Prior Movement of One Arm Facilitates Motor Adaptation in the Other.

Many movements in daily life are embedded in motion sequences that involve more than one limb, demanding the motor system to monitor and control different body parts in quick succession. During such movements, systematic changes in the environment or the body might require motor adaptation of specific segments. However, previous motor adaptation research has focused primarily on motion sequences produced by a single limb, or on simultaneous movements of several limbs. For example, adaptation to opposing force fields is possible in unimanual reaching tasks when the direction of a prior or subsequent movement is predictive of force field direction. It is unclear, however, whether multilimb sequences can support motor adaptation processes in a similar way. In the present study (38 females, 38 males), we investigated whether reaches can be adapted to different force fields in a bimanual motor sequence when the information about the perturbation is associated with the prior movement direction of the other arm. In addition, we examined whether prior perceptual (visual or proprioceptive) feedback of the opposite arm contributes to force field-specific motor adaptation. Our key finding is that only active participation in the bimanual sequential task supports pronounced adaptation. This result suggests that active segments in bimanual motion sequences are linked across limbs. If there is a consistent association between movement kinematics of the linked and goal movement, the learning process of the goal movement can be facilitated. More generally, if motion sequences are repeated often, prior segments can evoke specific adjustments of subsequent movements.SIGNIFICANCE STATEMENT Movements in a limb's motion sequence can be adjusted based on linked movements. A prerequisite is that kinematics of the linked movements correctly predict which adjustments are needed. We show that use of kinematic information to improve performance is even possible when a prior linked movement is performed with a different limb. For example, a skilled juggler might have learned how to correctly adjust his catching movement of the left hand when the right hand performed a throwing action in a specific way. Linkage is possibly a key mechanism of the human motor system for learning complex bimanual skills. Our study emphasizes that learning of specific movements should not be studied in isolation but within their motor sequence context.

Altered functional connectivity in the hippocampal and striatal systems after motor sequence learning consolidation in medial temporal lobe epilepsy individuals.

Both the hippocampal and striatal systems participate in motor sequence learning (MSL) in healthy subjects, and the prominent role of the hippocampal system in sleep-related consolidation has been demonstrated. However, some pathological states may change the functional dominance between these two systems in MSL consolidation. To better understand the functional performance within these two systems under the pathological condition of hippocampal impairment, we compared the functional differences after consolidation between patients with left medial temporal lobe epilepsy (LmTLE) and healthy control subjects (HCs). We assessed participants' performance on the finger-tapping task (FTT) during acquisition (on day 1) and after consolidation during sleep (on day 2). All participants underwent an MRI scan (T1 and resting state) before each FTT. We found that the LmTLE group showed performance deficits in offline consolidation compared to the HC group. The LmTLE group exhibited structural changes, such as decreased gray matter volume (GMV) in the left hippocampus and increased GMV in the right putamen (striatum). Our results also revealed that whereas the main effect of consolidation was observed in the hippocampus-related functional connection in the HC group, it was only evident in the striatum-related functional loop in the LmTLE group. Our findings indicated that LmTLE patients may rely more on the striatal system for offline consolidation because of structural impairments in the hippocampus. Additionally, this compensatory mechanism may not fully substitute for the role of the impaired hippocampus itself.NEW & NOTEWORTHY Motor sequence learning (MSL) relies on both the hippocampal and striatal systems, but whether functional performance is altered after MSL consolidation when the hippocampus is impaired remains unknown. Our results indicated that whereas the main effect of consolidation was observed in the hippocampus-related functional connection in the healthy control (HC) group, it was only evident in the striatum-related functional loop in the left medial temporal lobe epilepsy (LmTLE) group.

Cholinergic modulation of motor sequence learning.

