Striatal and hippocampal involvement in motor sequence chunking depends on the learning strategy

Coherent, time-shifted patterns of microstructural plasticity during motor-skill learning

Motor skill learning relies on neural plasticity in the motor and limbic systems. However, the spatial and temporal characteristics of these changes-and their microstructural underpinnings-remain unclear. Eighteen healthy males received 1 hour of training in a computer-based motion game, 4 times a week, for 4 consecutive weeks, while 14 untrained participants underwent scanning only. Performance improvements were observed in all trained participants. Serial myelin- and iron-sensitive multiparametric mapping at 3T during this period of intensive motor skill acquisition revealed temporally and spatially distributed, performance-related microstructural changes in the grey and white matter across a corticospinal-cerebellar-hippocampal circuit. Analysis of the trajectory of these transient changes suggested time-shifted cascades of plasticity from the dominant sensorimotor system to the contralateral hippocampus. In the cranial corticospinal tracts, changes in myelin-sensitive metrics during training in the posterior limb of the internal capsule were of greater magnitude in those who trained their upper limbs vs. lower limb trainees. Motor skill learning is associated with waves of grey and white matter plasticity, across a broad sensorimotor network.

Exploring pain interference with motor skill learning in humans: A systematic review

Motor learning underpins successful motor skill acquisition. Although it is well known that pain changes the way we move, it’s impact on motor learning is less clear. The aim of this systematic review was to synthesize evidence on the impact of experimental and clinical pain on task performance and activity-dependent plasticity measures across learning and explore these findings in relation to different pain and motor learning paradigms. Five databases were searched: Web of Science, Scopus, MEDLINE, Embase and CINAHL. Two reviewers independently screened the studies, extracted data, and assessed risk of bias using the Cochrane ROB2 and ROBIN-I. The overall strength of evidence was rated using the GRADE guidelines. Due to the heterogeneity of study methodologies a narrative synthesis was employed. Twenty studies were included in the review: fifteen experimental pain and five clinical pain studies, covering multiple motor paradigms. GRADE scores for all outcome measures suggested limited confidence in the reported effect for experimental pain and clinical pain, on motor learning. There was no impact of pain on any of the task performance measures following acquisition except for ‘accuracy’ during a tongue protrusion visuomotor task and ‘timing of errors’ during a motor adaptation locomotion task. Task performance measures at retention, and activity dependent measures at both acquisition and retention showed conflicting results. This review delivers a detailed synthesis of research studies exploring the impact of pain on motor learning. This is despite the challenges provided by the heterogeneity of motor learning paradigms, outcome measures and pain paradigms employed in these studies. The results highlight important questions for further research with the goal of strengthening the confidence of findings in this area.

Differences in implicit motor learning between adults who do and do not stutter

Implicit learning allows us to acquire complex motor skills through repeated exposure to sensory cues and repetition of motor behaviours, without awareness or effort. Implicit learning is also critical to the incremental fine-tuning of the perceptual-motor system. To understand how implicit learning and associated domain-general learning processes may contribute to motor learning differences in people who stutter, we investigated implicit finger-sequencing skills in adults who do (AWS) and do not stutter (ANS) on an Alternating Serial Reaction Time task. Our results demonstrated that, while all participants showed evidence of significant sequence-specific learning in their speed of performance, male AWS were slower and made fewer sequence-specific learning gains than their ANS counterparts. Although there were no learning gains evident in accuracy of performance, AWS performed the implicit learning task more accurately than ANS, overall. These findings may have implications for sex-based differences in the experience of developmental stuttering, for the successful acquisition of complex motor skills during development by individuals who stutter, and for the updating and automatization of speech motor plans during the therapeutic process.

