Improvements in the signal-to-noise ratio of motor cortex cells distinguish early versus late phases of motor skill learning

Learning and Control in Motor Cortex across Cell Types and Scales

The motor cortex is essential for controlling the flexible movements underlying complex behaviors. Behavioral flexibility involves the ability to integrate and refine new movements, thereby expanding an animal's repertoire. This review discusses recent strides in motor learning mechanisms across spatial and temporal scales, describing how neural networks are remodeled at the level of synapses, cell types, and circuits and across time as animals' learn new skills. It highlights how changes at each scale contribute to the evolving structure and function of neural circuits that accompanies the expansion and refinement of motor skills. We review new findings highlighted by advanced imaging techniques that have opened new vistas in optical physiology and neuroanatomy, revealing the complexity and adaptability of motor cortical circuits, crucial for learning and control. At the structural level, we explore the dynamic regulation of dendritic spines mediating corticocortical and thalamocortical inputs to the motor cortex. We delve into the role of perisynaptic astrocyte processes in maintaining synaptic stability during learning. We also examine the functional diversity among pyramidal neuron subtypes, their dendritic computations and unique contributions to single cell and network function. Further, we highlight how cortical activation is characterized by increased consistency and reduced strength as new movements are learned and how external inputs contribute to these changes. Finally, we consider the motor cortex's necessity as movements unfold over long time scales. These insights will continue to drive new research directions, enhancing our understanding of motor cortical circuit transformations that underpin behavioral changes expressed throughout an animal's life.

Selective direct motor cortical influence during naturalistic climbing

It remains poorly resolved when and how motor cortical output directly influences limb muscle activity through descending projections, which impedes mechanistic understanding of cortical movement control. Here we addressed this in mice performing an ethologically inspired all-limb climbing behavior. We quantified the direct influence of forelimb primary motor cortex (caudal forelimb area, CFA) on muscle activity comprehensively across the muscle activity states that occur during climbing. We found that CFA informs muscle activity pattern, mainly by selectively activating certain muscles while exerting much smaller, bidirectional effects on their antagonists. From Neuropixel recordings, we identified linear combinations (components) of motor cortical activity that covary with these effects, finding that these components differ from those that covary with muscle activity or kinematics. Collectively, our results reveal an instructive direct motor cortical influence on limb muscles that is selective within a motor behavior and reliant on a new type of neural activity subspace.

Information flow between motor cortex and striatum reverses during skill learning

The coordination of neural activity across brain areas during a specific behavior is often interpreted as neural communication involved in controlling the behavior. However, whether information relevant to the behavior is actually transferred between areas is often untested. Here, we used information-theoretic tools to quantify how motor cortex and striatum encode and exchange behaviorally relevant information about specific reach-to-grasp movement features during skill learning in rats. We found a temporal shift in the encoding of behaviorally relevant information during skill learning, as well as a reversal in the primary direction of behaviorally relevant information flow, from cortex-to-striatum during naive movements to striatum-to-cortex during skilled movements. Standard analytical methods that quantify the evolution of overall neural activity during learning-such as changes in neural signal amplitude or the overall exchange of information between areas-failed to capture these behaviorally relevant information dynamics. Using these standard methods, we instead found a consistent coactivation of overall neural signals during movement production and a bidirectional increase in overall information propagation between areas during learning. Our results show that skill learning is achieved through a transformation in how behaviorally relevant information is routed across cortical and subcortical brain areas and that isolating the components of neural activity relevant to and informative about behavior is critical to uncover directional interactions within a coactive and coordinated network.

Selective plasticity of layer 2/3 inputs onto distal forelimb controlling layer 5 corticospinal neurons with skilled grasp motor training

Layer 5 neurons of the neocortex receive their principal inputs from layer 2/3 neurons. We seek to identify the nature and extent of the plasticity of these projections with motor learning. Using optogenetic and viral intersectional tools to selectively stimulate distinct neuronal subsets in rat primary motor cortex, we simultaneously record from pairs of corticospinal neurons associated with distinct features of motor output control: distal forelimb vs. proximal forelimb. Activation of Channelrhodopsin2-expressing layer 2/3 afferents onto layer 5 in untrained animals produces greater monosynaptic excitation of neurons controlling the proximal forelimb. Following skilled grasp training, layer 2/3 inputs onto corticospinal neurons controlling the distal forelimb associated with skilled grasping become significantly stronger. Moreover, peak excitatory response amplitude nearly doubles while latency shortens, and excitatory-to-inhibitory latencies become significantly prolonged. These findings demonstrate distinct, highly segregated, and cell-specific plasticity of layer 2/3 projections during skilled grasp motor learning.

Modulation of neural spiking in motor cortex-cerebellar networks during sleep spindles

Sleep spindles appear to play an important role in learning new motor skills. Motor skill learning engages several regions in the brain with two important areas being the motor cortex (M1) and the cerebellum. However, the neurophysiological processes in these areas during sleep, especially how spindle oscillations affect local and cross-region spiking, are not fully understood. We recorded activity from the M1 and cerebellar cortex in 8 rats during spontaneous activity to investigate how sleep spindles in these regions are related to local spiking as well as cross-region spiking. We found that M1 firing was significantly changed during both M1 and cerebellum spindles and this spiking occurred at a preferred phase of the spindle. On average, M1 and cerebellum neurons showed most spiking at the M1 or cerebellum spindle peaks. These neurons also developed a preferential phase-locking to local or cross-area spindles with the greatest phase-locking value at spindle peaks; however, this preferential phase-locking wasn't significant for cerebellar neurons when compared to cerebellum spindles. Additionally, we found the percentage of task-modulated cells in the M1 and cerebellum that fired with non-uniform spike-phase distribution during M1/ cerebellum spindle peaks were greater in the rats that learned a reach-to-grasp motor task robustly. Finally, we found that spindle-band LFP coherence (for M1 and cerebellum LFPs) showed a positive correlation with success rate in the motor task. These findings support the idea that sleep spindles in both the M1 and cerebellum recruit neurons that participate in the awake task to support motor memory consolidation.Significance Statement Neural processing during sleep spindles is linked to memory consolidation. However, little is known about sleep activity in the cerebellum and whether cerebellum spindles can affect spiking activity in local or distant areas. We report the effect of sleep spindles on neuron activity in the M1 and cerebellum-specifically their firing rate and phase-locking to spindle oscillations. Our results indicate that awake practice neuronal activity is tempered during local M1 and cerebellum spindles, and during cross-region spindles, which may support motor skill learning. We describe spiking dynamics in motor networks spindle oscillations that may aid in the learning of skills. Our results support the sleep reactivation hypothesis and suggest that awake M1 activity may be reactivated during cerebellum spindles.