The cholinergic system plays a key role in motor function, but whether pharmacological modulation of cholinergic activity affects motor sequence learning is unknown. The acetylcholine receptor antagonist biperiden, an established treatment in movement disorders, reduces attentional modulation, but whether it influences motor sequence learning is not clear. Using a randomized, double-blind placebo-controlled crossover design, we tested 30 healthy young participants and showed that biperiden impairs the ability to learn sequential finger movements, accompanied by widespread oscillatory broadband power changes (4-25 Hz) in the motor sequence learning network after receiving biperiden, with greater power in the theta, alpha and beta bands over ipsilateral motor and bilateral parietal-occipital areas. The reduced early theta power during a repeated compared with random sequence, likely reflecting disengagement of top-down attention to sensory processes, was disrupted by biperiden. Alpha synchronization during repeated sequences reflects sensory gating and lower visuospatial attention requirements compared with visuomotor responses to random sequences. After biperiden, alpha synchronization was greater, potentially reflecting excessive visuospatial attention reduction, affecting visuomotor responding required to enable sequence learning. Beta oscillations facilitate sequence learning by integrating visual and somatosensory inputs, stabilizing repeated sequences and promoting prediction of the next stimulus. The beta synchronization after biperiden fits with a disruption of the selective visuospatial attention enhancement associated with initial sequence learning. These findings highlight the role of cholinergic processes in motor sequence learning.

Decreased long-range temporal correlations in the resting-state functional magnetic resonance imaging blood-oxygen-level-dependent signal reflect motor sequence learning up to 2 weeks following training.

Decreased long-range temporal correlations (LRTC) in brain signals can be used to measure cognitive effort during task execution. Here, we examined how learning a motor sequence affects long-range temporal memory within resting-state functional magnetic resonance imaging signal. Using the Hurst exponent (HE), we estimated voxel-wise LRTC and assessed changes over 5 consecutive days of training, followed by a retention scan 12 days later. The experimental group learned a complex visuomotor sequence while a complementary control group performed tightly matched movements. An interaction analysis revealed that HE decreases were specific to the complex sequence and occurred in well-known motor sequence learning associated regions including left supplementary motor area, left premotor cortex, left M1, left pars opercularis, bilateral thalamus, and right striatum. Five regions exhibited moderate to strong negative correlations with overall behavioral performance improvements. Following learning, HE values returned to pretraining levels in some regions, whereas in others, they remained decreased even 2 weeks after training. Our study presents new evidence of HE's possible relevance for functional plasticity during the resting-state and suggests that a cortical subset of sequence-specific regions may continue to represent a functional signature of learning reflected in decreased long-range temporal dependence after a period of inactivity.

Temporal cluster-based organization of sleep spindles underlies motor memory consolidation.

Sleep benefits motor memory consolidation, which is mediated by sleep spindle activity and associated memory reactivations during non-rapid eye movement (NREM) sleep. However, the particular role of NREM2 and NREM3 sleep spindles and the mechanisms triggering this memory consolidation process remain unclear. Here, simultaneous electroencephalographic and functional magnetic resonance imaging (EEG-fMRI) recordings were collected during night-time sleep following the learning of a motor sequence task. Adopting a time-based clustering approach, we provide evidence that spindles iteratively occur within clustered and temporally organized patterns during both NREM2 and NREM3 sleep. However, the clustering of spindles in trains is related to motor memory consolidation during NREM2 sleep only. Altogether, our findings suggest that spindles' clustering and rhythmic occurrence during NREM2 sleep may serve as an intrinsic rhythmic sleep mechanism for the timed reactivation and subsequent consolidation of motor memories, through synchronized oscillatory activity within a subcortical-cortical network involved during learning.

Human motor sequence learning drives transient changes in network topology and hippocampal connectivity early during memory consolidation.