Habitual or hyper-controlled behavior: OCD symptoms and explicit sequence learning

BACKGROUND AND OBJECTIVES

This study examined whether ritualistic behaviors characteristic of obsessive-compulsive disorder (OCD) are a product of dysfunctional goal-directed behavior leading to habitual behavior (Gillan & Robbins, 2014). We used an explicit motor sequence learning task to investigate the repetition of chunked action sequences across the OC spectrum. As sequential motor behavior is practiced, action movements appear to get bundled together, and the initial movement of the sequence activates the entire sequence, leaving it relatively insensitive to change. Therefore, compulsive behavior in OCD may be a result of failing to inhibit the full activation of an extensively learned action sequence.

METHODS

Fifty-seven participants across the range of OCD symptoms practiced one sequence and then tested on a novel sequence in which one of the middle movements was omitted. Optimal performance for the new sequence required goal-directed inhibition of the original sequence and goal-directed execution of the new sequence instead. To manipulate activation of goal-directed behavior, we added a dual-task condition with a competing auditory tonal N-Back task. Data were analyzed using mixed-effects models.

RESULTS

Although we did observe expected learning patterns during learning of the original sequence, slower reaction times for the new sequences, and higher errors in the dual-task condition, performance was not significantly related to either obsessive-compulsive symptoms or distress symptoms.

LIMITATIONS

The current study used an analog sample; replication in a treatment seeking sample is warranted.

CONCLUSIONS

These findings challenge the goal-directed dysfunction model of OCD.

Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits

Adaptive sequential behavior is a hallmark of human cognition. In particular, humans can learn to produce precise spatiotemporal sequences given a certain context. For instance, musicians can not only reproduce learned action sequences in a context-dependent manner, they can also quickly and flexibly reapply them in any desired tempo or rhythm without overwriting previous learning. Existing neural network models fail to account for these properties. We argue that this limitation emerges from the fact that sequence information (i.e., the position of the action) and timing (i.e., the moment of response execution) are typically stored in the same neural network weights. Here, we augment a biologically plausible recurrent neural network of cortical dynamics to include a basal ganglia-thalamic module which uses reinforcement learning to dynamically modulate action. This “associative cluster-dependent chain” (ACDC) model modularly stores sequence and timing information in distinct loci of the network. This feature increases computational power and allows ACDC to display a wide range of temporal properties (e.g., multiple sequences, temporal shifting, rescaling, and compositionality), while still accounting for several behavioral and neurophysiological empirical observations. Finally, we apply this ACDC network to show how it can learn the famous “Thunderstruck” song intro and then flexibly play it in a “bossa nova” rhythm without further training.

How do humans flexibly adapt action sequences? For instance, musicians can learn a song and quickly speed up or slow down the tempo, or even play the song following a completely different rhythm (e.g., a rock song using a bossa nova rhythm). In this work, we build a biologically plausible network of cortico-basal ganglia interactions that explains how this temporal flexibility may emerge in the brain. Crucially, our model factorizes sequence order and action timing, respectively represented in cortical and basal ganglia dynamics. This factorization allows full temporal flexibility, i.e. the timing of a learned action sequence can be recomposed without interfering with the order of the sequence. As such, our model is capable of learning asynchronous action sequences, and flexibly shift, rescale, and recompose them, while accounting for biological data.

Brain Activity and Functional Connectivity Patterns Associated With Fast and Slow Motor Sequence Learning in Late Middle Adulthood

The human brain undergoes structural and functional changes across the lifespan. The study of motor sequence learning in elderly subjects is of particularly interest since previous findings in young adults might not replicate during later stages of adulthood. The present functional magnetic resonance imaging (fMRI) study assessed the performance, brain activity and functional connectivity patterns associated with motor sequence learning in late middle adulthood. For this purpose, a total of 25 subjects were evaluated during early stages of learning [i.e., fast learning (FL)]. A subset of these subjects (n = 11) was evaluated after extensive practice of a motor sequence [i.e., slow learning (SL) phase]. As expected, late middle adults improved motor performance from FL to SL. Learning-related brain activity patterns replicated most of the findings reported previously in young subjects except for the lack of hippocampal activity during FL and the involvement of cerebellum during SL. Regarding functional connectivity, precuneus and sensorimotor lobule VI of the cerebellum showed a central role during improvement of novel motor performance. In the sample of subjects evaluated, connectivity between the posterior putamen and parietal and frontal regions was significantly decreased with aging during SL. This age-related connectivity pattern may reflect losses in network efficiency when approaching late adulthood. Altogether, these results may have important applications, for instance, in motor rehabilitation programs.

