife 2018;7:e35676 doi: 10.7554/eLife.35676

Abstract

The ability to flexibly adjust movement timing is important for everyday life. Although the basal ganglia and cerebellum have been implicated in monitoring of supra- and sub-second intervals, respectively, the underlying neuronal mechanism remains unclear. Here, we show that in monkeys trained to generate a self-initiated saccade at instructed timing following a visual cue, neurons in the caudate nucleus kept track of passage of time throughout the delay period, while those in the cerebellar dentate nucleus were recruited only during the last part of the delay period. Conversely, neuronal correlates of trial-by-trial variation of self-timing emerged earlier in the cerebellum than the striatum. Local inactivation of respective recording sites confirmed the difference in their relative contributions to supra- and sub-second intervals. These results suggest that the basal ganglia may measure elapsed time relative to the intended interval, while the cerebellum might be responsible for the fine adjustment of self-timing.

https://doi.org/10.7554/eLife.35676.001

Introduction

Action timing is crucial for organisms to interact with dynamic environments. To make timely movements, elapsed time often needs to be monitored and the timing of upcoming events needs to be accurately predicted. Previous studies have shown that, in addition to the cerebral cortex, both the basal ganglia and the cerebellum are implicated in this function (Buhusi and Meck, 2005). In humans, the underlying neural mechanism of self-timing has been examined by recording scalp potentials that develop gradually over the medial frontal cortex in anticipation of task-relevant events (contingent negative variation or CNV: Macar and Vidal, 2003; van Rijn et al., 2011). However, CNV sometimes provides only a poor indication of event timing (Kononowicz and van Rijn, 2011), possibly because it contains multiple components. In fact, the magnitudes of CNV are known to be correlated with neural activity in the thalamus, the basal ganglia, and the cerebellum (Nagai et al., 2004), indicating that the ramping-up of neuronal activity in the cerebral cortex is likely to be regulated by different signals arising from multiple subcortical structures.

More direct evidence for the role of subcortical signals in motor preparation has come from animal experiments. In monkeys, neurons in the motor thalamus exhibit a gradual rise in firing rate that predicts the timing of self-initiated movements (Costello et al., 2016; Tanaka, 2007), and their inactivation causes delayed self-timing (Tanaka, 2006; van Donkelaar et al., 2000). Recent studies in rodents have demonstrated that direct inputs from the thalamus are necessary for generating preparatory activity in the premotor cortex (Guo et al., 2017), while signals in the cortical network are also essential for generation (Murakami et al., 2014) and maintenance (Li et al., 2016) of ramping activity. Because the thalamus transmits signals from both the basal ganglia and the cerebellum, elucidation of the neuronal activity in these subcortical structures will address specific roles of respective cortico-subcortical loops in self-timing.

A prevailing hypothesis regarding subcortical contribution to timing is that the basal ganglia process supra-second interval timing while the cerebellum is involved in sub-second timing (Ivry and Spencer, 2004). Our recent analysis in monkeys showed that neurons in the cerebellar dentate nucleus exhibited preparatory activity only about a half second before self-initiated saccades, irrespective of the length of the mandatory delay interval that ranged from 400 to 2400 ms (Ohmae et al., 2017). These results indicate that neurons outside of the cerebellum must keep track of elapsed time to make temporally accurate movements in trials with supra-second delay intervals. Because neurons in the striatum are known to exhibit ramping activity during motor preparation (Schultz and Romo, 1992), signals in the cortico-basal ganglia loop might play this role.

To understand the roles of the basal ganglia and the cerebellum in self-timing, we compared activity of single neurons in the anterior part of the striatum (caudate nucleus) with those in the posterior part of the cerebellar dentate nucleus in monkeys performing the self-timed saccade task (Ashmore and Sommer, 2013; Kunimatsu and Tanaka, 2012, 2016; Tanaka, 2006, 2007). Because the previous studies have demonstrated that these subcortical regions have common anatomical connections with the medial and lateral frontal cortices involved in temporal processing (Dum and Strick, 2003; Strick et al., 2009; McFarland and Haber, 2001), we reasoned that these areas might have functional interactions. Although the previous studies of eye blink conditioning suggest that plasticity in the cerebellar cortex rather than the deep cerebellar nuclei plays a role in the learning of motor timing (Garcia and Mauk, 1998; Perrett et al., 1993; Raymond et al., 1996), neurons in the nuclei encode timing signals originated in the cerebellar cortex (Ten Brinke et al., 2017). Therefore, we explored signals in the dentate nucleus in this study because any significant computation in the lateral cerebellum must modify neuronal activity in the nucleus that may eventually regulate movement timing. We found that neurons in the striatum displayed a ramping-up of firing rate throughout the delay period, while the rate of rise of neuronal activity depended on the length of the mandatory delay interval. In contrast, neurons in the cerebellar dentate nucleus exhibited preparatory activity only during the last part of the delay period, while neuronal correlates of trial-by-trial variation of saccade latency started earlier in the cerebellum than the striatum. These results suggest that the striatum may play a role in monitoring the passage of time relative to the mandatory interval, while the cerebellum might play a role in the fine adjustment of self-timing in the range of hundreds of milliseconds.

Results

Time courses of preparatory activity for self-timing in the striatum and the cerebellum

We examined neuronal activity while two Japanese monkeys performed the self-timed saccade task (Figure 1A). In this task, the animals made a self-initiated memory-guided saccade to the location of the previously presented brief visual cue (100 ms). They received a liquid reward for saccades generated within a predetermined time interval that was indicated by color of the fixation point (FP) in each trial. Distributions of saccade latency during recording sessions shown in Figure 1B (Figure 1—source data 1) indicate that both monkeys flexibly adjusted movement timing depending on the given instructions.

Behavioral task and performance.

(A) Sequence of events in the self-timed saccade task (upper panel). During central fixation, a cue flashed briefly (100 ms) in the peripheral visual field. Monkeys were required to remember the cue location and maintain fixation until expiration of the predetermined mandatory delay interval that was indicated by color of the fixation point (lower panel). Animals received a reward if they correctly made a self-timed memory-guided saccade to the cue location after the mandatory delay period. (B) Distributions of saccade latency during recording sessions in two monkeys. Differently colored histograms represent the data for different mandatory intervals. Vertical lines indicate the end of the mandatory intervals (400, 1000, and 2200 ms). Inverted triangles denote medians.

https://doi.org/10.7554/eLife.35676.002

We recorded from 162 task-related neurons in the anterior part of the caudate nucleus (63 and 99 neurons for monkeys B and G, respectively) and 127 neurons in the posterior part of the cerebellar dentate nucleus (59 and 68, respectively, Figure 2). Of these, 100 striatal neurons (37 and 63 neurons for monkeys B and G, respectively) and 76 cerebellar neurons (29 and 47 neurons) showed elevated activity before self-timed saccades (see Materials and methods). Most of them exhibited increased activity for both saccade directions (55 and 66% for neuron in the caudate nucleus and the cerebellar dentate nucleus, respectively), while many showed a significant directional modulation (48/100 and 32/76, respectively; Wilcoxon rank sum test, p<0.05).

Recording and inactivation sites in monkey G.

Drawings indicate coronal sections of the striatum (left panel) and horizontal sections of the cerebellar dentate nucleus (right panel). Red and blue symbols indicate the sites of recording and muscimol injection, respectively. Data for the intermediate sections are projected anteriorly (striatum) or dorsally (dentate nucleus). Note that the scales for the left and right panels differ by three times. AC, anterior commissure; Caud, caudate nucleus; Put, putamen.

https://doi.org/10.7554/eLife.35676.004

Figure 3A shows a representative example of caudate neuron exhibiting a gradual ramp-up of firing rate before self-timed saccades for all three mandatory delay intervals. This neuron elevated activity immediately after cue appearance but the rate of rise of firing rate differed for different interval timing. In contrast, the representative cerebellar nuclear neuron shown in Figure 3B also exhibited a ramp-up activity, but the timing of ramp onset differed depending on the mandatory intervals, while the rate of rise of activity was roughly constant. The time courses of the population activity for each structure also gave a similar impression. Again, the ramp-up activity started just after cue onset in the striatum (Figure 3C, Figure 3—source data 1), while the firing modulation started late for trials with longer delay in the cerebellum (Figure 3D). The rate of rise of preparatory activity differed for different interval timing in the caudate nucleus (Figure 3C), while the activity was similar across all timing in the cerebellar dentate nucleus (Figure 3D). These results held consistent even when the same sets of data were aligned with saccade initiation (Figure 3—figure supplement 1). In general, the time courses of individual neuronal activity during the delay interval were more variable in the striatum than the cerebellum (Figure 3—figure supplement 2). Neurons in both structures did not show clear ramping activity during the standard memory-guided saccade task, in which animals generated a saccade in response to the FP offset (Figure 3—figure supplement 3). In addition to the delay period activity, the activity just before the cue onset in the self-timed task also tended to be different (striatum, 9.8 ± 5.6, 8.4 ± 9.2, 7.8 ± 8.6 spikes/s for short, medium and long intervals, respectively; cerebellum, 50.6 ± 22.3, 52.0 ± 27.0, 46.9 ± 21.9 spikes/s), although these differences were not statistically significant (one-way ANOVAs, p=0.33 and 0.40).

Figure 3 with 3 supplements see all
Comparison of single neuronal activity in the striatum and the cerebellar nucleus during the self-timed saccade task.

