Exploring Cerebellar Contribution to Motor Optimization in Temporal Prediction Through Cerebellar–Brain Inhibition

Exploring Cerebellar Contribution to Motor Optimization in Temporal Prediction Through Cerebellar–Brain Inhibition | 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 Research Article Exploring Cerebellar Contribution to Motor Optimization in Temporal Prediction Through Cerebellar–Brain Inhibition Sara Terranova, Alessandro Botta, Martina Putzolu, Gaia Bonassi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8778315/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The cerebellum integrates temporal information to optimize motor responses through prediction. Temporal prediction occurs through two mechanisms: rhythmic entrainment to recurring patterns or integration of single isolated intervals, guiding attention and enhancing perceptual and motor performance. However, the causal contribution of the cerebellum–M1 pathway to motor optimization across predictive contexts remains unexplored. This study aimed to assess whether functional cerebellum-M1connectivity is modulated during a temporal prediction task requiring motor response optimization via a predictive process. Thus, we investigated Cerebellar-Brain Inhibition (CBI), a transcranial magnetic stimulation (TMS) technique, during a temporal prediction (TP) task. Twenty participants received the CBI protocol at rest and during the TP-task. Response times (RTs) to the target were recorded in two predictive contexts: rhythmic (i.e., interstimulus intervals were constant, 900 ms) and single-interval condition (i.e., target’s timing estimation was based on the prior exposure to the train of stimuli). TMS was delivered at two time points of the task: at the warning signal, corresponding to the maintenance of timing information for motor preparation and at the target stimulus onset, indexing use of timing information for motor execution. We found a significant reduction of the CBI with respect to rest only when participants were engaged in the single-interval condition of the TP-task. Furthermore, this modulation was evident only when TMS was delivered at the warning stimulus appearance, and not at the target stimulus appearance. Our findings underscore the causal role of the cerebellum in motor optimization in a predictive context, critically in single-interval, memory-based timing. Cerebellum Cerebellar-Brain inhibition Temporal Prediction Response time Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Temporal prediction (TP) is a fundamental function of neural systems and can be defined as the brain's ability to anticipate future events by estimating time and event probabilities (Grabenhorst et al., 2025 ).TP arises spontaneously, without explicit instructions to rely on temporal information, making it a component of implicit timing (J. Coull & Nobre, 2008 ). Research on neural activations during TP(Cona et al., 2021 ; Nani et al., 2019 ; Ortuño et al., 2011 ; Radua et al., 2014 ; Teghil et al., 2019 ; Wiener et al., 2010 ) has identified a large network of cortical and subcortical regions mediating different aspects of TP in a context-dependent manner. In fact, prediction may arise in rhythmic context, through the exposure to simple rhythms (e.g., music or speech) or in memory-based or single-interval context, when prior exposure to an event creates a memory trace that informs the prediction (Large & Jones, 1999; Lawrance et al., 2014; Nobre & Van Ede, 2018; Correa et al., 2006 a; Shalev et al., 2019 ; Breska & Ivry, 2018 ; Correa et al., 2006 b; Terranova et al., 2023 ). RecentlyBreska & Ivry, 2018 have shown a double dissociation in the neural mechanisms underlying rhythmic and single-interval predictions. They elegantly showed that patients with cerebellar degeneration were impaired in forming single-interval predictions, while their performance in the rhythmic context was preserved; patients with Parkinson's disease, which primarily affected the basal ganglia, showed the opposite pattern. Temporal predictability has been shown to optimize sensory and motor processing (for a review see J. T. Coull et al., 2024 ). Studies consistently have shown that temporally predictable events are perceived more easily and more quickly, they are better encoded into working and long-term memory and are responded to more quickly(Nobre & van Ede, 2023 ). In fact, to identify TP ability, the outcome measure is usually the response time (RT) benefit, i.e., the decrease in response time to a target in a predictive context with respect to non-predictive ones. Other studies have shown that temporal predictability not only speeds responses but also simultaneously reduces the muscular effort needed to make these responses, suggesting that it might improve motor efficiency (Niemi & Näätänen, 1981 ). In support of this hypothesis, other neurophysiological studies showed improved muscle coordination and reduced motor cortex activity during TP (Hasbroucq et al., 1995 ; Tandonnet et al., 2006 ). In a recent study, we investigated the cerebellum’s causal role in TP in healthy subjects, exploring by means of transcranial Direct Current Stimulation (tDCS), whether perturbing cerebellar neural activity could modulate online TP according to temporal features of the context (rhythmic vs single-interval) (Terranova et al., 2023 ). We found that the inhibition of the cerebellar activity, induced by cathodal tDCS, modulated participants’ performance in a context-dependent manner. Specifically, for long sub-second intervals (900 or 1000 ms), we observed a specific reduction of response time within the single-interval context. This study confirms the cerebellum’s pivotal role in integrating the representation of time intervals stored in memory with the elapsed time likely for optimizing motor performance (Terranova et al., 2023 ). In the present study, we wanted to take a step forward and to directly test the involvement of the cerebellum-primary motor cortex (M1) pathway in motor optimization across different temporal predictive contexts. It is well known that the cerebellum’s predictive function—its ability to build internal models that anticipate the sensory outcomes of movement—helps to refine and smooth ongoing actions. Because of this, the pathway between the cerebellum and the M1 may contribute to improved motor efficiency when actions occur in predictable time patterns. However, previous evidence reported on the role of cerebellum in TP suggests that the influence of the cerebellum-M1 pathway may vary depending on the predictive context (rhythmic vs single-interval). The cerebellum-M1 connectivity can be studied via Cerebellar Brain Inhibition (CBI)(Ugawa et al., 1991 ). CBI is a paired-pulse protocol that involves a cerebellar conditioning pulse (conditioning stimulus, CS) applied 5–7 milliseconds before a test stimulus (test stimulus, TS) over the contralateral motor cortex, eliciting a transient inhibition of corticospinal excitability, as demonstrated by a reduction in the amplitude of the motor evoked potentials (MEP) in this condition (CS-TS), compared to the sole stimulation of the motor cortex (TS) (for a review see Fernandez et al., 2018 ). The inhibitory effect is driven by Purkinje cells, suppressing deep cerebellar nuclei output, which reduces excitatory signals from the thalamus to M1 (Daskalakis et al., 2004a ). CBI is commonly assessed using a double-cone coil over the cerebellum and a figure-of-eight coil over M1, with electrodes placed on the first dorsal interosseous muscle (FDI) (Ugawa et al., 1995a ; van Dun et al., 2017 ). Different studies demonstrated that CBI is a valuable tool for probing the connectivity of the cerebellum-M1 pathway during cerebellar-dependent motor (D. A. Spampinato et al., 2017a ; D. Spampinato & Celnik, 2021 ) or cognitive tasks (Ferrari et al., 2018 ; Schlerf et al., 2012a ). In this study, we investigated the cerebellum-M1 connectivity, via CBI paradigm, during a temporal prediction task across two temporal contexts: rhythmic (i.e., interstimulus intervals (ISIs) were constant, 900 ms) and single-interval condition (i.e., the estimation of the timing of the target was based on the prior exposure to the train of stimuli). Furthermore, here we explored different timings of possible activation of the cerebellar-M1 pathway during temporal prediction. The cerebellar-M1 connectivity was tested (i) when the warning signal appeared on the screen, corresponding to the maintenance of timing information to prepare the motor response, and (ii) when the target stimulus appeared on the screen, corresponding to the exploitation of timing information for the motor response. Finally, response times (RTs) to the target were recorded to obtain a direct measure of improved motor efficiency with temporal predictability. More specifically, we evaluated how participants’ response times decreased over the course of the single experimental session as they learned to predict the target’s onset time, thanks to the predictive context. We hypothesized that the CBI would be more strongly influenced in the single-interval context, which relies on memory-based prediction, while no significant changes in CBI were expected during the rhythmic one. In addition, we expected changes in CBI when the stimulation was delivered, when the warning signal appeared on the screen, that is, a preparatory phase in the task, in which expectation can presumably be modulated. Conversely, when the target appears on the screen, the motor response is expected; therefore, the expectation should already have been formed to guide the behaviour. These findings will provide new insights into the cerebellum's causal role in temporal prediction and its broader implications for timing-related motor functions. MATERIALS AND METHODS Participants Twenty right-handed volunteers (12 females, mean age ± SD: 28.9 ± 7.2 years) with normal or corrected-to-normal vision participated in this study. Five participants were excluded due to technical issues during the TMS procedure. All participants reported no history of professional musical training or engagement in amateur musical activities in the 3 years before testing (Breska & Ivry, 2018 ). None of the participants presented neurological, psychiatric, or other medical history or any contraindications to TMS, in accordance with the international safety guidelines for non-invasive brain stimulation (Rossi et al., 2021 ), and they provided written informed consent before inclusion in the study. The experimental procedure was approved by the ethics committee at the University of Genoa, in agreement with legal requirements and international norms stated in the adjourned declaration of Helsinki) (World Medical Association., 2001). A priori power analysis (within-subjects ANOVA, 5 measurements, α = .05, power = .80, f = 0.25) indicated a required sample of 21 participants. Sensitivity analysis confirmed that, with the final sample of 15 participants, the study remained well-powered to detect effects of f ≥ 0.29, reliably identifying medium-to-large effects. Experimental design The experimental protocol is presented in Fig. 1 . The experiment was conducted in a controlled, quiet environment, where participants were seated 60 cm away from the screen. Task design and response acquisition were controlled by E-Prime software (E-Prime 3.0, www.pstnet.com/eprime ). All participants completed three experimental blocks. In the initial block, baseline CBI measurements were obtained while participants' right arm and hand were relaxed and placed comfortably on a table, in a position that was realistically compatible with the hand's posture during the task. Subsequently, a practice session of the temporal prediction (TP) task was conducted without stimulation, allowing participants to familiarize themselves with the task. In the last block, the experimental session, CBI was delivered online while participants performed the TP task. Once the optimal M1 hotspot had been identified, to ensure precise targeting of M1 throughout the entire experiment, a neuronavigational system (Softaxic, E.M.S., Bologna, Italy) was employed to guide the positioning of the TMS coil over M1 and to maintain the same position throughout the experiment. Temporal prediction (TP) task Participants performed an implicit perceptual TP task (Breska & Ivry, 2018 ) designed to assess their ability to predict a target within a stream of visual stimuli with specific temporal properties. The task was carried out on a 17-inch computer screen (∼3.5° visual angle per side), where a sequence of visual stimuli, each lasting 100 ms, was presented. Each trial started with three or two red squares, followed by a fixation cross, acting as a warning signal (WS), and a green square, representing the target stimulus. Participants were instructed to press the spacebar with their right hand as quickly as possible when the target appeared. The trial sequence