A Rapid Form of Offline Consolidation in Skill Learning

Level of M1 GABAB predicts micro offline consolidation of motor learning during wakefulness

The consolidation process stabilizes a new initially labile memory. This consolidation could operate on a shorter timescale during wakefulness after initial motor learning. Within micro-offline learning states, sequences of simple individual actions learned through interleaved practice are condensed into a unified skill through a time-dependent consolidation process occurring during wakeful periods. While emerging evidence links Glutamate and GABA modulations in the primary motor cortex (M1) to motor learning, its relationship with micro-offline consolidation processes in brief resting states during motor learning is unclear. To investigate this issue, we employed Transcranial magnetic stimulation (TMS) to evaluate whether interindividual variation of different neurotransmitters at rest influences motor learning consolidation in humans. Our results point to the role of GABAB in micro-offline motor consolidation processes during motor learning in M1. This finding could have an important impact on planning neuropharmacology or non-invasive brain stimulation approaches in clinical domains, such as post-stroke rehabilitation.

Ensemble reactivations during brief rest drive fast learning of sequences

During motor learning, breaks in practice are known to facilitate behavioural optimizations. Although this process has traditionally been studied over long breaks that last hours to days1-6, recent studies in humans have demonstrated that rapid performance gains during early motor sequence learning are most pronounced after very brief breaks lasting seconds to minutes7-10. However, the precise causal neural mechanisms that facilitate performance gains after brief breaks remain poorly understood. Here we recorded neural ensemble activity in the motor cortex of macaques while they performed a visuomotor sequence learning task interspersed with brief breaks. We found that task-related neural cofiring patterns were reactivated during brief breaks. The rate and content of reactivations predicted the magnitude and pattern of subsequent performance gains. Of note, we found that performance gains and reactivations were positively correlated with cortical ripples (80-120 Hz oscillations) but anti-correlated with β bursts (13-30 Hz oscillations), which ultimately dominated breaks after the fast learning phase plateaued. We then applied 20 Hz epidural alternating current stimulation (ACS) to motor cortex, which reduced reactivation rates in a phase-specific and dose-dependent manner. Notably, 20 Hz ACS also eliminated performance gains. Overall, our results indicate that the reactivations of task ensembles during brief breaks are causal drivers of subsequent performance gains. β bursts compete with this process, possibly to support stable performance.

Exploring the Impact of Declarative Learning on the Consolidation of Acquired Motor Skills Under Valence Feedback

Implicit motor learning involves the acquisition and consolidation of motor skills without conscious awareness, influenced by various factors. Punishment and reward have been identified as significant modulators during training, impacting skill acquisition differently. Additionally, the role of a second declarative task in offline consolidation has been explored, affecting both stabilization and enhancement processes during wake and sleep periods. However, how valanced feedback and learning a secondary declarative task can influence the learning and consolidation of implicit motor learning has not been explored. This study investigates whether receiving monetary feedback during motor sequence learning influences consolidation when declarative knowledge about the task is disrupted by a second word-list task. Participants' skill levels were assessed during training, immediately after training, 15 min post-training (after performing the second task), and 24 h later after night sleep. Concurrently, brain synchrony was measured using electroencephalography (EEG) recording. Results indicate that monetary punishment leads to early enhancement and higher performance after the second task compared to reward and control groups. However, after 24 h, no significant enhancement was observed in any group, with differences between groups diminishing. EEG analysis revealed distinct brain subnetworks across alpha, beta, and unexpectedly delta network which traditionally associated with sleep-dependent consolidation. These findings shed light on the complex interplay between valanced feedback learning, declarative memory disruption, and offline consolidation in implicit motor learning, highlighting the dynamic nature of skill acquisition and retention, offering potential implications for targeted interventions and future research directions.

Intact ultrafast memory consolidation in adults with autism and neurotypicals with autism traits

The processes of learning and memory consolidation are closely interlinked. Therefore, to uncover statistical learning in autism spectrum disorder (ASD), an in-depth examination of memory consolidation is essential. Studies of the last five years have revealed that learning can take place not only during practice but also during micro rest (<1 min) between practice blocks, termed micro offline gains. The concept of micro offline gains refers to performance improvements during short rest periods interspersed with practice, rather than during practice itself. This phenomenon is crucial for the acquisition and consolidation of motor skills and has been observed across various learning contexts. Numerous studies on learning in autism have identified intact learning but there has been no investigation into this fundamental aspect of memory consolidation in autistic individuals to date. We conducted two studies with two different samples: 1) neurotypical adults with distinct levels of autistic traits (N = 166) and 2) ASD-diagnosed adults (NASD = 22, NNTP = 20). Participants performed a well-established probabilistic learning task, allowing us to measure two learning processes separately in the same experimental design: statistical learning (i.e., learning probability-based regularities) and visuomotor performance (i.e., speed-up regardless of probabilities). Here we show considerable individual differences in offline (between blocks) changes during statistical learning and between-blocks improvement during visuomotor performance. However, cumulative evidence from individual studies suggests that the degree of autistic traits and ASD status are not associated with micro offline gains, indicating that, like statistical learning, rapid memory consolidation is intact.

M1 recruitment during interleaved practice is important for encoding, not just consolidation, of skill memory

The primary motor cortex (M1) is crucial for motor skill learning. We examined its role in interleaved practice, which enhances retention (vs. repetitive practice) through M1-dependent consolidation. We hypothesized that cathodal transcranial direct current stimulation (ctDCS) to M1 would disrupt retention. We found that ctDCS reduced retention due to weakened encoding during acquisition, not disrupted consolidation. These results highlight M1's broad role in encoding and retention of novel motor skills.

Motor sequence learning elicits mu peak-specific corticospinal plasticity

Motor cortical (M1) transcranial magnetic stimulation (TMS) interventions increase corticospinal output and improve motor learning when delivered during sensorimotor mu rhythm trough but not peak phases, suggesting that the mechanisms supporting motor learning may be most active during mu trough phases. Based on these findings, we predicted that motor sequence learning-related corticospinal plasticity would be most evident when measured during mu trough phases. Healthy adults were assigned to either a sequence or no-sequence group. Participants in the sequence group practiced the implicit serial reaction time task (SRTT), which contained an embedded, repeating 12-item sequence. Participants in the no-sequence group practiced a version of the SRTT that contained no sequence. We measured mu phase-independent and mu phase-dependent MEP amplitudes using EEG-informed single-pulse TMS before, immediately after, and 30 minutes after the SRTT in both groups. All participants performed a retention test one hour after SRTT acquisition. In both groups, mu phase-independent MEP amplitudes increased following SRTT acquisition, but the pattern of mu phase-dependent MEP amplitude changes after SRTT acquisition differed between groups. Relative to the no-sequence group, the sequence group showed greater peak-specific MEP amplitude increases 30 minutes after SRTT acquisition. Further, the magnitude of these peak-specific MEP amplitude increases was negatively associated with the magnitude of sequence-specific learning. Contrary to our original hypothesis, results revealed that motor sequence-specific learning elicits peak-specific corticospinal plasticity. Findings provide first direct evidence for the presence of a mu phase-dependent motor learning mechanism in the human brain.