(A) A representative neuron in the caudate nucleus showing a ramp-up of activity during the delay period. Trials are sorted by saccade latency, and the rasters and corresponding spike density are shown for saccades in contralateral direction. Green, blue, and orange traces indicate data for short, medium, and long mandatory intervals, respectively. (B) A representative neuron in the cerebellar dentate nucleus. Data are shown for saccades in ipsilateral direction. (C) Time courses of the population activity for neurons in the caudate nucleus. Traces are the means of spike densities for individual neurons and are aligned on the cue onset (vertical line). Continuous and dashed traces indicate data for saccades in contralateral and ipsilateral directions, respectively. The filled and open triangles with horizontal bars indicate the mean ± SD of average saccade latency for contralateral and ipsilateral directions in each session, respectively. (D) Time courses of the population activity for neurons in the dentate nucleus. The population data in both structures aligned with saccade initiation are shown in Figure 3—figure supplement 1.

https://doi.org/10.7554/eLife.35676.005

To quantify the time courses of preparatory activity, the population of normalized activity in each condition was fit with a regression line incorporating a delay in ramp onset (Figure 4A, Materials and Methods). The left panel of Figure 4B summarizes the onset times of preparatory activity derived from the fitted lines and the distributions from bootstrap resampling for different conditions (Figure 4—source data 1). For all iterations (n = 1000), coefficients of determination (r2) of fitting were greater than 0.86, and averaged 0.96 ± 0.01 (SD) for both structures. Except for the shortest delay interval, the times of ramp onset differed significantly between neurons in the striatum and those in the cerebellum (p<0.05, bootstrap CIs). The right panel of Figure 4B plots the slopes of ramp-up activity for different conditions. Although the slopes differed among the mandatory delay intervals for neurons in both the striatum and cerebellum (one-way ANOVA, p<0.05), the difference was much greater in the striatum. Consistent with the results of ramp onset, the slopes for the medium and long delay conditions again showed a significant difference between neurons in the striatum and those in the cerebellum (p<0.05, bootstrap CIs). We also obtained similar results when the same sets of data were aligned with saccade initiation (Figure 4C, Figure 4—source data 1). Thus, while striatal neurons were active throughout the delay period, dentate nuclear neurons were recruited only during the last part of the delay period in the self-timed task.

Quantitative analysis for the time course of ramping activity during the delay interval.

(A) An example illustrating how we measured the onset and slope of ramp-up activity. For each condition, the trace of population of normalized activity (starting from 300 ms before cue onset and ending at the mean saccade latency) was fitted with two lines defined by three parameters (least squares, red lines). Data during 200 ms following 50 ms after cue onset were excluded to remove visual transients (dashed line). (B) Summary of ramp onset (left panel) and ramp-up slopes (right panel) for neurons in the striatum (n = 100, red circles) and the cerebellar nucleus (n = 76, blue triangles) based on data aligned with cue onset. Each data point indicates the values computed from the population data. Error bars with tick marks denote 2.5, 50 and 97.5 percentile of the results of the bootstrap analysis. Data points connected with solid and dashed lines indicate the data for ipsiversive and contraversive saccades, respectively. (C) Summary of ramp onsets (left panel) and slopes (right panel) computed for the data aligned with saccade initiation. Note that ramp onsets and slopes for the medium and long delay conditions differed significantly between the striatum and cerebellum.

https://doi.org/10.7554/eLife.35676.013

Neuronal correlates of trial-by-trial variation in self-timing

We have shown that neurons in the striatum flexibly alter the rate of rise of ramping activity depending on the instruction. However, in addition to the imposed task rule, the timing of self-initiated movements can also vary depending on stochastic fluctuation in neuronal activity. In fact, many previous studies sought neuronal correlates of trial-by-trial variation in self-timing under fixed conditions (Jazayeri and Shadlen, 2015; Maimon and Assad, 2006; Murakami et al., 2017; Ohmae et al., 2017; Soares et al., 2016; Tanaka, 2007).

To assess neuronal correlates of trial-by-trial variation, data for each neuron were divided into three groups according to saccade latency, and then the population of normalized activity was computed for each group. In the right panels in Figure 5A and B, the traces of spike density aligned on saccades are shifted in time so that the data alignments are placed at the mean saccade latencies relative to the cue onset (colored vertical lines, Figure 5—source data 1). For neurons in the striatum, preparatory activity started immediately after cue onset, but trial-by-trial variation was evident only during the very last part of the delay interval (Figure 5A). In contrast, for neurons in the cerebellar dentate nucleus, trial-by-trial variation started at the beginning of the preparatory activity except for trials with the shortest delay interval (Figure 5B).

Timing of trial-by-trial variation of ramp-up activity.

(A, B) For each condition, trials were divided into three groups according to saccade latency. Then, the data were normalized for each neuron, aligned with saccade initiation, and were shifted in time so that the times of saccades (colored vertical lines) were placed at the mean saccade latencies relative to the cue onset (right panels). On the left panels, data of the population activity were aligned with the cue onset (vertical black line). Inverted triangles indicate the time when the traces of normalized neuronal firing rate started to diverge as detected by repeated measures ANOVAs (p<0.01 for consecutive 40 ms). The baseline fluctuation (SD) of normalized neuronal activity was comparable between the recording sites (unpaired t-test, p=0.84). (C) Onset of trial-by-trial variation relative to the cue (left panel) or saccade initiation (right). Each point indicates the data derived from the analysis shown in (A) and (B). Error bars with three tick marks denote 2.5, 50 and 97.5 percentile of the results of the bootstrap analysis. Note that the trial-by-trial variation started earlier in the cerebellum than the striatum for medium and long intervals.

https://doi.org/10.7554/eLife.35676.015

We measured the timing of neuronal variation by comparing traces of individual neuronal activity among three groups for every millisecond (repeated measures ANOVA, p<0.01, Figure 5A and B, inverted triangles). This procedure was optimal to detect the earliest time of diverging point of normalized spike density profiles for the population of neurons, but did not take account of the trial-by-trial fluctuation of baseline firing rate. Since the coefficient of variation of baseline activity was greater in the striatum than the cerebellum (1.48 ± 1.34 vs. 0.52 ± 0.29, unpaired t-test, p<1.0E-8), the estimate of timing for the striatal neurons could be much later if the variation of baseline firing was considered. Figure 5C summarizes the onset times of neuronal variation and the distributions from bootstrap resampling, computed for the data aligned with either the cue onset or saccade initiation (Figure 5—source data 1). The trial-by-trial variation of neuronal activity started earlier in the cerebellum than the striatum except for trials with the shortest mandatory delay interval (p<0.05, bootstrap CIs). These results suggest that the cerebellum might be primarily responsible for the fine adjustment of self-timing, while the striatum may continuously monitor the time relative to the intended interval during the delay period.

Duration preference in individual neurons

Several lines of evidence suggest that some neurons in the basal ganglia and the cerebral cortex might be tuned to specific interval timing (Bartolo et al., 2014; Hayashi et al., 2015; Merchant et al., 2013b; Mita et al., 2009; Murakami et al., 2014). Indeed, we found that a subset of caudate neurons showed a preference for specific mandatory delay interval, although the population activity at the time of saccade initiation was roughly the same across intervals (Figure 3—figure supplement 1). Figure 6A illustrates three such examples exhibiting ramp-up activity that was greatest for the short, medium, and long mandatory delay intervals.

Figure 6 with 1 supplement see all
Duration preference of ramping activity.

(A) Three striatal neurons exhibiting a preference for specific mandatory delay interval. For each neuron, data were aligned with self-timed saccades and were shifted in time to place the end of traces at the time of mean saccade latencies (vertical lines). (B) Comparison of the magnitude of firing modulation between the striatum and the cerebellar dentate nucleus. Filled symbols indicate the data showing a significant difference (ANOVA, p<0.01). Bull’s eyes indicate the data for neurons shown in (A). Data for contraversive saccades only are shown for the striatum, while those for ipsiversive saccades only are shown for the cerebellum. Data for the opposite saccade directions are shown in Figure 6—figure supplement 1. Note that the data for the cerebellum clustered around the center, while those for the striatum varied. (C) Cumulative density functions for the distance from the center of the triangles for the data points in (B). Open and filled symbols indicate the data for saccades in ipsilateral and contralateral directions, respectively.

https://doi.org/10.7554/eLife.35676.017

To quantify the duration selectivity of preparatory activity in individual neurons, the firing rate 200 ms before self-timed saccades (black bars in Figure 6A) was compared across the delay intervals. Triangular plots in Figure 6B summarize the relative magnitude of the firing rate in individual neurons in the striatum and the cerebellum (Figure 6—source data 1). Among 31 (31%) caudate neurons exhibiting a significant duration selectivity (one-way ANOVA, p<0.01), 18, 8, and five neurons showed a preference for short, medium, and long delay intervals, respectively. In contrast, a smaller population of neurons in the cerebellar dentate nucleus (11/77, 14%) showed significant duration selectivity, with a preference for short, medium, and long intervals in 1, 8, and two neurons, respectively. We obtained similar results for the data of ipsiversive (striatum) and contraversive (cerebellum) saccades (Figure 6—figure supplement 1). In both structures, we found no topographic organization of neurons with or without duration selectivity.

We also compared degree of firing modulation depending on interval timing. Figure 6C plots the cumulative distributions of the distance of each data point from the center of the triangular plots in Figure 6B (Figure 6—source data 1). For both structures, data for saccades in the opposite directions almost perfectly overlap (filled and open symbols). The graph clearly shows that striatal neurons tend to have greater duration selectivity than neurons in the cerebellar dentate nucleus (Kolmogorov-Smirnov test; contra, p=7.7E-11; ipsi, p=5.3E-12), whereas no obvious preference for a specific duration was found in the population of neurons (Figure 3—figure supplement 1 and Figure 6B).

