Disengagement of motor cortex from movement control during long-term learning

Dynamics of striatal action selection and reinforcement learning

Spiny projection neurons (SPNs) in dorsal striatum are often proposed as a locus of reinforcement learning in the basal ganglia. Here, we identify and resolve a fundamental inconsistency between striatal reinforcement learning models and known SPN synaptic plasticity rules. Direct-pathway (dSPN) and indirect-pathway (iSPN) neurons, which promote and suppress actions, respectively, exhibit synaptic plasticity that reinforces activity associated with elevated or suppressed dopamine release. We show that iSPN plasticity prevents successful learning, as it reinforces activity patterns associated with negative outcomes. However, this pathological behavior is reversed if functionally opponent dSPNs and iSPNs, which promote and suppress the current behavior, are simultaneously activated by efferent input following action selection. This prediction is supported by striatal recordings and contrasts with prior models of SPN representations. In our model, learning and action selection signals can be multiplexed without interference, enabling learning algorithms beyond those of standard temporal difference models.

Motor learning refines thalamic influence on motor cortex

The primary motor cortex (M1) is central for the learning and execution of dexterous motor skills1-3, and its superficial layer (layers 2 and 3; hereafter, L2/3) is a key locus of learning-related plasticity1,4-6. It remains unknown how motor learning shapes the way in which upstream regions activate M1 circuits to execute learned movements. Here, using longitudinal axonal imaging of the main inputs to M1 L2/3 in mice, we show that the motor thalamus is the key input source that encodes learned movements in experts (animals trained for two weeks). We then use optogenetics to identify the subset of M1 L2/3 neurons that are strongly driven by thalamic inputs before and after learning. We find that the thalamic influence on M1 changes with learning, such that the motor thalamus preferentially activates the M1 neurons that encode learned movements in experts. Inactivation of the thalamic inputs to M1 in experts impairs learned movements. Our study shows that motor learning reshapes the thalamic influence on M1 to enable the reliable execution of learned movements.

An Open-Source Joystick Platform for Investigating Forelimb Motor Control, Auditory-Motor Integration, and Value-Based Decision-Making in Head-Fixed Mice

Investigation of neural processes underlying motor control requires behavioral readouts that capture the richness of actions, including both categorical (choice-based) information and motor execution (kinematics). We present an open-source platform for behavioral training of head-fixed mice that combines a stationary or retractable forelimb-based joystick, sound-presentation system, capacitive lick sensor, and water reward dispenser. The setup allows for the creation of multiple behavioral paradigms, two of which are highlighted here: a two-alternative forced-choice auditory-motor discrimination paradigm and a two-armed bandit value-based decision-making task. In the auditory-motor paradigm, mice learn to report high- or low-frequency tones by pushing or pulling the joystick. In the value-based paradigm, mice learn to push or pull the joystick based on the history of rewarded trials. In addition to reporting categorical choices, this setup provides a rich dataset of motor parameters that reflect components of the underlying learning and decision processes in both of these tasks. These kinematic parameters (including joystick speed and displacement, Fréchet similarity of trajectories, tortuosity, angular standard deviation, and movement vigor) provide key additional insights into the motor execution of choices that are not as readily assessed in other paradigms. The system's flexibility of task design, joystick readout, and ease of construction represent an advance compared with currently available manipulandum tasks in mice. We provide detailed schematics for constructing the setup and protocols for behavioral training using both paradigms, with the hope that this open-source resource is readily adopted by neuroscientists interested in mechanisms of sensorimotor integration, motor control, and choice behavior.

Selective direct motor cortical influence during naturalistic climbing in mice

It remains poorly resolved when and how motor cortical output directly influences limb muscle activity through descending projections, which impedes mechanistic understanding of motor control. Here we addressed this in mice performing an ethologically inspired climbing behavior. We quantified the direct influence of forelimb primary motor cortex (caudal forelimb area, CFA) on muscles across the muscle activity states expressed during climbing. We found that CFA instructs muscle activity pattern by selectively activating certain muscles, while less frequently activating or suppressing their antagonists. From Neuropixels recordings, we identified linear combinations (components) of motor cortical activity that covary with these effects. These components differ partially from those that covary with muscle activity and differ almost completely from those that covary with 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 distinct neural activity subspace.