Enhancing Prediction of Forelimb Movement Trajectory through a Calibrating-Feedback Paradigm Incorporating RAT Primary Motor and Agranular Cortical Ensemble Activity in the Goal-Directed Reaching Task

Complete reaching movements involve target sensing, motor planning, and arm movement execution, and this process requires the integration and communication of various brain regions. Previously, reaching movements have been decoded successfully from the motor cortex (M1) and applied to prosthetic control. However, most studies attempted to decode neural activities from a single brain region, resulting in reduced decoding accuracy during visually guided reaching motions. To enhance the decoding accuracy of visually guided forelimb reaching movements, we propose a parallel computing neural network using both M1 and medial agranular cortex (AGm) neural activities of rats to predict forelimb-reaching movements. The proposed network decodes M1 neural activities into the primary components of the forelimb movement and decodes AGm neural activities into internal feedforward information to calibrate the forelimb movement in a goal-reaching movement. We demonstrate that using AGm neural activity to calibrate M1 predicted forelimb movement can improve decoding performance significantly compared to neural decoders without calibration. We also show that the M1 and AGm neural activities contribute to controlling forelimb movement during goal-reaching movements, and we report an increase in the power of the local field potential (LFP) in beta and gamma bands over AGm in response to a change in the target distance, which may involve sensorimotor transformation and communication between the visual cortex and AGm when preparing for an upcoming reaching movement. The proposed parallel computing neural network with the internal feedback model improves prediction accuracy for goal-reaching movements.

Task-specific modulation of corticospinal neuron activity during motor learning in mice

Motor skill learning relies on the plasticity of the primary motor cortex as task acquisition drives cortical motor network remodeling. Large-scale cortical remodeling of evoked motor outputs occurs during the learning of corticospinal-dependent prehension behavior, but not simple, non-dexterous tasks. Here we determine the response of corticospinal neurons to two distinct motor training paradigms and assess the role of corticospinal neurons in the execution of a task requiring precise modulation of forelimb movement and one that does not. In vivo calcium imaging in mice revealed temporal coding of corticospinal activity coincident with the development of precise prehension movements, but not more simplistic movement patterns. Transection of the corticospinal tract and optogenetic regulation of corticospinal activity show the necessity for patterned corticospinal network activity in the execution of precise movements but not simplistic ones. Our findings reveal a critical role for corticospinal network modulation in the learning and execution of precise motor movements.

Emergent Low-Frequency Activity in Cortico-Cerebellar Networks with Motor Skill Learning

The motor cortex controls skilled arm movement by recruiting a variety of targets in the nervous system, and it is important to understand the emergent activity in these regions as refinement of a motor skill occurs. One fundamental projection of the motor cortex (M1) is to the cerebellum. However, the emergent activity in the motor cortex and the cerebellum that appears as a dexterous motor skill is consolidated is incompletely understood. Here, we report on low-frequency oscillatory (LFO) activity that emerges in cortico-cerebellar networks with learning the reach-to-grasp motor skill. We chronically recorded the motor and the cerebellar cortices in rats, which revealed the emergence of coordinated movement-related activity in the local-field potentials as the reaching skill consolidated. Interestingly, we found this emergent activity only in the rats that gained expertise in the task. We found that the local and cross-area spiking activity was coordinated with LFOs in proficient rats. Finally, we also found that these neural dynamics were more prominently expressed during accurate behavior in the M1. This work furthers our understanding on emergent dynamics in the cortico-cerebellar loop that underlie learning and execution of precise skilled movement.

Long-term stability of single neuron activity in the motor system

How an established behavior is retained and consistently produced by a nervous system in constant flux remains a mystery. One possible solution to ensure long-term stability in motor output is to fix the activity patterns of single neurons in the relevant circuits. Alternatively, activity in single cells could drift over time provided that the population dynamics are constrained to produce the same behavior. To arbitrate between these possibilities, we recorded single-unit activity in motor cortex and striatum continuously for several weeks as rats performed stereotyped motor behaviors-both learned and innate. We found long-term stability in single neuron activity patterns across both brain regions. A small amount of drift in neural activity, observed over weeks of recording, could be explained by concomitant changes in task-irrelevant aspects of the behavior. These results suggest that long-term stable behaviors are generated by single neuron activity patterns that are themselves highly stable.

Motor cortical influence relies on task-specific activity covariation

During limb movement, spinal circuits facilitate the alternating activation of antagonistic flexor and extensor muscles. Yet antagonist cocontraction is often required to stabilize joints, like when loads are handled. Previous results suggest that these different muscle activation patterns are mediated by separate flexion- and extension-related motor cortical output populations, while others suggest recruitment of task-specific populations. To distinguish between hypotheses, we developed a paradigm in which mice toggle between forelimb tasks requiring antagonist alternation or cocontraction and measured activity in motor cortical layer 5b. Our results conform to neither hypothesis: consistent flexion- and extension-related activity is not observed across tasks, and no task-specific populations are observed. Instead, activity covariation among motor cortical neurons dramatically changes between tasks, thereby altering the relation between neural and muscle activity. This is also observed specifically for corticospinal neurons. Collectively, our findings indicate that motor cortex drives different muscle activation patterns via task-specific activity covariation.

Neurotransmitters, Cell Types, and Circuit Mechanisms of Motor Skill Learning and Clinical Applications

Animals acquire motor skills to better survive and adapt to a changing environment. The ability to learn novel motor actions without disturbing learned ones is essential to maintaining a broad motor repertoire. During motor learning, the brain makes a series of adjustments to build novel sensory–motor relationships that are stored within specific circuits for long-term retention. The neural mechanism of learning novel motor actions and transforming them into long-term memory still remains unclear. Here we review the latest findings with regard to the contributions of various brain subregions, cell types, and neurotransmitters to motor learning. Aiming to seek therapeutic strategies to restore the motor memory in relative neurodegenerative disorders, we also briefly describe the common experimental tests and manipulations for motor memory in rodents.

Role of the Ipsilateral Primary Motor Cortex in the Visuo-Motor Network During Fine Contractions and Accurate Performance

It is widely recognized that continuous sensory feedback plays a crucial role in accurate motor control in everyday life. Feedback information is used to adapt force output and to correct errors. While primary motor cortex contralateral to the movement (cM1) plays a dominant role in this control, converging evidence supports the idea that ipsilateral primary motor cortex (iM1) also directly contributes to hand and finger movements. Similarly, when visual feedback is available, primary visual cortex (V1) and its interactions with the motor network also become important for accurate motor performance. To elucidate this issue, we performed and integrated behavioral and electroencephalography (EEG) measurements during isometric compression of a compliant rubber bulb, at 10% and 30% of maximum voluntary contraction, both with and without visual feedback. We used a semi-blind approach (functional source separation (FSS)) to identify separate functional sources of mu-frequency (8-13[Formula: see text]Hz) EEG responses in cM1, iM1 and V1. Here for the first time, we have used orthogonal FSS to extract multiple sources, by using the same functional constraint, providing the ability to extract different sources that oscillate in the same frequency range but that have different topographic distributions. We analyzed the single-trial timecourses of mu power event-related desynchronization (ERD) in these sources and linked them with force measurements to understand which aspects are most important for good task performance. Whilst the amplitude of mu power was not related to contraction force in any of the sources, it was able to provide information on performance quality. We observed stronger ERDs in both contralateral and ipsilateral motor sources during trials where contraction force was most consistently maintained. This effect was most prominent in the ipsilateral source, suggesting the importance of iM1 to accurate performance. This ERD effect was sustained throughout the duration of visual feedback trials, but only present at the start of no feedback trials, consistent with more variable performance in the absence of feedback. Overall, we found that the behavior of the ERD in iM1 was the most informative aspect concerning the accuracy of the contraction performance, and the ability to maintain a steady level of contraction. This new approach of using FSS to extract multiple orthogonal sources provides the ability to investigate both contralateral and ipsilateral nodes of the motor network without the need for additional information (e.g. electromyography). The enhanced signal-to-noise ratio provided by FSS opens the possibility of extracting complex EEG features on an individual trial basis, which is crucial for a more nuanced understanding of fine motor performance, as well as for applications in brain-computer interfacing.