Inactivation effects on self-timing

To explore the causal role of neuronal activity, the recording sites in both monkeys were reversibly inactivated by injecting a small amount of muscimol. Figure 7A illustrates the cumulative latency distributions of self-timed saccades before and during inactivation of the caudate nucleus in a single experimental session. During inactivation, contraversive self-timed saccades were delayed in trials with short (400 ms) and long (2200 ms) mandatory delay intervals (Wilcoxon rank-sum test; short, p=7.4E-3; long, p=4.8E-3), while ipsiversive saccades were facilitated for all delay intervals (short, p=4.5E-3; medium, p=1.2E-7; long, p=2.0E-4). Among six experiments (three experiments for each monkey), contraversive saccades were significantly delayed in three experiments, while ipsiversive saccades were significantly facilitated in four experiments (Wilcoxon rank-sum test, p<0.05 with Bonferroni correction). In the population as a whole, the delay of contraversive self-timed saccades was found only in trials with a long delay interval (Figure 7C, paired t-test, p=0.038, Figure 7—source data 1), while the facilitation of ipsiversive saccades was consistently observed for all delay intervals (short, p=0.044; medium, p=0.048; long, p=0.021). We also found a significant change in contraversive saccade latency in the standard memory-guided saccade trials (paired t-test, p=0.012), but neither ipsiversive memory-guided saccades nor visually-guided saccades in both directions were altered during inactivation. During inactivation of the striatum, variation of self-timed saccade latencies significantly increased in two, one and one experiments in trials with short, medium and long delay intervals, respectively (F-test, p<0.05 with Bonferroni correction), while the variation decreased in one experiment for all delay intervals. In the population, the variation of saccade latency remained unchanged in all conditions (Figure 7E, paired t-test, p>0.09, Figure 7—source data 1). These results indicate that signals in the caudate nucleus regulate self-timing, especially in trials with a long delay interval.

Effects of inactivation.

(A) Data from a representative experiment in the caudate nucleus. Cumulative distributions of saccade latencies are compared between trials before (black open circles) and during (colored circles) inactivation with muscimol. Different colors indicate different mandatory delay intervals. (B) A representative experiment in the cerebellar dentate nucleus. Note that inactivation effects were greatest for ipsiversive trials with short mandatory intervals. (C) Summary of inactivation effects on saccade latency for the caudate nucleus. Bars and whiskers indicate the means and 95% confidence intervals of the changes in median latencies for different conditions. Filled and open bars indicate the data for contraversive and ipsiversive saccades, respectively. Asterisk denotes a significant inactivation effect (paired t-test, p<0.05). (D) Inactivation effects on saccade latency in the cerebellar nucleus. (E, F) Summary of inactivation effects on variation of saccade latency for the caudate nucleus and the cerebellar nucleus, respectively. SD, standard deviation.

https://doi.org/10.7554/eLife.35676.021

Figure 7B plots data from a single inactivation experiment performed in the cerebellum. Inactivation of the dentate nucleus delayed ipsiversive self-timed saccades in trials with short (400 ms) and medium (1000 ms) mandatory delay intervals (Wilcoxon rank-sum test; short, p=1.3E-9; medium, p=4.1E-3), while it failed to alter the timing of contraversive saccades. Among six experiments (three for each monkey), ipsiversive self-timed saccades were delayed in four and three experiments in trials with short and medium delay intervals, respectively. For contraversive saccades, changes in self-timing were found in two experiments in trials with short delay intervals (Wilcoxon rank-sum test, p<0.05 with Bonferroni correction). In the population, a significant inactivation effect was found only for ipsiversive self-timed saccades with a short delay interval (Figure 7D, paired t-test, p=9.4E-3, Figure 7—source data 1). In addition, variation of ipsiversive self-timed saccade latencies significantly increased in four and two experiments in trials with short and long delay intervals, respectively. For contraversive saccades, the variation increased in four experiments in trials with short delay intervals (F-test, p<0.05 with Bonferroni correction). In the population as a whole, variation of self-timing increased for ipsiversive saccades with short delay interval (Figure 7F, paired t-test, p=0.041, Figure 7—source data 1) and contraversive saccades with short and long delay intervals (short, p=6.6E-3; long, p=0.030). In contrast, variation of saccade latency remained unchanged in the standard memory-guided saccade task. When we compared the coefficient of variation of self-timing, the values were statistically greater during inactivation than the control for ipsiversive saccades in trials with a short delay interval (paired t-test, p=0.042), indicating that the signals in the dentate nucleus may regulate the precision of self-timing in trials with a short delay interval. Because injections of saline into three effective sites in either structure failed to alter saccade latency in all conditions (Wilcoxon rank-sum test, p>0.05), the present results were not attributed to a non-specific volume effect. Taken together, the results of inactivation experiments were consistent with the notion that signals in the cerebellum mostly regulate sub-second timing while those in the striatum are important for supra-second timing.

Discussion

Our exploration of neuronal signals in the striatum and the cerebellar dentate nucleus during self-timing yielded four major findings. First, neurons in the striatum were active throughout the delay period and altered the rate of rise of firing rate depending on the timing instruction, while those in the cerebellum were recruited only during the last part of the delay period, exhibiting a similar time course of activity for different interval timings (Figures 3 and 4). Second, neuronal correlates of trial-by-trial variation started earlier in the cerebellum than the striatum (Figure 5), suggesting that the cerebellum might be essential for the fine adjustment of self-timing in each condition. Third, a subset of striatal neurons showed a clear preference for a specific interval, while such neuron was almost absent in the cerebellum (Figure 6). Finally, the effects of inactivation in the striatum were greater for supra-second timing, while those in the cerebellum were dominant for sub-second timing (Figure 7), indicating that the relative contributions of these subcortical structures were different for different intervals. Based on these observations, we conclude that neurons in the striatum keep track of elapsed time by representing relative timing to the intended interval, while those in the cerebellum may participate in the generation of self-timed movements with a certain precision.

Roles of the basal ganglia and cerebellum in self-timing

We found that individual neurons in the striatum continuously kept track of relative timing. A similar scalable ramping activity has been reported in the prefrontal cortex of rats performing the self-timing task within the range of a few seconds (Xu et al., 2014). Other recent studies have also shown that the transient neuronal activity in the rat striatum represents a scalable population code for interval timing (Mello et al., 2015; Wang et al., 2018). Such temporally-dependent transient signals might be integrated in time to generate monotonic ramping activity to keep track of elapsed time and for making decisions (Janssen and Shadlen, 2005; Mita et al., 2009; Murakami et al., 2014). Indeed, some striatal neurons in the latter study were active throughout the delay period and continuously represented passage of time, although the proportion of such neurons appeared to be relatively small (Mello et al., 2015).

It has been widely accepted that dopamine signaling in the striatum is essential for interval timing (Coull et al., 2011; Merchant et al., 2013a). Subjects with Parkinson’s disease, for example, show deficits in the estimation and production of interval timing within the range of seconds (Pastor et al., 1992) and exhibit a significant decrease in the magnitude of CNV (Ikeda et al., 1997). In experimental animals, local application of dopamine receptor antagonists into the striatum alters self-timing in monkeys (Kunimatsu and Tanaka, 2016), and more specifically, optogenetic inhibition of nigro-striatal pathways disrupts temporal discrimination in rodents (Soares et al., 2016). However, the firing of midbrain dopamine neurons is known to be generally phasic (Bromberg-Martin et al., 2010) and therefore appears unlikely to continuously keep track of elapsed time during motor preparation, while the gain of transient sensory response might carry temporal information (Soares et al., 2016). In contrast, the dopamine concentration in the striatum has been recently shown to exhibit a gradual increase when performing tasks requiring continuous behavioral control (Howe et al., 2013). Interestingly, in rats moving towards distant goals, the level of striatal dopamine was proportional to the relative distance (or time) to the goals, thereby showing a scalable monotonous increase. Although how the dopamine release is regulated within the striatum remains controversial (D'Souza and Craig, 2006), the continuous representation of relative timing in striatal neurons might be relevant to the regulation of dopamine release at the terminal.

Aside from scalable, linear representation of interval timing, the existence of non-linear, duration-selective neuronal representation has also been suggested (Hayashi et al., 2015; Merchant and Averbeck, 2017). Time-dependent firing modulation in individual neurons has been reported during motor preparation (Murakami et al., 2014), sensory-guided continuous movements (Schoppik and Lisberger, 2006) and the maintenance of spatial working memory (Constantinidis and Klingberg, 2016). In the present experiments, neurons in the striatum showed greater variation in peak firing rate for different mandatory delay intervals than the cerebellum, and a minority of striatal neurons exhibited a clear duration tuning (Figure 6). However, in the population as a whole, neuronal activity just before saccade initiation was comparable across interval timing, and the neuronal firing rate at a given moment represented the relative timing to the intended interval. Assuming that the duration-selective elements represent absolute timing, such non-linear temporal representation may not conform to the flexible representation of relative timing found in the neuronal population. Instead, the duration-selective elements might distinguish behavioral goals rather than provide a neuronal representation of elapsed time, although how these signals are used for self-timing remains uncertain.

Since the cerebellum is essential for accurate movements, the generation of self-initiated saccades at proper timing must be one of critical functions of the cerebellum. In this study, we found that neuronal correlates of stochastic variation of self-timed saccade started earlier in the cerebellum than the striatum, while preparatory activity started late in the cerebellum for supra-second intervals (Figure 5). Our recent analysis of neuronal activity in the cerebellum showed that trial-by-trial latency for sub-second timing was correlated with the rate of rise of preparatory activity, while that for supra-second timing was correlated with the times of activity onset (Ohmae et al., 2017). These results suggest that saccade latency for sub-second timing might depend largely on the magnitude of neuronal activity in the cerebellum, while that for supra-second timing might be regulated by the combination of cerebellar activity and the external signals triggering the activity.

Consistent with this hypothesis, the effects of cerebellar inactivation on saccade latency were dominant for sub-second intervals (Figure 7D), while inactivation often increased the variation of self-timing even for supra-second interval (Figure 7F). The latter findings were consistent with the previous results of stimulation experiments showing that signals in the cerebellum can modify self-timing for all intervals (Ohmae et al., 2017), although the inactivation effects on latency variation found in this study were relatively small for the long intervals. This could be because the cerebellum may have a potential to adjust timing only in the range of several hundreds of milliseconds, and because the inactivation effects of the cerebellum might be masked by the greater variation of duration estimation for supra- than sub-second intervals.