was presented in two contexts that differed in their temporal structure: rhythmic and single-interval. In the rhythmic context, the interstimulus intervals (ISIs) between all stimuli were the same as the WS-target interval (i.e., the interval between the fixation cross and the target, lasting 900ms); in this case, the target timing was fully predictable. The single-interval context consisted of only two red squares, instead of three, and the interval between the last square before the WS and the WS was randomly jittered with a mean duration that was 2.5 times the WS-target interval (− 13.3%, − 6.6%, 0%, + 6.6%, + 13.3 of 2.250 ms). In this case, the timing of the WS is out of phase with the expected rhythm, reducing the rhythmicity of the stimulus sequence. As a result, the estimation of the target timing relied on the prior exposure to the sequence of red squares. During the practice block, participants performed the two task contexts (rhythmic and single-interval), each consisting of 28 trials, presented in a randomized order. The experimental block included a total of 80 trials, divided according to the different stimulation conditions. All the conditions comprised the 25% catch trials, where no target was presented, to minimize the anticipatory responses. Each time participants responded too late (3 seconds from target onset), too early (before the onset of the target), or responded on a catch trial, a feedback message was displayed on the screen. Electromyographic (EMG) recording EMG was recorded from the right first dorsal interosseous muscle (FDI) with Ag-AgCl electrodes, arranged with a belly-tendon montage, with the ground electrodes on the right wrist. EMG signals were amplified and filtered (20 Hz to 1 kHz) using a D360 amplifier (Digitimer). The signals were sampled at 5000 Hz, digitized through a laboratory interface (Power 1401, Cambridge Electronic Design, Cambridge, UK), and stored on a personal computer for real-time display and subsequent offline data analysis. Transcranial Magnetic Stimulation (TMS) and CBI protocol The M1 excitability was assessed via a figure-of-eight-shaped coil connected to a Magstim 200 2 stimulator (Magstim Co., Whitland, Wales, UK) placed over the left M1 at the optimal position (hot spot) to elicit MEPs in the right FDI muscle with the handle pointing backward and ~ 45° away from the midline. Firstly, we determined the resting motor thresholds for the targeted muscles, defined as the lowest level of stimulation able to induce MEPs of at least 50 µV in target muscles with 50% probability (Rossini et al., 2015 ). Subsequently, we established the stimulator output intensity necessary to evoke motor potentials (MEPs) of 0.8–1.0 mV (S1mV) at rest; this intensity was kept for the test stimulus intensity during the CBI protocol. The coil orientation and target position were fixed at the hotspot using the neuronavigation system (SofTaxic, E.M.S., Bologna, Italy) to ensure consistent responses across both baseline and experimental blocks. The system operates based on digitized skull landmarks (nasion, inion, and two pre-auricular points), allowing the mapping of 40 uniformly distributed points on the scalp using the Polaris Vicra digitizer (Northern Digital). These points were then related to cerebral anatomy. For each subject, the navigation system estimated the Talairach coordinates of cortical sites underlying the coil positions using a stereotaxic template constructed from MRI data, with an accuracy of approximately 1 cm in Talairach space (Bonato et al., 2006 ). A standard paired-pulsed TMS protocol (Daskalakis et al., 2004b ; Pinto & Chen, 2001 ; Ugawa et al., 1995b ; Werhahn et al., 1996 ) was applied for assessing CBI before (CBI baseline ) and during the TP task (CBI task) . A double-cone coil (diameter of 110 mm) was placed 3 cm lateral to the inion, on the right cerebellar cortex, and a cerebellar conditioning pulse (conditioning stimulus, CS) was applied 5 milliseconds before a test stimulus (test stimulus, TS) over the contralateral (left) M1. This timing produces a transient inhibition of corticospinal excitability, as seen by a reduction in the amplitude of the motor-evoked potentials (MEPs) compared to stimulation of the motor cortex alone ( for a review, see Fernandez et al., 2018 ). The control stimulus intensity was set at 90% of the RMT (Bonassi et al., 2021 ; Carrillo et al., 2013 ; Torriero et al., 2011 ). CBI was expressed as a ratio of the MEP amplitude in the CS-TS condition to MEP amplitude in the TS condition; ratios below 1.0 indicate suppression. In the baseline block, 40 paired-pulse CS-TS and 40 single TS TMS stimuli were randomly delivered. During the experimental block, TMS stimuli were administered online at two distinct phases of the task: on the warning and on the target. Specifically, in the rhythmic and single-interval contexts, in both the CS-TS and TS trials, TS TMS stimuli were delivered at the Warning signal appearance (20 CS-TS and 20 TS alone) and at the Target stimulus appearance (20 CS-TS and 20 TS alone). Thus, CS on the cerebellum was delivered just 5 milliseconds before the warning signal and target stimulus appearance. Data analysis Peak-to-peak MEP amplitude (mV) was calculated offline for each condition. In all conditions (CBI baseline and CBI task ), MEPs preceded by EMG preactivation activity > 50 µV in the 100 ms preceding the TMS pulse were excluded from the analysis. We extracted the CBI parameter by calculating the ratio of the MEP amplitude of CS-TS to the MEP amplitude of TS alone, with values below 1.0 indicating MEP suppression. Response times (RTs) to the target were also collected, excluding outliers (RTs 3000 ms; mean ± 2 × SD [standard deviation]; 8%). The remaining trials were log-transformed to reduce the skewness of the distribution. Two subjects were excluded because of excessive trial loss (> 50%). Statistical analysis Mauchly's and Shapiro-Wilk’s tests confirmed the sphericity assumption and ensured the variables were normally distributed. Firstly, to exclude the possibility that any muscular activation during the task could influence task-related CBI changes, we compared the MEP test during the task separately for the target and warning phases with the baseline MEP test using paired sample t-tests. In addition, to rule out any modulation of the M1 activity during the TP task, we conducted a repeated measures ANOVA (RM ANOVA) on the MEP test data, with Context (Rhythmic and Single-Interval) and Phase (Warning and Target) as within-subject factors. Then, we calculated the CBI baseline as the ratio of the mean MEP amplitude in the CS-TS condition to the mean MEP amplitude in the TS condition in the baseline CBI block, and CBI task as the ratio of the mean MEP amplitude in the CS-TS condition to the average MEP of the Target and Warning phases in the TS condition in the experimental block. The CBI baseline data and the CBI task data were analysed via an RM ANOVA with Condition (Baseline, Single-interval Warning, Single-interval Target, Rhythmic Warning, and Rhythmic Target) as within-subject factors. Subsequently, the CBI task was normalized to CBI baseline (Normalized CBI = CBI task / CBI baseline ) for accurately characterizing task-related modulation. The Normalized CBI data were log-transformed to reduce the skewness inherent in MEP distributions. Normalized log CBI data were analysed via a 2x2 RM ANOVA with Context (Rhythmic and Single-Interval) and Phase (Warning and Target) as within-subject factors. A linear mixed-effects model (LMM) was carried out to assess the effect of temporal predictability on RTs, including Context (Rhythmic and Single-Interval) and Phase (Warning and Target) as fixed factors. For each condition, trials were divided into three bins of equal length (10 trials each) to assess changes over time within the block. This factor, henceforth referred to as Temporal Bin (with levels: Initial, Middle, Final), was included as a fixed factor in the model for exploring intra-block temporal dynamics. The LMM procedure was implemented using the lme4 package in R software ((Kuznetsova et al., 2017 ) and Jamovi. For model selection, a full fixed-effects model was used to determine which random effects should be added, identifying the most parsimonious structure that provided an adequate fit. By default, a random intercept for each participant was included to account for inter-individual variability. Similarly, we assessed which fixed effects should be retained in the model. Model fitting was performed using maximum likelihood estimation, improvements in fit via χ² statistics, as well as Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and the corresponding p-values. For similar top-down model-building approaches, see Bates et al., 2015 ; De Paepe et al., 2016 . All other statistical analyses were carried out using JASP (Version 0.19.3) [Apple Silicon], R software, and Jamovi. The alpha level was set to 0.05 for all tests. Post-hoc analyses with Bonferroni correction were applied to significant interactions. RESULTS Neurophysiological data No discomfort or adverse effects during TMS were reported or observed in any of the participants. Table 1 shows the mean ± SEM for data collected at baseline: TS MEP amplitude, CS-TS MEP amplitude, and the CBI baseline (CS-TS/TS ratio), along with the corresponding stimulation intensities (%MSO). Table 1 MEP values at baseline (amplitude and intensity of the stimulation). TS, test stimulus; CS, conditioning stimulus; MSO, Maximum Stimulator Output. TS condition MEP amplitude (mV) CS-TS condition MEP amplitude (mV) CBI baseline CS-TS/TS (ratio) M1 intensity (% MSO) Cerebellar intensity (% MSO) Mean 1.318 1.101 0.827 50.375 38.594 Std. Error of Mean 0.101 0.107 0.031 1.264 1.157 Std. Deviation 0.405 0.427 0.124 5.058 4.628 Table 2 shows the mean ± SEM for the TS MEP amplitude at baseline and during rhythmic and single-interval contexts in TP task, at the warning signal and at the target stimulus appearance. T-tests on TS MEP data revealed no significant difference in the mean MEP amplitude during the warning (t (15) = 1.18, p = 0.25) and target (t (15) = 0.95, p = 0.35) phases compared to the baseline. The RM ANOVA on the TS MEP data showed no significant main effects or interaction (all ps > 0.05, all Fs < 0.76), indicating that neither the task nor the stimulation phases altered the MEP amplitude. This confirms that any modulation of CBI task observed cannot be attributed to changes in the TS MEP amplitude. Table 2 TS condition MEP amplitude values (mV) recorded at baseline and during rhythmic and single-interval contexts in the TP task with TMS delivered at warning signal appearance and at target presentation. TS, test stimulus. Baseline Single Interval (Warning signal) Single Interval (Target) Rhythmic (Warning signal) Rhythmic (Target) Mean 1.317 1.149 1.138 1.172 1.226 Std. Error of Mean 0.101 0.101 0.138 0.129 0.130 Std. Deviation 0.405 0.405 0.554 0.518 0.519 The analysis of CBI baseline and the CBI task data revealed a significant main effect of Condition (F( 3,60 ) = 3.251, p = 0.018, h p 2 = 0.44). Post-hoc analysis revealed a significant decrease in inhibition in the single-interval context compared to the baseline, specifically when the TMS was delivered at the warning signal appearance (0.82 ± 0.12, 0.98 ± 0.16, p = 0.004, Cohen’s d = -0.78), with no difference between the other conditions (p > 0.05) (Fig. 2 ). The RM ANOVA on the Normalized CBI data revealed a Task x Phase significant interaction (F( 1,15 ) = 12.19, p = 0.003, h p 2 = 0.44). Post-hoc analysis showed normalized CBI value was larger (meaning that CBI was reduced with respect to baseline) in the single-Interval context when the TMS stimulus was delivered at the warning signal appearance (0.07 ± 0.01, -0.013 ± 0.022, p ≤ 0.01, Cohen's d = 0.64) (Fig. 3 ). No differences were observed in the target phase between the two contexts and vice versa (all ps > 0.05). No other main effects or interactions were found to be significant (p > 0.05). Reaction times (RTs) data RT data are shown in Fig. 4 ; for illustrative purposes, raw RT data are reported as normalized to each participant’s mean, considering trial-by-trial variability. Model comparisons indicated that the most parsimonious random-effects structure included a random intercept for Subject and random slopes for Context and Phase (AIC = -2561.7, BIC = -2534.8, χ²(3) = 216.4, p = 0.001). The contribution of fixed effects was evaluated by comparing the full model (including the three-way interaction: Phase × Context × Temporal Bin) to reduced models. Adding the main effects significantly improved model fit compared to the null model (AIC = -2408.3 vs 2379.5, BIC = -2376.1 vs -2363.4, 14.797, χ²(3) = 34.79, p = 0.001), and also adding the two-way interactions compared to the model only with the main effects (AIC = -2417.1, BIC = -2368.7, χ²(3) = 14.797, p = 0.001). In contrast, adding the