High-fidelity musculoskeletal modeling reveals that motor planning variability contributes to the speed-accuracy tradeoff

A long-standing challenge in motor neuroscience is to understand the relationship between movement speed and accuracy, known as the speed-accuracy tradeoff. Here, we introduce a biomechanically realistic computational model of three-dimensional upper extremity movements that reproduces well-known features of reaching movements. This model revealed that the speed-accuracy tradeoff, as described by Fitts’ law, emerges even without the presence of motor noise, which is commonly believed to underlie the speed-accuracy tradeoff. Next, we analyzed motor cortical neural activity from monkeys reaching to targets of different sizes. We found that the contribution of preparatory neural activity to movement duration (MD) variability is greater for smaller targets than larger targets, and that movements to smaller targets exhibit less variability in population-level preparatory activity, but greater MD variability. These results propose a new theory underlying the speed-accuracy tradeoff: Fitts’ law emerges from greater task demands constraining the optimization landscape in a fashion that reduces the number of ‘good’ control solutions (i.e., faster reaches). Thus, contrary to current beliefs, the speed-accuracy tradeoff could be a consequence of motor planning variability and not exclusively signal-dependent noise.

Towards Cell and Subtype Resolved Functional Organization: Mouse as a Model for the Cortical Control of Movement

Despite a long history of interrogation, the functional organization of motor cortex remains obscure. A major barrier has been the inability to measure and perturb activity with sufficient resolution to reveal clear functional elements within motor cortex and its associated circuits. Increasingly, the mouse has been employed as a model to facilitate application of contemporary approaches with the potential to surmount this barrier. In this brief essay, we consider these approaches and their use in the context of studies aimed at resolving the logic of motor cortical operation.

Long-term motor skill training with individually adjusted progressive difficulty enhances learning and promotes corticospinal plasticity

Motor skill acquisition depends on central nervous plasticity. However, behavioural determinants leading to long lasting corticospinal plasticity and motor expertise remain unexplored. Here we investigate behavioural and electrophysiological effects of individually tailored progressive practice during long-term motor skill training. Two groups of participants practiced a visuomotor task requiring precise control of the right digiti minimi for 6 weeks. One group trained with constant task difficulty, while the other group trained with progressively increasing task difficulty, i.e. continuously adjusted to their individual skill level. Compared to constant practice, progressive practice resulted in a two-fold greater performance at an advanced task level and associated increases in corticospinal excitability. Differences were maintained 8 days later, whereas both groups demonstrated equal retention 14 months later. We demonstrate that progressive practice enhances motor skill learning and promotes corticospinal plasticity. These findings underline the importance of continuously challenging patients and athletes to promote neural plasticity, skilled performance, and recovery.

Single-trial cross-area neural population dynamics during long-term skill learning

Mammalian cortex has both local and cross-area connections, suggesting vital roles for both local and cross-area neural population dynamics in cortically-dependent tasks, like movement learning. Prior studies of movement learning have focused on how single-area population dynamics change during short-term adaptation. It is unclear how cross-area dynamics contribute to movement learning, particularly long-term learning and skill acquisition. Using simultaneous recordings of rodent motor (M1) and premotor (M2) cortex and computational methods, we show how cross-area activity patterns evolve during reach-to-grasp learning in rats. The emergence of reach-related modulation in cross-area activity correlates with skill acquisition, and single-trial modulation in cross-area activity predicts reaction time and reach duration. Local M2 neural activity precedes local M1 activity, supporting top-down hierarchy between the regions. M2 inactivation preferentially affects cross-area dynamics and behavior, with minimal disruption of local M1 dynamics. Together, these results indicate that cross-area population dynamics are necessary for learned motor skills.

Machine Learning for 3D Kinematic Analysis of Movements in Neurorehabilitation

PURPOSE OF REVIEW

Recent advances in the machine learning field, especially in deep learning, provide the opportunity for automated, detailed, and unbiased analysis of motor behavior. Although there has not yet been wide use of these techniques in the motor rehabilitation field, they have great potential. In this review, I describe how the current state of machine learning can be applied to 3D kinematic analysis, and how this will have an impact on neurorehabilitation.

RECENT FINDINGS

Applications of deep learning methods, in the form of convolutional neural networks, have been revolutionary for image analysis such as face recognition and object detection in images, exceeding human level performance. Recent studies have shown applicability of these deep learning approaches to human posture and movement classification. It is to be expected that portable stereo-camera systems will bring 3D pose estimation into the clinical setting and allow the assessment of movement quality in response to interventions. Advances in machine learning can help automate the process of obtaining 3D kinematics of human movements and to identify/classify patterns of movement.

Corticospinal Pathways and Interactions Underpinning Dexterous Forelimb Movement of the Rodent

In 2013, Thomas Jessell published a paper with Andrew Miri and Eiman Azim that took on the task of examining corticospinal neuron function during movement (Miri et al., 2013). They took the view that a combination of approaches would be able to shed light on corticospinal function, and that this function must be considered in the context of corticospinal connectivity with spinal circuits. In this review, we will highlight recent developments in this area, along with new information regarding inputs and cross-connectivity of the corticospinal circuit with other circuits across the rodent central nervous system. The genetic and viral manipulations available in these animals have led to new insights into descending circuit interaction and function. As species differences exist in the circuitry profile that contributes to dexterous forelimb movements (Lemon, 2008; Yoshida and Isa, 2018), highlighting important advances in one model could help to compare and contrast with what is known about other models. We will focus on the circuitry underpinning dexterous forelimb movements, including some recent developments from systems besides the corticospinal tract, to build a more holistic understanding of sensorimotor circuits and their control of voluntary movement. The rodent corticospinal system is thus a central point of reference in this review, but not the only focus.

Emergent modular neural control drives coordinated motor actions

A remarkable feature of motor control is the ability to coordinate movements across distinct body parts into a consistent, skilled action. To reach and grasp an object, 'gross' arm and 'fine' dexterous movements must be coordinated as a single action. How the nervous system achieves this coordination is currently unknown. One possibility is that, with training, gross and fine movements are co-optimized to produce a coordinated action; alternatively, gross and fine movements may be modularly refined to function together. To address this question, we recorded neural activity in the primary motor cortex and dorsolateral striatum during reach-to-grasp skill learning in rats. During learning, the refinement of fine and gross movements was behaviorally and neurally dissociable. Furthermore, inactivation of the primary motor cortex and dorsolateral striatum had distinct effects on skilled fine and gross movements. Our results indicate that skilled movement coordination is achieved through emergent modular neural control.