In addition to the increased variation of self-timing, cerebellar inactivation also prolonged saccade latency in the standard memory-guided saccade task and the visually-guided saccade task (Figure 7D). Because the cerebellum is known to assist the cerebral cortex to boost motor commands during the initiation of somatic movements (Thach et al., 1992), the weak transient activity around the time of visually-triggered saccades found in this study (Figure 3—figure supplement 3B) might also play such a role. However, given the strong preparatory activity for self-timing, neurons in our recording sites in the dentate nucleus may not be simply providing motor commands for saccades. While the latency of visually-triggered saccades was only ~180 ms in our monkeys, the ramping activity started ~500 ms before self-timed saccades (Figure 3—figure supplement 1B), suggesting a role for motor planning. This was also supported by our previous findings that electrical stimulation applied to the dentate nucleus advanced self-timing without directly eliciting immediate saccades, and that the same stimulation pulses delivered during the delay period did not alter saccade latency in the standard memory-guided saccade task (Ohmae et al., 2017). Taken together, these results suggest that the signals in the cerebellar dentate nucleus may regulate timing of decisions for self-initiated movements. This function might gain importance in the situation where the precision of self-timing need to be preserved. Future studies may require consideration on these possibilities.

As a limitation of the present study, it should be noted that the other parts of the striatum and the deep cerebellar nuclei might represent sub- and supra-second intervals, respectively. Indeed, functional imaging studies often detect multiple loci in respective subcortical structures relevant to temporal information processing (e.g. Rao et al., 2001; Xu et al., 2006). However, the functional preference for short and long intervals for the cerebellum and the basal ganglia are also supported by many previous functional imaging and case studies (Lewis and Miall, 2003; Ivry and Spencer, 2004; Buhusi and Meck, 2005; Allman et al., 2014).

We recently found in monkeys that the trial-by-trial latency of self-timed saccades was inversely correlated with the pupil diameter just before the delay period (Suzuki et al., 2016). Importantly, the pupil diameter did not predict absolute timing, but was indicative of relative timing to the intended interval in each trial. Considering that the pupil diameter is well correlated with the noradrenergic signaling in the locus coeruleus (Aston-Jones and Cohen, 2005), and that these neurons send massive projections to the cerebellum (Olson and Fuxe, 1971), the stochastic variation of self-timing, cerebellar neuronal activity, and the pupil diameter might all be connected. Local manipulation of noradrenergic signaling in the cerebellum can provide a critical test for this possibility, whereas systemic pharmacological application complicates the interpretation of results (Suzuki and Tanaka, 2017).

Integration of subcortical signals

It remains unclear how the signals in the basal ganglia and the cerebellum are integrated to decide movement timing, although these areas have a common connection with the frontal and parietal cortices via the thalamus (Strick et al., 2009; McFarland and Haber, 2001; Prevosto et al., 2010). Recent anatomical data in rodents show that signals from the basal ganglia are sent to the superficial layers in the motor cortex via the thalamus, while those from the cerebellum are sent to deeper layers, suggesting that the basal ganglia and the cerebellum may play roles in preparing and triggering movements, respectively (Kaneko, 2013). Similarly, different subcortical signals for self-timing are possibly integrated through interplay between different layers in the cerebral cortex. In fact, individual neurons in the medial frontal cortex in monkeys are known to exhibit different time courses of activity during isochronous tapping (Merchant and Averbeck, 2017), suggesting that these signals might come from different subcortical sources. Alternatively, signals originating in the basal ganglia and the cerebellum might be separately processed in different areas in the cerebral cortex. For example, a recent study in rodents demonstrated that neurons in the medial prefrontal cortex signaled deterministic timing, while those in the premotor cortex represented stochastic timing during tasks requiring self-initiated movements (Murakami et al., 2017). These cortical areas might be specifically involved in the cortico-basal ganglia and the cortico-cerebellar loops, respectively. Furthermore, recent anatomical studies showed that the basal ganglia and cerebellum can mutually communicate with each other through disynaptic subcortical pathways (Bostan and Strick, 2018). Although the outputs from the cerebellar dentate nucleus have been shown to facilitate signals in the striatum through the thalamus in behaving animals (Chen et al., 2014), how these subcortical pathways can integrate signals for self-timing need to be examined in future study.

In addition to measuring single time intervals, the functional linkage between the basal ganglia and the cerebellum also appears to be essential for a variety of cognitive tasks. For temporal information processing, beat-based timing has been thought to be processed in the cortico-basal ganglia pathways (Teki et al., 2011), but recent analyses also suggest a role for the cerebellum (Ohmae et al., 2013; Teki and Griffiths, 2016). In addition, while proactive inhibition for demanding behavioral tasks requires intact basal ganglia (Frank et al., 2007), recent studies also show that the cerebellum is involved in countermanding (Ide and Li, 2011) as well as anti-saccade (Peterburs et al., 2015; Kunimatsu et al., 2016) paradigms. Furthermore, recent studies in rodents have detected neuronal modulation associated with temporal-difference prediction error of aversive stimuli in the cerebellum (Ohmae and Medina, 2015), although such signals have long been thought to be a hallmark of neural processes in the basal ganglia. All these recent studies indicate the necessity of future research linking neuronal processes in the basal ganglia with those in the cerebellum for comprehensive understanding of global network controlling behaviors.

Materials and methods

Animal preparation

Experiments were conducted on two female Japanese monkeys (Macaca fuscata, 7–8 kg). All experimental protocols were approved by the Hokkaido University Animal Care and Use Committee. Details of surgical procedures for implanting the head holder, eye coil, and recording cylinder are described elsewhere (Kunimatsu and Tanaka, 2016). All surgeries were performed using sterile procedures under general isoflurane anesthesia. Analgesics were administered during each surgery and the following few days.

Behavioral paradigms

During training and experimental sessions, monkeys were seated in a primate chair placed in a darkened booth. Visual stimuli were presented on a 24-inch cathode-ray tube monitor (refresh rate: 60 Hz) that was located 38 cm away from the eyes, and subtended a visual angle of 64 × 44°. We used three saccade paradigms: the self-timed memory-guided saccade task (Figure 1A), the standard memory-guided saccade task, and the visually-guided saccade task (Figure 3—figure supplement 3A). In the self-timed memory-guided saccade task (Ashmore and Sommer, 2013; Costello et al., 2016; Kunimatsu and Tanaka, 2012, 2016; Tanaka, 2006, 2007; Wang et al., 2018), monkeys were required to make a saccade to the location of a previously presented visual cue (100 ms) without any immediate external trigger. The FP disappeared only after the animals generated a self-timed saccade, as eye position deviated >3° from the FP. The mandatory delay interval following the cue onset was selected from short (400–700 ms), medium (1000–1600 ms), or long (2400–3100 ms) intervals that were indicated by FP color (Figure 1A, lower panel; cyan, blue and yellow squares for short, medium, and long intervals, respectively). In the standard memory-guided saccade task (Hikosaka and Wurtz, 1983), monkeys made a saccade to the remembered location of the visual cue in response to the FP offset (<400 ms) that occurred randomly during 700–2500 ms (uniform distribution) following cue onset. In this task, saccade timing was instructed externally by extinction of the FP. In the visually-guided saccade task, the saccade target appeared at the time of the FP offset (always 1600 ms following its appearance), and the animals made an immediate saccade to the target. To inform the monkeys of the trial type, the FP was red for the standard memory-guided saccade task and the visually-guided saccade task (Figure 3—figure supplement 3A). In all these tasks, the saccade target and visual cue were presented either 16° left or right of the FP. The size of the eye position window was 2° for initial fixation and 4° for the peripheral targets. Correct performance was reinforced with a liquid reward at the end of each trial. Each trial was presented in pseudo-random order within a block that consisted of 10 different trials (five trials in opposite directions).

Recording and inactivation procedures

To record from single neurons, a glass-coated tungsten electrode (Alpha Omega Engineering) guided by a 23-gauge stainless tube was lowered into the striatum (caudate nucleus, ±2 mm of the anterior commissure and 4–8 mm lateral to the midline) or the cerebellar dentate nucleus (6–8 mm posterior to the interaural line and 6–8 mm lateral) in separate experiments using a micromanipulator (MO-97S; Narishige). The location of electrode penetration was adjusted using the grid system (Crist Instruments). Signals obtained from the electrodes were amplified and filtered (0.3–10 kHz, Model 1800; A-M Systems). The waveform of action potentials of a single neuron was isolated using a real-time spike sorter with template-matching algorithms (ASD; Alpha Omega Engineering). We searched for the task-related neurons when monkeys performed a block of randomized saccade trials. Once we isolated the putative task-related neuron exhibiting a ramping activity before saccade initiation, we collected data for ≥6 trials for the further offline analysis. We did not include the data from presumably tonically active neurons in the caudate nucleus, which exhibited characteristic tonic firing pattern and wider action potentials (Aosaki et al., 1995). Neurons included for the analysis had low baseline firing rate and were considered as medium-spiny projection neurons and some GABAergic interneurons.

For the inactivation experiments, we manufactured injectrodes composed of epoxy-coated tungsten microelectrode (FHC Inc.) and silica tube (Polymicro Technology; Tachibana et al., 2008). The injectrode connected to a 10 μL Hamilton microsyringe was inserted through the guide tube, and a small amount of GABAA agonist (muscimol; 5 μg/μL, 1 μL for each site) was pressure-injected using a micropump (NanoJet; Chemyx Inc.) at the sites where the task-related neurons were previously recorded. For the inactivation experiments in the striatum, we infused muscimol into two sites along the same penetration (>500 μm apart). The inactivation effects were assessed by comparing eye movements before and 15–90 min after muscimol injection. We also injected saline in separate experiments to ensure that the effect was not due to any volume effect.

Histological procedures

Recording sites in one monkey were reconstructed from histological sections (Figure 2). At the end of the experiments, several electrolytic lesions were made at or near sites where task-related neurons were recorded by passing a direct current through the electrodes (10–20 μA, tip negative, 800–1000 μC). The animal was then deeply anesthetized with sodium pentobarbital (>60 mg/kg, i.p.) and perfused transcardially with 0.1 M phosphate buffer followed by 3.5% formalin. The brain was cut into 50 μm thick transverse (cerebellum) and coronal (striatum) sections using a freezing microtome, and each section was stained with cresyl violet. We reconstructed the location of each task-related neuron according to the depth and coordinates of electrode penetrations, and the locations relative to the marking lesions.