three-way interaction among Phase, Context, and Temporal Bin did not improve model fit (AIC = -2415.1, BIC = -2361.3, χ²(1) = 0.0098, p = 0.921). Therefore, the three-way interaction was removed, and the final model included all three fixed effects along with their two-way interactions. In the final model, there was a significant effect of Temporal Bin (F (2, 1551.3) = 8.09, p = < 0.001) accounted for by faster RTs in the final bin of trials (2.46 ± 0.138, p < 0.001) compared to the initial one (2.48 ± 0.177), and faster RTs in the middle bin (2.46 ± 0.142, p = 0.005) relative to the initial one. No difference emerged between middle and final bins (p = 1.000). We also observed significant Context x Phase (F(1, 1551.3) = 6.433, p = 0.011) and Context x Temporal Bin (F(2,1551.3) = 7.849, p 0.05). The Context x Temporal Bin interaction indicated that bin-to-bin speeding occurred only in the Single-Interval context: both the middle and final bins (2.46 ± 0.135 and 2.46 ± 0.131) were faster than the initial bin (2.50 ± 0.211, p 0.05) (Fig. 4 ). DISCUSSION Here, we explored for the first time the cerebellum-M1 pathway, by means of CBI, during a temporal prediction task to test the involvement of this pathway in motor optimization across different temporal predictive contexts. We tested two predictive contexts within the TP task, rhythmic and single-interval, and two critical time points, corresponding to the presentation of the warning signal and the target on the screen, which indexed the maintenance of timing information and the exploitation of timing information for motor response execution, respectively. CBI and motor performance were modulated only in the single-interval context The main result of our study was a significant reduction of CBI in the single-interval context compared with the rhythmic context. This effect was evident only when TMS was delivered at the warning signal phase of the task, with no differences when stimulation was applied at the target phase. Furthermore, our behavioural results showed a progressive decrease in response times across time bins only in the single-interval context, with both the middle and final bins being faster than the initial bin. No comparable modulation was observed in the rhythmic context, in which RTs remained stable across bins. The context-dependent modulation of CBI (observable only in the single-interval context) aligns with the literature highlighting the cerebellum's critical role in event timing, particularly in tasks that rely on memory-based representation of time intervals. Compelling evidence in patients with cerebellar damage showed intact performance in motor or perceptual timing tasks if they are required to execute repetitive movement or to perform timing estimation of regular patterns (for a review, see Breska & Ivry, 2016 ). However, it is well-established that patients with cerebellar degeneration show a wide range of impairment regarding temporal perception and temporal control of the movements (Bhanpuri et al., 2014 ; Franz et al., 1996 ; Ivry & Keele, 1989 ; Trillenberg et al., 2004 ). These deficits have been characterized as disruptions in event timing (Ivry et al., 2002 ), a process strongly linked to the cerebellum’s role in coordinating temporal aspects of both perception and motor control. The distinction between rhythmic and event timing has been investigated in connection with the concept of temporal prediction. Expanding on the neural processes involved in this ability, Breska & Ivry, 2018 , offered neuropsychological evidence for a double dissociation in the brain mechanisms underlying temporal prediction. Their findings demonstrated that individuals with Parkinson's disease, which primarily affects the nigrostriatal pathway of the basal ganglia, showed an alteration in the TP task within a rhythmic context but performed normally in a single-interval context. On the other hand, individuals with cerebellar degeneration exhibited impairments solely in making temporal predictions based on single-interval associations. In the wake of these findings, we recently demonstrated that in the single interval context, particularly for sub-second long intervals (corresponding to the 900 ms of ISI tested here), the cerebellum could play a crucial role in integrating the representation of time interval stored in memory with the elapsed time, thereby providing accurate temporal prediction (Terranova et al., 2023 ). Similarly to our finding, a significant reduction of CBI during tasks has been consistently reported in studies investigating the modulation of cerebellum-M1 connectivity in the context of motor learning (D. A. Spampinato et al., 2020 ). Specifically, these studies showed that CBI significantly decreased in adaptive learning tasks such as locomotor and visuomotor adaptation tasks (Jayaram et al., 2011 ; Schlerf et al., 2012b ; D. A. Spampinato et al., 2017b ) and sequence learning (Torriero et al., 2010 ). Specifically, EEG-TMS evidence (Fong et al., 2023 ) suggested that such modulation of cerebellar activity during visuomotor adaptive learning is critically tied to the activity of contralateral prefrontal cortex. Seminal works have recognized the cerebellum simultaneously as a “timing machine” and a “learning machine” (Eccles, 1967 ; Marr, 1969 ). Critically, a recent study (Tanaka et al., 2021 ) investigating CBI modulation during a temporal adaptive learning task (i.e., coincident timing skill task) highlighted the cerebellum's role in integrating temporal information to guide motor behavior and adjust actions in response to temporal perturbations, thereby linking temporal prediction processing with adaptive motor learning. Particularly, Tanaka and coworkers showed, in line with previous studies assessing CBI during motor learning, a consistent reduction in CBI associated with temporal adaptive learning (Tanaka et al., 2021 ). The modulation of CBI has been explained as reflecting a plastic change in the cerebellum, induced by motor learning, specifically in terms of long-term depression (LTD) of parallel fiber-Purkinje cells synapse, which interfere with the inhibition of M1 (Spampinato et al., 2017c ). Here, the single-interval task, in contrast to the rhythmic task, involved the creation of temporal expectancy by orienting attention to salient temporal features of events, memorizing it, and using information to guide behavior, mechanisms that could be shared with different kinds of error-based learning. Therefore, it could be argued that during single-interval prediction, LTD mechanisms might modulate cerebellar activity, potentially affecting cerebellum-M1 inhibition. In support of this, our behavioral data show that only in the single-interval predictive context response time significantly decreased across temporal bins, with participants responding faster in the final trials than in the initial ones. This pattern suggests that motor efficiency improved over the course of the task in single-interval predictive context thanks to learning mechanisms. Differently, in the rhythmic context, the response time is minimal from the outset of the task and exhibits no significant variation between the initial trials and the final ones, in agreement with the idea of memory and attention playing a larger role in learning in single-interval contexts and neural entrainment dominating in rhythmic contexts (Breska & Deouell, 2017 ). CBI was modulated at the warning signal and not at the target stimulus appearance We observed changes in CBI in the single-interval task only when the TMS was delivered at the warning signal appearance on the screen, and not when the TMS was delivered at the target stimulus appearance. Studies investigating the neural activity during orienting attention for time properties of an event showed that perceiving an event with temporal features automatically involved an implicit tracking of time, with attentional preparation gradually increasing and reaching its peak when the memorized interval has passed (Durstewitz, 2003 ). Studies utilizing event-related potentials (ERPs) have further clarified this mechanism by examining the temporal dynamics of expectation. These studies showed that valid cues predicting the timing of the target modulated several ERP components, linked to attentional anticipatory activity, including the contingent negative variation (CNV) and the attention-related potential (P300) (Breska & Deouell, 2014 , 2017 ; Capizzi et al., 2013 ; Miniussi et al., 1999 )(Breska & Deouell, 2014 , 2017 ; Capizzi et al., 2013 ; Miniussi et al., 1999 ). ERPs recorded during a target detection task (Miniussi et al., 1999 ) have demonstrated that ERP components associated with attentional shifts are modulated before the appearance of the target, influenced by the characteristics of the preceding cue. Differently, late ERP components were linked to target processing and may reflect adjustments in response preparation or decision-making processes (Miniussi et al., 1999 ). In our task, the warning signal acted as a cue, predicting target onset in 75% of trials at a fixed interval. This design likely allowed the engagement of similar mechanisms, whereby the warning cue triggered temporal anticipation and response preparation, in accordance with previous findings on cue-related modulations. Limitations One possible limitation of the study is that differences in CBI between the single-interval predictive context and the baseline may have been driven by changes in M1 excitability across these conditions. We can rule out this possibility since the comparison of MEP amplitudes between the two critical time points (i.e., warning and target phases) revealed no significant differences across conditions, thereby ruling out potential confounding effects related to M1 excitability during both the preparatory and response phases of the task, where TMS delivery could overlap with muscle activation and motor preparation or execution. Furthermore, we assessed potential differences in test MEP amplitudes across predictive contexts (rhythmic vs. single-interval) and time points (warning vs. target) finding no significant effects, further supporting the notion that the task-related observed differences in CBI cannot be explained by changes in M1 excitability. This result appears to diverge from previous studies reporting a facilitatory effect on M1 activity at the onset of a voluntary movement involving the FDI (e.g., pressing a button by flexing the index finger) (Kassavetis et al., 2011 ) or during movement preparation (D. A. Spampinato et al., 2017a ). In addition, research investigating the effects of single-pulse and paired-pulse TMS during the pre-movement period has demonstrated a gradual facilitation of motor evoked potentials (MEPs) as the voluntary movement onset approaches (Nikolova et al., 2006 ). Several potential factors could account for the absence of significant task-related modulation in motor excitability in our study. Firstly, anticipatory motor responses were strongly discouraged throughout the task, with an error feedback message on the screen if any responses were made prematurely, and the inclusion of 25% catch trials per condition, which strongly reduced motor activation before the onset of the target. Importantly, in several studies (e.g., Kassavetis et al., 2011 ) the TMS stimulus and the motor response were precisely aligned. In contrast, in our study, these two events were not perfectly synchronized, as TMS was delivered at target onset, while the subject's response could be delayed by a few milliseconds. Other limitations should be considered when interpreting the present findings. Although the sample size sensitivity analyses confirmed that the final sample provided sufficient power to detect the observed effects, larger cohort would allow a more detailed characterization of inter-individual variability in cerebellar–M1 connectivity. In addition, despite the use of neuronavigation to ensure consistent coil positioning, cerebellar TMS inherently lacks the spatial precision required to selectively target specific cerebellar areas, limiting anatomical specificity. The present study was also restricted to sub-second temporal intervals (900 ms), leaving open the question of whether similar modulation patterns would emerge for supra-second intervals, or in non-predictive contexts. Finally, the absence of concurrent neurophysiological recordings (e.g., EEG) limits direct assessment of how cerebellar–M1 interactions relate to broader network dynamics during temporal prediction. Conclusions Overall, our findings underscore the causal role of the cerebellum in temporal prediction, extending our previous results by highlighting its critical involvement in memory-based timing. Specifically, we demonstrated an online modulation of cerebellar-M1 connectivity, likely driven by LTD-like mechanisms, arising from the formation of temporal predictions in a single-interval context. This