Differential cannabinergic effects on temporal perception and production

Differential cannabinergic effects on temporal perception and production | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Differential cannabinergic effects on temporal perception and production Pavel Rueda-Orozco, Mario Martínez-Montalvo, Diana Ortega-Romero, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6499142/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Oct, 2025 Read the published version in Neuropsychopharmacology → Version 1 posted You are reading this latest preprint version Abstract Cannabinoids have traditionally been associated with motor and cognitive impairments, including slowness of movement and altered temporal perception. However, it remains unclear whether cannabinoids specifically affect the perception and/or production of temporal intervals. To explore these possibilities, we evaluated the effects of systemic administrations of the synthetic cannabinoid CP55940 on behavioral performance in male rats trained in three distinct paradigms designed to assess time interval perception and production. Systemic administration of CP55940 caused temporal overestimation in a fixed-interval task, which was primarily linked to impaired perception of elapsed time in the range of tens of seconds. In contrast, while the same treatment increased forelimb reach duration in a two-interval production task (in the hundreds of milliseconds range), these effects were more accurately attributed to a general reduction in movement speed rather than altered temporal processing. These findings were further confirmed in a third motor task, where animals executed a complex timed motor sequence with spatiotemporal constraints while running on a treadmill. Here, CP55940 administration slowed locomotion but did not disrupt motor timing. Our results demonstrate that, in addition to inducing motor slowing, systemic cannabinoid administration impairs temporal perception but preserves interval production, suggesting distinct underlying mechanisms for these two processes. Biological sciences/Neuroscience/Sensorimotor processing Biological sciences/Neuroscience/Learning and memory Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Numerous studies have reported that cannabinoid administration alters movement control and time perception. While there seems to be a consensus that low concentrations of cannabinoid agonists induce hyperlocomotion and high concentrations lead to hypomobility (Sañudo-Peña et al., 2000 ; Fernández-Ruiz et al., 2002 ; Katsidoni et al., 2013 ), it is not entirely clear how the cannabinoid system affects time perception (Han and Robinson, 2001 ; Crystal et al., 2003 ; Atakan et al., 2012 ). Preclinical studies on humans often report that cannabis users experience time as running slowly (Tinklenberg, 1972 ; Sewell et al., 2013 ). This distortion results in an overestimation of elapsed time; that is, subjects report that more time has passed than actually has. On the other hand, behavioral protocols where subjects are required to produce a time interval often report underestimations (i.e., subjects produce longer intervals than required; Sewell et al., 2013 ; Boggs et al., 2018 ). Similar results have been observed in rodents, where it has been demonstrated that systemic administrations of cannabinoids also induce an overestimation of elapsed time in “peak interval” procedures(Han and Robinson, 2001 ) or in interval discrimination procedures (Crystal et al., 2003 ). However, because time is not specifically sensed by a particular system or region of the brain (Buzsáki and Llinás, 2017 ), it has been proposed that the passage of time is sensed by a “distributed clock,” a network of structures including the motor and premotor cortices, the thalamus, the basal ganglia, the hippocampus, and the cerebellum (Merchant et al., 2013 ). These regions are linked to both motor and non-motor functions, and it has been proposed that various behavioral processes are involved in time interval estimation, differentiating between behaviors that involve estimating intervals associated with movement execution (interval and pattern motor timing) and those related to sensory stimuli (interval and pattern sensory timing) (Paton and Buonomano, 2018 ; Merchant and de Lafuente, 2024 ). However, it remains unclear whether these processes or behaviors share the same anatomical, neurophysiological, and pharmacological bases. In this context, cannabinoid receptor 1 (CBr1) is highly expressed in most of the brain regions typically associated with temporal processing, including the cerebellum, basal ganglia, and hippocampus, which show the highest levels of CBr1 (Tsou et al., 1998 ; Sañudo-Peña et al., 1999 ). This anatomical distribution suggests not only that various brain regions are implicated in temporal processing, but also that cannabinoids may affect aspects of temporal processing depending on their specific expression levels or localization within a given structure. On the other hand, certain time-processing effects attributed to cannabinoids may be more related to motor effects that influence timing; for instance, if an animal is slow, it will take more time to fulfill a given task. Hence, the covariance between temporal and motor parameters during execution in behavioral protocols may compromise interpretations on the exact nature of temporal processing, a topic that has recently been experimentally and theoretically studied elsewhere (Safaie et al., 2020 ; Robbe, 2023 ; de Lafuente et al., 2024 ). In this study, we addressed the possibility that covariance in temporal and execution parameters may mask the true nature of the behavioral effects of systemic administration of synthetic cannabinoids in rodents. To this aim, we implemented three behavioral protocols to simultaneously evaluate motor effects on movement speed and amplitude, as well as various aspects of interval estimation (e.g., motor timing and sensory timing) and time estimation on the scales of hundreds of milliseconds, seconds, and tens of seconds. First, we used a behavioral protocol where animals were trained to produce a coordinated forelimb movement in a 750 ms window (production component) but in a fixed-interval schedule of 30 s. We found that systemic administrations of CP55940 caused an overestimation of elapsed time in the peak-interval component of the task. However, upon analyzing movement production in the same animals and sessions, we found that the treatment induced longer coordinated forelimb movements in the temporal production component— a result typically interpreted as an underestimation of the temporal interval. This treatment also reduced movement speed. Hence, the second step to differentiate between time and speed effects was to introduce two production intervals— one of 750 ms and a longer one of 1250 ms. In this case, CP55940 injections induced slower movements and underestimations (i.e., longer movements) in both intervals, but the magnitude of the temporal effect was not proportional to the required interval. This observation suggests that temporal production was preserved, whereas speed control was not. To confirm this possibility, we trained animals to execute a complex locomotion-based motor sequence within a strict temporal constraint of 7 s (goal time) and with an implicit requirement to adjust their speed to the experimental context on a trial-by-trial basis. We found that the same CP55940 administrations reduced locomotion speed but spared the temporal component of the task. Furthermore, we observed that animals compensated for the reduced locomotion speed by adjusting the spatial and/or temporal component of the movement sequence to successfully maintain execution around the goal time. These results indicate that cannabinoids differentially affect the perception and production of temporal intervals and hence suggest different neuroanatomical mechanisms underlying these processes. MATERIALS AND METHODS Animals A total of 29 Long-Evans male rats were used in this study. Animals were housed in the satellite bioterium of our laboratory in home cages with stable temperature (23°C) and humidity (66%) and under a constant 12/12-hour light-dark cycle (lights on at 8 a.m.). Animals had free access to food and water and were constantly supervised by specialized personnel from the general facilities of our institute. All experimental procedures were conducted during the light phase of the cycle. Seven rats (300 to 650 g) were trained in the fixed-interval task and eight rats (300 to 650 g) in the two-interval production task; these animals were water-restricted and consumed all the water requirements during the training session (20 to 30 ml in 40 to 60 min per session, one session per day). Animal weight was monitored daily and maintained over 85% of the expected weight for their age. If the animals did not consume their daily water portion, then water was provided for short periods after the training. Animals were trained for six days a week with 24 hours of free access to water on the seventh day. Additionally, 14 rats were used for the treadmill spatiotemporal task. These animals were trained for 60–130 trials per day, six days a week, without any water restriction. Behavioral apparatus and training Fixed-interval schedule For this task, animals (n = 7) were trained in behavioral boxes, which have been previously described (Báez-Cordero et al., 2020 ; Luma et al., 2022 ; Pimentel-Farfan et al., 2022 ). These boxes were made with (50 x 50 x 50 cm) Plexiglas walls and equipped with two sets of levers, a water port, a green LED to indicate correct trials, and a white LED to indicate the availability of the set of levers (Fig. 1 a, left ). Each set of levers consists of two levers protruding 5 cm from the wall. All training and experimental procedures in this study were performed on the left set of levers. The levers can move vertically and are connected to a voltage transducer (3.5 cm = 2.5 V). Voltage signals were digitized and stored at 250 Hz through National Instruments cards (NI PXIe-6363) and LabView custom-made routines. In each session, we continuously recorded the vertical position of both levers. Animals were rewarded with water, which was delivered through the central water port using a solenoid valve. To accustom rats to the training conditions, they were placed in the training cages for one session (40 min) before the beginning of training and the water restriction. During the next session, the rats were deprived of water and trained to obtain rewards (drops of water) when approaching the water port. In subsequent sessions, animals were guided to obtain rewards by touching any lever and slightly moving it down (> 0.1 cm). We progressively increased the spatial threshold until the animals reliably displaced both levers simultaneously at least 2.6 cm. All animals quickly learned the rule of touch and could displace any lever in fewer than two sessions. Then, animals learned to displace both levers simultaneously. Rewards would only be delivered if the levers were simultaneously held under the spatial threshold (2.6 cm) for at least 50 ms (temporal threshold). Subsequently, in a range of three to five sessions, temporal thresholds were progressively increased to 750 ms. The rats were trained with these parameters for 100 to 120 sessions. Later, we started the fixed-interval schedule by introducing the rule that rewards would be delivered each time that the rats performed a correct response (coordinated lever press) after waiting for at least 30 seconds. When this response occurred after the appropriate time interval (30 s), the green light would turn on (1 s), indicating a correct trial, and a drop of water was delivered as a reward. The 30 s interval was continuously counted from the preceding reward, and once completed, the first correct response was rewarded (Fig. 1 a, right ). In each session, animals performed an unlimited number of trials in the lapse of 40 minutes to 1 hour. Most of the animals performed around 60 to 90 trials per session. Pharmacological manipulations were performed after at least 30 sessions of training in the fixed-interval schedule. All the execution parameters of the task are calculated based on the raw position data from the levers. Interlimb correlation was calculated as the Pearson correlation coefficient between the raw position of the left and right levers from the beginning of the successful bilateral movement to the delivery of the reward. Overshoot was the amount of time that the levers remained under the spatial threshold after the reward was delivered; this variable directly reflects movement duration. We calculated speed as the instantaneous difference in position in 4 ms time bins and report the average maximum per session. Two-interval production task The same behavioral chambers (fixed-interval schedule) were used in the two-interval task (n = 8). In this version, a white or a blue LED indicated the type of trial, specifically a 750 or 1250 ms temporal threshold. In this version, the animals were initially trained following the same process as in the previous task, except without