Data acquisition and analysis

Spike timing and eye movement data were sampled at 1 kHz and saved in files during the experiments. Further off-line analyses were performed using Matlab (MathWorks). Saccades were detected when angular eye velocity exceeded 40°/s and eye displacement was >3°. Trials were excluded from the analysis when saccades landed >5° from the cue location. Saccade latency was defined as the time from either cue onset (self-timed saccade task) or FP offset (other two tasks) to the time of saccade initiation. Although monkeys sometimes generated early saccades before the expiration of mandatory delay for reward (6–12% and 16–35% of self-timed trials for monkeys B and G, respectively, Figure 1B), these trials were also included for the analysis because the animals were supposed to monitor the elapsed time.

For quantification, we measured the firing rate during the following periods: (1) 300 ms just before the fixation point onset (baseline period), (2) 200 ms after the cue onset (visual period), (3) the 350 ms interval starting from 500 ms before saccades (delay period), and (4) 150 ms before saccade initiation (saccade period). In this study, we considered neurons with a significant firing modulation during the delay period according to post hoc multiple comparisons (one-way ANOVA followed by Scheffé’s method, p<0.05). The time course of neuronal activity for each condition was qualitatively examined by computing the spike density function using a Gaussian kernel (σ = 15 ms).

To compare the time courses of preparatory activity for neurons in different structures, we estimated the rate of rise of neuronal activity by fitting (least squares) a non-linear function with parameters of the baseline, slope, and onset time of ramp-up activity (Figure 4A). The normalized population activity was computed from the traces of spike density starting from 400 ms before cue onset and ending at the mean saccade latency. The data during the 200 ms following 50 ms after cue onset were excluded to eliminate visual transient. To obtain confidence intervals, we randomly resampled neurons with replacement to obtain replications of the same size as the original data set, and this was repeated 1000 times (bootstrap method, Figure 4B and C).

For the analysis of neuronal correlates of trial-by-trial variation in self-timing, the data for each neuron were divided into three groups according to saccade latencies, and then the population of normalized activity for saccades in the preferred direction was computed for each group (Figure 5). We compared normalized activity among three groups every millisecond (repeated measures ANOVA) and measured the timing of neuronal variation relative to either cue onset or saccade initiation when the traces significantly diverged (p<0.01) for >40 ms (Figure 5C). The distributions of timing of neuronal variation were again computed using the bootstrap method.

For the triangular plots in Figure 6B, the firing rate before saccades (200 ms) for each neuron was normalized for the sum of the activity in different conditions. The x-coordinate of each data point is the difference in normalized activity between the medium and long delay intervals, and the y-coordinate is the normalized response in the short delay interval times √3.

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Decision letter

  1. Naoshige Uchida
    Reviewing Editor; Harvard University, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Different contributions of preparatory activity in the basal ganglia and cerebellum to self-timing" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Naoshige Uchida as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Richard Ivry as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Robert S Turner (Reviewer #2); John Assad (Reviewer #3).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

The study compares the firing patterns of neurons in the striatum (the anterior part of the caudate nucleus) and the cerebellum (the posterior part of the cerebellar dentate nucleus) in a self-timed saccade task in two Japanese monkeys. Monkeys were trained to make a saccadic eye movement after a brief target cue presentation (100ms). To obtain reward, saccade onset had to be within a predetermined time interval (short, 400-700ms; medium, 1000-1600ms; long, 2400-3100ms) signaled by the color of the fixation point. The authors recorded the activity of neurons that 'elevated activity before self-timed saccade'. The authors made several interesting findings. First, the activity in the striatum started ramping shortly after the cue onset while that in the cerebellar nucleus started ramping only shortly before saccadic eye movement. Second, trial-by-trial fluctuation of saccade onset is more correlated with the pattern of activity in the cerebellar nucleus but not with that in the caudate nucleus. Third, the authors performed unilateral transient inactivation of each region using muscimol. During caudate inactivation, the onset of contraversive saccades was delayed in long interval trials, whereas that of ipsiversive saccades was shortened for all intervals. During cerebellar nucleus inactivation, a significant effect was found only for ipsiversive saccades in short interval trials. Furthermore, the coefficient of variation (CV) of saccade onset was not altered by caudate inactivation but increased in some conditions by cerebellar nucleus inactivation.

The possibility that neurons in these regions deferentially regulate self-timed movement initiation is of great interest, and this manuscript contains potentially very interesting results. The study is well designed, and the results appear to be high quality. The analyses are mostly appropriate (but see below) and text and illustrations are well prepared. Although all the three reviewers found this study to be of importance, they raised some concerns that we would like to see your response before recommending this work for publication at eLife.

Essential revisions:

1) The analyses of ramping activity were performed by aligning trials to cue onset. However, these analyses alone do not distinguish alternative hypotheses that need to be separated. For instance, these analyses do not distinguish whether the variability in ramping activity comes from different ramping patterns (e.g. different rates in ramp) or merely from the variation in movement onset timing. Some of the interpretations must distinguish these possibilities. It is, therefore, important to analyze the data by aligning trials to movement onset. We appreciate that you have already performed this in some analyses, but it is important to apply this method further and to more carefully interpret the results. Specifically, the measurement of ramping slope (Figure 4) should be made on data aligned to movement onset rather than cue onset. Moreover, a more conservative interpretation of the data (at least in Figure 5) is that the striatum activates considerably earlier than the cerebellum during self-timed movements; the "variability" argument is less convincing. That simpler interpretation is still consistent with the different roles that have been proposed for striatum and cerebellum in timing (seconds vs. sub-second).

Reviewer 3 provided a very thoughtful analysis of this issue, as summarized in the following paragraphs (taken directly from his/her review with some editing).

The major problem is that much of the analysis was performed with responses aligned to CUE onset rather than MOVEMENT onset (Figure 3 and Figure 4; I have seen this also in several other papers claiming that neurons show ramping before self-timed movements). The issue is that there is a *distribution* of movement times following cue onset. Most neurons in the motor system show activity preceding movement; these responses can be highly stereotyped relative to movement onset. If trials are aligned to the *cue time*, then on average among trials, even a stereotyped movement-related response will appear as a "ramp", because the variability in self-timed movement latency from trial-to-trial will smear out the average response. Even more dangerous, the distribution of movement times is broader with longer timed intervals than shorter intervals, as the authors showed (Figure 1B) and many other labs have reported. Thus, if trials are averaged aligned on cue onset, the "ramp" will appear shallower for late movements than early movements – i.e., the broader distribution of movement times will "smear out" the average activity more for long than short intervals. This is exactly the problem in Figure 3 and Figure 4. These data need to be re-analyzed aligned with respect to *movement time*, not cue time (i.e., averaging trials back from the movement onset, excluding the 200 ms period following the cue to omit the visual response to the cue). The cue-aligned smearing problem is especially a problem for the cerebellar data, because the apparent "ramp" begins only ~500 ms before the movement (as the authors note) and thus overlaps extensively with the distribution of movement times.

This has been done in Figure 5. Here the average striatal responses (Figure 5A) still show clear differences in ramping slopes between short, medium and long times, but there is not much difference in slope evident for the cerebellar neurons (Figure 5B). That is, the *slopes* of the rise in cerebellar activity do not appear so different between the sub-divided trials in each category (e.g., light blue, medium blue and dark blue), or between medium or long trials (short trials are difficult to compare, because they are likely contaminated with the cue response). The ramp slope should be quantitatively measured from these movement-aligned data, not from cue-aligned data.

These observations make it hard to evaluate the claim that the ramps diverge earlier in cerebellum than in striatum. Figure 5A may suggest that striatal activity before the movement is actually comprised of two components, a slow ramp with a slope that co-varies with movement time (short, medium or long) and fast, peri-movement response that is more stereotyped with respect to the movement onset. The cerebellar neurons seem to only have only a fast, peri-movement response, which is larger than the peri-movement response of the striatal neurons. The cerebellar neurons also have much higher intrinsic firing rates. This difference may make it harder to detect the divergence point of the striatal peri-movement component compared to the cerebellum. It is also problematic to argue that the cerebellar response controls the late-phase variability of the movement latency when the cerebellar responses appears to be so stereotyped relative to movement. That is, the stereotypy of the cerebellar response might suggest it arises "after the decision" to move whereas the more variable slow ramp in striatum might suggest it "contributes to the decision". As written now, casual readers may walk away with the idea that the cerebellum is somehow injecting variability into the movement time, whereas cerebellar responses seems fairly stereotyped relative to movement time.

2) Interpretation and General conclusion: The general conclusion regarding the role of the cerebellum hardly reaches beyond the results and thus is relatively uninformative. The authors conclude that Cb "might be primarily responsible for the stochastic variation of self-timing." This, of course, is just what the data show. But why would such a large and complicated structure as the Cb have such a simple job as adding noise to interval timing? Is there some advantage to the animal to implement "stochastic variation of self-timing?" In other words, is sub-second variation an aspect of behavior that benefits the animal and that the Cb is designed (by evolution) to implement? Or, do the authors believe that this kind of variation is merely performance noise? If performance noise, then is it possible the Cb is reflecting this variability without actually controlling it?

It appears that another way to describe the same results is that activity in the Cb is closely time-locked to the time of saccade onset, even that Cb is closely involved in triggering saccade onset. Granted, the Discussion section does allude to ideas that Cb may be involved in action triggering, but this explanation differs significantly from the one invoked throughout the manuscript ascribing a role to the Cb in "variation control".