Reorganization of Recurrent Layer 5 Corticospinal Networks Following Adult Motor Training

Recurrent synaptic connections between neighboring neurons are a key feature of mammalian cortex, accounting for the vast majority of cortical inputs. Although computational models indicate that reorganization of recurrent connectivity is a primary driver of experience-dependent cortical tuning, the true biological features of recurrent network plasticity are not well identified. Indeed, whether rewiring of connections between cortical neurons occurs during behavioral training, as is widely predicted, remains unknown. Here, we probe M1 recurrent circuits following motor training in adult male rats and find robust synaptic reorganization among functionally related layer 5 neurons, resulting in a 2.5-fold increase in recurrent connection probability. This reorganization is specific to the neuronal subpopulation most relevant for executing the trained motor skill, and behavioral performance was impaired following targeted molecular inhibition of this subpopulation. In contrast, recurrent connectivity is unaffected among neighboring layer 5 neurons largely unrelated to the trained behavior. Training-related corticospinal cells also express increased excitability following training. These findings establish the presence of selective modifications in recurrent cortical networks in adulthood following training.SIGNIFICANCE STATEMENT Recurrent synaptic connections between neighboring neurons are characteristic of cortical architecture, and modifications to these circuits are thought to underlie in part learning in the adult brain. We now show that there are robust changes in recurrent connections in the rat motor cortex upon training on a novel motor task. Motor training results in a 2.5-fold increase in recurrent connectivity, but only within the neuronal subpopulation most relevant for executing the new motor behavior; recurrent connectivity is unaffected among adjoining neurons that do not execute the trained behavior. These findings demonstrate selective reorganization of recurrent synaptic connections in the adult neocortex following novel motor experience, and illuminate fundamental properties of cortical function and plasticity.

Motor learning and metaplasticity in striatal neurons: relevance for Parkinson's disease

Nigro-striatal dopamine transmission is central to a wide range of neuronal functions, including skill learning, which is disrupted in several pathologies such as Parkinson's disease. The synaptic plasticity mechanisms, by which initial motor learning is stored for long time periods in striatal neurons, to then be gradually optimized upon subsequent training, remain unexplored. Addressing this issue is crucial to identify the synaptic and molecular mechanisms involved in striatal-dependent learning impairment in Parkinson's disease. In this study, we took advantage of interindividual differences between outbred rodents in reaching plateau performance in the rotarod incremental motor learning protocol, to study striatal synaptic plasticity ex vivo. We then assessed how this process is modulated by dopamine receptors and the dopamine active transporter, and whether it is impaired by overexpression of human α-synuclein in the mesencephalon; the latter is a progressive animal model of Parkinson's disease. We found that the initial acquisition of motor learning induced a dopamine active transporter and D1 receptors mediated long-term potentiation, under a protocol of long-term depression in striatal medium spiny neurons. This effect disappeared in animals reaching performance plateau. Overexpression of human α-synuclein reduced striatal dopamine active transporter levels, impaired motor learning, and prevented the learning-induced long-term potentiation, before the appearance of dopamine neuronal loss. Our findings provide evidence of a reorganization of cellular plasticity within the dorsolateral striatum that is mediated by dopamine receptors and dopamine active transporter during the acquisition of a skill. This newly identified mechanism of cellular memory is a form of metaplasticity that is disrupted in the early stage of synucleinopathies, such as Parkinson's disease, and that might be relevant for other striatal pathologies, such as drug abuse.

Cortical neural modulation by previous trial outcome differentiates fast- from slow-learning rats on a visuomotor directional choice task

To better understand the neural cortical underpinnings that explain behavioral differences in learning rate, we recorded single-unit activity in primary motor (M1) and secondary motor (M2) areas while rats learned to perform a directional (left or right) operant visuomotor association task. Analysis of neural activity during the early portion of the cue period showed that neural modulation in the motor cortex was most strongly associated with two task factors: the previous trial outcome (success or error) and the current trial's directional choice (left or right). Furthermore, the fast learners, defined as those who had steeper learning curves and required fewer learning sessions to reach criterion performance, encoded the previous trial outcome factor more strongly than the directional choice factor. Conversely, the slow learners encoded directional choice more strongly than previous trial outcome. These differences in task factor encoding were observed in both the percentage of neurons and the neural modulation depth. These results suggest that fast learning is accompanied by a stronger component of previous trial outcome in the modulation representation present in motor cortex, which therefore may be a contributing factor to behavioral differences in learning rate. NEW & NOTEWORTHY We chronically recorded neural activity as rats learned a visuomotor directional choice task from a naive state. Learning rates varied. Single-unit neural modulation of two motor areas revealed that the fast learners encoded previous trial outcome more strongly than directional choice, whereas the reverse was true for slow learners. This finding provides novel evidence that rat learning rate is strongly correlated with the strength of neural modulation by previous trial outcome in motor cortex.

Differential developmental changes in cortical representations of auditory-vocal stimuli in songbirds

Procedural skill learning requires iterative comparisons between feedback of self-generated motor output and a goal sensorimotor pattern. In juvenile songbirds, neural representations of both self-generated behaviors (each bird's own immature song) and the goal motor pattern (each bird's adult tutor song) are essential for vocal learning, yet little is known about how these behaviorally relevant stimuli are encoded. We made extracellular recordings during song playback in anesthetized juvenile and adult zebra finches ( Taeniopygia guttata) in adjacent cortical regions RA (robust nucleus of the arcopallium), AId (dorsal intermediate arcopallium), and RA cup, each of which is well situated to integrate auditory-vocal information: RA is a motor cortical region that drives vocal output, AId is an adjoining cortical region whose projections converge with basal ganglia loops for song learning in the dorsal thalamus, and RA cup surrounds RA and receives inputs from primary and secondary auditory cortex. We found strong developmental differences in neural selectivity within RA, but not in AId or RA cup. Juvenile RA neurons were broadly responsive to multiple songs but preferred juvenile over adult vocal sounds; in addition, spiking responses lacked consistent temporal patterning. By adulthood, RA neurons responded most strongly to each bird's own song with precisely timed spiking activity. In contrast, we observed a complete lack of song responsivity in both juvenile and adult AId, even though this region receives song-responsive inputs. A surprisingly large proportion of sites in RA cup of both juveniles and adults did not respond to song playback, and responsive sites showed little evidence of song selectivity. NEW & NOTEWORTHY Motor skill learning entails changes in selectivity for behaviorally relevant stimuli across cortical regions, yet the neural representation of these stimuli remains understudied. We investigated how information important for vocal learning in zebra finches is represented in regions analogous to infragranular layers of motor and auditory cortices during vs. after the developmentally regulated learning period. The results provide insight into how neurons in higher level stages of cortical processing represent stimuli important for motor skill learning.

Low-frequency cortical activity is a neuromodulatory target that tracks recovery after stroke

Recent work has highlighted the importance of transient low-frequency oscillatory (LFO; <4 Hz) activity in the healthy primary motor cortex during skilled upper-limb tasks. These brief bouts of oscillatory activity may establish the timing or sequencing of motor actions. Here, we show that LFOs track motor recovery post-stroke and can be a physiological target for neuromodulation. In rodents, we found that reach-related LFOs, as measured in both the local field potential and the related spiking activity, were diminished after stroke and that spontaneous recovery was closely correlated with their restoration in the perilesional cortex. Sensorimotor LFOs were also diminished in a human subject with chronic disability after stroke in contrast to two non-stroke subjects who demonstrated robust LFOs. Therapeutic delivery of electrical stimulation time-locked to the expected onset of LFOs was found to significantly improve skilled reaching in stroke animals. Together, our results suggest that restoration or modulation of cortical oscillatory dynamics is important for the recovery of upper-limb function and that they may serve as a novel target for clinical neuromodulation.