the long fixed-interval component. After session 120, the two-interval production schedule was introduced. Here, blocks of 20 trials were alternately presented, requiring rats to maintain the levers pressed for 1250 ms during block one (indicated by a blue LED) or 750 ms during block two (indicated by a white LED). After an inter-trial interval of 1.5 s, the levers were available so that the animals could freely perform the next trial. Most of the animals completed between 90 and 120 trials per session. Pharmacological manipulations were performed after at least 50 sessions of training in the two-interval production schedule. Spatiotemporal task The apparatus used for the spatiotemporal task consisted of a modified human treadmill (NordicTrack T6.1), equipped with Plexiglas walls (50 cm high) to restrict rats to the passable area of the belt (80 cm long by 20 cm wide; Hidalgo-Balbuena et al., 2019 ). The treadmill motor was controlled by a custom-made program (LabVIEW, National Instruments) and a multifunction computer board (NI USB-6353, National Instruments). A line of LEDs illuminated the entire apparatus, and the front wall of the treadmill was equipped with a drinking spout to deliver drops of sucrose solution. A laser-based photodetector gate, positioned 10 cm from the front wall, delimited the goal area. In this task, rats (n = 14) were trained to perform a stereotyped sequence of movements in at least 7 s while running on a motorized treadmill. Initially the animals were habituated to the elements of the setup, progressively adapting to the treadmill speed, which increased from 5 cm/s to 30 cm/s (3 sessions of 90 min, one session per day). Subsequent training sessions maintained a fixed treadmill speed of 30 cm/s (60–130 trials per session, one session per day). During the early phases of training, the animals started their trials at different locations of the treadmill. However, as training advanced, they quickly and spontaneously adapted their behavior and developed a new sequence of actions. At the start each trial, the animals began at the front of the treadmill; then, when the belt began to move, they were passively transported to the back, where they ran for an estimated time interval of 7 s, after which they accelerated to reach the front of the treadmill, or goal area. Incorrect trials (entering the goal area before the 7 s) were signaled with a 1.5 kHz, 65 dB auditory tone triggered by a beam break into the goal area and continued for 20 s. These trials were not rewarded. During each session, the average performance of the last 40 trials was continuously calculated, and sessions were terminated by reaching one of the following three criteria: 1) if the animals achieved over 72.5% of correct trials at any point during the session; 2) if the animals completed 60 correct trials; 3) if 130 trials were conducted. Once the animals reached a criterion of performance accuracy of ≥ 72.5% of correct responses over the last 40 trials of each session for ≥ 3 consecutive sessions, we implemented a random speed protocol. In this protocol, the belt speed varied unpredictably from trial to trial in a range of 27 cm/s to 33 cm/s. Throughout the training period, the experimenter was absent from the behavioral room. During all trial sessions, animals were continuously monitored with a high-speed CCD camera (acA640-120fc, Basler, 100 frames s − 1, 9 pixels cm − 1) positioned laterally to the treadmill. The animals’ positions were automatically extracted with a custom-made program (Vision, National Instruments) by calculating the center of mass. To quantify behavioral stereotypy, we extracted the position and speed time-courses from each trial across all sessions. Position or speed trajectories were aligned to the entrance times (i.e., end of the movement sequence). Administration of the cannabinergic agonist Systemic administration of the cannabinergic agonist CP55940 (Sigma and Cayman) was performed with vehicle, 5% dimethyl sulfoxide (DMSO; Merk) + 5% Cremophor (Sigma) in saline. Drugs were injected in volumes of 1 ml/kg. For each task, CP55940 concentrations of 0.01, 0.05, 0.1, and 0.2 mg/kg were injected in individual sessions with at least two drug-free sessions between CP55940 injections. All injections were performed 15 min before the behavioral sessions. Statistical analysis Behavioral data are presented as a median + 25th and 75th percentiles. Statistical comparisons between groups were performed with Mann-Whitney or Kruskal-Wallis tests as stated in each section. A Bonferroni post hoc test was used for multiple comparisons. Statistical analysis was performed using MATLAB software (The MathWorks, Inc.). Statistical differences were considered significant if P values were < 0.05. RESULTS Systemic administrations of CP55940 differentially impact in the perception and production of temporal intervals To explore the effects of cannabinergic agonism on temporal processing, we implemented a modified version of a bimanual coordination behavioral protocol for rats (Báez-Cordero et al, 2020 ; Pimentel-Farfan et al, 2022 ; Luma et al., 2022 ). First, as reported in the original protocol, rats (n = 7) were trained to vertically displace two levers simultaneously by a minimum of 2.6 cm and for a duration of at least 750 ms (Fig. 1 a, left ). This version of the task included an implicit interval production component (750 ms) but was not explicitly designed to assess temporal perception. Hence, we complemented this approach with a “peak-interval” design, where the levers became functional only 30 s after the last reward was delivered. That is, only the first coordinated lever press was rewarded after 30 s. Following the reward delivery, the time counter reset, and the levers remained inactive for an additional 30 seconds (Fig. 1 a, right ). Our results indicate that during the first training sessions, the animals continuously lever-pressed during most of the 30s period (Fig. 1 b, left ), and their presses lasted hundreds of milliseconds longer than the 750 ms required to obtain a reward (“overshoot,” Fig. 1 c, d up ), indicating poor temporal processing for both the perception of the 30-second long interval and the production of the 750 ms coordinated movement. However, after training, the animals limited their presses to a few seconds after the 30 s interval (Fig. 1 b, right ), indicating a good perception of the long interval. Conversely, at this stage, lever presses were more accurate, with only a few milliseconds of overshoot (Fig. 1 c, d bottom ), indicating that the movement duration was appropriately adjusted to meet the experimental demands. Hence, with this approach we were able to evaluate the perception and production of temporal intervals, as well as other aspects of bilateral movement, enhancing our understanding of potential effects on movement, temporal processing, or both. For instance, we were able to assess the coordination level (Fig. 1 e) and the speed at which the left and right forelimbs moved (Fig. 1 f). Interestingly, these parameters were more stable. Then, to create learning curves, the movement-related variables (Fig. 1 g-j) and the area under the curve (AUC) extracted from lever-pressing distributions (Fig. 1 k) were formally quantified on a session-by-session basis, revealing progressive improvements as training evolved. The previous data indicate that with this protocol, we can simultaneously assess two levels of temporal processing: one related to the perception of elapsed time on the scale of dozens of seconds (peak interval) and another concerning interval production on the scale of hundreds of milliseconds. Then, we explored the effects of systemic administrations of different doses of the synthetic cannabinoid agonist CP55940. Pharmacological sessions were started after at least 30 training sessions. Injections were administered 15 min before the behavioral sessions, and at least three treatment-free sessions were conducted in between treatment sessions. All animals in this group received at least two injections of each dose. Consistent with what was reported previously (Báez-Cordero et al, 2021), a dose-dependent effect was observed in movement duration and speed, with higher doses inducing slower and longer movements (Fig. 1 l-m; Overshoot K-W, df = 4; X 2 = 36.27, p < 0.0001 ). Nonetheless, other execution parameters, such as interlimb correlation or movement onset variability, were slightly changed with the highest dose (Fig. 1 n, o; interlimb correlation K-W, df = 4; X 2 = 18.98, p = 0.0008; BMOV K-W, df = 4; X 2 = 8.31, p = 0.0809 ). Longer movements could be interpreted as an underestimation of the 750 ms produced interval; however, when analyzing the behavior in the longer 30 s range, we observed a phenomenon that could be interpreted as an overestimation of the perceived interval. Higher doses of CP55940 resulted in lever presses homogeneously distributed throughout the entire interval (Fig. 1 p), reflecting significantly higher values of the AUC (Fig. 1 q; K-W, df = 4; X 2 = 42, p < 0.0001 ) and with cumulative peaks closer to or even under 30 s (Fig. 1 r; K-W, df = 4; X 2 = 19.2, p = 0.0005 ). The previous data suggest that CP55940 may differentially affect temporal processing at the perception level (30 s range) and production level (750 ms range). However, they could also indicate that temporal production is not impaired, as movement parameters may covary (i.e., slow movements could seem longer). To address this possibility, we trained a new group of animals (n = 8) in the same original bimanual coordination protocol but introduced another modification. In this case, we did not use the peak-interval component but only manipulated the temporal production component in the range of hundreds of milliseconds. Here, instead of using only a holding time threshold of 750 ms, we also introduced a 1250 ms threshold. The two intervals were presented in 20 alternating trial blocks, and sessions always started with the longest threshold (Fig. 2 a). After training in this version, animals produced movement trajectories with similar execution parameters (Fig. 2 b-g) and overshoots in both types of trials (Fig. 2 b, e). Similar overshoots indicate that the animals quickly adapted to the duration of the movement from a long (1250 ms) to a short (750 ms) reach. In this way, if animals control the interval production, movement duration must be close to the demanded interval, and the result would be better reflected in potential cannabinergic effects. In other words, if CP55950 induces an underestimation of interval production, it should impact both intervals equally. Conversely, if the slowness of movement were to impact the duration of movement, we could anticipate differential effects, such as longer movements for the 750 ms interval but not for the 1250 ms interval. After the training period (~ 150 sessions), we carried out pharmacological manipulations where CP55940 was administered 15 minutes before each experimental session, and each experimental session was conducted after at least two control sessions (free of drugs). Consistent with previous observations (Báez-Cordero et al., 2020 ; Monory et al., 2007; Sales-Carbonell et al., 2013 ), we found that systemic administrations of CP55940 produced dose-dependent motor impairments reflected in a decrease in interlimb correlation (Fig. 2 h; K-W, df = 9; X 2 = 20.81, p = 0.0135 ), without clear effects on bilateral movement onset variability (Fig. 2 i; K-W, df = 9; X 2 = 10.15, p = 0.3381 ) but with a significant reduction of the maximum movement speed (Fig. 3 j; K-W, df = 9; X 2 = 17.87, p = 0.0367 ). We also found a dose-dependent increase in overshoot in both intervals (Fig. 3 k; K-W, df = 9; X 2 = 108.6, p < 0.0001 ); however, this increase appeared to be more robust in the production of the 750 ms interval. Since overshoot directly reflects movement duration, this observation indicates that the impact on movement duration is not proportional to the required interval. This was confirmed when we analyzed the overshoot time relative to the magnitude of each time interval (Fig. 3 l; K-W, df = 9; X 2 = 60.56, p < 0.0001 ). These results indicate that systemic administrations of CP55940 induced differential effects based on the interval, which is more consistent with impairments in speed control than with the production of temporal intervals. Rats dynamically adjust their speed to maintain the spatiotemporal structure of a well-trained complex sequence of movements. Our previous results suggest differential effects of CP55940, with impairments in the perception of long temporal intervals in the range of dozens of seconds but systematically affecting speed control and sparing movement timing production in the range of hundreds of milliseconds. To further confirm this possibility, we performed the same pharmacological manipulations in a new cohort of animals (n = 14) but trained them to perform a complex motor sequence requiring active control of elapsed time and speed in a temporal range of about 7 seconds. To this aim, we used a modified version of the behavioral protocol established in (Rueda-Orozco and Robbe, 2015 ). We