3) Please provide more information regarding the rationale and the details of recording conditions. First, please provide more information concerning the locations sampled in striatum and dentate. Why were these locations chosen? What are the stereotaxic locations of the regions sampled? Were identical locations sampled in both animals? Do these regions of striatum and dentate "project to" (via their trans-thalamic pathways) the same regions of cortex? Is it possible that a different part of striatum might show sub-second variation related activity? or a different part of dentate show multi-second ramping activity?

A related question is whether the authors discriminated between the identifiable neuronal subtypes in the striatum? Were all or most of the neurons likely spiny projection neurons? Were TANs explicitly excluded from analysis?

4) To analyze the onset and slope of ramping activities, the authors fit a line incorporating onset and baseline (three parameters). It would be important to discuss how good the fit is (goodness of fit) at the level of single neurons and populations. Furthermore, it is important to report some data regarding neuron-to-neuron variability in firing patterns.

5) The interpretation of the inactivation experiments appears to warrant further clarification. There appears to be a significant change in the CV in the long interval condition (Figure 7F). Is this true? How does this relate to the authors' conclusions? Overall, it would be helpful to clarify how the authors interpret the inactivation results (Figure 7).

6) Last, but not least, there is a logical concern with your study. You compare activity in the input nucleus of the basal ganglia with activity in an output nucleus of the cerebellum. This difference is especially relevant given that current computational models of timing in these subcortical structures tend to focus on the striatum and cerebellar cortex (either Purkinje cells or the cerebellar cortical cells that provide input to the Purkinjes). We are not aware of models that posit a role for DCN neurons in "timing computations". In fact, some classic results in the eyeblink literature have shown that if the system is just driven by DCN (following lesions/inactivation of cerebellar cortex), you lose the adaptive timing of the CR. This has been taken to mean that inputs to DCN (from extracerebellar regions) are sufficient to associate the CS and US, but the adaptive timing of the CR comes from the cerebellar cortex.

This issue needs to be made explicit both in the Introduction and Discussion section. You need to outline the logic of comparing the striatum to the DCN, as well as outline how your results may be qualified by the fact that you have focused on this comparison. As things are now written, the framing makes it sound like the striatum and DCN provide a way to compare temporal processing/representation in the basal ganglia and cerebellum. For example, in the Introduction, you write, "These results indicate that neurons outside of the cerebellum must keep track of elapsed time to make temporally accurate movements in trials with suprasecond delay intervals", and then go on to make the argument that for turning to the striatum. This is reasonable, but note you are going from DCN observations to the conclusion that the tracking of elapsed time must be from "neurons outside of the cerebellum." This ignores the possibility that the tracking might be done in the cerebellar cortex with the projection from the cerebellar cortex to DCN introducing some sort of non-linearity/thresholding function that obscures more of a ramping-like function.

[Editors' note: further revisions were requested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Different contributions of preparatory activity in the basal ganglia and cerebellum for self-timing" for further consideration at eLife. Your revised article has reviewed by two peer reviewers, including Nao Uchida as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Rich Ivry as the Senior Editor.

The manuscript has been improved but there is one final issue that we would like to see addressed before making a final decision. Specifically, the reviewers thought that the data do not provide strong support for the broad claim that 'the cerebellum might be primarily responsible for the stochastic variation of self-timing'. We ask that you qualify this claim/inference in the manuscript.

I'm passing along the comments from reviewer 3 on this issue to help guide you in this endeavor.

Reviewer #3:

The paper has been improved. However, I am still skeptical of the claim that the cerebellum is more responsible for stochastic variation in self-timing than the striatum. The authors have tried to address this issue, but the data/arguments are not convincing. For example, in the Discussion section, the authors suggest that the cerebellum adds or maintains variation of saccade timing – but only for short estimated intervals. For longer intervals, the authors state "there was no need for the animals to maintain the variation of saccade timing within several hundreds of milliseconds". This begs the question of why there is there a "need" at all to maintain variation in timing. Perhaps inherent variation allows us to explore different intervals in a world with constantly changing temporal contingencies - but if so, why would we relax that exploratory bent for supra-second intervals?

Stepping back, there were two observations behind the claim that the cerebellum is responsible for stochastic variation. First, for the medium and long intervals, if trials are grouped according to precise movement time, the neuronal responses in the Cb appear to diverge a few hundred ms earlier than those of the striatum (Figure 5). This earlier divergence is based (per force) on a statistical criterion – when significant differences first arise based on the results of repeated-measures ANOVA. This is not a fair comparison between caudate and Cb. Average firing rates in caudate are far lower than in Cb dentate firing (Figure 3C-D). Moreover, I'm not sure about dentate nucleus firing statistics, but firing patterns in the Cb can be highly regular (e.g., Purkinje cells), which would reduce variance in spike counts for a given mean firing rate. For both these reasons, it might just be easier to DETECT the ANOVA-based divergence point for the cerebellar neurons than the caudate neurons, whereas we don't know how the brain actually pools signals from these two areas. I'm not exactly sure how to address this problem, but the authors could try to repeat their analysis with a subset of data matched for mean firing rates from the two areas (if possible). It would also be helpful to at least examine the coefficient of variation of firing rates between the two areas.

Second, the authors conclude the description of the inactivation experiment (end of Results section) with the statement that stochastic variation "might be controlled by signals in the cerebellum, but not in the striatum" - but this does not seem warranted from the inactivation experiment. The clearest result is that inactivation of Cb affected mean movement time most for short intervals while striatum affected mean movement time most for medium and long times. With respect to *variation* in latency, the effects of Cb inactivation were inconsistent at best. In addition, the largest change in movement-time variation with Cb inactivation was for short intervals, but this also caused the largest shift in mean movement latency of any area/interval/experiment, which might be expected to automatically increase the variation in latency (e.g., as per scalar expectancy theory of interval timing). But more generally, what is the hypothesized effect of inactivation on latency variation? Do the authors posit that the cerebellum controls precision, and thus that Cb inactivation would erode that precision? Is that interpretation not at odds with the results in Figure 5? Or do the authors hypothesize that the Cb adds noise to the timing process, in which case Cb inactivation might reduce variation?

In the end, the clearest result in the paper is the differential roles for the caudate and Cb for long and short intervals, respectively, supported by both electrophysiological recording and inactivation experiments. This alone is a nice contribution. The stochastic variation claims are not strongly supported (either empirically or theoretically). Of course, the authors could present the "divergence" argument in Figure 5, but in a more conservative manner, pointing out the difficulty in statistically distinguishing divergence points between neuronal populations with different firing rates and potentially different firing statistics. But I don't think that the cerebellar stochastic variation claims should be featured prominently in the paper (for example, the abstract should not conclude with that claim).

We also ask that you double check the new supplement 1 to Figure 3 (saccade-aligned responses). The SD of cue times seems narrow given the wide distributions of movement times in Figure 1B. Please verify that the figure is correct.

https://doi.org/10.7554/eLife.35676.025

Author response

Essential revisions:

1) The analyses of ramping activity were performed by aligning trials to cue onset. However, these analyses alone do not distinguish alternative hypotheses that need to be separated. For instance, these analyses do not distinguish whether the variability in ramping activity comes from different ramping patterns (e.g. different rates in ramp) or merely from the variation in movement onset timing. Some of the interpretations must distinguish these possibilities. It is, therefore, important to analyze the data by aligning trials to movement onset. We appreciate that you have already performed this in some analyses, but it is important to apply this method further and to more carefully interpret the results. Specifically, the measurement of ramping slope (Figure 4) should be made on data aligned to movement onset rather than cue onset. Moreover, a more conservative interpretation of the data (at least in Figure 5) is that the striatum activates considerably earlier than the cerebellum during self-timed movements; the "variability" argument is less convincing. That simpler interpretation is still consistent with the different roles that have been proposed for striatum and cerebellum in timing (seconds vs. sub-second).

Reviewer 3 provided a very thoughtful analysis of this issue, as summarized in the following paragraphs (taken directly from his/her review with some editing).

The major problem is that much of the analysis was performed with responses aligned to CUE onset rather than MOVEMENT onset (Figure 3 anf Figure 4; I have seen this also in several other papers claiming that neurons show ramping before self-timed movements). The issue is that there is a *distribution* of movement times following cue onset. Most neurons in the motor system show activity preceding movement; these responses can be highly stereotyped relative to movement onset. If trials are aligned to the *cue time*, then on average among trials, even a stereotyped movement-related response will appear as a "ramp", because the variability in self-timed movement latency from trial-to-trial will smear out the average response. Even more dangerous, the distribution of movement times is broader with longer timed intervals than shorter intervals, as the authors showed (Figure 1B) and many other labs have reported. Thus, if trials are averaged aligned on cue onset, the "ramp" will appear shallower for late movements than early movements – i.e., the broader distribution of movement times will "smear out" the average activity more for long than short intervals. This is exactly the problem in Figure 3 and Figure 4. These data need to be re-analyzed aligned with respect to *movement time*, not cue time (i.e., averaging trials back from the movement onset, excluding the 200 ms period following the cue to omit the visual response to the cue). The cue-aligned smearing problem is especially a problem for the cerebellar data, because the apparent "ramp" begins only ~500 ms before the movement (as the authors note) and thus overlaps extensively with the distribution of movement times.

This has been done in Figure 5. Here the average striatal responses (Figure 5A) still show clear differences in ramping slopes between short, medium and long times, but there is not much difference in slope evident for the cerebellar neurons (Figure 5B). That is, the *slopes* of the rise in cerebellar activity do not appear so different between the sub-divided trials in each category (e.g., light blue, medium blue and dark blue), or between medium or long trials (short trials are difficult to compare, because they are likely contaminated with the cue response). The ramp slope should be quantitatively measured from these movement-aligned data, not from cue-aligned data.