Selective regulation of corticostriatal synapses by astrocytic phagocytosis

In the adult brain, neural circuit homeostasis depends on the constant turnover of synapses via astrocytic phagocytosis mechanisms. However, it remains unclear whether this process occurs in a circuit-specific manner. Here, we reveal that astrocytes target and eliminate specific type of excitatory synapses in the striatum. Using model mice lacking astrocytic phagocytosis receptors in the dorsal striatum, we found that astrocytes constantly remove corticostriatal synapses rather than thalamostriatal synapses. This preferential elimination suggests that astrocytes play a selective role in modulating corticostriatal plasticity and functions via phagocytosis mechanisms. Supporting this notion, corticostriatal long-term potentiation and the early phase of motor skill learning are dependent on astrocytic phagocytic receptors. Together, our findings demonstrate that astrocytes contribute to the connectivity and plasticity of the striatal circuit by preferentially engulfing a specific subset of excitatory synapses within brain regions innervated by multiple excitatory sources.

Conjoint specification of action by neocortex and striatum

The interplay between two major forebrain structures-cortex and subcortical striatum-is critical for flexible, goal-directed action. Traditionally, it has been proposed that striatum is critical for selecting what type of action is initiated, while the primary motor cortex is involved in specifying the continuous parameters of an upcoming/ongoing movement. Recent data indicate that striatum may also be involved in specification. These alternatives have been difficult to reconcile because comparing very distinct actions, as is often done, makes essentially indistinguishable predictions. Here, we develop quantitative models to reveal a somewhat paradoxical insight: only comparing neural activity across similar actions makes strongly distinguishing predictions. We thus developed a novel reach-to-pull task in which mice reliably selected between two similar but distinct reach targets and pull forces. Simultaneous cortical and subcortical recordings were uniquely consistent with a model in which cortex and striatum jointly specify continuous parameters governing movement execution.

An Open-Source Joystick Platform for Investigating Forelimb Motor Control, Auditory-Motor Integration, and Value-Based Decision-Making in Head-Fixed Mice

Investigation of neural processes underlying motor control requires behavioral readouts that capture the richness of actions, including both categorical (choice-based) information and motor execution (kinematics). We present an open-source platform for behavioral training of head-fixed mice that combines a stationary or retractable forelimb-based joystick, sound-presentation system, capacitive lick sensor, and water reward dispenser. The setup allows for the creation of multiple behavioral paradigms, two of which are highlighted here: a two-alternative forced-choice auditory-motor discrimination paradigm, and a two-armed bandit value-based decision-making task. In the auditory-motor paradigm, mice learn to report high or low frequency tones by pushing or pulling the joystick. In the value-based paradigm, mice learn to push or pull the joystick based on the history of rewarded trials. In addition to reporting categorical choices, this setup provides a rich dataset of motor parameters that reflect components of the underlying learning and decision processes in both of these tasks. These kinematic parameters (including joystick speed and displacement, Fréchet similarity of trajectories, tortuosity, angular standard deviation, and movement vigor) provide key additional insights into the motor execution of choices that are not as readily assessed in other paradigms. The system's flexibility of task design, joystick readout, and ease of construction represent an advance compared to currently available manipulandum tasks in mice. We provide detailed schematics for constructing the setup and protocols for behavioral training using both paradigms, with the hope that this open-source resource is readily adopted by neuroscientists interested in mechanisms of sensorimotor integration, motor control, and choice behavior.

Significance Statement

Behavioral paradigms for experiments in head-restrained mice are important for investigating the relationship between neural activity and behavior. However, behavioral setups are often constrained by high cost, design complexity, and implementation challenges. Here, we present an open-source platform for behavioral training of head-fixed mice using a joystick manipulandum. The setup allows for the creation of multiple behavioral paradigms, including an auditory-motor discrimination paradigm, and a value-based decision-making task. We include detailed instructions for construction and implementation of the entire open-source behavioral platform.