In the last decade, the exclusive role of the hippocampus in human declarative learning has been challenged. Recently, we have shown that gains in performance observed in motor sequence learning (MSL) during the quiet rest periods interleaved with practice are associated with increased hippocampal activity, suggesting a role of this structure in motor memory reactivation. Yet, skill also develops offline as memory stabilizes after training and overnight. To examine whether the hippocampus contributes to motor sequence memory consolidation, here we used a network neuroscience strategy to track its functional connectivity offline 30 min and 24 h post learning using resting-state functional magnetic resonance imaging. Using a graph-analytical approach we found that MSL transiently increased network modularity, reflected in an increment in local information processing at 30 min that returned to baseline at 24 h. Within the same time window, MSL decreased the connectivity of a hippocampal-sensorimotor network, and increased the connectivity of a striatal-premotor network in an antagonistic manner. Finally, a supervised classification identified a low-dimensional pattern of hippocampal connectivity that discriminated between control and MSL data with high accuracy. The fact that changes in hippocampal connectivity were detected shortly after training supports a relevant role of the hippocampus in early stages of motor memory consolidation.

Somatosensory targeted memory reactivation enhances motor performance via hippocampal-mediated plasticity.

Increasing evidence suggests that reactivation of newly acquired memory traces during postlearning wakefulness plays an important role in memory consolidation. Here, we sought to boost the reactivation of a motor memory trace during postlearning wakefulness (quiet rest) immediately following learning using somatosensory targeted memory reactivation (TMR). Using functional magnetic resonance imaging, we examined the neural correlates of the reactivation process as well as the effect of the TMR intervention on brain responses elicited by task practice on 24 healthy young adults. Behavioral data of the post-TMR retest session showed a faster learning rate for the motor sequence that was reactivated as compared to the not-reactivated sequence. Brain imaging data revealed that motor, parietal, frontal, and cerebellar brain regions, which were recruited during initial motor learning, were specifically reactivated during the TMR episode and that hippocampo-frontal connectivity was modulated by the reactivation process. Importantly, the TMR-induced behavioral advantage was paralleled by dynamical changes in hippocampal activity and hippocampo-motor connectivity during task practice. Altogether, the present results suggest that somatosensory TMR during postlearning quiet rest can enhance motor performance via the modulation of hippocampo-cortical responses.

Anodal transcranial direct current stimulation does not enhance the effects of motor imagery training of a sequential finger-tapping task in young adults.

When applied over the primary motor cortex (M1), anodal transcranial direct current stimulation (a-tDCS) could enhance the effects of a single motor imagery training (MIt) session on the learning of a sequential finger-tapping task (SFTT). This study aimed to investigate the effect of a-tDCS on the learning of an SFTT during multiple MIt sessions. Two groups of 16 healthy young adults participated in three consecutive MIt sessions over 3 days, followed by a retention test 1 week later. They received active or sham a-tDCS during a MIt session in which they mentally rehearsed an eight-item complex finger sequence with their left hand. Before and after each session, and during the retention test, they physically repeated the sequence as quickly and accurately as possible. Both groups (i) improved their performance during the first two sessions, showing online learning; (ii) stabilised the level they reached during all training sessions, reflecting offline consolidation; and (iii) maintained their performance level one week later, showing retention. However, no significant difference was found between the groups, regardless of the MSL stage. These results emphasise the importance of performing several MIt sessions to maximise performance gains, but they do not support the additional effects of a-tDCS.

Differential effects of conventional and high-definition transcranial direct-current stimulation of the motor cortex on implicit motor sequence learning.