Persistent Increases of PKMζ in Sensorimotor Cortex Maintain Procedural Long-Term Memory Storage

Procedural motor learning and memory are accompanied by changes in synaptic plasticity, neural dynamics, and synaptogenesis. Missing is information on the spatiotemporal dynamics of the molecular machinery maintaining these changes. Here we examine whether persistent increases in PKMζ, an atypical protein kinase C (PKC) isoform, store long-term memory for a reaching task in rat sensorimotor cortex that could reveal the sites of procedural memory storage. Specifically, perturbing PKMζ synthesis (via antisense oligodeoxynucleotides) and blocking atypical PKC activity (via zeta inhibitory peptide [ZIP]) in S1/M1 disrupts and erases long-term motor memory maintenance, indicating atypical PKCs and specifically PKMζ store consolidated long-term procedural memories. Immunostaining reveals that PKMζ increases in S1/M1 layers II/III and V as performance improved to an asymptote. After storage for 1 month without reinforcement, the increase in M1 layer V persists without decrement. Thus, the persistent increases in PKMζ that store long-term procedural memory are localized to the descending output layer of the primary motor cortex.

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Perturbing PKMζ synthesis in S1/M1 slows the formation of skilled motor memory

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Blocking PKMζ activity specifically erases memories maintained without reinforcement

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Skilled motor learning induces the increase of PKMζ in S1/M1 layers II/III and V

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PKMζ sustains the engram for procedural motor memory in M1 layer V

Distinct Laterality in Forelimb-Movement Representations of Rat Primary and Secondary Motor Cortical Neurons with Intratelencephalic and Pyramidal Tract Projections

Two distinct motor areas, the primary and secondary motor cortices (M1 and M2), play crucial roles in voluntary movement in rodents. The aim of this study was to characterize the laterality in motor cortical representations of right and left forelimb movements. To achieve this goal, we developed a novel behavioral task, the Right-Left Pedal task, in which a head-restrained male rat manipulates a right or left pedal with the corresponding forelimb. This task enabled us to monitor independent movements of both forelimbs with high spatiotemporal resolution. We observed phasic movement-related neuronal activity (Go-type) and tonic hold-related activity (Hold-type) in isolated unilateral movements. In both M1 and M2, Go-type neurons exhibited bias toward contralateral preference, whereas Hold-type neurons exhibited no bias. The contralateral bias was weaker in M2 than M1. Moreover, we differentiated between intratelencephalic (IT) and pyramidal tract (PT) neurons using optogenetically evoked spike collision in rats expressing channelrhodopsin-2. Even in identified PT and IT neurons, Hold-type neurons exhibited no lateral bias. Go-type PT neurons exhibited bias toward contralateral preference, whereas IT neurons exhibited no bias. Our findings suggest a different laterality of movement representations of M1 and M2, in each of which IT neurons are involved in cooperation of bilateral movements, whereas PT neurons control contralateral movements.SIGNIFICANCE STATEMENT In rodents, the primary and secondary motor cortices (M1 and M2) are involved in voluntary movements via distinct projection neurons: intratelencephalic (IT) neurons and pyramidal tract (PT) neurons. However, it remains unclear whether the two motor cortices (M1 vs M2) and the two classes of projection neurons (IT vs PT) have different laterality of movement representations. We optogenetically identified these neurons and analyzed their functional activity using a novel behavioral task to monitor movements of the right and left forelimbs separately. We found that contralateral bias was reduced in M2 relative to M1, and in IT relative to PT neurons. Our findings suggest that the motor information processing that controls forelimb movement is coordinated by a distinct cell population.

Behaviorally Selective Engagement of Short-Latency Effector Pathways by Motor Cortex

Blocking motor cortical output with lesions or pharmacological inactivation has identified movements that require motor cortex. Yet, when and how motor cortex influences muscle activity during movement execution remains unresolved. We addressed this ambiguity using measurement and perturbation of motor cortical activity together with electromyography in mice during two forelimb movements that differ in their requirement for cortical involvement. Rapid optogenetic silencing and electrical stimulation indicated that short-latency pathways linking motor cortex with spinal motor neurons are selectively activated during one behavior. Analysis of motor cortical activity revealed a dramatic change between behaviors in the coordination of firing patterns across neurons that could account for this differential influence. Thus, our results suggest that changes in motor cortical output patterns enable a behaviorally selective engagement of short-latency effector pathways. The model of motor cortical influence implied by our findings helps reconcile previous observations on the function of motor cortex.

Reorganization of corticospinal output during motor learning

Motor learning is accompanied by widespread changes within the motor cortex, but it is unknown whether these changes are ultimately funneled through a stable corticospinal output channel or whether the corticospinal output itself is plastic. We investigated the consistency of the relationship between corticospinal neuron activity and movement through in vivo two-photon calcium imaging in mice learning a lever-press task. Corticospinal neurons exhibited heterogeneous correlations with movement, with the majority of movement-modulated neurons decreasing activity during movement. Individual cells changed their activity across days, which led to changed associations between corticospinal activity and movement. Unlike previous observations in layer 2/3, activity accompanying learned movements did not become more consistent with learning; instead, the activity of dissimilar movements became more decorrelated. These results indicate that the relationship between corticospinal activity and movement is dynamic and that the types of activity and plasticity are different from and possibly complementary to those in layer 2/3.

Learning in the Rodent Motor Cortex

The motor cortex is far from a stable conduit for motor commands and instead undergoes significant changes during learning. An understanding of motor cortex plasticity has been advanced greatly using rodents as experimental animals. Two major focuses of this research have been on the connectivity and activity of the motor cortex. The motor cortex exhibits structural changes in response to learning, and substantial evidence has implicated the local formation and maintenance of new synapses as crucial substrates of motor learning. This synaptic reorganization translates into changes in spiking activity, which appear to result in a modification and refinement of the relationship between motor cortical activity and movement. This review presents the progress that has been made using rodents to establish the motor cortex as an adaptive structure that supports motor learning.

Transformation of Cortex-wide Emergent Properties during Motor Learning

Learning involves a transformation of brain-wide operation dynamics. However, our understanding of learning-related changes in macroscopic dynamics is limited. Here, we monitored cortex-wide activity of the mouse brain using wide-field calcium imaging while the mouse learned a motor task over weeks. Over learning, the sequential activity across cortical modules became temporally more compressed, and its trial-by-trial variability decreased. Moreover, a new flow of activity emerged during learning, originating from premotor cortex (M2), and M2 became predictive of the activity of many other modules. Inactivation experiments showed that M2 is critical for the post-learning dynamics in the cortex-wide activity. Furthermore, two-photon calcium imaging revealed that M2 ensemble activity also showed earlier activity onset and reduced variability with learning, which was accompanied by changes in the activity-movement relationship. These results reveal newly emergent properties of macroscopic cortical dynamics during motor learning and highlight the importance of M2 in controlling learned movements.