Encoding and consolidation of motor sequence learning in young and older adults

Sleep benefits motor memory consolidation in young adults, but this benefit is reduced in older adults. Here we sought to understand whether differences in the neural bases of encoding between young and older adults contribute to aging-related differences in sleep-dependent consolidation of an explicit variant of the serial reaction time task (SRTT). Seventeen young and 18 older adults completed two sessions (nap, wake) one week apart. In the MRI, participants learned the SRTT. Following an afternoon interval either awake or with a nap (recorded with high-density polysomnography), performance on the SRTT was reassessed in the MRI. Imaging and behavioral results from SRTT performance showed clear sleep-dependent consolidation of motor sequence learning in older adults after a daytime nap, compared to an equal interval awake. Young adults, however, showed brain activity and behavior during encoding consistent with high SRTT performance prior to the sleep interval, and did not show further sleep-dependent performance improvements. Young adults did show reduced cortical activity following sleep, suggesting potential systems-level consolidation related to automatization. Sleep physiology data showed that sigma activity topography was affected by hippocampal and cortical activation prior to the nap in both age groups, and suggested a role of theta activity in sleep-dependent automatization in young adults. These results suggest that previously observed aging-related sleep-dependent consolidation deficits may be driven by aging-related deficiencies in fast learning processes. Here we demonstrate that when sufficient encoding strength is reached with additional training, older adults demonstrate intact sleep-dependent consolidation of motor sequence learning.

Explicit motor sequence learning after stroke: a neuropsychological study

Motor learning interacts with and shapes experience-dependent cerebral plasticity. In stroke patients with paresis of the upper limb, motor recovery was proposed to reflect a process of re-learning the lost/impaired skill, which interacts with rehabilitation. However, to what extent stroke patients with hemiparesis may retain the ability of learning with their affected limb remains an unsolved issue, that was addressed by this study. Nineteen patients, with a cerebrovascular lesion affecting the right or the left hemisphere, underwent an explicit motor learning task (finger tapping task, FTT), which was performed with the paretic hand. Eighteen age-matched healthy participants served as controls. Motor performance was assessed during the learning phase (i.e., online learning), as well as immediately at the end of practice, and after 90 min and 24 h (i.e., retention). Results show that overall, as compared to the control group, stroke patients, regardless of the side (left/right) of the hemispheric lesion, do not show a reliable practice-dependent improvement; consequently, no retention could be detected in the long-term (after 90 min and 24 h). The motor learning impairment was associated with subcortical damage, predominantly affecting the basal ganglia; conversely, it was not associated with age, time elapsed from stroke, severity of upper-limb motor and sensory deficits, and the general neurological condition. This evidence expands our understanding regarding the potential of post-stroke motor recovery through motor practice, suggesting a potential key role of basal ganglia, not only in implicit motor learning as previously pointed out, but also in explicit finger tapping motor tasks.

Recursion in action: An fMRI study on the generation of new hierarchical levels in motor sequences

Generation of hierarchical structures, such as the embedding of subordinate elements into larger structures, is a core feature of human cognition. Processing of hierarchies is thought to rely on lateral prefrontal cortex (PFC). However, the neural underpinnings supporting active generation of new hierarchical levels remain poorly understood. Here, we created a new motor paradigm to isolate this active generative process by means of fMRI. Participants planned and executed identical movement sequences by using different rules: a Recursive hierarchical embedding rule, generating new hierarchical levels; an Iterative rule linearly adding items to existing hierarchical levels, without generating new levels; and a Repetition condition tapping into short term memory, without a transformation rule. We found that planning involving generation of new hierarchical levels (Recursive condition vs. both Iterative and Repetition) activated a bilateral motor imagery network, including cortical and subcortical structures. No evidence was found for lateral PFC involvement in the generation of new hierarchical levels. Activity in basal ganglia persisted through execution of the motor sequences in the contrast Recursive versus Iteration, but also Repetition versus Iteration, suggesting a role of these structures in motor short term memory. These results showed that the motor network is involved in the generation of new hierarchical levels during motor sequence planning, while lateral PFC activity was neither robust nor specific. We hypothesize that lateral PFC might be important to parse hierarchical sequences in a multi‐domain fashion but not to generate new hierarchical levels.