These observations make it hard to evaluate the claim that the ramps diverge earlier in cerebellum than in striatum. Figure 5A may suggest that striatal activity before the movement is actually comprised of two components, a slow ramp with a slope that co-varies with movement time (short, medium or long) and fast, peri-movement response that is more stereotyped with respect to the movement onset. The cerebellar neurons seem to only have only a fast, peri-movement response, which is larger than the peri-movement response of the striatal neurons. The cerebellar neurons also have much higher intrinsic firing rates. This difference may make it harder to detect the divergence point of the striatal peri-movement component compared to the cerebellum. It is also problematic to argue that the cerebellar response controls the late-phase variability of the movement latency when the cerebellar responses appears to be so stereotyped relative to movement. That is, the stereotypy of the cerebellar response might suggest it arises "after the decision" to move whereas the more variable slow ramp in striatum might suggest it "contributes to the decision". As written now, casual readers may walk away with the idea that the cerebellum is somehow injecting variability into the movement time, whereas cerebellar responses seems fairly stereotyped relative to movement time.

We agree with the reviewer in that the variation of saccade timing must obscure the slope of ramping activity when the data are aligned with the cue onset. In the revised manuscript, we have added the data for the analysis of ramping activity with saccade alignment (Figure 4C). We also present the population data aligned with saccade initiation (Figure 3—figure supplement 1). The results were in good agreement with the original data aligned with the cue onset. The times of ramp onset greatly differed for different interval timing in the striatum but were comparable in the cerebellum (Figure 4C). The degree of changes in ramp slope for different intervals was greater in the striatum than the cerebellum (Figure 4D), although the slope differed for different intervals in both structures. We have modified the text in the Results section accordingly. Also, we have corrected the unit on the ordinate of Figure 4B right panel (from spikes/s/s to unit/s), because the quantification was made for the normalized activity as shown in Figure 4A.

As the reviewer pointed out, the preparatory activity in the cerebellum appears to be more stereotyped than that in the striatum. This was further elucidated as we quantified the inter-neuronal variation in response to the comment #4 below (Figure 3—figure supplement 2). However, we do not think that the cerebellum simply triggers movements following motor decisions, because the ramping activity was found before self-timed saccades only and started well before the movements (~500 ms) while the triggering of saccades occurred within ~180 ms in the standard memory-guided saccade task. Taken together with the results of inactivation and our previous stimulation study performed in other animals (Ohmae et al., 2017), we have added a paragraph to thoroughly discuss this important point (subsection “Roles of the basal ganglia and cerebellum in self-timing”, "In addition to the increased variation of self-timing, cerebellar inactivation also prolonged saccade latency in the standard memory-guided saccade task and the visually-guided saccade task (Figure 7D). […] Taken together, these results suggest that the signals in the cerebellar dentate nucleus may regulate timing of decisions for self-initiated movements. This function might gain importance in the situation where the precision of self-timing need to be preserved. Future studies may require consideration on these possibilities.").

2) Interpretation and General conclusion: The general conclusion regarding the role of the cerebellum hardly reaches beyond the results and thus is relatively uninformative. The authors conclude that Cb "might be primarily responsible for the stochastic variation of self-timing." This, of course, is just what the data show. But why would such a large and complicated structure as the Cb have such a simple job as adding noise to interval timing? Is there some advantage to the animal to implement "stochastic variation of self-timing?" In other words, is sub-second variation an aspect of behavior that benefits the animal and that the Cb is designed (by evolution) to implement? Or, do the authors believe that this kind of variation is merely performance noise? If performance noise, then is it possible the Cb is reflecting this variability without actually controlling it?

It appears that another way to describe the same results is that activity in the Cb is closely time-locked to the time of saccade onset, even that Cb is closely involved in triggering saccade onset. Granted, the Discussion section does allude to ideas that Cb may be involved in action triggering, but this explanation differs significantly from the one invoked throughout the manuscript ascribing a role to the Cb in "variation control".

The reviewer is right. Although the results of inactivation experiments clearly show that the signals in the cerebellar dentate nucleus controls the accuracy and precision of self-timing, it is difficult to interpret the results in terms of behavioral benefit in our experimental paradigm, at least for the long delay condition. We have inserted some lines in the Discussion section to mention this point, "This could be because the cerebellum may have a potential to adjust timing only in the range of several hundreds of milliseconds, and because the inactivation effects of the cerebellum might be masked by the greater variation of duration estimation for supra- than sub-second intervals. In other words, while our results suggest that the stochastic variation of self-timing may primarily reflect neuronal signals in the cerebellum, the cerebellum might not be fully engaged in current behavioral condition. For example, when the mandatory delay was long (e.g., 2200 ms), there was no need for the animals to maintain the variation of saccade timing within several hundreds of milliseconds.").

As for the alternative interpretation of triggering of saccades, we have added a paragraph to the Discussion section considering the roles for the cerebellum in self-timing, as stated in the response to the major comment #1 above. Because the preparatory activity started well before movements and was observed only for self-timed saccades, neuronal signals in the dentate nucleus are likely to play a role beyond simply triggering movements.

3) Please provide more information regarding the rationale and the details of recording conditions. First, please provide more information concerning the locations sampled in striatum and dentate. Why were these locations chosen? What are the stereotaxic locations of the regions sampled? Were identical locations sampled in both animals? Do these regions of striatum and dentate "project to" (via their trans-thalamic pathways) the same regions of cortex? Is it possible that a different part of striatum might show sub-second variation related activity? or a different part of dentate show multi-second ramping activity?

We chose the anterior striatum and the posterior dentate nucleus because they commonly project to the areas in the frontal and parietal cortices known to be related to self-timing, and because the previous studies found neurons associated with saccades in respective subcortical regions. Along with these information (Introduction), we now report the stereotaxic coordinates of recording sites in both monkeys(Materials and methods section). The last question is far beyond the scope of the present study, and we are unable to exclude these possibilities unless thoroughly recording from both structures. We have added a paragraph to the Discussion section to mention this ("As a limitation of the present study, it should be noted that the other parts of the striatum and the deep cerebellar nuclei might represent sub- and supra-second intervals, respectively. Indeed, functional imaging studies often detect multiple loci in respective subcortical structures relevant to temporal information processing (e.g., Rao et al., 2001; Xu et al., 2006). However, the functional preference for short and long intervals for the cerebellum and the basal ganglia are also supported by many functional imaging and case studies (Lewis and Miall, 2003; Ivry and Spencer, 2004; Buhusi and Meck, 2005; Allman et al., 2014).").

A related question is whether the authors discriminated between the identifiable neuronal subtypes in the striatum? Were all or most of the neurons likely spiny projection neurons? Were TANs explicitly excluded from analysis?

During experiments, we discriminated neuron type based on the baseline firing rate and the width of action potentials. Technically, we were able to exclude the tonically active neurons (TANs) easily, but our sample might also contain GABAergic interneurons in addition to the projection neurons. We have noted this point in the revised text (subsection “Recording and inactivation procedures”, "We did not include the data from presumably tonically active neurons in the caudate nucleus, which exhibited characteristic tonic firing pattern and wider action potentials (Aosaki et al., 1995). Neurons included for the analysis had low baseline firing rate and were considered as medium-spiny projection neurons and some GABAergic interneurons.").

4) To analyze the onset and slope of ramping activities, the authors fit a line incorporating onset and baseline (three parameters). It would be important to discuss how good the fit is (goodness of fit) at the level of single neurons and populations. Furthermore, it is important to report some data regarding neuron-to-neuron variability in firing patterns.

We now report the distribution and range of coefficient of determination of fitting for the population data in the Results section. As stated in the text, the quantification by line fitting was performed on the population activity obtained from each bootstrap resampling, not on individual neuronal activity. As suggested, we have compared the inter-neuronal variation of the time courses of preparatory activity between the recoding sites (Figure 3—figure supplement 2). As stated in the text (subsection “Time courses of preparatory activity for self-timing in the striatum and the cerebellum”) and in the figure legend, neuronal activity during the delay interval was more variable in the striatum than the cerebellum.

5) The interpretation of the inactivation experiments appears to warrant further clarification. There appears to be a significant change in the CV in the long interval condition (Figure 7F). Is this true? How does this relate to the authors' conclusions? Overall, it would be helpful to clarify how the authors interpret the inactivation results (Figure 7).

The effects of inactivation on saccade latency were verified by Wilcoxon rank-sum test with Bonferroni correction for individual experiments and by paired t-test for multiple experiments. We also verified the effects of inactivation on the variance of saccade latency by F-test with Bonferroni correction. These are stated in the Results section of the revised text. As stated in the Results section, we found significant changes in standard deviation (SD) for short and long delay intervals (Figure 7F), while changes in coefficient of variation (CV) were found only for short delay interval. Along with the discussion regarding the weak inactivation effects on latency variation for supra-second intervals, we have inserted some lines explaining the possible limitation of the experimental paradigm, as explained above in the response to the comment #2 (subsection “Roles of the basal ganglia and cerebellum in self-timing”, "…although the inactivation effects on latency variation found in this study were relatively small for the long intervals. This could be because the cerebellum may have a potential to adjust timing only in the range of several hundreds of milliseconds, and because the inactivation effects of the cerebellum might be masked by the greater variation of duration estimation for supra- than sub-second intervals. In other words, while our results suggest that the stochastic variation of self-timing may primarily reflect neuronal signals in the cerebellum, the cerebellum might not be fully engaged in current behavioral condition. For example, when the mandatory delay was long (e.g., 2200 ms), there was no need for the animals to maintain the variation of saccade timing within several hundreds of milliseconds.").

6) Last, but not least, there is a logical concern with your study. You compare activity in the input nucleus of the basal ganglia with activity in an output nucleus of the cerebellum. This difference is especially relevant given that current computational models of timing in these subcortical structures tend to focus on the striatum and cerebellar cortex (either Purkinje cells or the cerebellar cortical cells that provide input to the Purkinjes). We are not aware of models that posit a role for DCN neurons in "timing computations". In fact, some classic results in the eyeblink literature have shown that if the system is just driven by DCN (following lesions/inactivation of cerebellar cortex), you lose the adaptive timing of the CR. This has been taken to mean that inputs to DCN (from extracerebellar regions) are sufficient to associate the CS and US, but the adaptive timing of the CR comes from the cerebellar cortex.

