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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

Learning requires the traversal of inherently distinct cognitive states to produce behavioral adaptation. Yet, tools to explicitly measure these states with non-invasive imaging – and to assess their dynamics during learning – remain limited. Here, we describe an approach based on a distinct application of graph theory in which points in time are represented by network nodes, and similarities in brain states between two different time points are represented as network edges. We use a graph-based clustering technique to identify clusters of time points representing canonical brain states, and to assess the manner in which the brain moves from one state to another as learning progresses. We observe the presence of two primary states characterized by either high activation in sensorimotor cortex or high activation in a frontal-subcortical system. Flexible switching among these primary states and other less common states becomes more frequent as learning progresses, and is inversely correlated with individual differences in learning rate. These results are consistent with the notion that the development of automaticity is associated with a greater freedom to use cognitive resources for other processes. Taken together, our work offers new insights into the constrained, low dimensional nature of brain dynamics characteristic of early learning, which give way to less constrained, high-dimensional dynamics in later learning. •Proposed a time-time network for application of graph theory in brain networks.•Identified two canonical brain states associated with motor sequence learning.•The brain switches between states more frequently in later stages of learning.

Non-invasive transcranial stimulation of cerebellum and primary motor cortex (M1) has been shown to enhance motor learning. However, the mechanisms by which stimulation improves learning remain largely unknown. Here, we sought to shed light on the neural correlates of transcranial direct current stimulation (tDCS) during motor learning by simultaneously recording functional magnetic resonance imaging (fMRI). We found that right cerebellar tDCS, but not left M1 tDCS, led to enhanced sequence learning in the serial reaction time task. Performance was also improved following cerebellar tDCS compared to sham in a sequence production task, reflecting superior training effects persisting into the post-training period. These behavioral effects were accompanied by increased learning-specific activity in right M1, left cerebellum lobule VI, left inferior frontal gyrus and right inferior parietal lobule during cerebellar tDCS compared to sham. Despite the lack of group-level changes comparing left M1 tDCS to sham, activity increase in right M1, supplementary motor area, and bilateral middle frontal cortex, under M1 tDCS, was associated with better sequence performance. This suggests that lack of group effects in M1 tDCS relate to inter-individual variability in learning-related activation patterns. We further investigated how tDCS modulates effective connectivity in the cortico-striato-cerebellar learning network. Using dynamic causal modelling, we found altered connectivity patterns during both M1 and cerebellar tDCS when compared to sham. Specifically, during cerebellar tDCS, negative modulation of a connection from putamen to cerebellum was decreased for sequence learning only, effectively leading to decreased inhibition of the cerebellum. These results show specific effects of cerebellar tDCS on functional activity and connectivity in the motor learning network and may facilitate the optimization of motor rehabilitation involving cerebellar non-invasive stimulation.

Sleep benefits motor memory consolidation. This mnemonic process is thought to be mediated by thalamo-cortical spindle activity during NREM-stage2 sleep episodes as well as changes in striatal and hippocampal activity. However, direct experimental evidence supporting the contribution of such sleep-dependent physiological mechanisms to motor memory consolidation in humans is lacking. In the present study, we combined EEG and fMRI sleep recordings following practice of a motor sequence learning (MSL) task to determine whether spindle oscillations support sleep-dependent motor memory consolidation by transiently synchronizing and coordinating specialized cortical and subcortical networks. To that end, we conducted EEG source reconstruction on spindle epochs in both cortical and subcortical regions using novel deep-source localization techniques. Coherence-based metrics were adopted to estimate functional connectivity between cortical and subcortical structures over specific frequency bands. Our findings not only confirm the critical and functional role of NREM-stage2 sleep spindles in motor skill consolidation, but provide first-time evidence that spindle oscillations [11–17 Hz] may be involved in sleep-dependent motor memory consolidation by locally reactivating and functionally binding specific task-relevant cortical and subcortical regions within networks including the hippocampus, putamen, thalamus and motor-related cortical regions.

•Alpha coherence between premotor cortex and cerebellum decreases during learning.•Following 10Hz cerebellar stimulation we found: (1) decreased learning.•(2) Increased alpha power in premotor cortex.•(3) Increased alpha coherence between premotor cortex and cerebellum.•Alpha underlies information transfer in premotor-cerebellar loop during learning. 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. [Display omitted]

Sleep spindle activity has repeatedly been found to contribute to brain plasticity and consolidation of both declarative and procedural memories. Here we propose a framework for motor memory consolidation that outlines the essential contribution of the hierarchical and multi-scale periodicity of spindle activity, as well as of the synchronization and interaction of brain oscillations during this sleep-dependent process. We posit that the clustering of sleep spindles in ‘trains', together with the temporally organized alternation between spindles and associated refractory periods, is critical for efficient reprocessing and consolidation of motor memories. We further argue that the long-term retention of procedural memories relies on the synchronized (functional connectivity) local reprocessing of new information across segregated, but inter-connected brain regions that are involved in the initial learning process. Finally, we propose that oscillatory synchrony in the spindle frequency band may reflect the cross-structural reactivation, reorganization and consolidation of motor, and potentially declarative, memory traces within broader subcortical–cortical networks during sleep. This article is part of the Theo Murphy meeting issue ‘Memory reactivation: replaying events past, present and future'.

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. The present study highlights the significance of using long‐range temporal correlations (LRTC) within the rsfMRI BOLD signal as a potential sensitive biomarker for functional neuroplasticity. Our findings demonstrate that decreases in LRTC reflect sequence‐specific motor learning and performance improvements, and that these changes persist even after a two‐week break from training. These results suggest that alterations in functional dynamics represent the newly learned skill and support the use of LRTC as a sensitive measure of functional neuroplasticity resulting from complex motor learning.

Motor sequence learning under high levels of contextual interference (CI) disrupts initial performance but supports delayed test and transfer performance when compared to learning under low CI. Integrating findings from early behavioral work and more recent experimental efforts that incorporated neurophysiologic measures led to a novel account of the role of CI during motor sequence learning. This account focuses on important contributions from two neural regions—the dorsal premotor area and the SMA complex—that are recruited earlier and more extensively during the planning of a motor sequence in a high CI context. It is proposed that activation of these regions is critical to early adaptation of sequence structure amenable to long-term storage. Moreover, greater CI enhances access to newly acquired motor sequence knowledge through (1) the emergence of temporary functional connectivity between neural sites previously described as crucial to successful long-term performance of sequential behaviors, and (2) heightened excitability of M1—a key constituent of the temporary coupled neural circuits, and the primary candidate for storage of motor memory.

Motor sequence learning involves both oculomotor and manual motor systems, yet the role of the oculomotor system in the learning and execution of skilled arm movements remains underexplored. In the current work, the influence of sequence learning on the oculomotor system was investigated by testing 20 healthy adults for 3 days as they practiced an implicit motor learning task, the serial targeting task (STT). The STT contained a repeated sequence, which was interleaved with random sequences. This task was practiced on a KINARM robot that tracked both saccades and reaches. A delayed, 24-h retention test assessed sequence-specific motor learning. Sequence-specific changes across practice and learning were observed for both saccades and reaches; this was demonstrated by faster saccade and arm motor reaction times for the repeated sequence compared to random sequences. Notably, change in the oculomotor system occurred earlier in practice as compared to the manual motor system. Reaches were executed more quickly when led by express saccades (rapid eye movements occurring within 90–120 ms) compared to when they were preceded by regular latency (> 120 ms) saccades early in practice. Our findings highlight distinct yet interconnected functions between oculomotor and manual motor systems associated with implicit motor sequence learning.