first trained rats to perform a stereotyped and timed sequence of locomotion decelerations and accelerations while running on a motorized treadmill (Rueda-Orozco and Robbe, 2015 ; Hidalgo-Balbuena et al., 2019 ) (see methods). Once the animals learned the task, they adopted a stereotypical strategy consisting of the following steps (Fig. 3 a ) : 1) Animals started each trial at the front of the treadmill and learned to be passively transported to the rear part of the apparatus by the movement of the treadmill belt; 2) then, animals held their position in the back of the treadmill by trotting for a few seconds at the speed of the treadmill’s belt; 3) finally, animals performed a controlled acceleration across the treadmill to reach the front part again. This “front-back-front” strategy was extracted and analyzed from spatiotemporal trajectories reconstructed from high-speed video recordings on a trial-by-trial basis (Fig. 3 a, bottom) . Highly trained animals displayed low variability in sequence duration around goal time (Fig. 3 b) and significantly stereotyped execution trajectories (Fig. 3 c). Then, to understand how speed control or temporal representations interact and contribute to the general architecture of movement sequences, we implemented a second phase of training. Here, in each session, the treadmill speed (but not the temporal rule) varied randomly in a range of 27 to 33 cm/s on a trial-to-trial basis, so the animals were forced to dynamically adapt their locomotion speed based on their perception of the treadmill’s speed and on their representation of the 7 s temporal interval. Animals were trained in this version for at least 30 sessions. We found that, independently of the treadmill speed, the rats accurately maintained the sequence duration around goal time (Fig. 3 d) and displayed similar spatiotemporal trajectories (Fig. 3 e). These effects were observed in representative sessions from individual animals (Fig. 3 b-e) and robustly maintained in the group of animals (Fig. 3 f-g; Rat 04, Time K-W, df = 6, X 2 = 16.71, p = 0.01; Position K-W, df = 6; X 2 = 14.7. Group, Time K-W, df = 6; X 2 = 4.1; p = 0.663; Position K-W, df = 6; X 2 = 3.67; p = 0.721 ). These data indicate that the animals were somehow adjusting their behavior to maintain the duration of the sequence of movements, but they do not specify which movement parameter was adjusted to achieve this. For example, animals could have performed the sequence of movements in 7 seconds by holding their position at the back of the treadmill for varying durations according to the treadmill’s speed. Alternatively, the same outcome could arise from animals adjusting their speed in the last phase of the sequence according to the treadmill’s speed, either accelerating or decelerating during faster or slower trials, respectively. To evaluate these possibilities, we calculated the speed peaks during the last acceleration of the sequence on the treadmill. We found a linear relationship between the treadmill speeds and the peak speed reached by the animals, with a lower peak speed when the treadmill moved at lower speeds (27–29 cm/s) and a higher peak speed when the treadmill moved at higher speeds (31–33 cm/s). This behavior was observed in individual animals (Fig. 3 h, center ) and the group of animals (Fig. 3 h, right; Rat 04 K-W, df = 6; X 2 = 1683.2; p < 0.001 . Group K-W, df = 6; X 2 = 226.22; p < 0.001 ). These data indicate that the animals adjusted their speed to preserve the spatiotemporal structure of the sequence of movements, indicating that the animals maintained a representation of the 7 s interval. Hence, in this task we could potentially distinguish between the effects off cannabinoids on the overactive control of speed and those on the representation of time. After animals were trained in the new version of the protocol, we conducted the same pharmacological manipulations as in the two previous protocols. Fifteen minutes before each experimental session, we administered individual doses of CP55940 (0.01, 0.05, 0.1, or 0.2 mg/kg). Contrary to our observations regarding the duration of the bimanual movement, we found that cannabinergic administrations did not induce significant changes in the duration of the movement sequence (Fig. 4 a). This suggests that, for a complex and highly trained sequence, the temporal component was resistant to the treatment. On the other hand, when we analyzed the locomotion speed, we found a clear dose-dependent effect, with gradually slower accelerations associated with higher doses of CP55940 (Fig. 4 b). This finding confirms that the mechanisms controlling behavioral timing and speed are different and can be differentially affected by cannabinoids under these circumstances. During the execution of the sequence, the temporal component can be linked to the animals’ speed. Therefore, these data also raise the question as to how the temporal component is unaffected if the animals are running slowly. This was clarified when analyzing the spatial component of the motor sequence. Here, we also observed a clear dose-dependent effect (Fig. 4 c). In the control condition, spatiotemporal trajectories at different treadmill speeds overlapped (Fig. 4 d, right ), but speed trajectories showed a gradient, with higher and lower locomotion peaks corresponding to higher and lower treadmill speeds, respectively (Fig. 4 d, left ). However, with the higher doses of CP55940, the animals tended to express one of two strategies to compensate their slowness. Either they started the last acceleration almost as soon as they reached the rear end of the treadmill (Fig. 4 e, right )—that is, they did not hold their position for as long as they did in the control condition— or they did not let themselves be transported to the far end of the apparatus (Fig. 4 f, right ). Both strategies combined with movement slowness resulted in the animals completing the sequences close to the 7 s rule. As in the previous protocols, these effects were clearly visible in representative sessions from individual animals (Fig. 4 a-f) and in the group of animals (Fig. 4 g-i). These results suggest that, to maintain the target duration of the motor sequence under circumstances where cannabinoids cause a decrease in peak speeds, the animals implemented compensatory adjustments to their timing (not waiting for too long on the back of the treadmill) or their position on the treadmill (avoiding being carried entirely to the back). Altogether, this set of data confirms the existence of independent representations for space, speed control, and motor timing, and indicates that cannabinoids differentially affect these variables. DISCUSSION In this study, we evaluated the effects of systemic administration of the synthetic cannabinergic agonist CP55940 on interval perception and interval production across behavioral contexts with different spatiotemporal contents. Our results showed that cannabinoid administration differentially affected these processes, impairing the perception of long temporal intervals in the range of dozens of seconds while maintaining interval production in the range of hundreds of milliseconds. On the other hand, the same administrations systematically affected speed control independently of the spatiotemporal context, which is consistent with previous reports (Sañudo-Peña et al., 1999 ). Previous studies have suggested that cannabinoids alter the perception of elapsed time, often leading to overestimations in tasks requiring interval discrimination (Han and Robinson, 2001 ; Crystal et al., 2003 ; Sewell et al., 2013 ; Boggs et al., 2018 ). In agreement with this, we found that CP55940 systemic administrations led to an overestimation of elapsed time in a fixed-interval schedule task where rats were required to estimate a 30-second interval. This suggests that cannabinoids disrupt sensory-based time estimation, possibly due to their effects on cortico-striatal or cortico-hippocampal circuits, both of which are rich in CB1 receptor expression (Tsou et al., 1998 ; Egertova and Elphick, 2000 ) and have been implicated in interval timing (Bartolo et al., 2014 ; Mello et al., 2015 ; Bakhurin et al., 2017 ; Zhou et al., 2020 ; Shimbo et al., 2021 ). Altogether, our data suggest that while the cannabinergic system may be linked to sensory timing, interval production effects are most likely related to unspecific effects related to slowness in motor execution. On the other hand, in the two-interval production task, cannabinoid administrations caused longer forelimb movements in the production of time intervals in the range of hundreds of milliseconds. This result is typically interpreted as an underestimation of the required time interval, implying that cannabinoids may also affect motor timing. However, in our next set of experiments, when we used two production intervals (Fig. 2 ), one of 750 ms and another of 1250 ms, the temporal effects were not proportional to the interval duration, suggesting a potential motor confound. To explore this possibility, we evaluated the effects of cannabinoids on a spatiotemporal motor task that required executing a well-learned sequence of movements with a tight temporal constraint of 7 s (goal time). Under these conditions, CP55940 injections produced no significant disruption in motor timing precision. More importantly, despite a clear dose-dependent reduction in locomotion speed, the rats compensated by adjusting their spatial trajectories, thereby ensuring that the total sequence duration remained close to the goal time. These results not only confirm that the animals maintained a functional representation of the temporal interval, but also that they were able to sense the behavioral disparities induced by cannabinoids and compensate them by manipulating another execution variable, specifically position. Previous reports indicate that in this task, animals entrain their behavior to stable environmental variables, such as treadmill speed or length (Rueda-Orozco and Robbe, 2015 ; Jurado-Parras et al., 2020 ; Safaie et al., 2020 ), which makes it difficult to adapt to unpredictable changes in these variables and raises questions about the explicit representation of the sequence duration. However, in our experiments, animals were able to behaviorally adapt to subtle changes in treadmill speed. It is possible that this discrepancy arises from differences in task structure and training history. For example, in our training schedule, animals were first overtrained in a fixed-speed condition before transitioning to a random-speed protocol, which may have facilitated compensatory adjustments. Safaie et al. ( 2020 ) observed impairments in speed adaptation when animals underwent training with extreme speed variations (2–40 cm/s). In this study, we utilized a treadmill speed range of 27 to 33 cm/s, which may facilitate more precise adaptations​. Another possibility is that, in the spatiotemporal task, the time interval to estimate is implicitly embedded within a complex motor sequence that also includes rhythmic sensory feedback, such as the somatosensory input from the animal's paws while running (Hidalgo-Balbuena et al., 2019 ). In contrast, during the two-interval production task, the sensory feedback that the animals may receive to guide their behavior by keeping the levers pressed with their forepaws is continuous. What would be the neurobiological mechanism underlying these cannabinergic effects? Previous reports suggest that a combination of molecular and structural locations implicating the basal ganglia, hippocampus, and cerebellum may be responsible for these effects. First, the robust slowness of movements observed in all our behavioral protocols is most likely related to the activation of CBr1, located in the terminals of the direct pathway neurons of the substantia nigra pars reticulata. This effect has been consistently observed in different studies (Sales-Carbonell et al., 2013 ; Báez-Cordero et al., 2020 ; Soria-Gomez et al., 2021 ) and may result in unbalanced direct/indirect pathway activity, thereby enhancing inhibition of the motor thalamus (Aceves et al., 2011 ; Antonazzo et al., 2019 ; Báez-Cordero et al., 2020 ). Here, however, it remains to be established whether these effects are exclusively linked to the CBr1 located at the synapse or the mitochondrial compartment, as the latter has been linked to CBr1-induced catalepsy (Soria-Gomez et al., 2021 ). Second, the effects on sensory timing may be related to CBr1 located in the hippocampus, basal ganglia, and/or cerebellum, since these regions have been strongly implicated in temporal processing (refs reviews) and exhibit the highest expression of CBr1 receptors in the brain (Tsou et al., 1998 ; Egertova and Elphick, 2000 ; Mátyás et al., 2006 ). While striatal and hippocampal circuits may be primarily involved in interval perception and production (MacDonald et al., 2011 ; Salz et al., 2016 ; Shimbo et al., 2021 ), cerebellar circuits could contribute to compensatory mechanisms, allowing animals to adjust movement execution despite cannabinoid-induced slowness of movements (Kunimatsu et al., 2018 ; Andersen and Dalal, 2021 , 2024 ). Taken together, our results suggest that cannabinoids selectively impair time perception but not production, and their effects depend on the specific timescale and task demands. The overestimation of long intervals in the fixed-interval task supports a role for cannabinoids in disrupting sensory timing, whereas the elongation of short-interval movements suggests an influence on motor timing mechanisms. However, timing in well-learned motor sequences remains preserved, likely due to compensatory strategies that adjust spatial parameters to maintain temporal precision. Future studies should examine the specific neural circuits underlying these differential effects. Electrophysiological recordings or CBr1 receptor manipulations in the striatum, hippocampus and cerebellum could help to elucidate their individual contributions to sensory and motor timing influenced by cannabinoids. Declarations All experiments were approved by the Animal Ethics Committee of the Institute of Neurobiology at the National Autonomous University of Mexico (UNAM; protocol 102.A) and conformed to the principles outlined in the Guide for the Care and Use of Laboratory Animals (National Institute of Health). Every precaution was taken to minimize suffering, and the number of animals used in the experiments. ACKNOWLEDGMENTS Authors thank the support provided by all the members of Laboratory A-02 from the Institute of Neurobiology, UNAM; Martín García from the animal facility of INB; Óscar Prospéro García, for generous donations of valuable equipment; Adriana González from Unidad de Proteogenómica, INB; and Jessica Gonzalez-Norris for proofreading the manuscript. This manuscript is part of the requirements for obtaining a doctoral degree at the Posgrado en Ciencias Biológicas, UNAM, by MG Martínez-Montalvo. AUTHOR CONTRIBUTION Conceptualization: MGMM, PRO Methodology: MGMM, PRO Investigation: MGMM, DIOR, ASBC, JOSL, CIP, PRO Data Curation: MGMM, PRO Formal analysis: PRO Writing—original draft: MGMM, PRO Writing—review & editing: MGMM, DIOR, ASBC, JOSL, CIP, PRO Supervision: PRO Project administration: PRO, CIP Funding Acquisition: PRO FUNDING Mario Gabriel Martínez-Montalvo is supported by fellowship 925898 from CONAHCyT-México. This work was funded by grants: UNAM-DGAPA-PAPIIT: IG200424 (PRO) CONAHCyT: CF-2023-I-7 (PRO) COMPETING INTERESTS The authors declare no competing interests. References Aceves JJ, Rueda-Orozco PE, Hernandez-Martinez R, Galarraga E, Bargas J (2011) Bidirectional plasticity in striatonigral synapses: A switch to balance direct and indirect basal ganglia pathways. Learning and Memory 18. Andersen LM, Dalal SS (2021) The cerebellar clock: Predicting and timing somatosensory touch. Neuroimage 238:118202. 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Neuroscience 83:393–411 Available at: https://linkinghub.elsevier.com/retrieve/pii/S0306452297004363 . Zhou S, Masmanidis SC, Buonomano D V. (2020) Neural Sequences as an Optimal Dynamical Regime for the Readout of Time. Neuron 108:651–658.e5 Available at: https://doi.org/10.1016/j.neuron.2020.08.020 . Additional Declarations There is NO conflict of interest to disclose. Cite Share Download PDF Status: Published Journal Publication published 13 Oct, 2025 Read the published version in Neuropsychopharmacology → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6499142","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":447456363,"identity":"0465d893-7045-40a7-aca8-9d9ea6246cb7","order_by":0,"name":"Pavel Rueda-Orozco","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYLCCB2AygRFE8/ARpSUBQjIbgLSwkaKFTQJEEdRizn7G8EECw73EfvbkZ5Vfc+xk2BiYHz66gUeLZU+OsUECQ3HizJ5nZrdltyUDHcZmbJyDR4vBgbQ0iQSGhMQNNxLMbktuYwZq4WGTxqvl/LP0HyAt+2+kfyuW3FZPhJYbyccYwLZI5Jgxftx2mLAWyxmPD0skGCQYzzjzpliacdtxHjZmAn4x509s/PChIkG2vz1948ef26rt+dmbHz7G6zAkkoGZB0ziUY6kGAIYfxBQPQpGwSgYBSMTAABlx0aAc2vymAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-2801-803X","institution":"Instituto de Neurbobiología, UNAM","correspondingAuthor":true,"prefix":"","firstName":"Pavel","middleName":"","lastName":"Rueda-Orozco","suffix":""},{"id":447456364,"identity":"909afcd6-3b92-42a7-a532-21cd82df935f","order_by":1,"name":"Mario Martínez-Montalvo","email":"","orcid":"","institution":"Instituto de Neurbobiología, UNAM","correspondingAuthor":false,"prefix":"","firstName":"Mario","middleName":"","lastName":"Martínez-Montalvo","suffix":""},{"id":447456365,"identity":"c5a63610-918b-45f3-8942-cede836c4c27","order_by":2,"name":"Diana Ortega-Romero","email":"","orcid":"","institution":"Instituto de Neurbobiología, UNAM","correspondingAuthor":false,"prefix":"","firstName":"Diana","middleName":"","lastName":"Ortega-Romero","suffix":""},{"id":447456366,"identity":"bfe1db5d-e5d1-49ac-b478-a9b4723cf3ec","order_by":3,"name":"Ana Báez-Cordero","email":"","orcid":"","institution":"Instituto de Neurbobiología, UNAM","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Báez-Cordero","suffix":""},{"id":447456367,"identity":"b911b125-e1a7-4ed2-9068-b89afb748a3a","order_by":4,"name":"Oswaldo Sánchez-Lobato","email":"","orcid":"","institution":"Instituto de Neurbobiología, UNAM","correspondingAuthor":false,"prefix":"","firstName":"Oswaldo","middleName":"","lastName":"Sánchez-Lobato","suffix":""},{"id":447456368,"identity":"7f620e10-e3f6-4c13-a53d-6d151e7455c3","order_by":5,"name":"Claudia Pérez-Díaz","email":"","orcid":"","institution":"Instituto de Neurbobiología, UNAM","correspondingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Pérez-Díaz","suffix":""}],"badges":[],"createdAt":"2025-04-22 00:55:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6499142/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6499142/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41386-025-02262-5","type":"published","date":"2025-10-13T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81533200,"identity":"bf44517b-6b34-4f3e-80c2-0c518db8c985","added_by":"auto","created_at":"2025-04-28 09:52:38","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":962134,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCP55940 effects on the perception and production of temporal intervals.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e) Schematic representation of the behavioral set-up and task structure. \u003cstrong\u003eb\u003c/strong\u003e) Representative lever press rasters (left panels) and peri-reward histograms (right panels) during an early and a late training session. \u003cstrong\u003ec\u003c/strong\u003e) Representative average (shaded area 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentiles) trajectories the left and right levers (color coded) aligned to the reward onset (dotted red line) during an early (up) and late (bottom) training session. Boxplot representation of overshoot (\u003cstrong\u003ed\u003c/strong\u003e), interlimb correlation (\u003cstrong\u003ee\u003c/strong\u003e), and speed (\u003cstrong\u003ef\u003c/strong\u003e) for the same early (upper panels) and late (lower panels) sessions depicted in C. Average learning curves for a group of animals (n = 7) for the following variables: Interlimb correlation (\u003cstrong\u003eg\u003c/strong\u003e), bilateral movement onset variability (BMOV, \u003cstrong\u003eh\u003c/strong\u003e), overshoot (\u003cstrong\u003ei\u003c/strong\u003e), maximum lever speed (\u003cstrong\u003eJ\u003c/strong\u003e) and area under the curve (\u003cstrong\u003ek\u003c/strong\u003e). Data for learning curves are presented as median (solid line) + 75th and 25th percentiles (shaded area). Boxplot comparison of the effect of the CP55940 doses (color coded) on the bilateral execution variables (\u003cstrong\u003el-o\u003c/strong\u003e). \u003cstrong\u003ep\u003c/strong\u003e) Representative lever press raster and peri-reward histogram during one session under the highest dose of CP55940. Box plot comparison of CP55940 effects on the peri-reward histogram area under the curve (\u003cstrong\u003eq\u003c/strong\u003e) and moment of the peak lever response around reward onset (\u003cstrong\u003er\u003c/strong\u003e, peak interval). Boxplots represent median and 25th and 75th percentiles.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6499142/v1/7ef5f8036977d0629689ac18.jpeg"},{"id":81533205,"identity":"f0bd6f54-a394-4bb5-abd4-65e3a4010c2c","added_by":"auto","created_at":"2025-04-28 09:52:38","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":767855,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCP55940 effects on the production of two temporal intervals.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e) Schematic representation of the behavioral set-up. \u003cstrong\u003eb\u003c/strong\u003e) Representative average trajectories (shaded area, 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentiles) of the left and right levers (color coded) aligned to the reward onset (dotted red line) during long (1250 ms) and short (750 ms) movement blocks. Average learning curves for a group of animals (n = 7) for the following variables: interlimb correlation (\u003cstrong\u003ec\u003c/strong\u003e), bilateral movement onset variability (BMOV, \u003cstrong\u003ed\u003c/strong\u003e), overshoot (\u003cstrong\u003ee\u003c/strong\u003e), maximum lever speed (\u003cstrong\u003ef\u003c/strong\u003e) and trajectory variability (\u003cstrong\u003eg\u003c/strong\u003e). Data for learning curves are presented as median (solid line) + 75th and 25th percentiles (shaded area). Box plot comparison of the effect of different doses of CP55940 (color coded) on the different bilateral execution variables for the two types of trials: 750 ms and 1250 ms (\u003cstrong\u003eh-l\u003c/strong\u003e). Boxplots represent median and 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentiles. * and # represent significant differences (LSD post hoc test, p \u0026lt; 0.05) against control (0 mg/kg) and 0.2 mg/kg conditions, respectively.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6499142/v1/9a4d93d29f01dd860ab69ed9.jpeg"},{"id":81533199,"identity":"9acd724c-5da8-400f-b7fe-46474fdb9dab","added_by":"auto","created_at":"2025-04-28 09:52:38","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":453731,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatiotemporal behavioral protocol.\u003c/strong\u003e (\u003cstrong\u003ea\u003c/strong\u003e) Schematic representation of the different phases of the front-back-front strategy expressed by the animals in the spatiotemporal task (top). Trajectories for every trial and the average trajectory of a representative session are presented at the bottom. The different phases of the sequence are indicated in the average trajectory. Sequence duration (\u003cstrong\u003eb, d\u003c/strong\u003e) and representative average trajectories (\u003cstrong\u003ec, e\u003c/strong\u003e; shaded area represents\u003cstrong\u003e \u003c/strong\u003e+ 75\u003csup\u003eth\u003c/sup\u003e and 25\u003csup\u003eth\u003c/sup\u003e percentiles) for a highly trained animal during behavioral execution in the basic single-speed (b, c) and multiple-speed phases (color coded) of training. \u003cstrong\u003ef\u003c/strong\u003e) Sequence durations by groups of trials for a representative animal (left) and the group of animals (right) at different treadmill speeds (color-coded dots and boxes). \u003cstrong\u003eg\u003c/strong\u003e) Color-coded representative average position trajectories (left) and trajectory differences (center, right) with respect to the stereotypical position trajectory at the central speed for a representative animal (center) and for the group of animals (right). \u003cstrong\u003eh\u003c/strong\u003e) Same as in G but for speed. The central line and box in boxplots represent the median and 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentiles. Whiskers extend to the most extreme data points, excluding outliers. Bonferroni \u003cem\u003epost hoc\u003c/em\u003e test *** p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6499142/v1/5e1bb14a1f9b7b6a24292ee2.jpeg"},{"id":81533203,"identity":"9c7f41d9-fd37-48b9-8092-3d42528c0e1d","added_by":"auto","created_at":"2025-04-28 09:52:38","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":823088,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of systemic administration of CP55940 on the execution of a timed motor sequence.\u003c/strong\u003e Representative trial-by-trial sequence duration (dots represent individual trials; \u003cstrong\u003ea\u003c/strong\u003e), peak speed (\u003cstrong\u003eb\u003c/strong\u003e), and difference in position (\u003cstrong\u003ec\u003c/strong\u003e) for an animal under control conditions and different doses of CP55940 (color coded). \u003cstrong\u003ed-f\u003c/strong\u003e) Representative average speed (left) and position trajectories (right) at different treadmill speeds (color coded) during trials under control (d) and the highest dose (\u003cstrong\u003ee-f\u003c/strong\u003e) of CP55940. \u003cstrong\u003eg-i\u003c/strong\u003e) Group effects of the different doses of CP55940 (color coded) for sequence duration (g), speed (h), and position (i). Data are presented as median (solid line and dots) + 75\u003csup\u003eth\u003c/sup\u003e and 25\u003csup\u003eth\u003c/sup\u003e percentiles (shaded area).