Chronic Stroke Survivors Improve Reaching Accuracy by Reducing Movement Variability at the Trained Movement Speed

BACKGROUND

Recovery from stroke is often said to have "plateaued" after 6 to 12 months. Yet training can still improve performance even in the chronic phase. Here we investigate the biomechanics of accuracy improvements during a reaching task and test whether they are affected by the speed at which movements are practiced.

METHOD

We trained 36 chronic stroke survivors (57.5 years, SD ± 11.5; 10 females) over 4 consecutive days to improve endpoint accuracy in an arm-reaching task (420 repetitions/day). Half of the group trained using fast movements and the other half slow movements. The trunk was constrained allowing only shoulder and elbow movement for task performance.

RESULTS

Before training, movements were variable, tended to undershoot the target, and terminated in contralateral workspace (flexion bias). Both groups improved movement accuracy by reducing trial-to-trial variability; however, change in endpoint bias (systematic error) was not significant. Improvements were greatest at the trained movement speed and generalized to other speeds in the fast training group. Small but significant improvements were observed in clinical measures in the fast training group.

CONCLUSIONS

The reduction in trial-to-trial variability without an alteration to endpoint bias suggests that improvements are achieved by better control over motor commands within the existing repertoire. Thus, 4 days' training allows stroke survivors to improve movements that they can already make. Whether new movement patterns can be acquired in the chronic phase will need to be tested in longer term studies. We recommend that training needs to be performed at slow and fast movement speeds to enhance generalization.

Voluntary control of intracortical oscillations for reconfiguration of network activity

Voluntary control of oscillatory activity represents a key target in the self-regulation of brain function. Using a real-time closed-loop paradigm and simultaneous macro- and micro-electrode recordings, we studied the effects of self-induced intracortical oscillatory activity (4–8 Hz) in seven neurosurgical patients. Subjects learned to robustly and specifically induce oscillations in the target frequency, confirmed by increased oscillatory event density. We have found that the session-to-session variability in performance was explained by the functional long-range decoupling of the target area suggesting a training-induced network reorganization. Downstream effects on more local activities included progressive cross-frequency-coupling with gamma oscillations (30–120 Hz), and the dynamic modulation of neuronal firing rates and spike timing, indicating an improved temporal coordination of local circuits. These findings suggest that effects of voluntary control of intracortical oscillations can be exploited to specifically target plasticity processes to reconfigure network activity, with a particular relevance for memory function or skill acquisition.

An automated rat single pellet reaching system with high-speed video capture

BACKGROUND

Single pellet reaching is an established task for studying fine motor control in which rats reach for, grasp, and eat food pellets in a stereotyped sequence. Most incarnations of this task require constant attention, limiting the number of animals that can be tested and the number of trials per session. Automated versions allow more interventions in more animals, but must be robust and reproducible.

NEW METHOD

Our system automatically delivers single reward pellets for rats to grasp with their forepaw. Reaches are detected using real-time computer vision, which triggers video acquisition from multiple angles using mirrors. This allows us to record high-speed (>300 frames per second) video, and trigger interventions (e.g., optogenetics) with high temporal precision. Individual video frames are triggered by digital pulses that can be synchronized with behavior, experimental interventions, or recording devices (e.g., electrophysiology). The system is housed within a soundproof chamber with integrated lighting and ventilation, allowing multiple skilled reaching systems in one room.

RESULTS

We show that rats acquire the automated task similarly to manual versions, that the task is robust, and can be synchronized with optogenetic interventions.

COMPARISON WITH EXISTING METHODS

Existing skilled reaching protocols require high levels of investigator involvement, or, if ad libitum, do not allow for integration of high-speed, synchronized data collection.

CONCLUSION

This task will facilitate the study of motor learning and control by efficiently recording large numbers of skilled movements. It can be adapted for use with modern neurophysiology, which demands high temporal precision.

Sleep-Dependent Reactivation of Ensembles in Motor Cortex Promotes Skill Consolidation

Despite many prior studies demonstrating offline behavioral gains in motor skills after sleep, the underlying neural mechanisms remain poorly understood. To investigate the neurophysiological basis for offline gains, we performed single-unit recordings in motor cortex as rats learned a skilled upper-limb task. We found that sleep improved movement speed with preservation of accuracy. These offline improvements were linked to both replay of task-related ensembles during non-rapid eye movement (NREM) sleep and temporal shifts that more tightly bound motor cortical ensembles to movements; such offline gains and temporal shifts were not evident with sleep restriction. Interestingly, replay was linked to the coincidence of slow-wave events and bursts of spindle activity. Neurons that experienced the most consistent replay also underwent the most significant temporal shift and binding to the motor task. Significantly, replay and the associated performance gains after sleep only occurred when animals first learned the skill; continued practice during later stages of learning (i.e., after motor kinematics had stabilized) did not show evidence of replay. Our results highlight how replay of synchronous neural activity during sleep mediates large-scale neural plasticity and stabilizes kinematics during early motor learning.

During non-REM sleep in rats, consolidation and offline improvements of a recently learned motor skill are linked to synchronous reactivation of task-related neural ensembles.

Sleep has been shown to help in consolidating learned motor tasks. In other words, sleep can induce “offline” gains in a new motor skill even in the absence of further training. However, how sleep induces this change has not been clearly identified. One hypothesis is that consolidation of memories during sleep occurs by “reactivation” of neurons engaged during learning. In this study, we tested this hypothesis by recording populations of neurons in the motor cortex of rats while they learned a new motor skill and during sleep both before and after the training session. We found that subsets of task-relevant neurons formed highly synchronized ensembles during learning. Interestingly, these same neural ensembles were reactivated during subsequent sleep blocks, and the degree of reactivation was correlated with several metrics of motor memory consolidation. Specifically, after sleep, the speed at which animals performed the task while maintaining accuracy was increased, and the activity of the neuronal assembles were more tightly bound to motor action. Further analyses showed that reactivation events occurred episodically and in conjunction with spindle-oscillations—common bursts of brain activity seen during sleep. This observation is consistent with previous findings in humans that spindle-oscillations correlate with consolidation of learned tasks. Our study thus provides insight into the neuronal network mechanism supporting consolidation of motor memory during sleep and may lead to novel interventions that can enhance skill learning in both healthy and injured nervous systems.

Interaction between Neurogenesis and Hippocampal Memory System: New Vistas

During the last decade, the questions on the functionality of adult neurogenesis have changed their emphasis from if to how the adult-born neurons participate in a variety of memory processes. The emerging answers are complex because we are overwhelmed by a variety of behavioral tasks that apparently require new neurons to be performed optimally. With few exceptions, the hippocampal memory system seems to use the newly generated neurons for multiple roles. Adult neurogenesis has given the dentate gyrus new capabilities not previously thought possible within the scope of traditional synaptic plasticity. Looking at these new developments from the perspective of past discoveries, the science of adult neurogenesis has emerged from its initial phase of being, first, a surprising oddity and, later, exciting possibility, to the present state of being an integral part of mainstream neuroscience. The answers to many remaining questions regarding adult neurogenesis will come along only with our growing understanding of the functionality of the brain as a whole. This, in turn, will require integration of multiple levels of organization from molecules and cells to circuits and systems, ultimately resulting in comprehension of behavioral outcomes.