New and Noteworthy

Recent work suggests that motor learning's neural mechanisms may be most active during specific sensorimotor mu rhythm phases. If so, motor sequence learning-induced corticospinal plasticity should be more evident during some mu phases than others. Our results show that motor sequence-specific learning elicits corticospinal plasticity that is most prominent during mu peak phases. Further, this peak-specific plasticity correlates with learning. Findings establish the presence of a mu phase-dependent motor learning mechanism in the human brain.

Cingulate and striatal hubs are linked to early skill learning

Early skill learning develops in the context of activity changes in distributed cortico-subcortical regions. Here, we investigated network hubs-centers of information integration and transmission-within the brain network supporting early skill learning. We recorded magnetoencephalographic (MEG) brain activity in healthy human subjects who learned a moderately difficult sequence skill with their non-dominant left hand. We then computed network hub strength by summing top 10% functional connectivity over 86 parcellated brain regions (AAL3 atlas) and five brain oscillatory frequency bands (alpha, low-, high-beta, low- and high-gamma). Virtually all skill gains developed during rest intervals of early learning (micro-offline gains). MEG hub strength in the alpha band (8-13Hz) in bilateral anterior cingulate (ACC) and caudate and in the low-beta band (13-16Hz) in bilateral caudate and right putamen correlated with micro-offline gains. These regions linked strongly with the hippocampus, parahippocampal cortex, and lingual and fusiform gyri. Thus, alpha and low-beta brain oscillatory activity in cingulate and striatal regions appear to contribute as hubs of information integration and transmission during early skill learning.

Emerging perspectives for the study of the neural basis of motor behaviour

The 33rd Annual Meeting of the Society for the Neural Control of Movement (NCM) brought together over 500 experts to discuss recent advancements in motor control. This article highlights key topics from the conference, including the foundational mechanisms of motor control, the ongoing debate over the context-dependency of feedforward and feedback processes, and the interplay between motor and cognitive functions in learning, memory, and decision-making. It also presents innovative methods for studying movement in complex, real-world environments.

Rest the brain to learn new gait patterns after stroke

BACKGROUND

The ability to relearn a lost skill is critical to motor recovery after a stroke. Previous studies indicate that stroke typically affects the processes underlying motor control and execution but not the learning of those skills. However, these studies could be confounded by the presence of significant motor impairments. Furthermore, prior research involving the upper extremity indicates that stroke survivors have an advantage in offline motor learning when compared with controls. However, this has not been examined using motor acuity tasks (i.e., tasks focusing on the quality of executed actions) that have direct functional relevance to rehabilitation.

OBJECTIVE

Investigate how stroke affects leg motor skill learning during walking in stroke survivors.

METHODS

Twenty-five participants (10 stroke; 15 controls) were recruited for this prospective, case-control study. Participants learned a novel foot-trajectory tracking task on two consecutive days while walking on a treadmill. The task necessitated greater hip and knee flexion during the swing phase of the gait. Online learning was measured by comparing tracking error at the beginning and end of each practice session, offline (rest-driven) learning was measured by comparing the end of the first practice session to the beginning of the second, and retention was measured by comparing the beginning of the first practice session to the beginning of the second. Online learning, offline learning, and retention were compared between the stroke survivors and uninjured controls.

RESULTS

Stroke survivors improved their tracking performance on the first day (p = 0.033); however, the amount of learning in stroke survivors was lower in comparison with the control group on both days (p ≤ 0.05). Interestingly, stroke survivors showed higher offline learning gains when compared with uninjured controls (p = 0.011).

CONCLUSIONS

Even stroke survivors with no perceivable motor impairments have difficulty acquiring new motor skills related to walking, which may be related to the underlying neural damage caused at the time of stroke. Furthermore, stroke survivors may require longer training with adequate rest to acquire new motor skills.

Sleep improves accuracy, but not speed, of generalized motor learning in young and older adults and in individuals with Parkinson's disease

An essential aspect of motor learning is generalizing procedural knowledge to facilitate skill acquisition across diverse conditions. Here, we examined the development of generalized motor learning during initial practice-dependent learning, and how distinct components of learning are consolidated over longer timescales during wakefulness or sleep. In the first experiment, a group of young healthy volunteers engaged in a novel motor sequence task over 36 h in a two-arm experimental design (either morning-evening-morning, or evening-morning-evening) aimed at controlling for circadian confounders. The findings unveiled an immediate, rapid generalization of sequential learning, accompanied by an additional long-timescale performance gain. Sleep modulated accuracy, but not speed, above and beyond equivalent wake intervals. To further elucidate the role of sleep across ages and under neurodegenerative disorders, a second experiment utilized the same task in a group of early-stage, drug-naïve individuals with Parkinson's disease and in healthy individuals of comparable age. Participants with Parkinson's disease exhibited comparable performance to their healthy age-matched group with the exception of reduced performance in recalling motor sequences, revealing a disease-related cognitive shortfall. In line with the results found in young subjects, both groups exhibited improved accuracy, but not speed, following a night of sleep. This result emphasizes the role of sleep in skill acquisition and provides a potential framework for deeper investigation of the intricate relationship between sleep, aging, Parkinson's disease, and motor learning.

Desiderata for Normative Models of Synaptic Plasticity

Normative models of synaptic plasticity use computational rationales to arrive at predictions of behavioral and network-level adaptive phenomena. In recent years, there has been an explosion of theoretical work in this realm, but experimental confirmation remains limited. In this review, we organize work on normative plasticity models in terms of a set of desiderata that, when satisfied, are designed to ensure that a given model demonstrates a clear link between plasticity and adaptive behavior, is consistent with known biological evidence about neural plasticity and yields specific testable predictions. As a prototype, we include a detailed analysis of the REINFORCE algorithm. We also discuss how new models have begun to improve on the identified criteria and suggest avenues for further development. Overall, we provide a conceptual guide to help develop neural learning theories that are precise, powerful, and experimentally testable.

Finding Pattern in the Noise: Persistent Implicit Statistical Knowledge Impacts the Processing of Unpredictable Stimuli

Humans can extract statistical regularities of the environment to predict upcoming events. Previous research recognized that implicitly acquired statistical knowledge remained persistent and continued to influence behavior even when the regularities were no longer present in the environment. Here, in an fMRI experiment, we investigated how the persistence of statistical knowledge is represented in the brain. Participants (n = 32) completed a visual, four-choice, RT task consisting of statistical regularities. Two types of blocks constantly alternated with one another throughout the task: predictable statistical regularities in one block type and unpredictable ones in the other. Participants were unaware of the statistical regularities and their changing distribution across the blocks. Yet, they acquired the statistical regularities and showed significant statistical knowledge at the behavioral level not only in the predictable blocks but also in the unpredictable ones, albeit to a smaller extent. Brain activity in a range of cortical and subcortical areas, including early visual cortex, the insula, the right inferior frontal gyrus, and the right globus pallidus/putamen contributed to the acquisition of statistical regularities. The right insula, inferior frontal gyrus, and hippocampus as well as the bilateral angular gyrus seemed to play a role in maintaining this statistical knowledge. The results altogether suggest that statistical knowledge could be exploited in a relevant, predictable context as well as transmitted to and retrieved in an irrelevant context without a predictable structure.