modulation occurred when the TMS was delivered at warning signal onset, indicating that the cerebellum may play a key role in orienting attention and preparing for expected events based on temporal information stored in memory. Future investigations will need to explore how cerebellar-M1 connectivity is dynamically modulated across different temporal windows during the task. Declarations Competing interests Financial interests: S. Terranova, A. Botta, M. Putzolu, G. Bonassi, R. Simeon, and A. Marra have nothing to declare. L. Avanzino, has received speaker honoraria from Zambon and Bial and received research supports (Grants) from the EU Joint Programme—Neurodegenerative Disease Research (JPND) (2022 call) and Michael J. Fox Foundation (Fall 2022 biomarkers to support therapeutic trials program). She received research support from FRESCO foundation and the Italian Ministry of Health (Ricerca Finalizzata 2022 and 5 × 1000). E. Pelosin is part of the Advisory Board for M.J. Fox Foundation. She has received grants from the Italian Ministry of University and Research (PRIN 2022) and Michael J. Fox Foundation (Fall 2022 biomarkers to support therapeutic trials program), and research supports from Italian Ministry of Health (Ricerca Finalizzata 2022) and IRCCS Policlinico San Martino, Genova (5 × 1000, 2022). Ethics approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the local ethics committee. Consent to participate Informed consent was obtained from all individual participants included in the study. Funding Open access funding provided by Università degli Studi di Genova within the CRUI-CARE Agreement. The work is partly supported by #NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006) – A Multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022). The work is partly supported by Fresco Foundation and by Ricerca Corrente from Italian Ministry of Health (MOH). Author Contribution Conceptualization: [Sara Terranova, Elisa Pelosin, Laura Avanzino]; Methodology: [Sara Terranova, Elisa Pelosin, Laura Avanzino]; Formal analysis and investigation: [Sara Terranova, Alessandro Botta, Martina Putzolu, Gaia Bonassi, Rachele Simeon, Anna Marra]; Writing - original draft preparation: [Sara Terranova, Elisa Pelosin, Laura Avanzino]; Writing - review and editing: [Sara Terranova, Alessandro Botta, Martina Putzolu, Gaia Bonassi, Rachele Simeon, Anna Marra, Elisa Pelosin, Laura Avanzino]; Funding acquisition: [Laura Avanzino]; Resources: [Elisa Pelosin, Laura Avanzino]; Supervision: [Elisa Pelosin, Laura Avanzino]. Data Availability The data that support the findings of this study are available on request from the corresponding author. References Bates D, Mächler M, Bolker BM, Walker SC (2015) Fitting linear mixed-effects models using lme4. 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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-8778315","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587854204,"identity":"02c1637a-120b-4c33-92f1-03fa753b3ca8","order_by":0,"name":"Sara Terranova","email":"","orcid":"","institution":"University of Genoa","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Terranova","suffix":""},{"id":587854205,"identity":"ac1578f0-5578-4197-9782-4fc619d0afab","order_by":1,"name":"Alessandro Botta","email":"","orcid":"","institution":"IRCCS Azienda Ospedaliera Metropolitana","correspondingAuthor":false,"prefix":"","firstName":"Alessandro","middleName":"","lastName":"Botta","suffix":""},{"id":587854206,"identity":"d438d91e-bbd8-4ae9-a31c-db91a8015e24","order_by":2,"name":"Martina Putzolu","email":"","orcid":"","institution":"IRCCS Azienda Ospedaliera Metropolitana","correspondingAuthor":false,"prefix":"","firstName":"Martina","middleName":"","lastName":"Putzolu","suffix":""},{"id":587854207,"identity":"3116221b-8f8b-49ee-a0ee-22a50d7d489e","order_by":3,"name":"Gaia Bonassi","email":"","orcid":"","institution":"University of Genoa","correspondingAuthor":false,"prefix":"","firstName":"Gaia","middleName":"","lastName":"Bonassi","suffix":""},{"id":587854208,"identity":"2bb19395-2ac8-4e6b-a29a-bd04c030f80d","order_by":4,"name":"Rachele Simeon","email":"","orcid":"","institution":"University of Genoa","correspondingAuthor":false,"prefix":"","firstName":"Rachele","middleName":"","lastName":"Simeon","suffix":""},{"id":587854209,"identity":"153fb373-25a4-4f9f-8cc6-bd1d87294a4c","order_by":5,"name":"Anna Marra","email":"","orcid":"","institution":"University of Genoa","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Marra","suffix":""},{"id":587854210,"identity":"50025cd3-3f12-4e3f-8d1d-182795f4eb84","order_by":6,"name":"Elisa Pelosin","email":"","orcid":"","institution":"IRCCS Azienda Ospedaliera Metropolitana","correspondingAuthor":false,"prefix":"","firstName":"Elisa","middleName":"","lastName":"Pelosin","suffix":""},{"id":587854211,"identity":"845327c2-cac6-43da-b2ae-b7725056f92a","order_by":7,"name":"Laura Avanzino","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYBACPmTOAQYGGwYGZhDTALcWNjQtaTAtuPWwofEPwxh4tLCfMfvwg8EusX92+8PDBRXnE7ezs198wFDwB7cWnhzjmT0MyYkz7hxIODzjzO3Enc08xQb4HZZjzMDDwGzMcCPhwGHettuJGw7zpEng1cL/xpjxD0O9sfyNxAaglnMgLek/8GqRyDFm5mE4LGdwI5kBqOUAUAv7MfwhJvGsmFnG4Lic4Y00hsM8Z5KNgbYwSyQYGOPUws+fvJnxTUU1j9yN9MefeSrsZDecP/7ww4c/cji1QACqK3gMGBIIaEAH7A9I1DAKRsEoGAXDHAAA+E5NsODZ16YAAAAASUVORK5CYII=","orcid":"","institution":"University of Genoa","correspondingAuthor":true,"prefix":"","firstName":"Laura","middleName":"","lastName":"Avanzino","suffix":""}],"badges":[],"createdAt":"2026-02-03 16:23:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8778315/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8778315/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102329037,"identity":"459c7fdb-3c55-48b8-ae81-559fce0b96ba","added_by":"auto","created_at":"2026-02-10 14:57:26","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":504128,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eExperimental design. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(A) Baseline CBI: Cerebellar Brain Inhibition (CBI) assessed with paired-pulse TMS (40 TS, 40 CS–TS trials). A double-cone coil (110 mm) was placed 3 cm lateral to the inion over the right cerebellar cortex to deliver the conditioning stimulus (CS), applied 5 ms before a test stimulus (TS) over the contralateral (left) M1.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(B) Practice session: Participants trained on the temporal prediction (TP) task in rhythmic and single-interval conditions. (C) Experimental session: CBI measured during TP task performance, TMS stimuli were delivered either at the warning signal appearance (20 TS, 20 CS–TS) or at the target stimulus appearance (20 TS, 20 CS–TS). CBI, Cerebellar Brain Inhibition; TMS, Transcranial Magnetic Stimulation; TS, Test Stimulus; CS, Conditioning Stimulus; M1, primary motor cortex; TP, Temporal Prediction; WS, Warning signal. Created in BioRender. Pelosin, E. (2026) https://BioRender.com/08aiy7l.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8778315/v1/785695b2a9faf9e387d88c01.jpeg"},{"id":102329034,"identity":"e9de21ea-dc8a-47d8-93cf-bf20f1177421","added_by":"auto","created_at":"2026-02-10 14:57:26","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":216924,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCBI\u003c/em\u003e\u003csub\u003e\u003cem\u003ebaseline \u003c/em\u003e\u003c/sub\u003e\u003cem\u003eand the CBI\u003c/em\u003e\u003csub\u003e\u003cem\u003etask \u003c/em\u003e\u003c/sub\u003e\u003cem\u003edata, separated for Single-Interval and Warning context. The analysis revealed a significant reduction compared to the Baseline in the Single Interval context when the TS TMS stimulus was delivered at the Warning signal appearance. No differences were found in the other conditions (p \u0026gt; 0.05). ** p ≤ 0.01. The values plotted are the minimum and the maximum (whiskers), the median, the 25th percentile and the 75th percentile (hinges).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8778315/v1/45db12dddd4cb8acd625df90.jpeg"},{"id":102329035,"identity":"33919c23-7d42-4e5c-a039-8ece5989f5ee","added_by":"auto","created_at":"2026-02-10 14:57:26","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":248029,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eNormalized CBI data. The Normalized CBI data are expressed as mean ± standard error of the mean (SEM) separated for Warning and Target phase.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNormalized CBI values greater than 1 indicate reduced cerebello-cortical inhibition during the task compared to baseline (i.e., less inhibition), while values less than 1 indicate increased inhibition during the task relative to baseline (i.e., more inhibition); values equal to 1 represent no difference between baseline and task. CBI values were significantly higher in Single-Interval compared to the Rhythmic context \u003c/em\u003ewhen the TS TMS stimulus was delivered at the Warning signal appearance\u003cem\u003e, with no difference between the two for the Target phase. The straight line indicates the median, while the dashed lines represent the quartiles. \u003c/em\u003e** p ≤ 0.01\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8778315/v1/c572adf67d6023eda842232b.jpeg"},{"id":102397315,"identity":"a36f7289-791d-414b-a5cb-9896ada16631","added_by":"auto","created_at":"2026-02-11 10:15:22","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":224014,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFor illustrative purposes, Response Times (RT) were normalized at the subject level and are shown as a function of Context (Rhythmic, Single Interval) and Temporal Bin (Initial, Middle, Final).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRTs were normalized by computing each participant’s mean RT across all conditions and expressing individual trials as a ratio relative to this mean (1 = subject mean).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eValues above 1 indicate slower than mean RTs, whereas values below 1 indicate faster than mean RTs.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIn the Single Interval context, bin-to-bin speeding was observed, with both the Middle and Final bins being faster than the Initial bin (p \u0026lt; .001). No modulation across bins emerged in the Rhythmic context (p = 1.000). No other main effects or interactions were significant (all ps \u0026gt; 0.05).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLight colors indicate slower than mean RTs, whereas dark colors indicate faster than mean RTs.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8778315/v1/3a890064b07e7c44ae34ee32.jpeg"},{"id":106154201,"identity":"cbde5d03-eb5d-4cab-86b3-a9e209dd3bec","added_by":"auto","created_at":"2026-04-04 14:55:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2080538,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8778315/v1/983930d7-8123-48ce-8551-016d28175698.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Cerebellar Contribution to Motor Optimization in Temporal Prediction Through Cerebellar–Brain Inhibition","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eTemporal prediction (TP) is a fundamental function of neural systems and can be defined as the brain's ability to anticipate future events by estimating time and event probabilities (Grabenhorst et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).TP arises spontaneously, without explicit instructions to rely on temporal information, making it a component of implicit timing (J. Coull \u0026amp; Nobre, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearch on neural activations during TP(Cona et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nani et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ortu\u0026ntilde;o et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Radua et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Teghil et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wiener et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) has identified a large network of cortical and subcortical regions mediating different aspects of TP in a context-dependent manner. In fact, prediction may arise in rhythmic context, through the exposure to simple rhythms (e.g., music or speech) or in memory-based or single-interval context, when prior exposure to an event creates a memory trace that informs the prediction (Large \u0026amp; Jones, 1999; Lawrance et al., 2014; Nobre \u0026amp; Van Ede, 2018; Correa et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003ea; Shalev et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Breska \u0026amp; Ivry, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Correa et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003eb; Terranova et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). RecentlyBreska \u0026amp; Ivry, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e have shown a double dissociation in the neural mechanisms underlying rhythmic and single-interval predictions. They elegantly showed that patients with cerebellar degeneration were impaired in forming single-interval predictions, while their performance in the rhythmic context was preserved; patients with Parkinson's disease, which primarily affected the basal ganglia, showed the opposite pattern.