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6499142/v1/04e5e00ee12be19d56a40ec1.jpeg"},{"id":93464446,"identity":"40dd8170-56ca-4613-adff-361f24973196","added_by":"auto","created_at":"2025-10-14 07:06:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3789557,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6499142/v1/9c6bd92b-68e3-4859-9ad1-1e5e282bc0b5.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Differential cannabinergic effects on temporal perception and production","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eNumerous studies have reported that cannabinoid administration alters movement control and time perception. While there seems to be a consensus that low concentrations of cannabinoid agonists induce hyperlocomotion and high concentrations lead to hypomobility (Sa\u0026ntilde;udo-Pe\u0026ntilde;a et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Fern\u0026aacute;ndez-Ruiz et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Katsidoni et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), it is not entirely clear how the cannabinoid system affects time perception (Han and Robinson, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Crystal et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Atakan et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Preclinical studies on humans often report that cannabis users experience time as running slowly (Tinklenberg, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1972\u003c/span\u003e; Sewell et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This distortion results in an overestimation of elapsed time; that is, subjects report that more time has passed than actually has. On the other hand, behavioral protocols where subjects are required to produce a time interval often report underestimations (i.e., subjects produce longer intervals than required; Sewell et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Boggs et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similar results have been observed in rodents, where it has been demonstrated that systemic administrations of cannabinoids also induce an overestimation of elapsed time in \u0026ldquo;peak interval\u0026rdquo; procedures(Han and Robinson, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) or in interval discrimination procedures (Crystal et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). However, because time is not specifically sensed by a particular system or region of the brain (Buzs\u0026aacute;ki and Llin\u0026aacute;s, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), it has been proposed that the passage of time is sensed by a \u0026ldquo;distributed clock,\u0026rdquo; a network of structures including the motor and premotor cortices, the thalamus, the basal ganglia, the hippocampus, and the cerebellum (Merchant et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These regions are linked to both motor and non-motor functions, and it has been proposed that various behavioral processes are involved in time interval estimation, differentiating between behaviors that involve estimating intervals associated with movement execution (interval and pattern motor timing) and those related to sensory stimuli (interval and pattern sensory timing) (Paton and Buonomano, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Merchant and de Lafuente, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, it remains unclear whether these processes or behaviors share the same anatomical, neurophysiological, and pharmacological bases. In this context, cannabinoid receptor 1 (CBr1) is highly expressed in most of the brain regions typically associated with temporal processing, including the cerebellum, basal ganglia, and hippocampus, which show the highest levels of CBr1 (Tsou et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Sa\u0026ntilde;udo-Pe\u0026ntilde;a et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). This anatomical distribution suggests not only that various brain regions are implicated in temporal processing, but also that cannabinoids may affect aspects of temporal processing depending on their specific expression levels or localization within a given structure. On the other hand, certain time-processing effects attributed to cannabinoids may be more related to motor effects that influence timing; for instance, if an animal is slow, it will take more time to fulfill a given task. Hence, the covariance between temporal and motor parameters during execution in behavioral protocols may compromise interpretations on the exact nature of temporal processing, a topic that has recently been experimentally and theoretically studied elsewhere (Safaie et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Robbe, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; de Lafuente et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we addressed the possibility that covariance in temporal and execution parameters may mask the true nature of the behavioral effects of systemic administration of synthetic cannabinoids in rodents. To this aim, we implemented three behavioral protocols to simultaneously evaluate motor effects on movement speed and amplitude, as well as various aspects of interval estimation (e.g., motor timing and sensory timing) and time estimation on the scales of hundreds of milliseconds, seconds, and tens of seconds. First, we used a behavioral protocol where animals were trained to produce a coordinated forelimb movement in a 750 ms window (production component) but in a fixed-interval schedule of 30 s. We found that systemic administrations of CP55940 caused an overestimation of elapsed time in the peak-interval component of the task. However, upon analyzing movement production in the same animals and sessions, we found that the treatment induced longer coordinated forelimb movements in the temporal production component\u0026mdash; a result typically interpreted as an underestimation of the temporal interval. This treatment also reduced movement speed. Hence, the second step to differentiate between time and speed effects was to introduce two production intervals\u0026mdash; one of 750 ms and a longer one of 1250 ms. In this case, CP55940 injections induced slower movements and underestimations (i.e., longer movements) in both intervals, but the magnitude of the temporal effect was not proportional to the required interval. This observation suggests that temporal production was preserved, whereas speed control was not. To confirm this possibility, we trained animals to execute a complex locomotion-based motor sequence within a strict temporal constraint of 7 s (goal time) and with an implicit requirement to adjust their speed to the experimental context on a trial-by-trial basis. We found that the same CP55940 administrations reduced locomotion speed but spared the temporal component of the task. Furthermore, we observed that animals compensated for the reduced locomotion speed by adjusting the spatial and/or temporal component of the movement sequence to successfully maintain execution around the goal time. These results indicate that cannabinoids differentially affect the perception and production of temporal intervals and hence suggest different neuroanatomical mechanisms underlying these processes.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnimals\u003c/h2\u003e \u003cp\u003eA total of 29 Long-Evans male rats were used in this study. Animals were housed in the satellite bioterium of our laboratory in home cages with stable temperature (23\u0026deg;C) and humidity (66%) and under a constant 12/12-hour light-dark cycle (lights on at 8 a.m.). Animals had free access to food and water and were constantly supervised by specialized personnel from the general facilities of our institute. All experimental procedures were conducted during the light phase of the cycle. Seven rats (300 to 650 g) were trained in the fixed-interval task and eight rats (300 to 650 g) in the two-interval production task; these animals were water-restricted and consumed all the water requirements during the training session (20 to 30 ml in 40 to 60 min per session, one session per day). Animal weight was monitored daily and maintained over 85% of the expected weight for their age. If the animals did not consume their daily water portion, then water was provided for short periods after the training. Animals were trained for six days a week with 24 hours of free access to water on the seventh day. Additionally, 14 rats were used for the treadmill spatiotemporal task. These animals were trained for 60\u0026ndash;130 trials per day, six days a week, without any water restriction.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBehavioral apparatus and training\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFixed-interval schedule\u003c/h2\u003e \u003cp\u003eFor this task, animals (n\u0026thinsp;=\u0026thinsp;7) were trained in behavioral boxes, which have been previously described (B\u0026aacute;ez-Cordero et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Luma et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pimentel-Farfan et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These boxes were made with (50 x 50 x 50 cm) Plexiglas walls and equipped with two sets of levers, a water port, a green LED to indicate correct trials, and a white LED to indicate the availability of the set of levers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, \u003cb\u003eleft\u003c/b\u003e). Each set of levers consists of two levers protruding 5 cm from the wall. All training and experimental procedures in this study were performed on the left set of levers. The levers can move vertically and are connected to a voltage transducer (3.5 cm\u0026thinsp;=\u0026thinsp;2.5 V). Voltage signals were digitized and stored at 250 Hz through National Instruments cards (NI PXIe-6363) and LabView custom-made routines. In each session, we continuously recorded the vertical position of both levers. Animals were rewarded with water, which was delivered through the central water port using a solenoid valve.\u003c/p\u003e \u003cp\u003eTo accustom rats to the training conditions, they were placed in the training cages for one session (40 min) before the beginning of training and the water restriction. During the next session, the rats were deprived of water and trained to obtain rewards (drops of water) when approaching the water port. In subsequent sessions, animals were guided to obtain rewards by touching any lever and slightly moving it down (\u0026gt;\u0026thinsp;0.1 cm). We progressively increased the spatial threshold until the animals reliably displaced both levers simultaneously at least 2.6 cm. All animals quickly learned the rule of touch and could displace any lever in fewer than two sessions. Then, animals learned to displace both levers simultaneously. Rewards would only be delivered if the levers were simultaneously held under the spatial threshold (2.6 cm) for at least 50 ms (temporal threshold). Subsequently, in a range of three to five sessions, temporal thresholds were progressively increased to 750 ms. The rats were trained with these parameters for 100 to 120 sessions. Later, we started the fixed-interval schedule by introducing the rule that rewards would be delivered each time that the rats performed a correct response (coordinated lever press) after waiting for at least 30 seconds. When this response occurred after the appropriate time interval (30 s), the green light would turn on (1 s), indicating a correct trial, and a drop of water was delivered as a reward. The 30 s interval was continuously counted from the preceding reward, and once completed, the first correct response was rewarded (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, \u003cb\u003eright\u003c/b\u003e). In each session, animals performed an unlimited number of trials in the lapse of 40 minutes to 1 hour. Most of the animals performed around 60 to 90 trials per session.\u003c/p\u003e \u003cp\u003ePharmacological manipulations were performed after at least 30 sessions of training in the fixed-interval schedule. All the execution parameters of the task are calculated based on the raw position data from the levers. Interlimb correlation was calculated as the Pearson correlation coefficient between the raw position of the left and right levers from the beginning of the successful bilateral movement to the delivery of the reward. Overshoot was the amount of time that the levers remained under the spatial threshold after the reward was delivered; this variable directly reflects movement duration. We calculated speed as the instantaneous difference in position in 4 ms time bins and report the average maximum per session.