Dynamics of striatal action selection and reinforcement learning

Spiny projection neurons (SPNs) in dorsal striatum are often proposed as a locus of reinforcement learning in the basal ganglia. Here, we identify and resolve a fundamental inconsistency between striatal reinforcement learning models and known SPN synaptic plasticity rules. Direct-pathway (dSPN) and indirect-pathway (iSPN) neurons, which promote and suppress actions, respectively, exhibit synaptic plasticity that reinforces activity associated with elevated or suppressed dopamine release. We show that iSPN plasticity prevents successful learning, as it reinforces activity patterns associated with negative outcomes. However, this pathological behavior is reversed if functionally opponent dSPNs and iSPNs, which promote and suppress the current behavior, are simultaneously activated by efferent input following action selection. This prediction is supported by striatal recordings and contrasts with prior models of SPN representations. In our model, learning and action selection signals can be multiplexed without interference, enabling learning algorithms beyond those of standard temporal difference models.

The role of motor cortex in motor sequence execution depends on demands for flexibility

The role of the motor cortex in executing motor sequences is widely debated, with studies supporting disparate views. Here we probe the degree to which the motor cortex's engagement depends on task demands, specifically whether its role differs for highly practiced, or 'automatic', sequences versus flexible sequences informed by external cues. To test this, we trained rats to generate three-element motor sequences either by overtraining them on a single sequence or by having them follow instructive visual cues. Lesioning motor cortex showed that it is necessary for flexible cue-driven motor sequences but dispensable for single automatic behaviors trained in isolation. However, when an automatic motor sequence was practiced alongside the flexible task, it became motor cortex dependent, suggesting that an automatic motor sequence fails to consolidate subcortically when the same sequence is produced also in a flexible context. A simple neural network model recapitulated these results and offered a circuit-level explanation. Our results critically delineate the role of the motor cortex in motor sequence execution, describing the conditions under which it is engaged and the functions it fulfills, thus reconciling seemingly conflicting views about motor cortex's role in motor sequence generation.

Dynamic lateralization in contralateral-projecting corticospinal neurons during motor learning

Understanding the adaptability of the motor cortex in response to bilateral motor tasks is crucial for advancing our knowledge of neural plasticity and motor learning. Here we aim to investigate the dynamic lateralization of contralateral-projecting corticospinal neurons (cpCSNs) during such tasks. Utilizing in vivo two-photon calcium imaging, we observe cpCSNs in mice performing a "left-right" lever-press task. Our findings reveal heterogeneous populational dynamics in cpCSNs: a marked decrease in activity during ipsilateral motor learning, in contrast to maintained activity during contralateral motor learning. Notably, individual cpCSNs show dynamic shifts in engagement with ipsilateral and contralateral movements, displaying an evolving pattern of activation over successive days. It suggests that cpCSNs exhibit adaptive changes in activation patterns in response to ipsilateral and contralateral movements, highlighting a flexible reorganization during motor learning This reconfiguration underscores the dynamic nature of cortical lateralization in motor learning and offers insights for neuromotor rehabilitation.

Motor cortex is responsible for motoric dynamics in striatum and the execution of both skilled and unskilled actions

Striatum and its predominant input, motor cortex, are responsible for the selection and performance of purposive movement, but how their interaction guides these processes is not understood. To establish its neural and behavioral contributions, we bilaterally lesioned motor cortex and recorded striatal activity and reaching performance daily, capturing the lesion's direct ramifications within hours of the intervention. We observed reaching impairment and an absence of striatal motoric activity following lesion of motor cortex, but not parietal cortex control lesions. Although some aspects of performance began to recover after 8-10 days, striatal projection and interneuronal dynamics did not-eventually entering a non-motor encoding state that aligned with persisting kinematic control deficits. Lesioned mice also exhibited a profound inability to switch motor plans while locomoting, reminiscent of clinical freezing of gait (FOG). Our results demonstrate the necessity of motor cortex in generating trained and untrained actions as well as striatal motoric dynamics.