Improved discriminability of spatiotemporal neural patterns in rat motor cortical areas as directional choice learning progresses

Animals learn to choose a proper action among alternatives to improve their odds of success in food foraging and other activities critical for survival. Through trial-and-error, they learn correct associations between their choices and external stimuli. While a neural network that underlies such learning process has been identified at a high level, it is still unclear how individual neurons and a neural ensemble adapt as learning progresses. In this study, we monitored the activity of single units in the rat medial and lateral agranular (AGm and AGl, respectively) areas as rats learned to make a left or right side lever press in response to a left or right side light cue. We noticed that rat movement parameters during the performance of the directional choice task quickly became stereotyped during the first 2–3 days or sessions. But learning the directional choice problem took weeks to occur. Accompanying rats' behavioral performance adaptation, we observed neural modulation by directional choice in recorded single units. Our analysis shows that ensemble mean firing rates in the cue-on period did not change significantly as learning progressed, and the ensemble mean rate difference between left and right side choices did not show a clear trend of change either. However, the spatiotemporal firing patterns of the neural ensemble exhibited improved discriminability between the two directional choices through learning. These results suggest a spatiotemporal neural coding scheme in a motor cortical neural ensemble that may be responsible for and contributing to learning the directional choice task.

Electrifying the motor engram: effects of tDCS on motor learning and control

Learning to control our movements is accompanied by neuroplasticity of motor areas of the brain. The mechanisms of neuroplasticity are diverse and produce what is referred to as the motor engram, i.e., the neural trace of the motor memory. Transcranial direct current stimulation (tDCS) alters the neural and behavioral correlates of motor learning, but its precise influence on the motor engram is unknown. In this review, we summarize the effects of tDCS on neural activity and suggest a few key principles: (1) Firing rates are increased by anodal polarization and decreased by cathodal polarization, (2) anodal polarization strengthens newly formed associations, and (3) polarization modulates the memory of new/preferred firing patterns. With these principles in mind, we review the effects of tDCS on motor control, motor learning, and clinical applications. The increased spontaneous and evoked firing rates may account for the modulation of dexterity in non-learning tasks by tDCS. The facilitation of new association may account for the effect of tDCS on learning in sequence tasks while the ability of tDCS to strengthen memories of new firing patterns may underlie the effect of tDCS on consolidation of skills. We then describe the mechanisms of neuroplasticity of motor cortical areas and how they might be influenced by tDCS. We end with current challenges for the fields of brain stimulation and motor learning.

Motor cortex single-neuron and population contributions to compensation for multiple dynamic force fields

To elucidate how primary motor cortex (M1) neurons contribute to the performance of a broad range of different and even incompatible motor skills, we trained two monkeys to perform single-degree-of-freedom elbow flexion/extension movements that could be perturbed by a variety of externally generated force fields. Fields were presented in a pseudorandom sequence of trial blocks. Different computer monitor background colors signaled the nature of the force field throughout each block. There were five different force fields: null field without perturbing torque, assistive and resistive viscous fields proportional to velocity, a resistive elastic force field proportional to position and a resistive viscoelastic field that was the linear combination of the resistive viscous and elastic force fields. After the monkeys were extensively trained in the five field conditions, neural recordings were subsequently made in M1 contralateral to the trained arm. Many caudal M1 neurons altered their activity systematically across most or all of the force fields in a manner that was appropriate to contribute to the compensation for each of the fields. The net activity of the entire sample population likewise provided a predictive signal about the differences in the time course of the external forces encountered during the movements across all force conditions. The neurons showed a broad range of sensitivities to the different fields, and there was little evidence of a modular structure by which subsets of M1 neurons were preferentially activated during movements in specific fields or combinations of fields.

Task-phase-specific dynamics of basal forebrain neuronal ensembles

Cortically projecting basal forebrain neurons play a critical role in learning and attention, and their degeneration accompanies age-related impairments in cognition. Despite the impressive anatomical and cell-type complexity of this system, currently available data suggest that basal forebrain neurons lack complexity in their response fields, with activity primarily reflecting only macro-level brain states such as sleep and wake, onset of relevant stimuli and/or reward obtainment. The current study examined the spiking activity of basal forebrain neuron populations across multiple phases of a selective attention task, addressing, in particular, the issue of complexity in ensemble firing patterns across time. Clustering techniques applied to the full population revealed a large number of distinct categories of task-phase-specific activity patterns. Unique population firing-rate vectors defined each task phase and most categories of task-phase-specific firing had counterparts with opposing firing patterns. An analogous set of task-phase-specific firing patterns was also observed in a population of posterior parietal cortex neurons. Thus, consistent with the known anatomical complexity, basal forebrain population dynamics are capable of differentially modulating their cortical targets according to the unique sets of environmental stimuli, motor requirements, and cognitive processes associated with different task phases.

Rapid acquisition of novel interface control by small ensembles of arbitrarily selected primary motor cortex neurons

Pioneering studies demonstrated that novel degrees of freedom could be controlled individually by directly encoding the firing rate of single motor cortex neurons, without regard to each neuron's role in controlling movement of the native limb. In contrast, recent brain-computer interface work has emphasized decoding outputs from large ensembles that include substantially more neurons than the number of degrees of freedom being controlled. To bridge the gap between direct encoding by single neurons and decoding output from large ensembles, we studied monkeys controlling one degree of freedom by comodulating up to four arbitrarily selected motor cortex neurons. Performance typically exceeded random quite early in single sessions and then continued to improve to different degrees in different sessions. We therefore examined factors that might affect performance. Performance improved with larger ensembles. In contrast, other factors that might have reflected preexisting synaptic architecture-such as the similarity of preferred directions-had little if any effect on performance. Patterns of comodulation among ensemble neurons became more consistent across trials as performance improved over single sessions. Compared with the ensemble neurons, other simultaneously recorded neurons showed less modulation. Patterns of voluntarily comodulated firing among small numbers of arbitrarily selected primary motor cortex (M1) neurons thus can be found and improved rapidly, with little constraint based on the normal relationships of the individual neurons to native limb movement. This rapid flexibility in relationships among M1 neurons may in part underlie our ability to learn new movements and improve motor skill.

The neural correlates of learned motor acuity

We recently defined a component of motor skill learning as "motor acuity," quantified as a shift in the speed-accuracy trade-off function for a task. These shifts are primarily driven by reductions in movement variability. To determine the neural correlates of improvement in motor acuity, we devised a motor task compatible with magnetic resonance brain imaging that required subjects to make finely controlled wrist movements under visual guidance. Subjects were imaged on day 1 and day 5 while they performed this task and were trained outside the scanner on intervening days 2, 3, and 4. The potential confound of performance changes between days 1 and 5 was avoided by constraining movement time to a fixed duration. After training, subjects showed a marked increase in success rate and a reduction in trial-by-trial variability for the trained task but not for an untrained control task, without changes in mean trajectory. The decrease in variability for the trained task was associated with increased activation in contralateral primary motor and premotor cortical areas and in ipsilateral cerebellum. A global nonlocalizing multivariate analysis confirmed that learning was associated with increased overall brain activation. We suggest that motor acuity is acquired through increases in the number of neurons recruited in contralateral motor cortical areas and in ipsilateral cerebellum, which could reflect increased signal-to-noise ratio in motor output and improved state estimation for feedback corrections, respectively.