Neural Competitive Queuing of Ordinal Structure Underlies Skilled Sequential Action

Fluent retrieval and execution of movement sequences is essential for daily activities, but the neural mechanisms underlying sequence planning remain elusive. Here participants learned finger press sequences with different orders and timings and reproduced them in a magneto-encephalography (MEG) scanner. We classified the MEG patterns for each press in the sequence and examined pattern dynamics during preparation and production. Our results demonstrate the “competitive queuing” (CQ) of upcoming action representations, extending previous computational and non-human primate recording studies to non-invasive measures in humans. In addition, we show that CQ reflects an ordinal template that generalizes across specific motor actions at each position. Finally, we demonstrate that CQ predicts participants’ production accuracy and originates from parahippocampal and cerebellar sources. These results suggest that the brain learns and controls multiple sequences by flexibly combining representations of specific actions and interval timing with high-level, parallel representations of sequence position.

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Non-invasive recordings in humans show “competitive queuing” of upcoming actions

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Queuing gradient mainly reflects a high-level template of sequence position

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Queuing gradient originates in ipsilateral parahippocampal and cerebellar areas

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Strength of queuing gradient predicts production accuracy

Walking, Gross Motor Development, and Brain Functional Connectivity in Infants and Toddlers

Infant gross motor development is vital to adaptive function and predictive of both cognitive outcomes and neurodevelopmental disorders. However, little is known about neural systems underlying the emergence of walking and general gross motor abilities. Using resting state fcMRI, we identified functional brain networks associated with walking and gross motor scores in a mixed cross-sectional and longitudinal cohort of infants at high and low risk for autism spectrum disorder, who represent a dimensionally distributed range of motor function. At age 12 months, functional connectivity of motor and default mode networks was correlated with walking, whereas dorsal attention and posterior cingulo-opercular networks were implicated at age 24 months. Analyses of general gross motor function also revealed involvement of motor and default mode networks at 12 and 24 months, with dorsal attention, cingulo-opercular, frontoparietal, and subcortical networks additionally implicated at 24 months. These findings suggest that changes in network-level brain-behavior relationships underlie the emergence and consolidation of walking and gross motor abilities in the toddler period. This initial description of network substrates of early gross motor development may inform hypotheses regarding neural systems contributing to typical and atypical motor outcomes, as well as neurodevelopmental disorders associated with motor dysfunction.

The Role of Human Primary Motor Cortex in the Production of Skilled Finger Sequences

Human primary motor cortex (M1) is essential for producing dexterous hand movements. Although distinct subpopulations of neurons are activated during single-finger movements, it remains unknown whether M1 also represents sequences of multiple finger movements. Using novel multivariate functional magnetic resonance imaging (fMRI) analysis techniques and combining evidence from both 3T and 7T fMRI data, we found that after 5 d of intense practice, premotor and parietal areas encoded the different movement sequences. There was little or no evidence for a sequence representation in M1. Instead, activity patterns in M1 could be fully explained by a linear combination of patterns for the constituent individual finger movements, with the strongest weight on the first finger of the sequence. Using passive replay of sequences, we show that this first-finger effect is due to neuronal processes involved in the active execution, rather than to a hemodynamic nonlinearity. These results suggest that M1 receives increased input from areas with sequence representations at the initiation of a sequence, but that M1 activity itself relates to the execution of component finger presses only. These results improve our understanding of the representation of finger sequences in the human neocortex after short-term training and provide important methodological advances for the study of long-term skill development.SIGNIFICANCE STATEMENT There is clear evidence that human primary motor cortex (M1) is essential for producing individuated finger movements, such as pressing a button. Over and above its involvement in movement execution, it is less clear whether M1 also plays a role in learning and controlling sequences of multiple finger movements, such as when playing the piano. Using cutting-edge multivariate fMRI analysis and carefully controlled experiments, we demonstrate here that, while premotor areas clearly show a sequence representation, activity patterns in M1 can be fully explained from the patterns for individual finger movements. The results provide important new insights into the interplay of M1 and premotor cortex for learning of sequential movements.