\u003c/p\u003e \u003cp\u003eTemporal predictability has been shown to optimize sensory and motor processing (for a review see J. T. Coull et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Studies consistently have shown that temporally predictable events are perceived more easily and more quickly, they are better encoded into working and long-term memory and are responded to more quickly(Nobre \u0026amp; van Ede, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In fact, to identify TP ability, the outcome measure is usually the response time (RT) benefit, i.e., the decrease in response time to a target in a predictive context with respect to non-predictive ones. Other studies have shown that temporal predictability not only speeds responses but also simultaneously reduces the muscular effort needed to make these responses, suggesting that it might improve motor efficiency (Niemi \u0026amp; N\u0026auml;\u0026auml;t\u0026auml;nen, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). In support of this hypothesis, other neurophysiological studies showed improved muscle coordination and reduced motor cortex activity during TP (Hasbroucq et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Tandonnet et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn a recent study, we investigated the cerebellum\u0026rsquo;s causal role in TP in healthy subjects, exploring by means of transcranial Direct Current Stimulation (tDCS), whether perturbing cerebellar neural activity could modulate online TP according to temporal features of the context (rhythmic vs single-interval) (Terranova et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We found that the inhibition of the cerebellar activity, induced by cathodal tDCS, modulated participants\u0026rsquo; performance in a context-dependent manner. Specifically, for long sub-second intervals (900 or 1000 ms), we observed a specific reduction of response time within the single-interval context. This study confirms the cerebellum\u0026rsquo;s pivotal role in integrating the representation of time intervals stored in memory with the elapsed time likely for optimizing motor performance (Terranova et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the present study, we wanted to take a step forward and to directly test the involvement of the cerebellum-primary motor cortex (M1) pathway in motor optimization across different temporal predictive contexts. It is well known that the cerebellum\u0026rsquo;s predictive function\u0026mdash;its ability to build internal models that anticipate the sensory outcomes of movement\u0026mdash;helps to refine and smooth ongoing actions. Because of this, the pathway between the cerebellum and the M1 may contribute to improved motor efficiency when actions occur in predictable time patterns. However, previous evidence reported on the role of cerebellum in TP suggests that the influence of the cerebellum-M1 pathway may vary depending on the predictive context (rhythmic vs single-interval).\u003c/p\u003e \u003cp\u003eThe cerebellum-M1 connectivity can be studied via Cerebellar Brain Inhibition (CBI)(Ugawa et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). CBI is a paired-pulse protocol that involves a cerebellar conditioning pulse (conditioning stimulus, CS) applied 5\u0026ndash;7 milliseconds before a test stimulus (test stimulus, TS) over the contralateral motor cortex, eliciting a transient inhibition of corticospinal excitability, as demonstrated by a reduction in the amplitude of the motor evoked potentials (MEP) in this condition (CS-TS), compared to the sole stimulation of the motor cortex (TS) (for a review see Fernandez et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The inhibitory effect is driven by Purkinje cells, suppressing deep cerebellar nuclei output, which reduces excitatory signals from the thalamus to M1 (Daskalakis et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2004a\u003c/span\u003e). CBI is commonly assessed using a double-cone coil over the cerebellum and a figure-of-eight coil over M1, with electrodes placed on the first dorsal interosseous muscle (FDI) (Ugawa et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1995a\u003c/span\u003e; van Dun et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDifferent studies demonstrated that CBI is a valuable tool for probing the connectivity of the cerebellum-M1 pathway during cerebellar-dependent motor (D. A. Spampinato et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e; D. Spampinato \u0026amp; Celnik, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) or cognitive tasks (Ferrari et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Schlerf et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we investigated the cerebellum-M1 connectivity, via CBI paradigm, during a temporal prediction task across two temporal contexts: rhythmic (i.e., interstimulus intervals (ISIs) were constant, 900 ms) and single-interval condition (i.e., the estimation of the timing of the target was based on the prior exposure to the train of stimuli).\u003c/p\u003e \u003cp\u003eFurthermore, here we explored different timings of possible activation of the cerebellar-M1 pathway during temporal prediction. The cerebellar-M1 connectivity was tested (i) when the warning signal appeared on the screen, corresponding to the maintenance of timing information to prepare the motor response, and (ii) when the target stimulus appeared on the screen, corresponding to the exploitation of timing information for the motor response.\u003c/p\u003e \u003cp\u003eFinally, response times (RTs) to the target were recorded to obtain a direct measure of improved motor efficiency with temporal predictability. More specifically, we evaluated how participants\u0026rsquo; response times decreased over the course of the single experimental session as they learned to predict the target\u0026rsquo;s onset time, thanks to the predictive context.\u003c/p\u003e \u003cp\u003eWe hypothesized that the CBI would be more strongly influenced in the single-interval context, which relies on memory-based prediction, while no significant changes in CBI were expected during the rhythmic one. In addition, we expected changes in CBI when the stimulation was delivered, when the warning signal appeared on the screen, that is, a preparatory phase in the task, in which expectation can presumably be modulated. Conversely, when the target appears on the screen, the motor response is expected; therefore, the expectation should already have been formed to guide the behaviour.\u003c/p\u003e \u003cp\u003eThese findings will provide new insights into the cerebellum's causal role in temporal prediction and its broader implications for timing-related motor functions.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eTwenty right-handed volunteers (12 females, mean age\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 28.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2 years) with normal or corrected-to-normal vision participated in this study. Five participants were excluded due to technical issues during the TMS procedure. All participants reported no history of professional musical training or engagement in amateur musical activities in the 3 years before testing (Breska \u0026amp; Ivry, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). None of the participants presented neurological, psychiatric, or other medical history or any contraindications to TMS, in accordance with the international safety guidelines for non-invasive brain stimulation (Rossi et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and they provided written informed consent before inclusion in the study. The experimental procedure was approved by the ethics committee at the University of Genoa, in agreement with legal requirements and international norms stated in the adjourned declaration of Helsinki) (World Medical Association., 2001).\u003c/p\u003e \u003cp\u003eA priori power analysis (within-subjects ANOVA, 5 measurements, α\u0026thinsp;=\u0026thinsp;.05, power = .80, f\u0026thinsp;=\u0026thinsp;0.25) indicated a required sample of 21 participants. Sensitivity analysis confirmed that, with the final sample of 15 participants, the study remained well-powered to detect effects of f\u0026thinsp;\u0026ge;\u0026thinsp;0.29, reliably identifying medium-to-large effects.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExperimental design\u003c/h3\u003e\n\u003cp\u003eThe experimental protocol is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The experiment was conducted in a controlled, quiet environment, where participants were seated 60 cm away from the screen. Task design and response acquisition were controlled by E-Prime software (E-Prime 3.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.pstnet.com/eprime\u003c/span\u003e\u003cspan address=\"http://www.pstnet.com/eprime\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All participants completed three experimental blocks.\u003c/p\u003e \u003cp\u003eIn the initial block, baseline CBI measurements were obtained while participants' right arm and hand were relaxed and placed comfortably on a table, in a position that was realistically compatible with the hand's posture during the task. Subsequently, a practice session of the temporal prediction (TP) task was conducted without stimulation, allowing participants to familiarize themselves with the task.\u003c/p\u003e \u003cp\u003eIn the last block, the experimental session, CBI was delivered online while participants performed the TP task. Once the optimal M1 hotspot had been identified, to ensure precise targeting of M1 throughout the entire experiment, a neuronavigational system (Softaxic, E.M.S., Bologna, Italy) was employed to guide the positioning of the TMS coil over M1 and to maintain the same position throughout the experiment.\u003c/p\u003e\n\u003ch3\u003eTemporal prediction (TP) task\u003c/h3\u003e\n\u003cp\u003eParticipants performed an implicit perceptual TP task (Breska \u0026amp; Ivry, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) designed to assess their ability to predict a target within a stream of visual stimuli with specific temporal properties. The task was carried out on a 17-inch computer screen (\u0026sim;3.5\u0026deg; visual angle per side), where a sequence of visual stimuli, each lasting 100 ms, was presented. Each trial started with three or two red squares, followed by a fixation cross, acting as a warning signal (WS), and a green square, representing the target stimulus. Participants were instructed to press the spacebar with their right hand as quickly as possible when the target appeared.\u003c/p\u003e \u003cp\u003eThe trial sequence was presented in two contexts that differed in their temporal structure: rhythmic and single-interval. In the rhythmic context, the interstimulus intervals (ISIs) between all stimuli were the same as the WS-target interval (i.e., the interval between the fixation cross and the target, lasting 900ms); in this case, the target timing was fully predictable. The single-interval context consisted of only two red squares, instead of three, and the interval between the last square before the WS and the WS was randomly jittered with a mean duration that was 2.5 times the WS-target interval (\u0026minus;\u0026thinsp;13.3%, \u0026minus;\u0026thinsp;6.6%, 0%, +\u0026thinsp;6.6%, +\u0026thinsp;13.3 of 2.250 ms). In this case, the timing of the WS is out of phase with the expected rhythm, reducing the rhythmicity of the stimulus sequence. As a result, the estimation of the target timing relied on the prior exposure to the sequence of red squares.\u003c/p\u003e \u003cp\u003e During the practice block, participants performed the two task contexts (rhythmic and single-interval), each consisting of 28 trials, presented in a randomized order. The experimental block included a total of 80 trials, divided according to the different stimulation conditions. All the conditions comprised the 25% catch trials, where no target was presented, to minimize the anticipatory responses. Each time participants responded too late (3 seconds from target onset), too early (before the onset of the target), or responded on a catch trial, a feedback message was displayed on the screen.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eElectromyographic (EMG) recording\u003c/h3\u003e\n\u003cp\u003eEMG was recorded from the right first dorsal interosseous muscle (FDI) with Ag-AgCl electrodes, arranged with a belly-tendon montage, with the ground electrodes on the right wrist. EMG signals were amplified and filtered (20 Hz to 1 kHz) using a D360 amplifier (Digitimer). The signals were sampled at 5000 Hz, digitized through a laboratory interface (Power 1401, Cambridge Electronic Design, Cambridge, UK), and stored on a personal computer for real-time display and subsequent offline data analysis.