Conventional transcranial direct-current stimulation (tDCS) delivered to the primary motor cortex (M1) has been shown to enhance implicit motor sequence learning (IMSL). Conventional tDCS targets M1 but also the motor association cortices (MAC), making the precise contribution of these areas to IMSL presently unclear. We aimed to address this issue by comparing conventional tDCS of M1 and MAC to 4 * 1 high-definition (HD) tDCS, which more focally targets M1. In this mixed-factorial, sham-controlled, crossover study in 89 healthy young adults, we used mixed-effects models to analyse sequence-specific and general learning effects in the acquisition and short- and long-term consolidation phases of IMSL, as measured by the serial reaction time task. Conventional tDCS did not influence general learning, improved sequence-specific learning during acquisition (anodal: M = 42.64 ms, sham: M = 32.87 ms, p = .041), and seemingly deteriorated it at long-term consolidation (anodal: M = 75.37 ms, sham: M = 86.63 ms, p = .019). HD tDCS did not influence general learning, slowed performance specifically in sequential blocks across all learning phases (all p's < .050), and consequently deteriorated sequence-specific learning during acquisition (anodal: M = 24.13 ms, sham: M = 35.67 ms, p = .014) and long-term consolidation (anodal: M = 60.03 ms, sham: M = 75.01 ms, p = .002). Our findings indicate that the observed superior conventional tDCS effects on IMSL are possibly attributable to a generalized stimulation of M1 and/or adjacent MAC, rather than M1 alone. Alternatively, the differential effects can be attributed to cathodal inhibition of other cortical areas involved in IMSL by the 4 * 1 HD tDCS return electrodes, and/or more variable electric field strengths induced by HD tDCS, compared with conventional tDCS.

Effect of high-endurance exercise intervention on sleep-dependent procedural memory consolidation in individuals with schizophrenia: a randomized controlled trial.

BACKGROUND: Little is known about the effects of physical exercise on sleep-dependent consolidation of procedural memory in individuals with schizophrenia. We conducted a randomized controlled trial (RCT) to assess the effectiveness of physical exercise in improving this cognitive function in schizophrenia. METHODS: A three-arm parallel open-labeled RCT took place in a university hospital. Participants were randomized and allocated into either the high-intensity-interval-training group (HIIT), aerobic-endurance exercise group (AE), or psychoeducation group for 12 weeks, with three sessions per week. Seventy-nine individuals with schizophrenia spectrum disorder were contacted and screened for their eligibility. A total of 51 were successfully recruited in the study. The primary outcome was sleep-dependent procedural memory consolidation performance as measured by the finger-tapping motor sequence task (MST). Assessments were conducted during baseline and follow-up on week 12. RESULTS: The MST performance scored significantly higher in the HIIT (n = 17) compared to the psychoeducation group (n = 18) after the week 12 intervention (p < 0.001). The performance differences between the AE (n = 16) and the psychoeducation (p = 0.057), and between the AE and the HIIT (p = 0.999) were not significant. Yet, both HIIT (p < 0.0001) and AE (p < 0.05) showed significant within-group post-intervention improvement. CONCLUSIONS: Our results show that HIIT and AE were effective at reverting the defective sleep-dependent procedural memory consolidation in individuals with schizophrenia. Moreover, HIIT had a more distinctive effect compared to the control group. These findings suggest that HIIT may be a more effective treatment to improve sleep-dependent memory functions in individuals with schizophrenia than AE alone.

Suppression of Motor Sequence Learning and Execution Through Anodal Cerebellar Transcranial Electrical Stimulation.

Cerebellum (CB) and primary motor cortex (M1) have been associated with motor learning, with different putative roles. Modulation of task performance through application of transcranial direct current stimulation (TDCS) to brain structures provides causal evidence for their engagement in the task. Studies evaluating and comparing TDCS to these structures have provided conflicting results, however, likely due to varying paradigms and stimulation parameters. Here we applied TDCS to CB and M1 within the same experimental design, to enable direct comparison of their roles in motor sequence learning. We examined the effects of anodal TDCS during motor sequence learning in 60 healthy participants, randomly allocated to CB-TDCS, M1-TDCS, or Sham stimulation groups during a serial reaction time task. Key to the design was an equal number of repeated and random sequences. Reaction times (RTs) to implicitly learned and random sequences were compared between groups using ANOVAs and post hoc t-tests. A speed-accuracy trade-off was excluded by analogous analysis of accuracy scores. An interaction was observed between whether responses were to learned or random sequences and the stimulation group. Post hoc analyses revealed a preferential slowing of RTs to implicitly learned sequences in the group receiving CB-TDCS. Our findings provide evidence that CB function can be modulated through transcranial application of a weak electrical current, that the CB and M1 cortex perform separable functions in the task, and that the CB plays a specific role in motor sequence learning during implicit motor sequence learning.