Changes in corticospinal excitability during reach adaptation in force fields

Both abrupt and gradually imposed perturbations produce adaptive changes in motor output, but the neural basis of adaptation may be distinct. Here, we measured the state of the primary motor cortex (M1) and the corticospinal network during adaptation by measuring motor-evoked potentials (MEPs) before reach onset using transcranial magnetic stimulation of M1. Subjects reached in a force field in a schedule in which the field was introduced either abruptly or gradually over many trials. In both groups, by end of the training, muscles that countered the perturbation in a given direction increased their activity during the reach (labeled as the on direction for each muscle). In the abrupt group, in the period before the reach toward the on direction, MEPs in these muscles also increased, suggesting a direction-specific increase in the excitability of the corticospinal network. However, in the gradual group, these MEP changes were missing. After training, there was a period of washout. The MEPs did not return to baseline. Rather, in the abrupt group, off direction MEPs increased to match on direction MEPs. Therefore, we observed changes in corticospinal excitability in the abrupt but not gradual condition. Abrupt training includes the repetition of motor commands, and repetition may be the key factor that produces this plasticity. Furthermore, washout did not return MEPs to baseline, suggesting that washout engaged a new network that masked but did not erase the effects of previous adaptation. Abrupt but not gradual training appears to induce changes in M1 and/or corticospinal networks.

How is a motor skill learned? Change and invariance at the levels of task success and trajectory control

The public pays large sums of money to watch skilled motor performance. Notably, however, in recent decades motor skill learning (performance improvement beyond baseline levels) has received less experimental attention than motor adaptation (return to baseline performance in the setting of an external perturbation). Motor skill can be assessed at the levels of task success and movement quality, but the link between these levels remains poorly understood. We devised a motor skill task that required visually guided curved movements of the wrist without a perturbation, and we defined skill learning at the task level as a change in the speed-accuracy trade-off function (SAF). Practice in restricted speed ranges led to a global shift of the SAF. We asked how the SAF shift maps onto changes in trajectory kinematics, to establish a link between task-level performance and fine motor control. Although there were small changes in mean trajectory, improved performance largely consisted of reduction in trial-to-trial variability and increase in movement smoothness. We found evidence for improved feedback control, which could explain the reduction in variability but does not preclude other explanations such as an increased signal-to-noise ratio in cortical representations. Interestingly, submovement structure remained learning invariant. The global generalization of the SAF across a wide range of difficulty suggests that skill for this task is represented in a temporally scalable network. We propose that motor skill acquisition can be characterized as a slow reduction in movement variability, which is distinct from faster model-based learning that reduces systematic error in adaptation paradigms.

A computational model of use-dependent motor recovery following a stroke: optimizing corticospinal activations via reinforcement learning can explain residual capacity and other strength recovery dynamics

This paper describes a computational model of use-dependent recovery of movement strength following a stroke. The model frames the problem of strength recovery as that of learning appropriate activations of residual corticospinal neurons to their target motoneuronal pools. For example, for an agonist/antagonist muscle pair, we assume the motor system must learn to activate preserved agonist-exciting corticospinal neurons and deactivate preserved antagonist-exciting corticospinal neurons. The model incorporates a biologically plausible reinforcement learning algorithm for adjusting cell activation patterns-stochastic search-using generated limb force as the teaching signal to adjust the synaptic weights that determine cell activations. The model makes predictions consistent with clinical and brain imaging data, such as that patients can achieve an increase in strength after appearing to reach a recovery plateau (i.e., "residual capacity"), that the differential effect of a dose of movement practice will be greater earlier in recovery, and that force-related brain activation will increase in secondary motor areas following a stroke. An interesting prediction that could be explored clinically is that temporarily inhibiting subpopulations of more powerfully connected corticospinal neurons during late movement training will allow the motor system to optimize corticospinal neurons with a weaker influence, whose optimization was blocked by the rapid optimization of more strongly connected neurons early in training.

Anterior cingulate neurons in the rat map anticipated effort and reward to their associated action sequences

Goal-directed behaviors require the consideration and expenditure of physical effort. The anterior cingulate cortex (ACC) appears to play an important role in evaluating effort and reward and in organizing goal-directed actions. Despite agreement regarding the involvement of the ACC in these processes, the way in which effort-, reward-, and motor-related information is registered by networks of ACC neurons is poorly understood. To contrast ACC responses to effort, reward, and motor behaviors, we trained rats on a reversal task in which the selected paths on a track determined the level of effort or reward. Effort was presented in the form of an obstacle that was climbed to obtain reward. We used single-unit recordings to identify neural correlates of effort- and reward-guided behaviors. During periods of outcome anticipation, 52% of recorded ACC neurons responded to the specific route taken to the reward while 21% responded prospectively to effort and 12% responded prospectively to reward. In addition, effort- and reward-selective neurons typically responded to the route, suggesting that these cells integrated motor-related activity with expectations of future outcomes. Furthermore, the activity of ACC neurons did not discriminate between choice and forced trials or respond to a more generalized measure of outcome value. Nearly all neural responses to effort and reward occurred after path selection and were restricted to discrete temporal/spatial stages of the task. Together, these findings support a role for the ACC in integrating route-specific actions, effort, and reward in the service of sustaining discrete movements through an effortful series of goal-directed actions.

Are we ready for a natural history of motor learning?

Here we argue that general principles with regard to the contributions of the cerebellum, basal ganglia, and primary motor cortex to motor learning can begin to be inferred from explicit comparison across model systems and consideration of phylogeny. Both the cerebellum and the basal ganglia have highly conserved circuit architecture in vertebrates. The cerebellum has consistently been shown to be necessary for adaptation of eye and limb movements. The precise contribution of the basal ganglia to motor learning remains unclear but one consistent finding is that they are necessary for early acquisition of novel sequential actions. The primary motor cortex allows independent control of joints and construction of new movement synergies. We suggest that this capacity of the motor cortex implies that it is a necessary locus for motor skill learning, which we argue is the ability to execute selected actions with increasing speed and precision.

Neuroplasticity of the sensorimotor cortex during learning

We will discuss some of the current issues in understanding plasticity in the sensorimotor (SM) cortices on the behavioral, neurophysiological, and synaptic levels. We will focus our paper on reaching and grasping movements in the rat. In addition, we will discuss our preliminary work utilizing inhibition of protein kinase Mζ (PKMζ), which has recently been shown necessary and sufficient for the maintenance of long-term potentiation (LTP) (Ling et al., 2002). With this new knowledge and inhibitors to this system, as well as the ability to overexpress this system, we can start to directly modulate LTP and determine its influence on behavior as well as network level processing dependent at least in part due to this form of LTP. We will also briefly introduce the use of brain machine interface (BMI) paradigms to ask questions about sensorimotor plasticity and discuss current analysis techniques that may help in our understanding of neuroplasticity.