\u003c/p\u003e\n\u003ch3\u003eTranscranial Magnetic Stimulation (TMS) and CBI protocol\u003c/h3\u003e\n\u003cp\u003eThe M1 excitability was assessed via a figure-of-eight-shaped coil connected to a Magstim 200\u003csup\u003e2\u003c/sup\u003e stimulator (Magstim Co., Whitland, Wales, UK) placed over the left M1 at the optimal position (hot spot) to elicit MEPs in the right FDI muscle with the handle pointing backward and ~\u0026thinsp;45\u0026deg; away from the midline. Firstly, we determined the resting motor thresholds for the targeted muscles, defined as the lowest level of stimulation able to induce MEPs of at least 50 \u0026micro;V in target muscles with 50% probability (Rossini et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Subsequently, we established the stimulator output intensity necessary to evoke motor potentials (MEPs) of 0.8\u0026ndash;1.0 mV (S1mV) at rest; this intensity was kept for the test stimulus intensity during the CBI protocol. The coil orientation and target position were fixed at the hotspot using the neuronavigation system (SofTaxic, E.M.S., Bologna, Italy) to ensure consistent responses across both baseline and experimental blocks. The system operates based on digitized skull landmarks (nasion, inion, and two pre-auricular points), allowing the mapping of 40 uniformly distributed points on the scalp using the Polaris Vicra digitizer (Northern Digital). These points were then related to cerebral anatomy. For each subject, the navigation system estimated the Talairach coordinates of cortical sites underlying the coil positions using a stereotaxic template constructed from MRI data, with an accuracy of approximately 1 cm in Talairach space (Bonato et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA standard paired-pulsed TMS protocol (Daskalakis et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004b\u003c/span\u003e; Pinto \u0026amp; Chen, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Ugawa et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1995b\u003c/span\u003e; Werhahn et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) was applied for assessing CBI before (CBI\u003csub\u003ebaseline\u003c/sub\u003e) and during the TP task (CBI\u003csub\u003etask)\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eA double-cone coil (diameter of 110 mm) was placed 3 cm lateral to the inion, on the right cerebellar cortex, and a cerebellar conditioning pulse (conditioning stimulus, CS) was applied 5 milliseconds before a test stimulus (test stimulus, TS) over the contralateral (left) M1. This timing produces a transient inhibition of corticospinal excitability, as seen by a reduction in the amplitude of the motor-evoked potentials (MEPs) compared to stimulation of the motor cortex alone (\u003cem\u003efor a review, see\u003c/em\u003e Fernandez et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The control stimulus intensity was set at 90% of the RMT (Bonassi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Carrillo et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Torriero et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). CBI was expressed as a ratio of the MEP amplitude in the CS-TS condition to MEP amplitude in the TS condition; ratios below 1.0 indicate suppression.\u003c/p\u003e \u003cp\u003eIn the baseline block, 40 paired-pulse CS-TS and 40 single TS TMS stimuli were randomly delivered. During the experimental block, TMS stimuli were administered online at two distinct phases of the task: on the warning and on the target. Specifically, in the rhythmic and single-interval contexts, in both the CS-TS and TS trials, TS TMS stimuli were delivered at the Warning signal appearance (20 CS-TS and 20 TS alone) and at the Target stimulus appearance (20 CS-TS and 20 TS alone).\u003c/p\u003e \u003cp\u003eThus, CS on the cerebellum was delivered just 5 milliseconds before the warning signal and target stimulus appearance.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003ePeak-to-peak MEP amplitude (mV) was calculated offline for each condition. In all conditions (CBI\u003csub\u003ebaseline\u003c/sub\u003e and CBI\u003csub\u003etask\u003c/sub\u003e), MEPs preceded by EMG preactivation activity\u0026thinsp;\u0026gt;\u0026thinsp;50 \u0026micro;V in the 100 ms preceding the TMS pulse were excluded from the analysis. We extracted the CBI parameter by calculating the ratio of the MEP amplitude of CS-TS to the MEP amplitude of TS alone, with values below 1.0 indicating MEP suppression.\u003c/p\u003e \u003cp\u003eResponse times (RTs) to the target were also collected, excluding outliers (RTs\u0026thinsp;\u0026lt;\u0026thinsp;100 ms, \u0026gt;\u0026thinsp;3000 ms; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;2 \u0026times; SD [standard deviation]; 8%). The remaining trials were log-transformed to reduce the skewness of the distribution. Two subjects were excluded because of excessive trial loss (\u0026gt;\u0026thinsp;50%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eMauchly's and Shapiro-Wilk\u0026rsquo;s tests confirmed the sphericity assumption and ensured the variables were normally distributed.\u003c/p\u003e \u003cp\u003eFirstly, to exclude the possibility that any muscular activation during the task could influence task-related CBI changes, we compared the MEP test during the task separately for the target and warning phases with the baseline MEP test using paired sample t-tests.\u003c/p\u003e \u003cp\u003eIn addition, to rule out any modulation of the M1 activity during the TP task, we conducted a repeated measures ANOVA (RM ANOVA) on the MEP test data, with Context (Rhythmic and Single-Interval) and Phase (Warning and Target) as within-subject factors.\u003c/p\u003e \u003cp\u003eThen, we calculated the CBI\u003csub\u003ebaseline\u003c/sub\u003e as the ratio of the mean MEP amplitude in the CS-TS condition to the mean MEP amplitude in the TS condition in the baseline CBI block, and CBI\u003csub\u003etask\u003c/sub\u003e as the ratio of the mean MEP amplitude in the CS-TS condition to the average MEP of the Target and Warning phases in the TS condition in the experimental block.\u003c/p\u003e \u003cp\u003eThe CBI\u003csub\u003ebaseline\u003c/sub\u003e data and the CBI\u003csub\u003etask\u003c/sub\u003e data were analysed via an RM ANOVA with Condition (Baseline, Single-interval Warning, Single-interval Target, Rhythmic Warning, and Rhythmic Target) as within-subject factors.\u003c/p\u003e \u003cp\u003eSubsequently, the CBI\u003csub\u003etask\u003c/sub\u003e was normalized to CBI\u003csub\u003ebaseline\u003c/sub\u003e (Normalized CBI\u0026thinsp;=\u0026thinsp;CBI\u003csub\u003etask\u003c/sub\u003e/ CBI\u003csub\u003ebaseline\u003c/sub\u003e) for accurately characterizing task-related modulation. The Normalized CBI data were log-transformed to reduce the skewness inherent in MEP distributions. Normalized log CBI data were analysed via a 2x2 RM ANOVA with Context (Rhythmic and Single-Interval) and Phase (Warning and Target) as within-subject factors.\u003c/p\u003e \u003cp\u003eA linear mixed-effects model (LMM) was carried out to assess the effect of temporal predictability on RTs, including Context (Rhythmic and Single-Interval) and Phase (Warning and Target) as fixed factors. For each condition, trials were divided into three bins of equal length (10 trials each) to assess changes over time within the block. This factor, henceforth referred to as Temporal Bin (with levels: Initial, Middle, Final), was included as a fixed factor in the model for exploring intra-block temporal dynamics.\u003c/p\u003e \u003cp\u003eThe LMM procedure was implemented using the lme4 package in R software ((Kuznetsova et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Jamovi. For model selection, a full fixed-effects model was used to determine which random effects should be added, identifying the most parsimonious structure that provided an adequate fit. By default, a random intercept for each participant was included to account for inter-individual variability. Similarly, we assessed which fixed effects should be retained in the model. Model fitting was performed using maximum likelihood estimation, improvements in fit via χ\u0026sup2; statistics, as well as Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and the corresponding p-values. For similar top-down model-building approaches, see Bates et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; De Paepe et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAll other statistical analyses were carried out using JASP (Version 0.19.3) [Apple Silicon], R software, and Jamovi. The alpha level was set to 0.05 for all tests. Post-hoc analyses with Bonferroni correction were applied to significant interactions.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eNeurophysiological data\u003c/h2\u003e\n\u003cp\u003eNo discomfort or adverse effects during TMS were reported or observed in any of the participants.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\n\u003cp\u003eTable 1 shows the mean \u0026plusmn; SEM for data collected at baseline:\u0026nbsp; TS MEP amplitude, CS-TS MEP amplitude, and the CBI\u003csub\u003ebaseline \u003c/sub\u003e(CS-TS/TS ratio), along with the corresponding stimulation intensities (%MSO).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u003cem\u003eMEP values at baseline (amplitude and intensity of the stimulation). TS, test stimulus; CS, conditioning stimulus; MSO, Maximum Stimulator Output.\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTS\u003c/p\u003e\n\u003cp\u003econdition\u003c/p\u003e\n\u003cp\u003eMEP amplitude (mV)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCS-TS condition\u003c/p\u003e\n\u003cp\u003eMEP amplitude (mV)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCBI\u003csub\u003ebaseline\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eCS-TS/TS (ratio)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eM1 intensity (% MSO)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCerebellar intensity (% MSO)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.318\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.827\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50.375\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38.594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eStd. Error of Mean\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.107\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.264\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.157\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eStd. Deviation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.405\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.427\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.124\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.058\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.628\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 2 shows the mean \u0026plusmn; SEM for the TS MEP amplitude at baseline and during rhythmic and single-interval contexts in TP task, at the warning signal and at the target stimulus appearance. T-tests on TS MEP data revealed no significant difference in the mean MEP amplitude during the warning (t (15) = 1.18, p = 0.25) and target (t (15) = 0.95,\u0026nbsp; p = 0.35) phases compared to the baseline.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\n\u003cp\u003eThe RM ANOVA on the TS MEP data showed no significant main effects or interaction (all ps \u0026gt; 0.05, all Fs \u0026lt; 0.76), indicating that neither the task nor the stimulation phases altered the MEP amplitude. This confirms that any modulation of CBI\u003csub\u003etask\u003c/sub\u003e observed cannot be attributed to changes in the TS MEP amplitude.\u003c/p\u003e\n\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u003cem\u003eTS condition MEP amplitude values (mV) recorded at baseline and during rhythmic and single-interval contexts in the TP task with TMS delivered at warning signal appearance and at target presentation.\u0026nbsp; TS, test stimulus.