Rest to Promote Learning: A Brain Default Mode Network Perspective

The brain often switches freely between focused attention and divergent thinking, and the Default Mode Network (DMN) is activated during brain rest. Since its discovery, the DMN, together with its function and characteristics, indicates that learning does not stop when the brain "rests". Therefore, DMN plays an important role in learning. Neural activities such as beta wave rhythm regulation, "subconscious" divergence thinking mode initiation, hippocampal function, and neural replay occur during default mode, all of which explains that "rest" promotes learning. This paper summarized the function and neural mechanism of DMN in learning and proposed that the DMN plays an essential role in learning, which is that it enables rest to promote learning.

Children exhibit a developmental advantage in the offline processing of a learned motor sequence

Changes in specific behaviors across the lifespan are frequently reported as an inverted-U trajectory. That is, young adults exhibit optimal performance, children are conceptualized as developing systems progressing towards this ideal state, and older adulthood is characterized by performance decrements. However, not all behaviors follow this trajectory, as there are instances in which children outperform young adults. Here, we acquired data from 7-35 and >55 year-old participants and assessed potential developmental advantages in motor sequence learning and memory consolidation. Results revealed no credible evidence for differences in initial learning dynamics among age groups, but 7- to 12-year-old children exhibited smaller sequence-specific learning relative to adolescents, young adults and older adults. Interestingly, children demonstrated the greatest performance gains across the 5 h and 24 h offline periods, reflecting enhanced motor memory consolidation. These results suggest that children exhibit an advantage in the offline processing of recently learned motor sequences.

Older and younger adults differ in time course of skill acquisition but not in overall improvement in a bimanual visuomotor tracking task

Manual motor performance declines with age, but the extent to which age influences the acquisition of new skills remains a topic of debate. Here, we examined whether older healthy adults show less training-dependent performance improvements during a single session of a bimanual pinch task than younger adults. We also explored whether physical and cognitive factors, such as grip strength or motor-cognitive ability, are associated with performance improvements. Healthy younger (n = 16) and older (n = 20) adults performed three training blocks separated by short breaks. Participants were tasked with producing visually instructed changes in pinch force using their right and left thumb and index fingers. Task complexity was varied by shifting between bimanual mirror-symmetric and inverse-asymmetric changes in pinch force. Older adults generally displayed higher visuomotor force tracking errors during the more complex inverse-asymmetric task compared to younger adults. Both groups showed a comparable net decrease in visuomotor force tracking error over the entire session, but their improvement trajectories differed. Young adults showed enhanced visuomotor tracking error only in the first block, while older adults exhibited a more gradual improvement over the three training blocks. Furthermore, grip strength and performance on a motor-cognitive test battery scaled positively with individual performance improvements during the first block in both age groups. Together, the results show subtle age-dependent differences in the rate of bimanual visuomotor skill acquisition, while overall short-term learning ability is maintained.

Maintenance of Procedural Motor Memory across Brief Rest Periods Requires the Hippocampus

Research on the role of the hippocampus in memory acquisition has generally focused on active learning. But to understand memory, it is at least as important to understand processes that happen offline, during both wake and sleep. In a study of patients with amnesia, we previously demonstrated that although a functional hippocampus is not necessary for the acquisition of procedural motor memory during training session, it is required for its offline consolidation during sleep. Here, we investigated whether an intact hippocampus is also required for the offline consolidation of procedural motor memory while awake. Patients with amnesia due to hippocampal damage (n = 4, all male) and demographically matched controls (n = 10, 8 males) trained on the finger tapping motor sequence task. Learning was measured as gains in typing speed and was divided into online (during task execution) and offline (during interleaved 30 s breaks) components. Amnesic patients and controls showed comparable total learning, but differed in the pattern of performance improvement. Unlike younger adults, who gain speed across breaks, both groups gained speed only while typing. Only controls retained these gains over the breaks; amnesic patients slowed down and compensated for these losses during subsequent typing. In summary, unlike their peers, whose motor performance remained stable across brief breaks in typing, amnesic patients showed evidence of impaired access to motor procedural memory. We conclude that in addition to being necessary for the offline consolidation of motor memories during sleep, the hippocampus maintains access to motor memory across brief offline periods during wake.

Across-task binding: The development of a representation in learning a continuous movement sequence

Across-task binding is defined as the stimulus/response of one task being linked to the response of another task. The purpose of the present experiment was to determine across-task binding in a continuous movement sequence task with an auditory task of high and low pitch tones and the development of a movement sequence representation. According to the two systems theory of sequence learning, we expected that the developed representation in the across-task binding context relies on the multi-dimensional system rather than on the unidimensional system which is restricted to a set of modules where each module processed information along one task/dimension. An inter-manual transfer design was used to disentangle the sequence representations. The mirror transfer test required the same pattern of muscle activation and joint angles (motor coordinates) in the contralateral limb as experienced during the acquisition phase, while in the non-mirror transfer test, the visual-spatial locations (spatial coordinates) of the target waveform were reinstated. The main finding was that consistently combining visual-spatial positions in a sequence and auditory dimensions such as the tone pitch does not rely on a multidimensional system as predicted by the two-systems theory.

Micro-consolidation occurs when learning an implicit motor sequence, but is not influenced by HIIT exercise

We investigated if micro-consolidation, a phenomenon recently discovered during the brief rest periods between practice when learning an explicit motor sequence, generalises to learning an implicit motor sequence task. We demonstrate micro-consolidation occurs in the absence of explicit sequence awareness. We also investigated the effect of a preceding bout of high-intensity exercise, as exercise is known to augment the consolidation of new motor skills. Micro-consolidation was not modified by exercise.

The dorsomedial prefrontal cortex prioritizes social learning during rest

Sociality is a defining feature of the human experience: We rely on others to ensure survival and cooperate in complex social networks to thrive. Are there brain mechanisms that help ensure we quickly learn about our social world to optimally navigate it? We tested whether portions of the brain's default network engage "by default" to quickly prioritize social learning during the memory consolidation process. To test this possibility, participants underwent functional MRI (fMRI) while viewing scenes from the documentary film, Samsara. This film shows footage of real people and places from around the world. We normed the footage to select scenes that differed along the dimension of sociality, while matched on valence, arousal, interestingness, and familiarity. During fMRI, participants watched the "social" and "nonsocial" scenes, completed a rest scan, and a surprise recognition memory test. Participants showed superior social (vs. nonsocial) memory performance, and the social memory advantage was associated with neural pattern reinstatement during rest in the dorsomedial prefrontal cortex (DMPFC), a key node of the default network. Moreover, it was during early rest that DMPFC social pattern reinstatement was greatest and predicted subsequent social memory performance most strongly, consistent with the "prioritization" account. Results simultaneously update 1) theories of memory consolidation, which have not addressed how social information may be prioritized in the learning process, and 2) understanding of default network function, which remains to be fully characterized. More broadly, the results underscore the inherent human drive to understand our vastly social world.