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTwo-interval production task\u003c/h3\u003e\n\u003cp\u003eThe same behavioral chambers (fixed-interval schedule) were used in the two-interval task (n\u0026thinsp;=\u0026thinsp;8). In this version, a white or a blue LED indicated the type of trial, specifically a 750 or 1250 ms temporal threshold. In this version, the animals were initially trained following the same process as in the previous task, except without the long fixed-interval component. After session 120, the two-interval production schedule was introduced. Here, blocks of 20 trials were alternately presented, requiring rats to maintain the levers pressed for 1250 ms during block one (indicated by a blue LED) or 750 ms during block two (indicated by a white LED). After an inter-trial interval of 1.5 s, the levers were available so that the animals could freely perform the next trial. Most of the animals completed between 90 and 120 trials per session. Pharmacological manipulations were performed after at least 50 sessions of training in the two-interval production schedule.\u003c/p\u003e\n\u003ch3\u003eSpatiotemporal task\u003c/h3\u003e\n\u003cp\u003eThe apparatus used for the spatiotemporal task consisted of a modified human treadmill (NordicTrack T6.1), equipped with Plexiglas walls (50 cm high) to restrict rats to the passable area of the belt (80 cm long by 20 cm wide; Hidalgo-Balbuena et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The treadmill motor was controlled by a custom-made program (LabVIEW, National Instruments) and a multifunction computer board (NI USB-6353, National Instruments). A line of LEDs illuminated the entire apparatus, and the front wall of the treadmill was equipped with a drinking spout to deliver drops of sucrose solution. A laser-based photodetector gate, positioned 10 cm from the front wall, delimited the goal area. In this task, rats (n\u0026thinsp;=\u0026thinsp;14) were trained to perform a stereotyped sequence of movements in at least 7 s while running on a motorized treadmill. Initially the animals were habituated to the elements of the setup, progressively adapting to the treadmill speed, which increased from 5 cm/s to 30 cm/s (3 sessions of 90 min, one session per day). Subsequent training sessions maintained a fixed treadmill speed of 30 cm/s (60\u0026ndash;130 trials per session, one session per day). During the early phases of training, the animals started their trials at different locations of the treadmill. However, as training advanced, they quickly and spontaneously adapted their behavior and developed a new sequence of actions. At the start each trial, the animals began at the front of the treadmill; then, when the belt began to move, they were passively transported to the back, where they ran for an estimated time interval of 7 s, after which they accelerated to reach the front of the treadmill, or goal area. Incorrect trials (entering the goal area before the 7 s) were signaled with a 1.5 kHz, 65 dB auditory tone triggered by a beam break into the goal area and continued for 20 s. These trials were not rewarded.\u003c/p\u003e \u003cp\u003eDuring each session, the average performance of the last 40 trials was continuously calculated, and sessions were terminated by reaching one of the following three criteria: 1) if the animals achieved over 72.5% of correct trials at any point during the session; 2) if the animals completed 60 correct trials; 3) if 130 trials were conducted. Once the animals reached a criterion of performance accuracy of \u0026ge;\u0026thinsp;72.5% of correct responses over the last 40 trials of each session for \u0026ge;\u0026thinsp;3 consecutive sessions, we implemented a random speed protocol. In this protocol, the belt speed varied unpredictably from trial to trial in a range of 27 cm/s to 33 cm/s. Throughout the training period, the experimenter was absent from the behavioral room. During all trial sessions, animals were continuously monitored with a high-speed CCD camera (acA640-120fc, Basler, 100 frames s\u0026thinsp;\u0026minus;\u0026thinsp;1, 9 pixels cm\u0026thinsp;\u0026minus;\u0026thinsp;1) positioned laterally to the treadmill. The animals\u0026rsquo; positions were automatically extracted with a custom-made program (Vision, National Instruments) by calculating the center of mass. To quantify behavioral stereotypy, we extracted the position and speed time-courses from each trial across all sessions. Position or speed trajectories were aligned to the entrance times (i.e., end of the movement sequence).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAdministration of the cannabinergic agonist\u003c/h2\u003e \u003cp\u003eSystemic administration of the cannabinergic agonist CP55940 (Sigma and Cayman) was performed with vehicle, 5% dimethyl sulfoxide (DMSO; Merk)\u0026thinsp;+\u0026thinsp;5% Cremophor (Sigma) in saline. Drugs were injected in volumes of 1 ml/kg. For each task, CP55940 concentrations of 0.01, 0.05, 0.1, and 0.2 mg/kg were injected in individual sessions with at least two drug-free sessions between CP55940 injections. All injections were performed 15 min before the behavioral sessions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eBehavioral data are presented as a median\u0026thinsp;+\u0026thinsp;25th and 75th percentiles. Statistical comparisons between groups were performed with Mann-Whitney or Kruskal-Wallis tests as stated in each section. A Bonferroni post hoc test was used for multiple comparisons. Statistical analysis was performed using MATLAB software (The MathWorks, Inc.). Statistical differences were considered significant if P values were \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSystemic administrations of CP55940 differentially impact in the perception and production of temporal intervals\u003c/h2\u003e \u003cp\u003eTo explore the effects of cannabinergic agonism on temporal processing, we implemented a modified version of a bimanual coordination behavioral protocol for rats (B\u0026aacute;ez-Cordero et al, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pimentel-Farfan et al, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Luma et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). First, as reported in the original protocol, rats (n\u0026thinsp;=\u0026thinsp;7) were trained to vertically displace two levers simultaneously by a minimum of 2.6 cm and for a duration of at least 750 ms (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, \u003cb\u003eleft\u003c/b\u003e). This version of the task included an implicit interval production component (750 ms) but was not explicitly designed to assess temporal perception. Hence, we complemented this approach with a \u0026ldquo;peak-interval\u0026rdquo; design, where the levers became functional only 30 s after the last reward was delivered. That is, only the first coordinated lever press was rewarded after 30 s. Following the reward delivery, the time counter reset, and the levers remained inactive for an additional 30 seconds (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, \u003cb\u003eright\u003c/b\u003e). Our results indicate that during the first training sessions, the animals continuously lever-pressed during most of the 30s period (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, \u003cb\u003eleft\u003c/b\u003e), and their presses lasted hundreds of milliseconds longer than the 750 ms required to obtain a reward (\u0026ldquo;overshoot,\u0026rdquo; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, d \u003cb\u003eup\u003c/b\u003e), indicating poor temporal processing for both the perception of the 30-second long interval and the production of the 750 ms coordinated movement. However, after training, the animals limited their presses to a few seconds after the 30 s interval (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, \u003cb\u003eright\u003c/b\u003e), indicating a good perception of the long interval. Conversely, at this stage, lever presses were more accurate, with only a few milliseconds of overshoot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, d \u003cb\u003ebottom\u003c/b\u003e), indicating that the movement duration was appropriately adjusted to meet the experimental demands. Hence, with this approach we were able to evaluate the perception and production of temporal intervals, as well as other aspects of bilateral movement, enhancing our understanding of potential effects on movement, temporal processing, or both. For instance, we were able to assess the coordination level (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee) and the speed at which the left and right forelimbs moved (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). Interestingly, these parameters were more stable. Then, to create learning curves, the movement-related variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg-j) and the area under the curve (AUC) extracted from lever-pressing distributions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ek) were formally quantified on a session-by-session basis, revealing progressive improvements as training evolved. The previous data indicate that with this protocol, we can simultaneously assess two levels of temporal processing: one related to the perception of elapsed time on the scale of dozens of seconds (peak interval) and another concerning interval production on the scale of hundreds of milliseconds. Then, we explored the effects of systemic administrations of different doses of the synthetic cannabinoid agonist CP55940. Pharmacological sessions were started after at least 30 training sessions. Injections were administered 15 min before the behavioral sessions, and at least three treatment-free sessions were conducted in between treatment sessions. All animals in this group received at least two injections of each dose. Consistent with what was reported previously (B\u0026aacute;ez-Cordero et al, 2021), a dose-dependent effect was observed in movement duration and speed, with higher doses inducing slower and longer movements (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003el-m; Overshoot K-W, \u003cem\u003edf\u0026thinsp;=\u0026thinsp;4; X\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;36.27, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/em\u003e). Nonetheless, other execution parameters, such as interlimb correlation or movement onset variability, were slightly changed with the highest dose (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003en, o; interlimb correlation K-W, \u003cem\u003edf\u0026thinsp;=\u0026thinsp;4; X\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;18.98, p\u0026thinsp;=\u0026thinsp;0.0008;\u003c/em\u003e BMOV K-W, \u003cem\u003edf\u0026thinsp;=\u0026thinsp;4; X\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;8.31, p\u0026thinsp;=\u0026thinsp;0.0809\u003c/em\u003e). Longer movements could be interpreted as an underestimation of the 750 ms produced interval; however, when analyzing the behavior in the longer 30 s range, we observed a phenomenon that could be interpreted as an overestimation of the perceived interval. Higher doses of CP55940 resulted in lever presses homogeneously distributed throughout the entire interval (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ep), reflecting significantly higher values of the AUC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eq; K-W, \u003cem\u003edf\u0026thinsp;=\u0026thinsp;4; X\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/em\u003e) and with cumulative peaks closer to or even under 30 s (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003er; K-W, \u003cem\u003edf\u0026thinsp;=\u0026thinsp;4; X\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;19.2, p\u0026thinsp;=\u0026thinsp;0.0005\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe previous data suggest that CP55940 may differentially affect temporal processing at the perception level (30 s range) and production level (750 ms range). However, they could also indicate that temporal production is not impaired, as movement parameters may covary (i.e., slow movements could seem longer). To address this possibility, we trained a new group of animals (n\u0026thinsp;=\u0026thinsp;8) in the same original bimanual coordination protocol but introduced another modification. In this case, we did not use the peak-interval component but only manipulated the temporal production component in the range of hundreds of milliseconds. Here, instead of using only a holding time threshold of 750 ms, we also introduced a 1250 ms threshold. The two intervals were presented in 20 alternating trial blocks, and sessions always started with the longest threshold (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). After training in this version, animals produced movement trajectories with similar execution parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb-g) and overshoots in both types of trials (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, e). Similar overshoots indicate that the animals quickly adapted to the duration of the movement from a long (1250 ms) to a short (750 ms) reach. In this way, if animals control the interval production, movement duration must be close to the demanded interval, and the result would be better reflected in potential cannabinergic effects. In other words, if CP55950 induces an underestimation of interval production, it should impact both intervals equally. Conversely, if the slowness of movement were to impact the duration of movement, we could anticipate differential effects, such as longer movements for the 750 ms interval but not for the 1250 ms interval. After the training period (~\u0026thinsp;150 sessions), we carried out pharmacological manipulations where CP55940 was administered 15 minutes before each experimental session, and each experimental session was conducted after at least two control sessions (free of drugs). Consistent with previous observations (B\u0026aacute;ez-Cordero et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Monory et al., 2007; Sales-Carbonell et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), we found that systemic administrations of CP55940 produced dose-dependent motor impairments reflected in a decrease in interlimb correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh; K-W, \u003cem\u003edf\u0026thinsp;=\u0026thinsp;9; X\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;20.81, p\u0026thinsp;=\u0026thinsp;0.0135\u003c/em\u003e), without clear effects on bilateral movement onset variability (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ei; K-W, \u003cem\u003edf\u0026thinsp;=\u0026thinsp;9; X\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;10.15, p\u0026thinsp;=\u0026thinsp;0.3381\u003c/em\u003e) but with a significant reduction of the maximum movement speed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ej; K-W, \u003cem\u003edf\u0026thinsp;=\u0026thinsp;9; X\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;17.87, p\u0026thinsp;=\u0026thinsp;0.0367\u003c/em\u003e). We also found a dose-dependent increase in overshoot in both intervals (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ek; K-W, \u003cem\u003edf\u0026thinsp;=\u0026thinsp;9; X\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;108.