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.

Cortico-basal ganglia plasticity in motor learning

One key function of the brain is to control our body's movements, allowing us to interact with the world around us. Yet, many motor behaviors are not innate but require learning through repeated practice. Among the brain's motor regions, the cortico-basal ganglia circuit is particularly crucial for acquiring and executing motor skills, and neuronal activity in these regions is directly linked to movement parameters. Cell-type-specific adaptations of activity patterns and synaptic connectivity support the learning of new motor skills. Functionally, neuronal activity sequences become structured and associated with learned movements. On the synaptic level, specific connections become potentiated during learning through mechanisms such as long-term synaptic plasticity and dendritic spine dynamics, which are thought to mediate functional circuit plasticity. These synaptic and circuit adaptations within the cortico-basal ganglia circuitry are thus critical for motor skill acquisition, and disruptions in this plasticity can contribute to movement disorders.

Recording of single-unit activities with flexible micro-electrocorticographic array in rats for decoding of whole-body navigation

Objective.Micro-electrocorticographic (μECoG) arrays are able to record neural activities from the cortical surface, without the need to penetrate the brain parenchyma. Owing in part to small electrode sizes, previous studies have demonstrated that single-unit spikes could be detected from the cortical surface, and likely from Layer I neurons of the neocortex. Here we tested the ability to useμECoG arrays to decode, in rats, body position during open field navigation, through isolated single-unit activities.Approach. μECoG arrays were chronically implanted onto primary motor cortex (M1) of Wistar rats, and neural recording was performed in awake, behaving rats in an open-field enclosure. The signals were band-pass filtered between 300-3000 Hz. Threshold-crossing spikes were identified and sorted into distinct units based on defined criteria including waveform morphology and refractory period. Body positions were derived from video recordings. We used gradient-boosting machine to predict body position based on previous 100 ms of spike data, and correlation analyses to elucidate the relationship between position and spike patterns.Main results.Single-unit spikes could be extracted during chronic recording fromμECoG, and spatial position could be decoded from these spikes with a mean absolute error of prediction of 0.135 and 0.090 in the x- and y- dimensions (of a normalized range from 0 to 1), and Pearson's r of 0.607 and 0.571, respectively.Significance. μECoG can detect single-unit activities that likely arise from superficial neurons in the cortex and is a promising alternative to intracortical arrays, with the added benefit of scalability to cover large cortical surface with minimal incremental risks. More studies should be performed in human related to its use as brain-machine interface.

Selective consolidation of learning and memory via recall-gated plasticity

In a variety of species and behavioral contexts, learning and memory formation recruits two neural systems, with initial plasticity in one system being consolidated into the other over time. Moreover, consolidation is known to be selective; that is, some experiences are more likely to be consolidated into long-term memory than others. Here, we propose and analyze a model that captures common computational principles underlying such phenomena. The key component of this model is a mechanism by which a long-term learning and memory system prioritizes the storage of synaptic changes that are consistent with prior updates to the short-term system. This mechanism, which we refer to as recall-gated consolidation, has the effect of shielding long-term memory from spurious synaptic changes, enabling it to focus on reliable signals in the environment. We describe neural circuit implementations of this model for different types of learning problems, including supervised learning, reinforcement learning, and autoassociative memory storage. These implementations involve synaptic plasticity rules modulated by factors such as prediction accuracy, decision confidence, or familiarity. We then develop an analytical theory of the learning and memory performance of the model, in comparison to alternatives relying only on synapse-local consolidation mechanisms. We find that recall-gated consolidation provides significant advantages, substantially amplifying the signal-to-noise ratio with which memories can be stored in noisy environments. We show that recall-gated consolidation gives rise to a number of phenomena that are present in behavioral learning paradigms, including spaced learning effects, task-dependent rates of consolidation, and differing neural representations in short- and long-term pathways.