Timing of transcranial direct current stimulation at M1 does not affect motor sequence learning

Administering anodal transcranial direct current stimulation (tDCS) at the primary motor cortex (M1) at various temporal loci relative to motor training is reported to affect subsequent performance gains. Stimulation administered in conjunction with motor training appears to offer the most robust benefit that emerges during offline epochs. This conclusion is made, however, based on between-experiment comparisons that involved varied methodologies. The present experiment addressed this shortcoming by administering the same 15-minute dose of anodal tDCS at M1 before, during, or after practice of a serial reaction time task (SRTT). It was anticipated that exogenous stimulation during practice with a novel SRTT would facilitate offline gains. Ninety participants were randomly assigned to one of four groups: tDCS before practice, tDCS during practice, tDCS after practice, or no tDCS. Each participant was exposed to 15 min of 2 mA of tDCS and motor training of an eight-element SRTT. The anode was placed at the right M1 with the cathode at the left M1, and the left hand was used to execute the SRTT. Test blocks were administered 1 and 24 h after practice concluded. The results revealed significant offline gain for all conditions at the 1-hour and 24-hour test blocks. Importantly, exposure to anodal tDCS at M1 at any point before, during, or after motor training failed to change the trajectory of skill development as compared to the no-stimulation control condition. These data add to the growing body of evidence questioning the efficacy of a single bout of exogenous stimulation as an adjunct to motor training for fostering skill learning.

Transcranial alternating current stimulation does not affect microscale learning

A theory has been posited that microscale learning, which involves short intervals of a few seconds during explicit motor skill learning, considerably enhances performance. This phenomenon correlates with diminished beta-band activity in the frontal and parietal regions. However, there is a lack of neurophysiological studies regarding the relationship between microscale learning and implicit motor skill learning. In the present study, we aimed to determine the effects of transcranial alternating current stimulation (tACS) during short rest periods on microscale learning in an implicit motor task. We investigated the effects of 20-Hz β-tACS delivered during short rest periods while participants performed an implicit motor task. In Experiments 1 and 2, β-tACS targeted the right dorsolateral prefrontal cortex and the right frontoparietal network, respectively. The participants performed a finger-tapping task using their nondominant left hand, and microscale learning was separately analyzed for micro-online gains (MOnGs) and micro-offline gains (MOffGs). Contrary to our expectations, β-tACS exhibited no statistically significant effects on MOnGs or MOffGs in either Experiment 1 or Experiment 2. In addition, microscale learning during the performance of the implicit motor task was improved by MOffGs in the early learning phase and by MOnGs in the late learning phase. These results revealed that the stimulation protocol employed in this study did not affect microscale learning, indicating a novel aspect of microscale learning in implicit motor tasks. This is the first study to examine microscale learning in implicit motor tasks and may provide baseline information that will be useful in future studies.

Comparison of online, offline, and hybrid hypotheses of motor sequence learning using a quantitative model that incorporate reactive inhibition

Two hypotheses have been advanced for when motor sequence learning occurs: offline between bouts of practice or online concurrently with practice. A third possibility is that learning occurs both online and offline. A complication for differentiating between those hypotheses is a process known as reactive inhibition, whereby performance worsens over consecutively executed sequences, but dissipates during breaks. We advance a new quantitative modeling framework that incorporates reactive inhibition and in which the three learning accounts can be implemented. Our results show that reactive inhibition plays a far larger role in performance than is appreciated in the literature. Across four groups of participants in which break times and correct sequences per trial were varied, the best overall fits were provided by a hybrid model. The version of the offline model that does not account for reactive inhibition, which is widely assumed in the literature, had the worst fits. We discuss implications for extant hypotheses and directions for future research.

Procedural Motor Memory Deficits in Patients With Long-COVID

BACKGROUND AND OBJECTIVES

At least 15% of patients who recover from acute severe acute respiratory syndrome coronavirus 2 infection experience lasting symptoms ("Long-COVID") including "brain fog" and deficits in declarative memory. It is not known if Long-COVID affects patients' ability to form and retain procedural motor skill memories. The objective was to determine the ability of patients with Long-COVID to acquire and consolidate a new procedural motor skill over 2 training days. The primary outcome was to determine difference in early learning, measured as the increase in correct sequence typing speed over the initial 11 practice trials of a new skill. The secondary outcomes were initial and final typing speed on days 1 and 2, learning rate, overnight consolidation, and typing accuracy.

METHODS

In this prospective, cross-sectional, online, case-control study, participants learned a sequential motor skill over 2 consecutive days (NCT05746624). Patients with Long-COVID (reporting persistent post-coronavirus disease 2019 [COVID-19] symptoms for more than 4 weeks) were recruited at the NIH. Patients were matched one-to-one by age and sex to controls recruited during the pandemic using a crowd-sourcing platform. Selection criteria included age 18-90 years, English speaking, right-handed, able to type with the left hand, denied active fever or respiratory infection, and no previous task exposure. Data were also compared with an age-matched and sex-matched control group who performed the task online before the COVID-19 pandemic (prepandemic controls).

RESULTS

In total, 105 of 236 patients contacted agreed to participate and completed the experiment (mean ± SD age 46 ± 12.8 years, 82% female). Both healthy control groups had 105 participants (mean age 46 ± 13.1 and 46 ± 11.9 years, 82% female). Early learning was comparable across groups (Long-COVID: 0.36 ± 0.24 correct sequences/second, pandemic controls: 0.36 ± 0.53 prepandemic controls: 0.38 ± 0.57, patients vs pandemic controls [CI -0.068 to 0.067], vs prepandemic controls [CI -0.084 to 0.052], and between controls [CI -0.083 to 0.053], p = 0.82). Initial and final typing speeds on days 1 and 2 were slower in patients than controls. Patients with Long-COVID showed a significantly reduced overnight consolidation and a nonsignificant trend to reduced learning rates.

DISCUSSION

Early learning was comparable in patients with Long-COVID and controls. Anomalous initial performance is consistent with executive dysfunction. Reduction in overnight consolidation may relate to deficits in procedural memory formation.