Involvement of the prefrontal cortex in motor sequence learning: A functional near-infrared spectroscopy (fNIRS) study.

Our previous functional near-infrared spectroscopy (fNIRS) study on motor sequence learning (Polskaia et al., 2020) did not detect the same decrease in activity in the left dorsolateral prefrontal cortex (DLPFC) associated with movement automaticity, as reported by Wu et al. (2004). This was partly attributed to insufficient practice time to reach neural efficiency. Therefore, we sought to expand on our previous work to better understand the contribution of the prefrontal cortex (PFC) to motor sequence learning by examining learning across a longer period of time. Participants were randomly assigned to one of two groups: control or trained. fNIRS was acquired at three time points: pre-test, post-test, and retention. Participants performed four sequences (S1, S2, S3, and S4) of right-hand finger tapping. The trained group also underwent four days of practice of S1 and S2. No group differences in the left DLPFC and ventrolateral (VLPFC) were found between sessions for S1 and S2. Our findings revealed increased contribution from the right VLPFC in post-test for the trained group, which may reflect the active retrieval of explicit information from long-term memory. Our results suggest that despite additional practice time, explicit motor sequence learning requires the continued involvement of the PFC.

Conscious awareness of others' actions during observational learning does not benefit motor skill performance.

The conscious awareness of motor success during motor learning has recently been revealed as a learning factor. In these studies, participants had to learn a motor sequence and to detect when they assumed the execution had reached a maximal fluidity. The consciousness groups showed better motor performance during a delayed post-training test than the non-consciousness control groups. Based on the "similar mechanism" hypothesis between observational and physical practice, we tested this beneficial effect of the conscious awareness of action in an observational learning context. In the present study, two groups learned a motor sequence task by observing a videotaped human model performing the task. However, only the consciousness group had to detect the maximal fluidity of the learning human model during observational practice. Unpredictably, no difference was detected between groups during the post-training test. However, the consciousness group outperformed the non-consciousness control group for tests that assessed the motor knowledges.

Rapid hippocampal plasticity supports motor sequence learning.

Recent evidence suggests that gains in performance observed while humans learn a novel motor sequence occur during the quiet rest periods interleaved with practice (micro-offline gains, MOGs). This phenomenon is reminiscent of memory replay observed in the hippocampus during spatial learning in rodents. Whether the hippocampus is also involved in the production of MOGs remains currently unknown. Using a multimodal approach in humans, here we show that activity in the hippocampus and the precuneus increases during the quiet rest periods and predicts the level of MOGs before asymptotic performance is achieved. These functional changes were followed by rapid alterations in brain microstructure in the order of minutes, suggesting that the same network that reactivates during the quiet periods of training undergoes structural plasticity. Our work points to the involvement of the hippocampal system in the reactivation of procedural memories.

Neurocomputational modeling of speech motor development.

This review describes a computational approach for modeling the development of speech motor control in infants. We address the development of two levels of control: articulation of individual speech sounds (defined here as phonemes, syllables, or words for which there is an optimized motor program) and production of sound sequences such as phrases or sentences. We describe the DIVA model of speech motor control and its application to the problem of learning individual sounds in the infant's native language. Then we describe the GODIVA model, an extension of DIVA, and how chunking of frequently produced phoneme sequences is implemented within it.

Disentangling Effects of Memory Storage and Inter-articulator Coordination on Generalization in Speech Motor Sequence Learning.