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/em\u003e); however, this increase appeared to be more robust in the production of the 750 ms interval. Since overshoot directly reflects movement duration, this observation indicates that the impact on movement duration is not proportional to the required interval. This was confirmed when we analyzed the overshoot time relative to the magnitude of each time interval (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003el; K-W, \u003cem\u003edf\u0026thinsp;=\u0026thinsp;9; X\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;60.56, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/em\u003e). These results indicate that systemic administrations of CP55940 induced differential effects based on the interval, which is more consistent with impairments in speed control than with the production of temporal intervals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eRats dynamically adjust their speed to maintain the spatiotemporal structure of a well-trained complex sequence of movements.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOur previous results suggest differential effects of CP55940, with impairments in the perception of long temporal intervals in the range of dozens of seconds but systematically affecting speed control and sparing movement timing production in the range of hundreds of milliseconds. To further confirm this possibility, we performed the same pharmacological manipulations in a new cohort of animals (n\u0026thinsp;=\u0026thinsp;14) but trained them to perform a complex motor sequence requiring active control of elapsed time and speed in a temporal range of about 7 seconds. To this aim, we used a modified version of the behavioral protocol established in (Rueda-Orozco and Robbe, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). We first trained rats to perform a stereotyped and timed sequence of locomotion decelerations and accelerations while running on a motorized treadmill (Rueda-Orozco and Robbe, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hidalgo-Balbuena et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) (see methods). Once the animals learned the task, they adopted a stereotypical strategy consisting of the following steps (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e: 1) Animals started each trial at the front of the treadmill and learned to be passively transported to the rear part of the apparatus by the movement of the treadmill belt; 2) then, animals held their position in the back of the treadmill by trotting for a few seconds at the speed of the treadmill\u0026rsquo;s belt; 3) finally, animals performed a controlled acceleration across the treadmill to reach the front part again. This \u0026ldquo;front-back-front\u0026rdquo; strategy was extracted and analyzed from spatiotemporal trajectories reconstructed from high-speed video recordings on a trial-by-trial basis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, \u003cb\u003ebottom)\u003c/b\u003e. Highly trained animals displayed low variability in sequence duration around goal time (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) and significantly stereotyped execution trajectories (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Then, to understand how speed control or temporal representations interact and contribute to the general architecture of movement sequences, we implemented a second phase of training. Here, in each session, the treadmill speed (but not the temporal rule) varied randomly in a range of 27 to 33 cm/s on a trial-to-trial basis, so the animals were forced to dynamically adapt their locomotion speed based on their perception of the treadmill\u0026rsquo;s speed and on their representation of the 7 s temporal interval. Animals were trained in this version for at least 30 sessions. We found that, independently of the treadmill speed, the rats accurately maintained the sequence duration around goal time (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed) and displayed similar spatiotemporal trajectories (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). These effects were observed in representative sessions from individual animals (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb-e) and robustly maintained in the group of animals (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef-g; Rat 04, Time K-W, \u003cem\u003edf\u0026thinsp;=\u0026thinsp;6, X\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;16.71, p\u0026thinsp;=\u0026thinsp;0.01;\u003c/em\u003e Position K-W, \u003cem\u003edf\u0026thinsp;=\u0026thinsp;6; X\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;14.7. Group, Time K-W, \u003cem\u003edf\u0026thinsp;=\u0026thinsp;6; X\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;4.1; p\u0026thinsp;=\u0026thinsp;0.663;\u003c/em\u003e Position K-W, \u003cem\u003edf\u0026thinsp;=\u0026thinsp;6; X\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;3.67; p\u0026thinsp;=\u0026thinsp;0.721\u003c/em\u003e). These data indicate that the animals were somehow adjusting their behavior to maintain the duration of the sequence of movements, but they do not specify which movement parameter was adjusted to achieve this. For example, animals could have performed the sequence of movements in 7 seconds by holding their position at the back of the treadmill for varying durations according to the treadmill\u0026rsquo;s speed. Alternatively, the same outcome could arise from animals adjusting their speed in the last phase of the sequence according to the treadmill\u0026rsquo;s speed, either accelerating or decelerating during faster or slower trials, respectively. To evaluate these possibilities, we calculated the speed peaks during the last acceleration of the sequence on the treadmill. We found a linear relationship between the treadmill speeds and the peak speed reached by the animals, with a lower peak speed when the treadmill moved at lower speeds (27\u0026ndash;29 cm/s) and a higher peak speed when the treadmill moved at higher speeds (31\u0026ndash;33 cm/s). This behavior was observed in individual animals (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh, \u003cb\u003ecenter\u003c/b\u003e) and the group of animals (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh, \u003cb\u003eright;\u003c/b\u003e Rat 04 K-W, \u003cem\u003edf\u0026thinsp;=\u0026thinsp;6; X\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;1683.2; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e. Group K-W, \u003cem\u003edf\u0026thinsp;=\u0026thinsp;6; X\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;226.22; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e). These data indicate that the animals adjusted their speed to preserve the spatiotemporal structure of the sequence of movements, indicating that the animals maintained a representation of the 7 s interval. Hence, in this task we could potentially distinguish between the effects off cannabinoids on the overactive control of speed and those on the representation of time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter animals were trained in the new version of the protocol, we conducted the same pharmacological manipulations as in the two previous protocols. Fifteen minutes before each experimental session, we administered individual doses of CP55940 (0.01, 0.05, 0.1, or 0.2 mg/kg). Contrary to our observations regarding the duration of the bimanual movement, we found that cannabinergic administrations did not induce significant changes in the duration of the movement sequence (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). This suggests that, for a complex and highly trained sequence, the temporal component was resistant to the treatment. On the other hand, when we analyzed the locomotion speed, we found a clear dose-dependent effect, with gradually slower accelerations associated with higher doses of CP55940 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). This finding confirms that the mechanisms controlling behavioral timing and speed are different and can be differentially affected by cannabinoids under these circumstances. During the execution of the sequence, the temporal component can be linked to the animals\u0026rsquo; speed. Therefore, these data also raise the question as to how the temporal component is unaffected if the animals are running slowly. This was clarified when analyzing the spatial component of the motor sequence. Here, we also observed a clear dose-dependent effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). In the control condition, spatiotemporal trajectories at different treadmill speeds overlapped (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed, \u003cb\u003eright\u003c/b\u003e), but speed trajectories showed a gradient, with higher and lower locomotion peaks corresponding to higher and lower treadmill speeds, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed, \u003cb\u003eleft\u003c/b\u003e). However, with the higher doses of CP55940, the animals tended to express one of two strategies to compensate their slowness. Either they started the last acceleration almost as soon as they reached the rear end of the treadmill (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee, \u003cb\u003eright\u003c/b\u003e)\u0026mdash;that is, they did not hold their position for as long as they did in the control condition\u0026mdash; or they did not let themselves be transported to the far end of the apparatus (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef, \u003cb\u003eright\u003c/b\u003e). Both strategies combined with movement slowness resulted in the animals completing the sequences close to the 7 s rule. As in the previous protocols, these effects were clearly visible in representative sessions from individual animals (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-f) and in the group of animals (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg-i). These results suggest that, to maintain the target duration of the motor sequence under circumstances where cannabinoids cause a decrease in peak speeds, the animals implemented compensatory adjustments to their timing (not waiting for too long on the back of the treadmill) or their position on the treadmill (avoiding being carried entirely to the back). Altogether, this set of data confirms the existence of independent representations for space, speed control, and motor timing, and indicates that cannabinoids differentially affect these variables.