Functional connectivity via the dorsolateral prefrontal cortex in the late phase of rest periods predicts offline learning

The relationship between offline learning gains and functional connectivity (FC) has been investigated in several studies. They have focused on average motor task performance and resting-state FC across subjects. Generally, individual differences are seen in both offline learning gain and neurophysiological profiles in resting-state FC. However, few studies have focused on the relationship between individual differences in offline learning gain and temporal characteristics of resting-state FC. The present study aimed to clarify this relationship between the two profiles. Thirty-four healthy right-handed participants performed a force-controlled motor task. Electroencephalography was performed during the 15-minute wakeful rest period between tasks. The results revealed a significant correlation between offline learning gain and FC between the contralateral dorsolateral prefrontal cortex (DLPFC) and contralateral primary motor cortex (M1), and ipsilateral primary somatosensory cortex (S1) during late phase of the rest interval. These results are consistent with the findings of previous studies showing the FC between M1, which is necessary for awake offline learning, and DLPFC, which is related to motor control. Additionally, sensory feedback related to force control may be caused by the interaction between contralateral DLPFC and ipsilateral S1. Our study shed light on the temporal profiles of resting-state FC associated with individual differences in offline learning.

Dyad motor learning in a wrist-robotic environment: Learning together is better than learning alone

OBJECTIVE

Dyad motor practice is characterized by two learners alternating between physical and observational practice, which can lead to better motor outcomes and reduce practice time compared to physical practice alone. Robot-assisted therapy has become an established neurorehabilitation tool but is limited by high therapy cost and access. Implementing dyad practice in robot-assisted rehabilitation has the potential to improve therapeutic outcomes and/or to achieve them faster. This study aims to determine the effects of dyad practice on motor performance in a wrist-robotic environment to evaluate its potential use in robotic rehabilitation settings.

METHODS

Forty-two healthy participants (18-35 years) were randomized into three groups (n = 14): Dyad practice, physical practice with rest and physical practice without rest. Participants practiced a 2 degree-of-freedom gamified wrist movement task for 20 trials using a custom-made wrist robotic device. A motor performance score (MPS) that captured temporal and spatial time-series kinematics was computed at baseline, the end of training and 24 h later to assess retention.

RESULTS

MPS did not differ between groups at baseline. All groups revealed significant performance gains by the end of training. However, dyads outperformed the other groups at the end of training (p < 0.001) and showed higher retention after 24-h (p = 0.02). Median MPS improved by 46.5% in dyads, 25.3% in physical practice-rest, and 33.6% in physical practice-no rest at the end of training compared to baseline.

CONCLUSION

Compared to physical practice alone, dyad practice leads to superior motor outcomes in a robot-assisted motor learning task. Dyads still outperformed their counterparts 24-h after practice.

IMPACT STATEMENT

Improving motor function in complex motor tasks without increasing required practice time, dyad practice can optimize therapeutic resources. This is particularly impactful in robot-assisted rehabilitation regimens as it would help to improve patients' outcomes and increase care efficiency.

Memory consolidation of sequence learning and dynamic adaptation during wakefulness

Motor learning involves acquiring new movement sequences and adapting motor commands to novel conditions. Labile motor memories, acquired through sequence learning and dynamic adaptation, undergo a consolidation process during wakefulness after initial training. This process stabilizes the new memories, leading to long-term memory formation. However, it remains unclear if the consolidation processes underlying sequence learning and dynamic adaptation are independent and if distinct neural regions underpin memory consolidation associated with sequence learning and dynamic adaptation. Here, we first demonstrated that the initially labile memories formed during sequence learning and dynamic adaptation were stabilized against interference through time-dependent consolidation processes occurring during wakefulness. Furthermore, we found that sequence learning memory was not disrupted when immediately followed by dynamic adaptation and vice versa, indicating distinct mechanisms for sequence learning and dynamic adaptation consolidation. Finally, by applying patterned transcranial magnetic stimulation to selectively disrupt the activity in the primary motor (M1) or sensory (S1) cortices immediately after sequence learning or dynamic adaptation, we found that sequence learning consolidation depended on M1 but not S1, while dynamic adaptation consolidation relied on S1 but not M1. For the first time in a single experimental framework, this study revealed distinct neural underpinnings for sequence learning and dynamic adaptation consolidation during wakefulness, with significant implications for motor skill enhancement and rehabilitation.

Effect of cognitive load on time spent offline during wakefulness

Humans continuously alternate between online attention to the current environment and offline attention to internally generated thought and imagery. This may be a fundamental feature of the waking brain, but remains poorly understood. Here, we took a data-driven approach to defining online and offline states of wakefulness, using machine learning methods applied to measures of sensory responsiveness, subjective report, electroencephalogram (EEG), and pupil diameter. We tested the effect of cognitive load on the structure and prevalence of online and offline states, hypothesizing that time spent offline would increase as cognitive load of an ongoing task decreased. We also expected that alternation between online and offline states would persist even in the absence of a cognitive task. As in prior studies, we arrived at a three-state model comprised of one online state and two offline states. As predicted, when cognitive load was high, more time was spent online. Also as predicted, the same three states were present even when participants were not performing a task. These observations confirm our method is successful at isolating seconds-long periods of offline time. Varying cognitive load may be a useful way to manipulate time spent in at least one of these offline states in future experimental studies.

Brain plasticity underlying sleep-dependent motor consolidation after motor imagery

Motor imagery can, similarly to physical practice, improve motor performance through experience-based plasticity. Using magnetoencephalography, we investigated changes in brain activity associated with offline consolidation of motor sequence learning through physical practice or motor imagery. After an initial training session with either physical practice or motor imagery, participants underwent overnight consolidation. As control condition, participants underwent wake-related consolidation after training with motor imagery. Behavioral analyses revealed that overnight consolidation of motor learning through motor imagery outperformed wake-related consolidation (95% CI [0.02, 0.07], P < 0.001, RP2 = 0.05). As regions of interest, we selected the generators of event-related synchronization/desynchronization of alpha (8-12 Hz) and beta (15-30 Hz) oscillations, which predicted the level of performance on the motor sequence. This yielded a primary sensorimotor-premotor network for alpha oscillations and a cortico-cerebellar network for beta oscillations. The alpha network exhibited increased neural desynchronization after overnight consolidation compared to wake-related consolidation. By contrast, the beta network exhibited an increase in neural synchronization after wake-related consolidation compared to overnight consolidation. We provide the first evidence of parallel brain plasticity underlying behavioral changes associated with sleep-dependent consolidation of motor skill learning through motor imagery and physical practice.

Generalization of procedural motor sequence learning after a single practice trial

When humans begin learning new motor skills, they typically display early rapid performance improvements. It is not well understood how knowledge acquired during this early skill learning period generalizes to new, related skills. Here, we addressed this question by investigating factors influencing generalization of early learning from a skill A to a different, but related skill B. Early skill generalization was tested over four experiments (N = 2095). Subjects successively learned two related motor sequence skills (skills A and B) over different practice schedules. Skill A and B sequences shared ordinal (i.e., matching keypress locations), transitional (i.e., ordered keypress pairs), parsing rule (i.e., distinct sequence events like repeated keypresses that can be used as a breakpoint for segmenting the sequence into smaller units) structures, or possessed no structure similarities. Results showed generalization for shared parsing rule structure between skills A and B after only a single 10-second practice trial of skill A. Manipulating the initial practice exposure to skill A (1 to 12 trials) and inter-practice rest interval (0-30 s) between skills A and B had no impact on parsing rule structure generalization. Furthermore, this generalization was not explained by stronger sensorimotor mapping between individual keypress actions and their symbolic representations. In contrast, learning from skill A did not generalize to skill B during early learning when the sequences shared only ordinal or transitional structure features. These results document sequence structure that can be very rapidly generalized during initial learning to facilitate generalization of skill.