Generalization in motor control is the extent to which motor learning affects movements in situations different than those in which it originally occurred. Recent data on orofacial speech movements indicates that motor sequence learning generalizes to novel syllable sequences containing phonotactically illegal, but previously practiced, consonant clusters. Practicing an entire syllable, however, results in even larger performance gains compared to practicing just its clusters. These patterns of generalization could reflect language-general changes in phonological memory storage and/or inter-articulator coordination during motor sequence learning. To disentangle these factors, we conducted two experiments in which talkers intensively practiced producing novel syllables containing illegal onset and coda clusters over two consecutive days. During the practice phases of both experiments, we observed that, through repetition, talkers gradually produced the syllables with fewer errors, indicative of learning. After learning, talkers were tested for generalization to single syllables (Experiment 1) or syllable pairs (Experiment 2) that overlapped to varying degrees with the practiced syllables. Across both experiments, we found that performance improvements from practicing syllables with illegal clusters partially generalized to novel syllables that contained those clusters, but performance was more error prone if the clusters occurred in a different syllable position (onset versus coda) as in practice, demonstrating that inter-articulator coordination is contextually sensitive. Furthermore, changing the position of a cluster was found to be more deleterious to motor performance during the production of the second syllables in syllable pairs, which required talkers to store more phonological material in memory prior to articulation, compared to single syllables. This interaction effect reveals a complex interplay between memory storage and inter-articulator coordination on generalization in speech motor sequence learning.

Deep brain stimulation of the ventrointermediate nucleus of the thalamus to treat essential tremor improves motor sequence learning.

The network of brain structures engaged in motor sequence learning comprises the same structures as those involved in tremor, including basal ganglia, cerebellum, thalamus, and motor cortex. Deep brain stimulation (DBS) of the ventrointermediate nucleus of the thalamus (VIM) reduces tremor, but the effects on motor sequence learning are unknown. We investigated whether VIM stimulation has an impact on motor sequence learning and hypothesized that stimulation effects depend on the laterality of electrode location. Twenty patients (age: 38-81 years; 12 female) with VIM electrodes implanted to treat essential tremor (ET) successfully performed a serial reaction time task, varying whether the stimuli followed a repeating pattern or were selected at random, during which VIM-DBS was either on or off. Analyses of variance were applied to evaluate motor sequence learning performance according to reaction times (RTs) and accuracy. An interaction was observed between whether the sequence was repeated or random and whether VIM-DBS was on or off (F[1,18] = 7.89, p = .012). Motor sequence learning, reflected by reduced RTs for repeated sequences, was greater with DBS on than off (T[19] = 2.34, p = .031). Stimulation location correlated with the degree of motor learning, with greater motor learning when stimulation targeted the lateral VIM (n = 23, ρ = 0.46; p = .027). These results demonstrate the beneficial effects of VIM-DBS on motor sequence learning in ET patients, particularly with lateral VIM electrode location, and provide evidence for a role for the VIM in motor sequence learning.

Stress Modulates the Balance between Hippocampal and Motor Networks during Motor Memory Processing.

The functional interaction between hippocampo- and striato-cortical regions during motor sequence learning is essential to trigger optimal memory consolidation. Based on previous evidence from other memory domains that stress alters the balance between these systems, we investigated whether exposure to stress prior to motor learning modulates motor memory processes. Seventy-two healthy young individuals were exposed to a stressful or nonstressful control intervention prior to training on a motor sequence learning task in a magnetic resonance imaging (MRI) scanner. Consolidation was assessed with an MRI retest after a sleep episode. Behavioral results indicate that stress prior to learning did not influence motor performance. At the neural level, stress induced both a larger recruitment of sensorimotor regions and a greater disengagement of hippocampo-cortical networks during training. Brain-behavior regression analyses showed that while this stress-induced shift from (hippocampo-)fronto-parietal to motor networks was beneficial for initial performance, it was detrimental for consolidation. Our results provide the first experimental evidence that stress modulates the neural networks recruited during motor memory processing and therefore effectively unify concepts and mechanisms from diverse memory fields. Critically, our findings suggest that intersubject variability in brain responses to stress determines the impact of stress on motor learning and subsequent consolidation.