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, we evaluated the effects of systemic administration of the synthetic cannabinergic agonist CP55940 on interval perception and interval production across behavioral contexts with different spatiotemporal contents. Our results showed that cannabinoid administration differentially affected these processes, impairing the perception of long temporal intervals in the range of dozens of seconds while maintaining interval production in the range of hundreds of milliseconds. On the other hand, the same administrations systematically affected speed control independently of the spatiotemporal context, which is consistent with previous reports (Sa\u0026ntilde;udo-Pe\u0026ntilde;a et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Previous studies have suggested that cannabinoids alter the perception of elapsed time, often leading to overestimations in tasks requiring interval discrimination (Han and Robinson, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Crystal et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Sewell et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Boggs et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In agreement with this, we found that CP55940 systemic administrations led to an overestimation of elapsed time in a fixed-interval schedule task where rats were required to estimate a 30-second interval. This suggests that cannabinoids disrupt sensory-based time estimation, possibly due to their effects on cortico-striatal or cortico-hippocampal circuits, both of which are rich in CB1 receptor expression (Tsou et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Egertova and Elphick, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and have been implicated in interval timing (Bartolo et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mello et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bakhurin et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shimbo et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Altogether, our data suggest that while the cannabinergic system may be linked to sensory timing, interval production effects are most likely related to unspecific effects related to slowness in motor execution.\u003c/p\u003e \u003cp\u003eOn the other hand, in the two-interval production task, cannabinoid administrations caused longer forelimb movements in the production of time intervals in the range of hundreds of milliseconds. This result is typically interpreted as an underestimation of the required time interval, implying that cannabinoids may also affect motor timing. However, in our next set of experiments, when we used two production intervals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), one of 750 ms and another of 1250 ms, the temporal effects were not proportional to the interval duration, suggesting a potential motor confound. To explore this possibility, we evaluated the effects of cannabinoids on a spatiotemporal motor task that required executing a well-learned sequence of movements with a tight temporal constraint of 7 s (goal time). Under these conditions, CP55940 injections produced no significant disruption in motor timing precision. More importantly, despite a clear dose-dependent reduction in locomotion speed, the rats compensated by adjusting their spatial trajectories, thereby ensuring that the total sequence duration remained close to the goal time. These results not only confirm that the animals maintained a functional representation of the temporal interval, but also that they were able to sense the behavioral disparities induced by cannabinoids and compensate them by manipulating another execution variable, specifically position. Previous reports indicate that in this task, animals entrain their behavior to stable environmental variables, such as treadmill speed or length (Rueda-Orozco and Robbe, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Jurado-Parras et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Safaie et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which makes it difficult to adapt to unpredictable changes in these variables and raises questions about the explicit representation of the sequence duration. However, in our experiments, animals were able to behaviorally adapt to subtle changes in treadmill speed. It is possible that this discrepancy arises from differences in task structure and training history. For example, in our training schedule, animals were first overtrained in a fixed-speed condition before transitioning to a random-speed protocol, which may have facilitated compensatory adjustments. Safaie et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) observed impairments in speed adaptation when animals underwent training with extreme speed variations (2\u0026ndash;40 cm/s). In this study, we utilized a treadmill speed range of 27 to 33 cm/s, which may facilitate more precise adaptations​. Another possibility is that, in the spatiotemporal task, the time interval to estimate is implicitly embedded within a complex motor sequence that also includes rhythmic sensory feedback, such as the somatosensory input from the animal's paws while running (Hidalgo-Balbuena et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In contrast, during the two-interval production task, the sensory feedback that the animals may receive to guide their behavior by keeping the levers pressed with their forepaws is continuous.\u003c/p\u003e \u003cp\u003eWhat would be the neurobiological mechanism underlying these cannabinergic effects? Previous reports suggest that a combination of molecular and structural locations implicating the basal ganglia, hippocampus, and cerebellum may be responsible for these effects. First, the robust slowness of movements observed in all our behavioral protocols is most likely related to the activation of CBr1, located in the terminals of the direct pathway neurons of the substantia nigra pars reticulata. This effect has been consistently observed in different studies (Sales-Carbonell et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; B\u0026aacute;ez-Cordero et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Soria-Gomez et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and may result in unbalanced direct/indirect pathway activity, thereby enhancing inhibition of the motor thalamus (Aceves et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Antonazzo et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; B\u0026aacute;ez-Cordero et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Here, however, it remains to be established whether these effects are exclusively linked to the CBr1 located at the synapse or the mitochondrial compartment, as the latter has been linked to CBr1-induced catalepsy (Soria-Gomez et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Second, the effects on sensory timing may be related to CBr1 located in the hippocampus, basal ganglia, and/or cerebellum, since these regions have been strongly implicated in temporal processing (refs reviews) and exhibit the highest expression of CBr1 receptors in the brain (Tsou et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Egertova and Elphick, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; M\u0026aacute;ty\u0026aacute;s et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). While striatal and hippocampal circuits may be primarily involved in interval perception and production (MacDonald et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Salz et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Shimbo et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), cerebellar circuits could contribute to compensatory mechanisms, allowing animals to adjust movement execution despite cannabinoid-induced slowness of movements (Kunimatsu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Andersen and Dalal, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Taken together, our results suggest that cannabinoids selectively impair time perception but not production, and their effects depend on the specific timescale and task demands. The overestimation of long intervals in the fixed-interval task supports a role for cannabinoids in disrupting sensory timing, whereas the elongation of short-interval movements suggests an influence on motor timing mechanisms. However, timing in well-learned motor sequences remains preserved, likely due to compensatory strategies that adjust spatial parameters to maintain temporal precision. Future studies should examine the specific neural circuits underlying these differential effects. Electrophysiological recordings or CBr1 receptor manipulations in the striatum, hippocampus and cerebellum could help to elucidate their individual contributions to sensory and motor timing influenced by cannabinoids.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll experiments were approved by the Animal Ethics Committee of the Institute of Neurobiology at the National Autonomous University of Mexico (UNAM; protocol 102.A) and conformed to the principles outlined in the Guide for the Care and Use of Laboratory Animals (National Institute of Health). Every precaution was taken to minimize suffering, and the number of animals used in the experiments.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors thank the support provided by all the members of Laboratory A-02 from the Institute of Neurobiology, UNAM; Martín García from the animal facility of INB; Óscar Prospéro García, for generous donations of valuable equipment; Adriana González from Unidad de Proteogenómica, INB; and Jessica Gonzalez-Norris for proofreading the manuscript. This manuscript is part of the requirements for obtaining a doctoral degree at the Posgrado en Ciencias Biológicas, UNAM, by MG Martínez-Montalvo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: MGMM, PRO\u003c/p\u003e\n\u003cp\u003eMethodology: MGMM, PRO\u003c/p\u003e\n\u003cp\u003eInvestigation: MGMM, DIOR, ASBC, JOSL, CIP, PRO\u003c/p\u003e\n\u003cp\u003eData Curation: MGMM, PRO\u003c/p\u003e\n\u003cp\u003eFormal analysis: PRO\u003c/p\u003e\n\u003cp\u003eWriting—original draft: MGMM, PRO\u003c/p\u003e\n\u003cp\u003eWriting—review \u0026amp; editing: MGMM, DIOR, ASBC, JOSL, CIP, PRO\u003c/p\u003e\n\u003cp\u003eSupervision: PRO\u003c/p\u003e\n\u003cp\u003eProject administration: PRO, CIP\u003c/p\u003e\n\u003cp\u003eFunding Acquisition: PRO\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMario Gabriel Martínez-Montalvo is supported by fellowship 925898 from CONAHCyT-México.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis work was funded by grants:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUNAM-DGAPA-PAPIIT: IG200424 (PRO)\u003c/p\u003e\n\u003cp\u003eCONAHCyT: CF-2023-I-7 (PRO)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAceves JJ, Rueda-Orozco PE, Hernandez-Martinez R, Galarraga E, Bargas J (2011) Bidirectional plasticity in striatonigral synapses: A switch to balance direct and indirect basal ganglia pathways. 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Neuron 108:651\u0026ndash;658.e5 Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuron.2020.08.020\u003c/span\u003e\u003cspan address=\"10.1016/j.neuron.2020.08.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6499142/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6499142/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCannabinoids have traditionally been associated with motor and cognitive impairments, including slowness of movement and altered temporal perception. However, it remains unclear whether cannabinoids specifically affect the perception and/or production of temporal intervals. To explore these possibilities, we evaluated the effects of systemic administrations of the synthetic cannabinoid CP55940 on behavioral performance in male rats trained in three distinct paradigms designed to assess time interval perception and production. Systemic administration of CP55940 caused temporal overestimation in a fixed-interval task, which was primarily linked to impaired perception of elapsed time in the range of tens of seconds. In contrast, while the same treatment increased forelimb reach duration in a two-interval production task (in the hundreds of milliseconds range), these effects were more accurately attributed to a general reduction in movement speed rather than altered temporal processing. These findings were further confirmed in a third motor task, where animals executed a complex timed motor sequence with spatiotemporal constraints while running on a treadmill. Here, CP55940 administration slowed locomotion but did not disrupt motor timing. Our results demonstrate that, in addition to inducing motor slowing, systemic cannabinoid administration impairs temporal perception but preserves interval production, suggesting distinct underlying mechanisms for these two processes.\u003c/p\u003e","manuscriptTitle":"Differential cannabinergic effects on temporal perception and production","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 09:52:34","doi":"10.21203/rs.3.rs-6499142/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"97a3bf4f-5a17-483f-84db-108b9939fc9b","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47627583,"name":"Biological sciences/Neuroscience/Sensorimotor processing"},{"id":47627584,"name":"Biological sciences/Neuroscience/Learning and memory"}],"tags":[],"updatedAt":"2025-10-14T07:06:14+00:00","versionOfRecord":{"articleIdentity":"rs-6499142","link":"https://doi.org/10.1038/s41386-025-02262-5","journal":{"identity":"neuropsychopharmacology","isVorOnly":false,"title":"Neuropsychopharmacology"},"publishedOn":"2025-10-13 04:00:00","publishedOnDateReadable":"October 13th, 2025"},"versionCreatedAt":"2025-04-28 09:52:34","video":"","vorDoi":"10.1038/s41386-025-02262-5","vorDoiUrl":"https://doi.org/10.1038/s41386-025-02262-5","workflowStages":[]},"version":"v1","identity":"rs-6499142","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6499142","identity":"rs-6499142","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}