Rethinking dopamine-guided action sequence learning

As opposed to those requiring a single action for reward acquisition, tasks necessitating action sequences demand that animals learn action elements and their sequential order and sustain the behaviour until the sequence is completed. With repeated learning, animals not only exhibit precise execution of these sequences but also demonstrate enhanced smoothness and efficiency. Previous research has demonstrated that midbrain dopamine and its major projection target, the striatum, play crucial roles in these processes. Recent studies have shown that dopamine from the substantia nigra pars compacta (SNc) and the ventral tegmental area (VTA) serve distinct functions in action sequence learning. The distinct contributions of dopamine also depend on the striatal subregions, namely the ventral, dorsomedial and dorsolateral striatum. Here, we have reviewed recent findings on the role of striatal dopamine in action sequence learning, with a focus on recent rodent studies.

Refinement of efficient encodings of movement in the dorsolateral striatum throughout learning

The striatum is required for normal action selection, movement, and sensorimotor learning. Although action-specific striatal ensembles have been well documented, it is not well understood how these ensembles are formed and how their dynamics may evolve throughout motor learning. Here we used longitudinal 2-photon Ca2+ imaging of dorsal striatal neurons in head-fixed mice as they learned to self-generate locomotion. We observed a significant activation of both direct- and indirect-pathway spiny projection neurons (dSPNs and iSPNs, respectively) during early locomotion bouts and sessions that gradually decreased over time. For dSPNs, onset- and offset-ensembles were gradually refined from active motion-nonspecific cells. iSPN ensembles emerged from neurons initially active during opponent actions before becoming onset- or offset-specific. Our results show that as striatal ensembles are progressively refined, the number of active nonspecific striatal neurons decrease and the overall efficiency of the striatum information encoding for learned actions increases.

A combinatorial neural code for long-term motor memory

Motor skill repertoire can be stably retained over long periods, but the neural mechanism underlying stable memory storage remains poorly understood. Moreover, it is unknown how existing motor memories are maintained as new motor skills are continuously acquired. Here we tracked neural representation of learned actions throughout a significant portion of a mouse's lifespan, and we show that learned actions are stably retained in motor memory in combination with context, which protects existing memories from erasure during new motor learning. We used automated home-cage training to establish a continual learning paradigm in which mice learned to perform directional licking in different task contexts. We combined this paradigm with chronic two-photon imaging of motor cortex activity for up to 6 months. Within the same task context, activity driving directional licking was stable over time with little representational drift. When learning new task contexts, new preparatory activity emerged to drive the same licking actions. Learning created parallel new motor memories while retaining the previous memories. Re-learning to make the same actions in the previous task context re-activated the previous preparatory activity, even months later. At the same time, continual learning of new task contexts kept creating new preparatory activity patterns. Context-specific memories, as we observed in the motor system, may provide a solution for stable memory storage throughout continual learning. Learning in new contexts produces parallel new representations instead of modifying existing representations, thus protecting existing motor repertoire from erasure.

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.

Exploration biases forelimb reaching strategies

The brain can generate actions, such as reaching to a target, using different movement strategies. We investigate how such strategies are learned in a task where perched head-fixed mice learn to reach to an invisible target area from a set start position using a joystick. This can be achieved by learning to move in a specific direction or to a specific endpoint location. As mice learn to reach the target, they refine their variable joystick trajectories into controlled reaches, which depend on the sensorimotor cortex. We show that individual mice learned strategies biased to either direction- or endpoint-based movements. This endpoint/direction bias correlates with spatial directional variability with which the workspace was explored during training. Model-free reinforcement learning agents can generate both strategies with similar correlation between variability during training and learning bias. These results provide evidence that reinforcement of individual exploratory behavior during training biases the reaching strategies that mice learn.

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.