This issue needs to be made explicit both in the Introduction and Discussion section. You need to outline the logic of comparing the striatum to the DCN, as well as outline how your results may be qualified by the fact that you have focused on this comparison. As things are now written, the framing makes it sound like the striatum and DCN provide a way to compare temporal processing/representation in the basal ganglia and cerebellum. For example, in the Introduction, you write, "These results indicate that neurons outside of the cerebellum must keep track of elapsed time to make temporally accurate movements in trials with suprasecond delay intervals", and then go on to make the argument that for turning to the striatum. This is reasonable, but note you are going from DCN observations to the conclusion that the tracking of elapsed time must be from "neurons outside of the cerebellum." This ignores the possibility that the tracking might be done in the cerebellar cortex with the projection from the cerebellar cortex to DCN introducing some sort of non-linearity/thresholding function that obscures more of a ramping-like function.

As the reviewer pointed out, the previous studies of eye blink conditioning clearly show that the cerebellar cortex is necessary for learning of stimulus timing while the deep cerebellar nuclei (DCN) only are sufficient for the expression of conditioned response. Furthermore, it appears to be reasonable that the local circuits within the cortex play a major computational role in the cerebellum. However, because the DCN (and the vestibular nuclei) serve as only output node of the cerebellum, any results of computation in the cerebellum must be transmitted through the DCN to the other brain regions. We therefore recorded from the dentate nucleus to explore the neuronal representation of timing in the cerebellum. To clarify this, we have added a few sentences to the revised Introduction ("Although the previous studies of eye blink conditioning suggest that plasticity in the cerebellar cortex rather than the deep cerebellar nuclei plays a role in the learning of motor timing (Garcia and Mauk, 1998; Perrett et al., 1993; Raymond et al., 1998), neurons in the nuclei encode timing signals originated in the cerebellar cortex (Ten Brinke et al., 2017). Therefore, we explored signals in the dentate nucleus in this study because any significant computation in the lateral cerebellum must modify neuronal activity in the nucleus that may eventually regulate movement timing.").

[Editors' note: further revisions were requested prior to acceptance, as described below.]

The manuscript has been improved but there is one final issue that we would like to see addressed before making a final decision. Specifically, the reviewers thought that the data do not provide strong support for the broad claim that 'the cerebellum might be primarily responsible for the stochastic variation of self-timing'. We ask that you qualify this claim/inference in the manuscript.

I'm passing along the comments from reviewer 3 on this issue to help guide you in this endeavor.

Reviewer #3:

The paper has been improved. However, I am still skeptical of the claim that the cerebellum is more responsible for stochastic variation in self-timing than the striatum. The authors have tried to address this issue, but the data/arguments are not convincing. For example, in the Discussion section, the authors suggest that the cerebellum adds or maintains variation of saccade timing – but only for short estimated intervals. For longer intervals, the authors state "there was no need for the animals to maintain the variation of saccade timing within several hundreds of milliseconds". This begs the question of why there is there a "need" at all to maintain variation in timing. Perhaps inherent variation allows us to explore different intervals in a world with constantly changing temporal contingencies – but if so, why would we relax that exploratory bent for supra-second intervals?

We agree with the reviewer in that there was no need for the animals to maintain variation of saccade timing in our behavioral paradigm. We have removed the relevant sentences from the text in the Discussion section.

Stepping back, there were two observations behind the claim that the cerebellum is responsible for stochastic variation. First, for the medium and long intervals, if trials are grouped according to precise movement time, the neuronal responses in the Cb appear to diverge a few hundred ms earlier than those of the striatum (Figure 5). This earlier divergence is based (per force) on a statistical criterion – when significant differences first arise based on the results of repeated-measures ANOVA. This is not a fair comparison between caudate and Cb. Average firing rates in caudate are far lower than in Cb dentate firing (Figure 3C-D). Moreover, I'm not sure about dentate nucleus firing statistics, but firing patterns in the Cb can be highly regular (e.g., Purkinje cells), which would reduce variance in spike counts for a given mean firing rate. For both these reasons, it might just be easier to DETECT the ANOVA-based divergence point for the cerebellar neurons than the caudate neurons, whereas we don't know how the brain actually pools signals from these two areas. I'm not exactly sure how to address this problem, but the authors could try to repeat their analysis with a subset of data matched for mean firing rates from the two areas (if possible). It would also be helpful to at least examine the coefficient of variation of firing rates between the two areas.

In response to this comment, we now report the coefficient of variation of baseline firing rates for the two areas (subsection “Neuronal correlates of trial-by-trial variation in self-timing”). As the reviewer pointed out, the baseline firing was more regular and the magnitude of preparatory activity was much greater in the cerebellum than the caudate nucleus. It is worth noting that the analyses in Figure 5 were based on the normalized activity for individual neurons, and that the repeated measures ANOVAs were performed on the spike density profiles derived from three latency groups. This procedure was optimal to detect the earliest diverging point of mean firing rate across groups, while the variation of baseline activity in each trial did not greatly confound the present results. We have added some lines to clarify this point (subsection “Neuronal correlates of trial-by-trial variation in self-timing”). In addition, we now also report that the SDs of normalized baseline activity for the spike density profiles used for the analysis were not different between the recording sites in Figure 5 legend.

Second, the authors conclude the description of the inactivation experiment (end of Results) with the statement that stochastic variation "might be controlled by signals in the cerebellum, but not in the striatum" – but this does not seem warranted from the inactivation experiment. The clearest result is that inactivation of Cb affected mean movement time most for short intervals while striatum affected mean movement time most for medium and long times. With respect to *variation* in latency, the effects of Cb inactivation were inconsistent at best. In addition, the largest change in movement-time variation with Cb inactivation was for short intervals, but this also caused the largest shift in mean movement latency of any area/interval/experiment, which might be expected to automatically increase the variation in latency (e.g., as per scalar expectancy theory of interval timing). But more generally, what is the hypothesized effect of inactivation on latency variation? Do the authors posit that the cerebellum controls precision, and thus that Cb inactivation would erode that precision? Is that interpretation not at odds with the results in Figure 5? Or do the authors hypothesize that the Cb adds noise to the timing process, in which case Cb inactivation might reduce variation?

We agree with the reviewer. In the revised manuscript, we have removed the claim that the cerebellum may control the stochastic variation. Instead, we now mention that the stochastic variation started earlier in the Cb than the striatum, and that the Cb might play a role in the fine adjustment of timing.

In the end, the clearest result in the paper is the differential roles for the caudate and Cb for long and short intervals, respectively, supported by both electrophysiological recording and inactivation experiments. This alone is a nice contribution. The stochastic variation claims are not strongly supported (either empirically or theoretically). Of course, the authors could present the "divergence" argument in Figure 5, but in a more conservative manner, pointing out the difficulty in statistically distinguishing divergence points between neuronal populations with different firing rates and potentially different firing statistics. But I don't think that the cerebellar stochastic variation claims should be featured prominently in the paper (for example, the abstract should not conclude with that claim).

We appreciate these positive comments. In the revised manuscript, we only report the fact that the stochastic variation started earlier in the cerebellum but have eliminated the argument about its role. Accordingly, we have also modified the Abstract.

We also ask that you double check the new supplement 1 to Figure 3 (saccade-aligned responses). The SD of cue times seems narrow given the wide distributions of movement times in Figure 1B. Please verify that the figure is correct.

We have verified that they are correct. In both Figure 3 and Figure 3—figure supplement 1, the error bar indicates the inter-experimental variability and plots the SD of means for individual recording sessions. We now clarify this point in figure legends.

https://doi.org/10.7554/eLife.35676.026

Article and author information

Author details

  1. Jun Kunimatsu

    1. Department of Physiology, Hokkaido University School of Medicine, Sapporo, Japan
    2. Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Writing—original draft, Writing—review and editing
    For correspondence
    kunimatsu.jun@gmail.com
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8003-0650
  2. Tomoki W Suzuki

    Department of Physiology, Hokkaido University School of Medicine, Sapporo, Japan
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7085-1594
  3. Shogo Ohmae

    1. Department of Physiology, Hokkaido University School of Medicine, Sapporo, Japan
    2. Department of Neuroscience, Baylor College of Medicine, Houston, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1726-4961
  4. Masaki Tanaka

    Department of Physiology, Hokkaido University School of Medicine, Sapporo, Japan
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing—original draft, Project administration, Writing—review and editing
    For correspondence
    masaki@med.hokudai.ac.jp
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6177-1314

Funding

Ministry of Education, Culture, Sports, Science and Technology (24800001)

  • Jun Kunimatsu

Ministry of Education, Culture, Sports, Science and Technology (25119005)

  • Masaki Tanaka

Ministry of Education, Culture, Sports, Science and Technology (17H03539)

  • Masaki Tanaka

Ministry of Education, Culture, Sports, Science and Technology (18H05523)

  • Masaki Tanaka

Takeda Science Foundation

  • Masaki Tanaka

Uehara Memorial Foundation

  • Masaki Tanaka

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank T Mori, A Hironaka for technical assistance, M Suzuki for her administrative help, M Takei and Y Hirata for manufacturing equipment, and all lab members for comments and discussions. We are also grateful to M Takada and K Inoue in the Primate Research Institute of Kyoto University for their insightful comments regarding the recording sites. Animals were provided by the National Bio-Resource Project. This work was supported partly by grants from the Ministry of Education, Culture, Sports, Science and Technology of Japan, the Takeda Science Foundation, and the Uehara Memorial Foundation.

Ethics

Animal experimentation: All experimental protocols were evaluated and approved by the Hokkaido University Animal Care and Use Committee (#13-0114). All surgery was performed under general isoflurane anesthesia, and every effort was made to minimize suffering.

Reviewing Editor

  1. Naoshige Uchida, Harvard University, United States

Publication history

  1. Received: February 5, 2018
  2. Accepted: July 1, 2018
  3. Accepted Manuscript published: July 2, 2018 (version 1)
  4. Version of Record published: July 17, 2018 (version 2)

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

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