Brain white matter correlates of learning ankle tracking using a wearable device: importance of the superior longitudinal fasciculus II.

BACKGROUND: Wearable devices have been found effective in training ankle control in patients with neurological diseases. However, the neural mechanisms associated with using wearable devices for ankle training remain largely unexplored. This study aimed to investigate the ankle tracking performance and brain white matter changes associated with ankle tracking learning using a wearable-device system and the behavior-brain structure relationships in middle-aged and older adults. METHODS: Twenty-six middle-aged and older adults (48-75 years) participated in this study. Participants underwent 5-day ankle tracking learning with their non-dominant foot using a custom-built ankle tracking system equipped with a wearable sensor and a sensor-computer interface for real-time visual feedback and data acquisition. Repeated and random sequences of target tracking trajectories were both used for learning and testing. Ankle tracking performance, calculated as the root-mean-squared-error (RMSE) between the target and actual ankle trajectories, and brain diffusion spectrum MR images were acquired at baseline and retention tests. The general fractional anisotropy (GFA) values of eight brain white matter tracts of interest were calculated to indicate their integrity. Two-way (Sex × Time) mixed repeated measures ANOVA procedures were used to investigate Sex and Time effects on RMSE and GFA. Correlations between changes in RMSE and those in GFA were analyzed, controlling for age and sex. RESULTS: After learning, both male and female participants reduced the RMSE of tracking repeated and random sequences (both p < 0.001). Among the eight fiber tracts, the right superior longitudinal fasciculus II (R SLF II) was the only one which showed both increased GFA (p = 0.039) after learning and predictive power of reductions in RMSE for random sequence tracking with its changes in GFA [ß = 0.514, R2 change = 0.259, p = 0.008]. CONCLUSIONS: Our findings implied that interactive tracking movement learning using wearable sensors may place high demands on the attention, sensory feedback integration, and sensorimotor transformation functions of the brain. Therefore, the SLF II, which is known to perform these brain functions, showed corresponding neural plasticity after such learning, and its plasticity also predicted the behavioral gains. The SLF II appears to be a very important anatomical neural correlate involved in such learning paradigms.

Alpha oscillations modulate premotor-cerebellar connectivity in motor learning: Insights from transcranial alternating current stimulation.

Alpha oscillations (8-13 Hz) have been suggested to play an important role in dynamic neural processes underlying learning and memory. The goal of this study was to scrutinize the role of alpha oscillations in communication within a cortico-cerebellar network implicated in motor sequence learning. To this end, we conducted two EEG experiments using a serial reaction time task. In the first experiment, we explored changes in alpha power and cross-channel alpha coherence as subjects learned a motor sequence. We found a gradual decrease in spectral alpha power over left premotor cortex (PMC) and sensorimotor cortex (SM1) during learning blocks. In addition, alpha coherence between left PMC/SM1 and left cerebellar crus I was specifically decreased during sequence learning, possibly reflecting a functional decoupling in the broader motor learning network. In the second experiment in a different cohort, we applied 10Hz transcranial alternating current stimulation (tACS), a method shown to entrain local oscillatory activity, to left M1 (lM1) and right cerebellum (rCB) during sequence learning. We observed a tendency for diminished learning following rCB tACS compared to sham, but not following lM1 tACS. Learning-related alpha power following rCB tACS was increased in left PMC, possibly reflecting increase in local inhibitory neural activity. Importantly, learning-specific alpha coherence between left PMC and right cerebellar lobule VIIb was enhanced following rCB tACS. These findings provide strong evidence for a causal role of alpha oscillations in controlling information transfer in a premotor-cerebellar loop during motor sequence learning. Our findings are consistent with a model in which sequence learning may be impaired by enhancing premotor cortical alpha oscillation via external modulation of cerebellar oscillations.