\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBaseline\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSingle Interval (Warning signal)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSingle Interval (Target)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRhythmic (Warning signal)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRhythmic (Target)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.317\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.149\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.138\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.172\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.226\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eStd. Error of Mean\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.138\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.129\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.130\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eStd. Deviation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.405\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.405\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.554\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.518\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.519\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe analysis of CBI\u003csub\u003ebaseline\u003c/sub\u003e and the CBI\u003csub\u003etask\u003c/sub\u003e data revealed a significant main effect of Condition (F(\u003csub\u003e3,60\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;3.251, p\u0026thinsp;=\u0026thinsp;0.018, h\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e = 0.44). Post-hoc analysis revealed a significant decrease in inhibition in the single-interval context compared to the baseline, specifically when the TMS was delivered at the warning signal appearance (0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12, 0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16, p\u0026thinsp;=\u0026thinsp;0.004, Cohen\u0026rsquo;s d = -0.78), with no difference between the other conditions (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe RM ANOVA on the Normalized CBI data revealed a Task x Phase significant interaction (F(\u003csub\u003e1,15\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;12.19, p\u0026thinsp;=\u0026thinsp;0.003, h\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e = 0.44). Post-hoc analysis showed normalized CBI value was larger (meaning that CBI was reduced with respect to baseline) in the single-Interval context when the TMS stimulus was delivered at the warning signal appearance (0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01, -0.013\u0026thinsp;\u0026plusmn;\u0026thinsp;0.022, p\u0026thinsp;\u0026le;\u0026thinsp;0.01, Cohen's d\u0026thinsp;=\u0026thinsp;0.64) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). No differences were observed in the target phase between the two contexts and vice versa (all ps\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eNo other main effects or interactions were found to be significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eReaction times (RTs) data\u003c/h2\u003e\n\u003cp\u003eRT data are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e; for illustrative purposes, raw RT data are reported as normalized to each participant\u0026rsquo;s mean, considering trial-by-trial variability.\u003c/p\u003e\n\u003cp\u003eModel comparisons indicated that the most parsimonious random-effects structure included a random intercept for Subject and random slopes for Context and Phase (AIC = -2561.7, BIC = -2534.8, \u0026chi;\u0026sup2;(3)\u0026thinsp;=\u0026thinsp;216.4, p\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003eThe contribution of fixed effects was evaluated by comparing the full model (including the three-way interaction: Phase \u0026times; Context \u0026times; Temporal Bin) to reduced models.\u003c/p\u003e\n\u003cp\u003eAdding the main effects significantly improved model fit compared to the null model (AIC = -2408.3 vs 2379.5, BIC = -2376.1 vs -2363.4, 14.797, \u0026chi;\u0026sup2;(3)\u0026thinsp;=\u0026thinsp;34.79, p\u0026thinsp;=\u0026thinsp;0.001), and also adding the two-way interactions compared to the model only with the main effects (AIC = -2417.1, BIC = -2368.7, \u0026chi;\u0026sup2;(3)\u0026thinsp;=\u0026thinsp;14.797, p\u0026thinsp;=\u0026thinsp;0.001). In contrast, adding the three-way interaction among Phase, Context, and Temporal Bin did not improve model fit (AIC = -2415.1, BIC = -2361.3, \u0026chi;\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;0.0098, p\u0026thinsp;=\u0026thinsp;0.921). Therefore, the three-way interaction was removed, and the final model included all three fixed effects along with their two-way interactions.\u003c/p\u003e\n\u003cp\u003eIn the final model, there was a significant effect of Temporal Bin (F (2, 1551.3)\u0026thinsp;=\u0026thinsp;8.09, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001) accounted for by faster RTs in the final bin of trials (2.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.138, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to the initial one (2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.177), and faster RTs in the middle bin (2.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.142, p\u0026thinsp;=\u0026thinsp;0.005) relative to the initial one. No difference emerged between middle and final bins (p\u0026thinsp;=\u0026thinsp;1.000).\u003c/p\u003e\n\u003cp\u003eWe also observed significant Context x Phase (F(1, 1551.3)\u0026thinsp;=\u0026thinsp;6.433, p\u0026thinsp;=\u0026thinsp;0.011) and Context x Temporal Bin (F(2,1551.3)\u0026thinsp;=\u0026thinsp;7.849, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) two-way interactions. For the Context x Phase interaction, post-hoc comparison analysis did not reveal any significant pairwise differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The Context x Temporal Bin interaction indicated that bin-to-bin speeding occurred only in the Single-Interval context: both the middle and final bins (2.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.135 and 2.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.131) were faster than the initial bin (2.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.211, p \u0026lt; .001). No such modulation emerged in the Rhythmic context, where RTs remained stable across bins (p\u0026thinsp;=\u0026thinsp;1.000). No other main effect or interaction resulted significant (all ps\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eHere, we explored for the first time the cerebellum-M1 pathway, by means of CBI, during a temporal prediction task to test the involvement of this pathway in motor optimization across different temporal predictive contexts. We tested two predictive contexts within the TP task, rhythmic and single-interval, and two critical time points, corresponding to the presentation of the warning signal and the target on the screen, which indexed the maintenance of timing information and the exploitation of timing information for motor response execution, respectively.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCBI and motor performance were modulated only in the single-interval context\u003c/h2\u003e \u003cp\u003eThe main result of our study was a significant reduction of CBI in the single-interval context compared with the rhythmic context. This effect was evident only when TMS was delivered at the warning signal phase of the task, with no differences when stimulation was applied at the target phase. Furthermore, our behavioural results showed a progressive decrease in response times across time bins only in the single-interval context, with both the middle and final bins being faster than the initial bin. No comparable modulation was observed in the rhythmic context, in which RTs remained stable across bins.\u003c/p\u003e \u003cp\u003eThe context-dependent modulation of CBI (observable only in the single-interval context) aligns with the literature highlighting the cerebellum's critical role in event timing, particularly in tasks that rely on memory-based representation of time intervals. Compelling evidence in patients with cerebellar damage showed intact performance in motor or perceptual timing tasks if they are required to execute repetitive movement or to perform timing estimation of regular patterns (for a review, see Breska \u0026amp; Ivry, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, it is well-established that patients with cerebellar degeneration show a wide range of impairment regarding temporal perception and temporal control of the movements (Bhanpuri et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Franz et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Ivry \u0026amp; Keele, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Trillenberg et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). These deficits have been characterized as disruptions in event timing (Ivry et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), a process strongly linked to the cerebellum\u0026rsquo;s role in coordinating temporal aspects of both perception and motor control.\u003c/p\u003e \u003cp\u003eThe distinction between rhythmic and event timing has been investigated in connection with the concept of temporal prediction. Expanding on the neural processes involved in this ability, Breska \u0026amp; Ivry, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, offered neuropsychological evidence for a double dissociation in the brain mechanisms underlying temporal prediction. Their findings demonstrated that individuals with Parkinson's disease, which primarily affects the nigrostriatal pathway of the basal ganglia, showed an alteration in the TP task within a rhythmic context but performed normally in a single-interval context. On the other hand, individuals with cerebellar degeneration exhibited impairments solely in making temporal predictions based on single-interval associations. In the wake of these findings, we recently demonstrated that in the single interval context, particularly for sub-second long intervals (corresponding to the 900 ms of ISI tested here), the cerebellum could play a crucial role in integrating the representation of time interval stored in memory with the elapsed time, thereby providing accurate temporal prediction (Terranova et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly to our finding, a significant reduction of CBI during tasks has been consistently reported in studies investigating the modulation of cerebellum-M1 connectivity in the context of motor learning (D. A. Spampinato et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Specifically, these studies showed that CBI significantly decreased in adaptive learning tasks such as locomotor and visuomotor adaptation tasks (Jayaram et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Schlerf et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e; D. A. Spampinato et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e) and sequence learning (Torriero et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Specifically, EEG-TMS evidence (Fong et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) suggested that such modulation of cerebellar activity during visuomotor adaptive learning is critically tied to the activity of contralateral prefrontal cortex.\u003c/p\u003e \u003cp\u003eSeminal works have recognized the cerebellum simultaneously as a \u0026ldquo;timing machine\u0026rdquo; and a \u0026ldquo;learning machine\u0026rdquo; (Eccles, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1967\u003c/span\u003e; Marr, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1969\u003c/span\u003e). Critically, a recent study (Tanaka et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) investigating CBI modulation during a temporal adaptive learning task (i.e., coincident timing skill task) highlighted the cerebellum's role in integrating temporal information to guide motor behavior and adjust actions in response to temporal perturbations, thereby linking temporal prediction processing with adaptive motor learning. Particularly, Tanaka and coworkers showed, in line with previous studies assessing CBI during motor learning, a consistent reduction in CBI associated with temporal adaptive learning (Tanaka et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe modulation of CBI has been explained as reflecting a plastic change in the cerebellum, induced by motor learning, specifically in terms of long-term depression (LTD) of parallel fiber-Purkinje cells synapse, which interfere with the inhibition of M1 (Spampinato et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017c\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHere, the single-interval task, in contrast to the rhythmic task, involved the creation of temporal expectancy by orienting attention to salient temporal features of events, memorizing it, and using information to guide behavior, mechanisms that could be shared with different kinds of error-based learning. Therefore, it could be argued that during single-interval prediction, LTD mechanisms might modulate cerebellar activity, potentially affecting cerebellum-M1 inhibition.\u003c/p\u003e \u003cp\u003eIn support of this, our behavioral data show that only in the single-interval predictive context response time significantly decreased across temporal bins, with participants responding faster in the final trials than in the initial ones. This pattern suggests that motor efficiency improved over the course of the task in single-interval predictive context thanks to learning mechanisms.