Integrating across behaviors and timescales to understand the neural control of movement

The nervous system evolved to enable navigation throughout the environment in the pursuit of resources. Evolutionarily newer structures allowed increasingly complex adaptations but necessarily added redundancy. A dominant view of movement neuroscientists is that there is a one-to-one mapping between brain region and function. However, recent experimental data is hard to reconcile with the most conservative interpretation of this framework, suggesting a degree of functional redundancy during the performance of well-learned, constrained behaviors. This apparent redundancy likely stems from the bidirectional interactions between the various cortical and subcortical structures involved in motor control. We posit that these bidirectional connections enable flexible interactions across structures that change depending upon behavioral demands, such as during acquisition, execution or adaptation of a skill. Observing the system across both multiple actions and behavioral timescales can help isolate the functional contributions of individual structures, leading to an integrated understanding of the neural control of movement.

Local and global predictors of synapse elimination during motor learning

During learning, synaptic connections between excitatory neurons in the brain display considerable dynamism, with new connections being added and old connections eliminated. Synapse elimination offers an opportunity to understand the features of synapses that the brain deems dispensable. However, with limited observations of synaptic activity and plasticity in vivo, the features of synapses subjected to elimination remain poorly understood. Here, we examined the functional basis of synapse elimination in the apical dendrites of L2/3 neurons in the primary motor cortex throughout motor learning. We found no evidence that synapse elimination is facilitated by a lack of activity or other local forms of plasticity. Instead, eliminated synapses display asynchronous activity with nearby synapses, suggesting that functional synaptic clustering is a critical component of synapse survival. In addition, eliminated synapses show delayed activity timing with respect to postsynaptic output. Thus, synaptic inputs that fail to be co-active with their neighboring synapses or are mistimed with neuronal output are targeted for elimination.

Divergent Learning-Related Transcriptional States of Cortical Glutamatergic Neurons

Experience-dependent gene expression reshapes neural circuits, permitting the learning of knowledge and skills. Most learning involves repetitive experiences during which neurons undergo multiple stages of functional and structural plasticity. Currently, the diversity of transcriptional responses underlying dynamic plasticity during repetition-based learning is poorly understood. To close this gap, we analyzed single-nucleus transcriptomes of L2/3 glutamatergic neurons of the primary motor cortex after 3 d motor skill training or home cage control in water-restricted male mice. "Train" and "control" neurons could be discriminated with high accuracy based on expression patterns of many genes, indicating that recent experience leaves a widespread transcriptional signature across L2/3 neurons. These discriminating genes exhibited divergent modes of coregulation, differentiating neurons into discrete clusters of transcriptional states. Several states showed gene expressions associated with activity-dependent plasticity. Some of these states were also prominent in the previously published reference, suggesting that they represent both spontaneous and task-related plasticity events. Markedly, however, two states were unique to our dataset. The first state, further enriched by motor training, showed gene expression suggestive of late-stage plasticity with repeated activation, which is suitable for expected emergent neuronal ensembles that stably retain motor learning. The second state, equally found in both train and control mice, showed elevated levels of metabolic pathways and norepinephrine sensitivity, suggesting a response to common experiences specific to our experimental conditions, such as water restriction or circadian rhythm. Together, we uncovered divergent transcriptional responses across L2/3 neurons, each potentially linked with distinct features of repetition-based motor learning such as plasticity, memory, and motivation.

Cortical circuit dynamics underlying motor skill learning: from rodents to humans

Motor learning is crucial for the survival of many animals. Acquiring a new motor skill involves complex alterations in both local neural circuits in many brain regions and long-range connections between them. Such changes can be observed anatomically and functionally. The primary motor cortex (M1) integrates information from diverse brain regions and plays a pivotal role in the acquisition and refinement of new motor skills. In this review, we discuss how motor learning affects the M1 at synaptic, cellular, and circuit levels. Wherever applicable, we attempt to relate and compare findings in humans, non-human primates, and rodents. Understanding the underlying principles shared by different species will deepen our understanding of the neurobiological and computational basis of motor learning.