Prefrontal stimulation as a tool to disrupt hippocampal and striatal reactivations underlying fast motor memory consolidation

BACKGROUND

Recent evidence suggests that hippocampal replay in humans support rapid motor memory consolidation during epochs of wakefulness interleaved with task practice.

OBJECTIVES/HYPOTHESES

The goal of this study was to test whether such reactivation patterns can be modulated with experimental interventions and in turn influence fast consolidation. We hypothesized that non-invasive brain stimulation targeting hippocampal and striatal networks via the prefrontal cortex would influence brain reactivation and the rapid form of motor memory consolidation.

METHODS

Theta-burst stimulation was applied to a prefrontal cluster functionally connected to both the hippocampus and striatum of young healthy participants before they learned a motor sequence task in a functional magnetic resonance imaging (fMRI) scanner. Neuroimaging data acquired during task practice and the interleaved rest epochs were analyzed to comprehensively characterize the effect of stimulation on the neural processes supporting fast motor memory consolidation.

RESULTS

Our results collectively show that active, as compared to control, theta-burst stimulation of the prefrontal cortex hindered fast motor memory consolidation. Converging evidence from both univariate and multivariate analyses of fMRI data indicate that active stimulation disrupted hippocampal and caudate responses during inter-practice rest, presumably altering the reactivation of learning-related patterns during the micro-offline consolidation episodes. Last, stimulation altered the link between the brain and the behavioral markers of the fast consolidation process.

CONCLUSION

These results suggest that stimulation targeting deep brain regions via the prefrontal cortex can be used to modulate hippocampal and striatal reactivations in the human brain and influence motor memory consolidation.

Sleep-related motor skill consolidation and generalizability after physical practice, motor imagery, and action observation

Sleep benefits the consolidation of motor skills learned by physical practice, mainly through periodic thalamocortical sleep spindle activity. However, motor skills can be learned without overt movement through motor imagery or action observation. Here, we investigated whether sleep spindle activity also supports the consolidation of non-physically learned movements. Forty-five electroencephalographic sleep recordings were collected during a daytime nap after motor sequence learning by physical practice, motor imagery, or action observation. Our findings reveal that a temporal cluster-based organization of sleep spindles underlies motor memory consolidation in all groups, albeit with distinct behavioral outcomes. A daytime nap offers an early sleep window promoting the retention of motor skills learned by physical practice and motor imagery, and its generalizability toward the inter-manual transfer of skill after action observation. Findings may further have practical impacts with the development of non-physical rehabilitation interventions for patients having to remaster skills following peripherical or brain injury.

Desiderata for normative models of synaptic plasticity

Normative models of synaptic plasticity use a combination of mathematics and computational simulations to arrive at predictions of behavioral and network-level adaptive phenomena. In recent years, there has been an explosion of theoretical work on these models, but experimental confirmation is relatively limited. In this review, we organize work on normative plasticity models in terms of a set of desiderata which, when satisfied, are designed to guarantee that a model has a clear link between plasticity and adaptive behavior, consistency with known biological evidence about neural plasticity, and specific testable predictions. We then discuss how new models have begun to improve on these criteria and suggest avenues for further development. As prototypes, we provide detailed analyses of two specific models - REINFORCE and the Wake-Sleep algorithm. We provide a conceptual guide to help develop neural learning theories that are precise, powerful, and experimentally testable.

Combined low-frequency brain oscillatory activity and behavior predict future errors in human motor skill

Human skills are composed of sequences of individual actions performed with utmost precision. When occasional errors occur, they may have serious consequences, for example, when pilots are manually landing a plane. In such cases, the ability to predict an error before it occurs would clearly be advantageous. Here, we asked whether it is possible to predict future errors in a keyboard procedural human motor skill. We report that prolonged keypress transition times (KTTs), reflecting slower speed, and anomalous delta-band oscillatory activity in cingulate-entorhinal-precuneus brain regions precede upcoming errors in skill. Combined anomalous low-frequency activity and prolonged KTTs predicted up to 70% of future errors. Decoding strength (posterior probability of error) increased progressively approaching the errors. We conclude that it is possible to predict future individual errors in skill sequential performance.

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.

Initial motor skill performance predicts future performance, but not learning

People show vast variability in skill performance and learning. What determines a person's individual performance and learning ability? In this study we explored the possibility to predict participants' future performance and learning, based on their behavior during initial skill acquisition. We recruited a large online multi-session sample of participants performing a sequential tapping skill learning task. We used machine learning to predict future performance and learning from raw data acquired during initial skill acquisition, and from engineered features calculated from the raw data. Strong correlations were observed between initial and final performance, and individual learning was not predicted. While canonical experimental tasks developed and selected to detect average effects may constrain insights regarding individual variability, development of novel tasks may shed light on the underlying mechanism of individual skill learning, relevant for real-life scenarios.

Distinct frequencies balance segregation with interaction between different memory types within a prefrontal circuit

Once formed, the fate of memory is uncertain. Subsequent offline interactions between even different memory types (actions versus words) modify retention.^1,2,3,4,5,6 These interactions may occur due to different oscillations functionally linking together different memory types within a circuit.^{7,8,9,10,11,12,13} With memory processing driving the circuit, it may become less susceptible to external influences.¹⁴ We tested this prediction by perturbing the human brain with single pulses of transcranial magnetic stimulation (TMS) and simultaneously measuring the brain activity changes with electroencephalography (EEG^15,16,17). Stimulation was applied over brain areas that contribute to memory processing (dorsolateral prefrontal cortex, DLPFC; primary motor cortex, M1) at baseline and offline, after memory formation, when memory interactions are known to occur.^1,4,6,10,18 The EEG response decreased offline (compared with baseline) within the alpha/beta frequency bands when stimulation was applied to the DLPFC, but not to M1. This decrease exclusively followed memory tasks that interact, revealing that it was due specifically to the interaction, not task performance. It remained even when the order of the memory tasks was changed and so was present, regardless of how the memory interaction was produced. Finally, the decrease within alpha power (but not beta) was correlated with impairment in motor memory, whereas the decrease in beta power (but not alpha) was correlated with impairment in word-list memory. Thus, different memory types are linked to different frequency bands within a DLPFC circuit, and the power of these bands shapes the balance between interaction and segregation between these memories.

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.