Network-wide reorganization of procedural memory during NREM sleep revealed by fMRI

Sleep is necessary for the optimal consolidation of newly acquired procedural memories. However, the mechanisms by which motor memory traces develop during sleep remain controversial in humans, as this process has been mainly investigated indirectly by comparing pre- and post-sleep conditions. Here, we used functional magnetic resonance imaging and electroencephalography during sleep following motor sequence learning to investigate how newly-formed memory traces evolve dynamically over time. We provide direct evidence for transient reactivation followed by downscaling of functional connectivity in a cortically-dominant pattern formed during learning, as well as gradual reorganization of this representation toward a subcortically-dominant consolidated trace during non-rapid eye movement (NREM) sleep. Importantly, the putamen functional connectivity within the consolidated network during NREM sleep was related to overnight behavioral gains. Our results demonstrate that NREM sleep is necessary for two complementary processes: the restoration and reorganization of newly-learned information during sleep, which underlie human motor memory consolidation.

DOI:http://dx.doi.org/10.7554/eLife.24987.001

The idea that, while you sleep, you could be honing skills such as the ability to play a musical instrument may sound like science fiction. But studies have shown that sleep, in addition to being beneficial for physical and mental health, also enhances memories laid down during the day. The process by which the brain strengthens these memories is called consolidation, but exactly how this process works is unclear.

Memories are thought to persist as altered connections between neurons, often referred to as memory traces. When we practice a skill, we activate the neurons encoding that skill over and over again, strengthening the connections between them. However, if this process were to continue unchecked, eventually the connections would become saturated and no further increases in strength could occur. One possible solution to this problem is that sleep enhances skill learning by downscaling connections across the brain as a whole, thereby freeing up capacity for further learning. Alternatively, sleep may reorganize an initially unstable memory trace into a more robust form with the potential to last a lifetime.

To test these possibilities, Vahdat et al. asked healthy volunteers to practice a finger-tapping task while lying inside a brain scanner, and then to sleep inside that scanner for 2–3 hours. When the volunteers returned to the scanner the next morning and attempted the task again, they performed better than they had the previous night. Their brains also showed a different pattern of activity when performing the task after a night’s sleep.

So what had happened overnight? As the volunteers lay awake inside the scanner, their brains reactivated the memory trace formed during learning. However, as they entered a stage of non-dreaming sleep called non-REM sleep, this activity became weaker. At the same time, a new pattern of activity – the one that would dominate the scan the next morning – began to emerge. Whereas the post-learning activity was mainly in the brain’s outer layer, the cortex, the new pattern included other areas that are deeper within the brain. The activity of one deeper region in particular, the putamen, predicted how well the volunteers would perform the task the next day.

Non-REM sleep thus strengthens memories via two complementary processes. It suppresses the initial memory trace formed during learning, and reorganizes the newly-learned information into a more stable state. These results might explain why people who are sleep-deprived often have impaired motor skills and memories. The findings also open up the possibility of enhancing newly learned skills by manipulating brain circuits during non-REM sleep.