\u003c/p\u003e \u003cp\u003eDifferently, in the rhythmic context, the response time is minimal from the outset of the task and exhibits no significant variation between the initial trials and the final ones, in agreement with the idea of memory and attention playing a larger role in learning in single-interval contexts and neural entrainment dominating in rhythmic contexts (Breska \u0026amp; Deouell, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCBI was modulated at the warning signal and not at the target stimulus appearance\u003c/h2\u003e \u003cp\u003eWe observed changes in CBI in the single-interval task only when the TMS was delivered at the warning signal appearance on the screen, and not when the TMS was delivered at the target stimulus appearance.\u003c/p\u003e \u003cp\u003eStudies investigating the neural activity during orienting attention for time properties of an event showed that perceiving an event with temporal features automatically involved an implicit tracking of time, with attentional preparation gradually increasing and reaching its peak when the memorized interval has passed (Durstewitz, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Studies utilizing event-related potentials (ERPs) have further clarified this mechanism by examining the temporal dynamics of expectation. These studies showed that valid cues predicting the timing of the target modulated several ERP components, linked to attentional anticipatory activity, including the contingent negative variation (CNV) and the attention-related potential (P300) (Breska \u0026amp; Deouell, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Capizzi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Miniussi et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1999\u003c/span\u003e)(Breska \u0026amp; Deouell, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Capizzi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Miniussi et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eERPs recorded during a target detection task (Miniussi et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) have demonstrated that ERP components associated with attentional shifts are modulated before the appearance of the target, influenced by the characteristics of the preceding cue. Differently, late ERP components were linked to target processing and may reflect adjustments in response preparation or decision-making processes (Miniussi et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our task, the warning signal acted as a cue, predicting target onset in 75% of trials at a fixed interval. This design likely allowed the engagement of similar mechanisms, whereby the warning cue triggered temporal anticipation and response preparation, in accordance with previous findings on cue-related modulations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eOne possible limitation of the study is that differences in CBI between the single-interval predictive context and the baseline may have been driven by changes in M1 excitability across these conditions.\u003c/p\u003e \u003cp\u003eWe can rule out this possibility since the comparison of MEP amplitudes between the two critical time points (i.e., warning and target phases) revealed no significant differences across conditions, thereby ruling out potential confounding effects related to M1 excitability during both the preparatory and response phases of the task, where TMS delivery could overlap with muscle activation and motor preparation or execution. Furthermore, we assessed potential differences in test MEP amplitudes across predictive contexts (rhythmic vs. single-interval) and time points (warning vs. target) finding no significant effects, further supporting the notion that the task-related observed differences in CBI cannot be explained by changes in M1 excitability. This result appears to diverge from previous studies reporting a facilitatory effect on M1 activity at the onset of a voluntary movement involving the FDI (e.g., pressing a button by flexing the index finger) (Kassavetis et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) or during movement preparation (D. A. Spampinato et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e). In addition, research investigating the effects of single-pulse and paired-pulse TMS during the pre-movement period has demonstrated a gradual facilitation of motor evoked potentials (MEPs) as the voluntary movement onset approaches (Nikolova et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Several potential factors could account for the absence of significant task-related modulation in motor excitability in our study. Firstly, anticipatory motor responses were strongly discouraged throughout the task, with an error feedback message on the screen if any responses were made prematurely, and the inclusion of 25% catch trials per condition, which strongly reduced motor activation before the onset of the target. Importantly, in several studies (e.g., Kassavetis et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) the TMS stimulus and the motor response were precisely aligned. In contrast, in our study, these two events were not perfectly synchronized, as TMS was delivered at target onset, while the subject's response could be delayed by a few milliseconds.\u003c/p\u003e \u003cp\u003eOther limitations should be considered when interpreting the present findings. Although the sample size sensitivity analyses confirmed that the final sample provided sufficient power to detect the observed effects, larger cohort would allow a more detailed characterization of inter-individual variability in cerebellar\u0026ndash;M1 connectivity. In addition, despite the use of neuronavigation to ensure consistent coil positioning, cerebellar TMS inherently lacks the spatial precision required to selectively target specific cerebellar areas, limiting anatomical specificity. The present study was also restricted to sub-second temporal intervals (900 ms), leaving open the question of whether similar modulation patterns would emerge for supra-second intervals, or in non-predictive contexts. Finally, the absence of concurrent neurophysiological recordings (e.g., EEG) limits direct assessment of how cerebellar\u0026ndash;M1 interactions relate to broader network dynamics during temporal prediction.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOverall, our findings underscore the causal role of the cerebellum in temporal prediction, extending our previous results by highlighting its critical involvement in memory-based timing. Specifically, we demonstrated an online modulation of cerebellar-M1 connectivity, likely driven by LTD-like mechanisms, arising from the formation of temporal predictions in a single-interval context.\u003c/p\u003e \u003cp\u003eThis modulation occurred when the TMS was delivered at warning signal onset, indicating that the cerebellum may play a key role in orienting attention and preparing for expected events based on temporal information stored in memory. Future investigations will need to explore how cerebellar-M1 connectivity is dynamically modulated across different temporal windows during the task.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eFinancial interests: S. Terranova, A. Botta, M. Putzolu, G. Bonassi, R. Simeon, and A. Marra have nothing to declare. L. Avanzino, has received speaker honoraria from Zambon and Bial and received research supports (Grants) from the EU Joint Programme\u0026mdash;Neurodegenerative Disease Research (JPND) (2022 call) and Michael J. Fox Foundation (Fall 2022 biomarkers to support therapeutic trials program). She received research support from FRESCO foundation and the Italian Ministry of Health (Ricerca Finalizzata 2022 and 5 \u0026times; 1000). E. Pelosin is part of the Advisory Board for M.J. Fox Foundation. She has received grants from the Italian Ministry of University and Research (PRIN 2022) and Michael J. Fox Foundation (Fall 2022 biomarkers to support therapeutic trials program), and research supports from Italian Ministry of Health (Ricerca Finalizzata 2022) and IRCCS Policlinico San Martino, Genova (5 \u0026times; 1000, 2022).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics approval\u003c/strong\u003e \u003cp\u003e All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the local ethics committee.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003e Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eOpen access funding provided by Universit\u0026agrave; degli Studi di Genova within the CRUI-CARE Agreement. The work is partly supported by #NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006) \u0026ndash; A Multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022).\u003c/p\u003e \u003cp\u003eThe work is partly supported by Fresco Foundation and by Ricerca Corrente from Italian Ministry of Health (MOH).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: [Sara Terranova, Elisa Pelosin, Laura Avanzino]; Methodology: [Sara Terranova, Elisa Pelosin, Laura Avanzino]; Formal analysis and investigation: [Sara Terranova, Alessandro Botta, Martina Putzolu, Gaia Bonassi, Rachele Simeon, Anna Marra]; Writing - original draft preparation: [Sara Terranova, Elisa Pelosin, Laura Avanzino]; Writing - review and editing: [Sara Terranova, Alessandro Botta, Martina Putzolu, Gaia Bonassi, Rachele Simeon, Anna Marra, Elisa Pelosin, Laura Avanzino]; Funding acquisition: [Laura Avanzino]; Resources: [Elisa Pelosin, Laura Avanzino]; Supervision: [Elisa Pelosin, Laura Avanzino].\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBates D, M\u0026auml;chler M, Bolker BM, Walker SC (2015) Fitting linear mixed-effects models using lme4. 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Bull World Health Organ 79(4):373\u0026ndash;374\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":false,"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":"Cerebellum, Cerebellar-Brain inhibition, Temporal Prediction, Response time","lastPublishedDoi":"10.21203/rs.3.rs-8778315/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8778315/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe cerebellum integrates temporal information to optimize motor responses through prediction. Temporal prediction occurs through two mechanisms: rhythmic entrainment to recurring patterns or integration of single isolated intervals, guiding attention and enhancing perceptual and motor performance. However, the causal contribution of the cerebellum\u0026ndash;M1 pathway to motor optimization across predictive contexts remains unexplored.\u003c/p\u003e \u003cp\u003eThis study aimed to assess whether functional cerebellum-M1connectivity is modulated during a temporal prediction task requiring motor response optimization via a predictive process. Thus, we investigated Cerebellar-Brain Inhibition (CBI), a transcranial magnetic stimulation (TMS) technique, during a temporal prediction (TP) task.\u003c/p\u003e \u003cp\u003eTwenty participants received the CBI protocol at rest and during the TP-task. Response times (RTs) to the target were recorded in two predictive contexts: rhythmic (i.e., interstimulus intervals were constant, 900 ms) and single-interval condition (i.e., target\u0026rsquo;s timing estimation was based on the prior exposure to the train of stimuli). TMS was delivered at two time points of the task: \u003cem\u003eat\u003c/em\u003e the warning signal, corresponding to the maintenance of timing information for motor preparation and \u003cem\u003eat\u003c/em\u003e the target stimulus onset, indexing use of timing information for motor execution.\u003c/p\u003e \u003cp\u003eWe found a significant reduction of the CBI with respect to rest only when participants were engaged in the single-interval condition of the TP-task. Furthermore, this modulation was evident only when TMS was delivered \u003cem\u003eat\u003c/em\u003e the warning stimulus appearance, and not at the target stimulus appearance.\u003c/p\u003e \u003cp\u003eOur findings underscore the causal role of the cerebellum in motor optimization in a predictive context, critically in single-interval, memory-based timing.\u003c/p\u003e","manuscriptTitle":"Exploring Cerebellar Contribution to Motor Optimization in Temporal Prediction Through Cerebellar–Brain Inhibition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 14:57:21","doi":"10.21203/rs.3.rs-8778315/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":"4b4cf145-5e5f-4d8a-b979-0ef5f5b2a8a7","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-04T14:55:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 14:57:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8778315","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8778315","identity":"rs-8778315","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}