Age-related effects on online and offline learning in visuo-spatial working memory

Learning results from online (within-session) and offline (between-sessions) changes. Heterogeneity of age-related effects in learning may be ascribed to aging differentially affecting these two processes. We investigated the contribution of online and offline consolidation in visuo-spatial working memory (vWM). Younger and older participants performed a vWM task on day one and after nine days, allowing us to disentangle online and offline learning effects. To test whether offline consolidation needs continuous practice, two additional groups of younger and older adults performed the same vWM task in between the two assessments. Similarly to other cognitive domains, older adults improved vWM through online (during session one) but not through offline learning. Practice was necessary to improve vWM between sessions in older participants. Younger adults instead exhibited only offline improvement, regardless of practice. The findings suggest that while online learning remains efficient in aging, practice is instead required to support more fragile offline mechanisms.

Breaking the barriers to designing online experiments: A novel open-source platform for supporting procedural skill learning experiments

BACKGROUND

Motor learning experiments are typically performed in laboratory environments, which can be time-consuming and require dedicated equipment/personnel, thus limiting the ability to gather data from large samples. To address this problem, some researchers have transitioned to unsupervised online experiments, showing advantages in participant recruitment without losing validity. However, most online platforms require coding experience or time-consuming setups to create and run experiments, limiting their usage across the field.

METHOD

To tackle this issue, an open-source web-based platform was developed (https://experiments.neurro-lab.engin.umich.edu/) to create, run, and manage procedural skill learning experiments without coding or setup requirements. The feasibility of the platform and the comparability of the results between supervised (n = 17) and unsupervised (n = 24) were tested in 41 naive right-handed participants using an established sequential finger tapping task. The study also tested if a previously reported rapid form of offline consolidation (i.e., microscale learning) in procedural skill learning could be replicated with the developed platform and evaluated the extent of interlimb transfer associated with the finger tapping task.

RESULTS

The results indicated that the performance metrics were comparable between the supervised and unsupervised groups (all p's > 0.05). The learning curves, mean tapping speeds, and micro-scale learning were similar to previous studies. Training led to significant improvements in mean tapping speed (2.22 ± 1.48 keypresses/s, p < 0.001) and a significant interlimb transfer of learning (1.22 ± 1.43 keypresses/s, p < 0.05).

CONCLUSIONS

The results show that the presented platform may serve as a valuable tool for conducting online procedural skill-learning experiments.

Reactivation-induced motor skill modulation does not operate at a rapid micro-timescale level

Abundant evidence shows that consolidated memories are susceptible to modifications following their reactivation. Processes of memory consolidation and reactivation-induced skill modulation have been commonly documented after hours or days. Motivated by studies showing rapid consolidation in early stages of motor skill acquisition, here we asked whether motor skill memories are susceptible to modifications following brief reactivations, even at initial stages of learning. In a set of experiments, we collected crowdsourced online motor sequence data to test whether post-encoding interference and performance enhancement occur following brief reactivations in early stages of learning. Results indicate that memories forming during early learning are not susceptible to interference nor to enhancement within a rapid reactivation-induced time window, relative to control conditions. This set of evidence suggests that reactivation-induced motor skill memory modulation might be dependent on consolidation at the macro-timescale level, requiring hours or days to occur.

Manipulating the Rapid Consolidation Periods in a Learning Task Affects General Skills More than Statistical Learning and Changes the Dynamics of Learning

Memory consolidation processes have traditionally been investigated from the perspective of hours or days. However, recent developments in memory research have shown that memory consolidation processes could occur even within seconds, possibly because of the neural replay of just practiced memory traces during short breaks. Here, we investigate this rapid form of consolidation during statistical learning. We aim to answer (1) whether this rapid consolidation occurs in implicit statistical learning and general skill learning, and (2) whether the duration of rest periods affects these two learning types differently. Human participants performed a widely used statistical learning task-the alternating serial reaction time (ASRT) task-that enables us to measure implicit statistical and general skill learning separately. The ASRT task consisted of 25 learning blocks with a rest period between the blocks. In a between-subjects design, the length of the rest periods was fixed at 15 or 30 s, or the participants could control the length themselves. We found that the duration of rest periods does not affect the amount of statistical knowledge acquired but does change the dynamics of learning. Shorter rest periods led to better learning during the learning blocks, whereas longer rest periods promoted learning also in the between-block rest periods, possibly because of the higher amount of replay. Moreover, we found weaker general skill learning in the self-paced group than in the fixed rest period groups. These results suggest that distinct learning processes are differently affected by the duration of short rest periods.

Improving old tricks as new: Young adults learn from repeating everyday activities

The notion that young healthy adults can substantially improve in activities that are part of their daily routine is often overlooked because it is assumed that such activities have come to be fully mastered. We followed, in young healthy adults, the effects of repeated executions of the Timed-Up-and-Go (TUG) task, a clinical test that assesses the ability to execute motor activities relevant to daily function-rising from a seated position, walking, turning and returning to a seated position. The participants (N = 15) performed 18 consecutive trials of the TUG in one session, and were retested on the following day and a week later. The participants were video recorded and wore inertial measurement units. Task execution times improved robustly; performance was well fitted by a power function, with large gains at the beginning of the session and nearing plateau in later trials, as one would expect in the learning of a novel task. Moreover, these gains were well retained overnight and a week later, with further gains accruing in the subsequent test-sessions. Significant intra-session and inter-session changes occurred in step kinematics as well; some aspects underwent inter-sessions recalibrations, but other aspects showed delayed inter-session changes, suggesting post-practice memory consolidation processes. Even common everyday tasks can be improved upon by practice; a small number of consecutive task repetitions can trigger lasting gains in young healthy individuals performing highly practiced routine tasks. This new learning in highly familiar tasks proceeded in a time-course characteristic of the acquisition of novel 'how to' (procedural) knowledge.

Applied strategies of neuroplasticity

Various levels of somatotopic organization are present throughout the human nervous system. However, this organization can change when needed based on environmental demands, a phenomenon known as neuroplasticity. Neuroplasticity can occur when learning a new motor skill, adjusting to life after blindness, or following a stroke. Following an injury, these neuroplastic changes can be adaptive or maladaptive, and often occur regardless of whether rehabilitation occurs or not. But not all movements produce neuroplasticity, nor do all rehabilitation interventions. Here, we focus on research regarding how to maximize adaptive neuroplasticity while also minimizing maladaptive plasticity, known as applied neuroplasticity. Emphasis is placed on research exploring how best to apply neuroplastic principles to training environments and rehabilitation protocols. By studying and applying these principles in research and clinical practice, it is hoped that learning of skills and regaining of function and independence can be optimized.

Robust enhancement of motor sequence learning with 4 mA transcranial electric stimulation

BACKGROUND AND OBJECTIVES

Motor learning experiments with transcranial direct current stimulation (tDCS) at 2 mA have produced mixed results. We hypothesize that tDCS boosts motor learning provided sufficiently high field intensity on the motor cortex.