DOI:http://dx.doi.org/10.7554/eLife.24987.002

Taking the brakes off the learning curve

Motor learning is characterized by patterns of cerebello-striato-cortical activations shifting in time, yet the early dynamic and function of these activations remains unclear. Five groups of subjects underwent either continuous or intermittent theta-burst stimulation of one cerebellar hemisphere, or no stimulation just before learning a new motor sequence during fMRI scanning. We identified three phases during initial learning: one rapid, one slow, and one quasi-asymptotic performance phase. These phases were not changed by left cerebellar stimulation. Right cerebellar inhibition, however, accelerated learning and enhanced brain activation in critical motor learning-related areas during the first phase, continuing with reduced brain activation but high-performance in late phase. Right cerebellar excitation did not affect the early learning process, but slowed learning significantly in late phase, along with increased brain activation. We conclude that the right cerebellum is a key factor coordinating other neuronal loops in the early acquisition of an explicit motor sequential skill. Hum Brain Mapp 38:1676-1691, 2017. © 2016 Wiley Periodicals, Inc.

Pramipexole Impairs Stimulus-Response Learning in Healthy Young Adults

Dopaminergic therapy has paradoxical effects on cognition in Parkinson's disease (PD) patients, with some functions worsened and others improved. The dopamine overdose hypothesis is proposed as an explanation for these opposing effects of medication taking into account the varying levels of dopamine within different brain regions in PD. The detrimental effects of medication on cognition have been attributed to exogenous dopamine overdose in brain regions with spared dopamine levels in PD. It has been demonstrated that learning is most commonly worsened by dopaminergic medication. The current study aimed to investigate whether the medication-related learning impairment exhibited in PD patients is due to a main effect of medication by evaluating the dopamine overdose hypothesis in healthy young adults. Using a randomized, double-blind, placebo-controlled design, 40 healthy young undergraduate students completed a stimulus-response learning task. Half of the participants were treated with 0.5 mg of pramipexole, a dopamine agonist, whereas the other half were treated with a placebo. We found that stimulus-response learning was significantly impaired in participants on pramipexole relative to placebo controls. These findings are consistent with the dopamine overdose hypothesis and suggest that dopaminergic medication impairs learning independent of PD pathology. Our results have important clinical implications for conditions treated with pramipexole, particularly PD, restless leg syndrome, some forms of dystonia, and potentially depression.

The hippocampus: a special place for time

Many findings have demonstrated that memories of past events are temporally organized. It is well known that the hippocampus is critical for such episodic memories, but, until recently, little was known about the temporal organization of mnemonic representations in the hippocampus. Recent developments in human and animal research have revealed important insights into the role of the hippocampus in learning and retrieving sequences of events. Here, we review these findings, including lesion and single-unit recording studies in rodents, functional magnetic resonance imaging studies in humans, and computational models that link findings from these studies to the anatomy of the hippocampal circuit. The findings converge toward the idea that the hippocampus is essential for learning sequences of events, allowing the brain to distinguish between memories for conceptually similar but temporally distinct episodes, and to associate representations of temporally contiguous, but otherwise unrelated experiences.

Chunking improves symbolic sequence processing and relies on working memory gating mechanisms

Chunking, namely the grouping of sequence elements in clusters, is ubiquitous during sequence processing, but its impact on performance remains debated. Here, we found that participants who adopted a consistent chunking strategy during symbolic sequence learning showed a greater improvement of their performance and a larger decrease in cognitive workload over time. Stronger reliance on chunking was also associated with higher scores in a WM updating task, suggesting the contribution of WM gating mechanisms to sequence chunking. Altogether, these results indicate that chunking is a cost-saving strategy that enhances effectiveness of symbolic sequence learning.

Motor skill learning between selection and execution

Learning motor skills evolves from the effortful selection of single movement elements to their combined fast and accurate production. We review recent trends in the study of skill learning which suggest a hierarchical organization of the representations that underlie such expert performance, with premotor areas encoding short sequential movement elements (chunks) or particular component features (timing/spatial organization). This hierarchical representation allows the system to utilize elements of well-learned skills in a flexible manner. One neural correlate of skill development is the emergence of specialized neural circuits that can produce the required elements in a stable and invariant fashion. We discuss the challenges in detecting these changes with fMRI.