Layer 5 Intratelencephalic Neurons in the Motor Cortex Stably Encode Skilled Movement

The primary motor cortex (M1) and the dorsal striatum play a critical role in motor learning and the retention of learned behaviors. Motor representations of corticostriatal ensembles emerge during motor learning. In the coordinated reorganization of M1 and the dorsal striatum for motor learning, layer 5a (L5a) which connects M1 to the ipsilateral and contralateral dorsal striatum, should be a key layer. Although M1 L5a neurons represent movement-related activity in the late stage of learning, it is unclear whether the activity is retained as a memory engram. Here, using Tlx3-Cre male transgenic mice, we conducted two-photon calcium imaging of striatum-projecting L5a intratelencephalic (IT) neurons in forelimb M1 during late sessions of a self-initiated lever-pull task and in sessions after 6 d of nontraining following the late sessions. We found that trained male animals exhibited stable motor performance before and after the nontraining days. At the same time, we found that M1 L5a IT neurons strongly represented the well-learned forelimb movement but not uninstructed orofacial movements. A subset of M1 L5a IT neurons consistently coded the well-learned forelimb movement before and after the nontraining days. Inactivation of M1 IT neurons after learning impaired task performance when the lever was made heavier or when the target range of the pull distance was narrowed. These results suggest that a subset of M1 L5a IT neurons continuously represent skilled movement after learning and serve to fine-tune the kinematics of well-learned movement.SIGNIFICANCE STATEMENT Motor memory persists even when it is not used for a while. IT neurons in L5a of the M1 gradually come to represent skilled forelimb movements during motor learning. However, it remains to be determined whether these changes persist over a long period and how these neurons contribute to skilled movements. Here, we show that a subset of M1 L5a IT neurons retain information for skilled forelimb movements even after nontraining days. Furthermore, suppressing the activity of these neurons during skilled forelimb movements impaired behavioral stability and adaptability. Our results suggest the importance of M1 L5a IT neurons for tuning skilled forelimb movements over a long period.

Motor cortex is required for flexible but not automatic motor sequences

How motor cortex contributes to motor sequence execution is much debated, with studies supporting disparate views. Here we probe the degree to which motor cortex's engagement depends on task demands, specifically whether its role differs for highly practiced, or 'automatic', sequences versus flexible sequences informed by external events. To test this, we trained rats to generate three-element motor sequences either by overtraining them on a single sequence or by having them follow instructive visual cues. Lesioning motor cortex revealed that it is necessary for flexible cue-driven motor sequences but dispensable for single automatic behaviors trained in isolation. However, when an automatic motor sequence was practiced alongside the flexible task, it became motor cortex-dependent, suggesting that subcortical consolidation of an automatic motor sequence is delayed or prevented when the same sequence is produced also in a flexible context. A simple neural network model recapitulated these results and explained the underlying circuit mechanisms. Our results critically delineate the role of motor cortex in motor sequence execution, describing the condition under which it is engaged and the functions it fulfills, thus reconciling seemingly conflicting views about motor cortex's role in motor sequence generation.

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.

Exploration biases how forelimb reaches to a spatial target are learned

The brain can learn to generate actions, such as reaching to a target, using different movement strategies. Understanding how different variables bias which strategies are learned to produce such a reach is important for our understanding of the neural bases of movement. Here we introduce a novel spatial forelimb target task in which perched head-fixed mice learn to reach to a circular target area from a set start position using a joystick. These reaches can be achieved by learning to move into a specific direction or to a specific endpoint location. We find that mice gradually learn to successfully reach the covert target. With time, they refine their initially exploratory complex joystick trajectories into controlled targeted reaches. The execution of these controlled reaches depends on the sensorimotor cortex. Using a probe test with shifting start positions, we show that individual mice learned to use strategies biased to either direction or endpoint-based movements. The degree of endpoint learning bias was correlated with the spatial directional variability with which the workspace was explored early in training. Furthermore, we demonstrate that reinforcement learning model agents exhibit a similar correlation between directional variability during training and learned strategy. These results provide evidence that individual exploratory behavior during training biases the control strategies that mice use to perform forelimb covert target reaches.