METHODS

In a single-blinded design, 108 healthy participants received either anodal (N = 36) or cathodal (N = 36) tDCS at 4 mA total, or no stimulation (N = 36) while they practiced a 12-min sequence learning task. Anodal stimulation was delivered across four electrode pairs (1 mA each), with anodes above the right parietal lobe and cathodes above the right frontal lobe. Cathodal stimulation, with reversed polarities, served as an active control for sensation, while the no-stimulation condition established baseline performance. fMRI-localized targets on the primary motor cortex in 10 subjects were used in current flow models to optimize electrode placement for maximal field intensity. A single electrode montage was then selected for all participants.

RESULTS

We found a significant difference in performance with anodal vs. cathodal stimulation (Cohen's d = 0.71) and vs. no stimulation (d = 0.56). This effect persisted for at least 1 h, and subsequent learning for a new sequence and the opposite hand also improved. Sensation ratings were comparable in the active groups and did not exceed moderate levels. Current flow models suggest the new electrode montage can achieve stronger motor cortex polarization than alternative montages.

CONCLUSION

The present paradigm shows a medium to large effect size and is well-tolerated. It may serve as a go-to experiment for future studies on motor learning and tDCS.

Reward timing matters in motor learning

Reward timing, that is, the delay after which reward is delivered following an action is known to strongly influence reinforcement learning. Here, we asked if reward timing could also modulate how people learn and consolidate new motor skills. In 60 healthy participants, we found that delaying reward delivery by a few seconds influenced motor learning. Indeed, training with a short reward delay (1 s) induced continuous improvements in performance, whereas a long reward delay (6 s) led to initially high learning rates that were followed by an early plateau in the learning curve and a lower performance at the end of training. Participants who learned the skill with a long reward delay also exhibited reduced overnight memory consolidation. Overall, our data show that reward timing affects the dynamics and consolidation of motor learning, a finding that could be exploited in future rehabilitation programs.

Reward and plasticity: Implications for neurorehabilitation

Neuroplasticity follows nervous system injury in the presence or absence of rehabilitative treatments. Rehabilitative interventions can be used to modulate adaptive neuroplasticity, reducing motor impairment and improving activities of daily living in patients with brain lesions. Learning principles guide some rehabilitative interventions. While basic science research has shown that reward combined with training enhances learning, this principle has been only recently explored in the context of neurorehabilitation. Commonly used reinforcers may be more or less rewarding depending on the individual or the context in which the task is performed. Studies in healthy humans showed that both reward and punishment can enhance within-session motor performance; but reward, and not punishment, improves consolidation and retention of motor skills. On the other hand, neurorehabilitative training after brain lesions involves complex tasks (e.g., walking and activities of daily living). The contribution of reward to neurorehabilitation is incompletely understood. Here, we discuss recent research on the role of reward in neurorehabilitation and the needed directions of future research.

Does an interference task immediately after practice prevent memory consolidation of sequence-specific learning?

[Purpose] Learning of movement procedures (sequence learning) is essential in physical therapy. Studies have shown that sequence-specific learning may be integrated from an early stage. This study examines the effect of an interference task on the retention of sequence-specific learning. [Participants and Methods] Young adults were randomly divided into a control group and an interference task group, and two experiments were performed. In each experiment, the control group practiced task A in both the acquisition phase and the retention phase four to five hours later. The Interference group practiced task A in the acquisition phase followed by task B, which is similar to the interference task, and then performed task A in the retention phase four to five hours later. In Experiment 2, the amount of practice for task A in the practice phase was 25% of that in Experiment 1. [Results] Sequence-specific learning occurred in the early stages of practice. In particular, the performance of Experiment 1 reached the ceiling. The results of the retention test showed no significant interference effect due to similar tasks. [Conclusion] Implicit sequence-specific learning stabilizes performance early and is not affected by interference tasks.

Resting-State Functional Connectivity in the Dorsal Attention Network Relates to Behavioral Performance in Spatial Attention Tasks and May Show Task-Related Adaptation

Between-subject variability in cognitive performance has been related to inter-individual differences in functional brain networks. Targeting the dorsal attention network (DAN) we questioned (i) whether resting-state functional connectivity (FC) within the DAN can predict individual performance in spatial attention tasks and (ii) whether there is short-term adaptation of DAN-FC in response to task engagement. Twenty-seven participants first underwent resting-state fMRI (PRE run), they subsequently performed different tasks of spatial attention [including visual search (VS)] and immediately afterwards received another rs-fMRI (POST run). Intra- and inter-hemispheric FC between core hubs of the DAN, bilateral intraparietal sulcus (IPS) and frontal eye field (FEF), was analyzed and compared between PRE and POST. Furthermore, we investigated rs-fMRI-behavior correlations between the DAN-FC in PRE/POST and task performance parameters. The absolute DAN-FC did not change from PRE to POST. However, different significant rs-fMRI-behavior correlations were revealed for intra-/inter-hemispheric connections in the PRE and POST run. The stronger the FC between left FEF and IPS before task engagement, the better was the learning effect (improvement of reaction times) in VS (r = 0.521, p = 0.024). And the faster the VS (mean RT), the stronger was the FC between right FEF and IPS after task engagement (r = −0.502, p = 0.032). To conclude, DAN-FC relates to the individual performance in spatial attention tasks supporting the view of functional brain networks as priors for cognitive ability. Despite a high inter- and intra-individual stability of DAN-FC, the change of FC-behavior correlations after task performance possibly indicates task-related adaptation of the DAN, underlining that behavioral experiences may shape intrinsic brain activity. However, spontaneous state fluctuations of the DAN-FC over time cannot be fully ruled out as an alternative explanation.

Prefrontal stimulation prior to motor sequence learning alters multivoxel patterns in the striatum and the hippocampus

Motor sequence learning (MSL) is supported by dynamical interactions between hippocampal and striatal networks that are thought to be orchestrated by the prefrontal cortex. In the present study, we tested whether individually-tailored theta-burst stimulation of the dorsolateral prefrontal cortex (DLPFC) prior to MSL can modulate multivoxel response patterns in the stimulated cortical area, the hippocampus and the striatum. Response patterns were assessed with multivoxel correlation structure analyses of functional magnetic resonance imaging data acquired during task practice and during resting-state scans before and after learning/stimulation. Results revealed that, across stimulation conditions, MSL induced greater modulation of task-related DLPFC multivoxel patterns than random practice. A similar learning-related modulatory effect was observed on sensorimotor putamen patterns under inhibitory stimulation. Furthermore, MSL as well as inhibitory stimulation affected (posterior) hippocampal multivoxel patterns at post-intervention rest. Exploratory analyses showed that MSL-related brain patterns in the posterior hippocampus persisted into post-learning rest preferentially after inhibitory stimulation. These results collectively show that prefrontal stimulation can alter multivoxel brain patterns in deep brain regions that are critical for the MSL process. They also suggest that stimulation influenced early offline consolidation processes as evidenced by a stimulation-induced modulation of the reinstatement of task pattern into post-learning wakeful rest.