Daily two-photon neuronal population imaging with targeted single-cell electrophysiology and subcellular imaging in auditory cortex of behaving mice

Quantitative and mechanistic understanding of learning and long-term memory at the level of single neurons in living brains require highly demanding techniques. A specific need is to precisely label one cell whose firing output property is pinpointed amidst a functionally characterized large population of neurons through the learning process and then investigate the distribution and properties of dendritic inputs. Here, we disseminate an integrated method of daily two-photon neuronal population Ca²⁺ imaging through an auditory associative learning course, followed by targeted single-cell loose-patch recording and electroporation of plasmid for enhanced chronic Ca²⁺ imaging of dendritic spines in the targeted cell. Our method provides a unique solution to the demand, opening a solid path toward the hard-cores of how learning and long-term memory are physiologically carried out at the level of single neurons and synapses.

Combined robot motor assistance with neural circuit-based virtual reality (NeuCir-VR) lower extremity rehabilitation training in patients after stroke: a study protocol for a single-centre randomised controlled trial

INTRODUCTION

Improving lower extremity motor function is the focus and difficulty of post-stroke rehabilitation treatment. More recently, robot-assisted and virtual reality (VR) training are commonly used in post-stroke rehabilitation and are considered feasible treatment methods. Here, we developed a rehabilitation system combining robot motor assistance with neural circuit-based VR (NeuCir-VR) rehabilitation programme involving procedural lower extremity rehabilitation with reward mechanisms, from muscle strength training, posture control and balance training to simple and complex ground walking training. The study aims to explore the effectiveness and neurological mechanisms of combining robot motor assistance and NeuCir-VR lower extremity rehabilitation training in patients after stroke.

METHODS AND ANALYSIS

This is a single-centre, observer-blinded, randomised controlled trial. 40 patients with lower extremity hemiparesis after stroke will be recruited and randomly divided into a control group (combined robot assistance and VR training) and an intervention group (combined robot assistance and NeuCir-VR training) by the ratio of 1:1. Each group will receive five 30 min sessions per week for 4 weeks. The primary outcome will be Fugl-Meyer assessment of the lower extremity. Secondary outcomes will include Berg Balance Scale, Modified Ashworth Scale and functional connectivity measured by resting-state functional MRI. Outcomes will be measured at baseline (T0), post-intervention (T1) and follow-ups (T2-T4).

ETHICS, REGISTRATION AND DISSEMINATION

The trial was approved by the Ethics Committee of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Chinese Traditional Medicine (Grant No. 2019-014). The results will be submitted to a peer-reviewed journal or at a conference.

TRIAL REGISTRATION NUMBER

ChiCTR2100052133.

Corticospinal neurons from motor and somatosensory cortices exhibit different temporal activity dynamics during motor learning

The ability to learn motor skills implicates an improvement in accuracy, speed and consistency of movements. Motor control is related to movement execution and involves corticospinal neurons (CSp), which are broadly distributed in layer 5B of the motor and somatosensory cortices. CSp neurons innervate the spinal cord and are functionally diverse. However, whether CSp activity differs between different cortical areas throughout motor learning has been poorly explored. Given the importance and interaction between primary motor (M1) and somatosensory (S1) cortices related to movement, we examined the functional roles of CSp neurons in both areas. We induced the expression of GCaMP7s calcium indicator to perform photometric calcium recordings from layer 5B CSp neurons simultaneously in M1 and S1 cortices and track their activity while adult mice learned and performed a cued lever-press task. We found that during early learning sessions, the population calcium activity of CSp neurons in both cortices during movement did not change significantly. In late learning sessions the peak amplitude and duration of calcium activity CSp neurons increased in both, M1 and S1 cortices. However, S1 and M1 CSp neurons display a different temporal dynamic during movements that occurred when animals learned the task; both M1 and S1 CSp neurons activate before movement initiation, however, M1 CSp neurons continue active during movement performance, reinforcing the idea of the diversity of the CSp system and suggesting that CSp neuron activity in M1 and S1 cortices throughout motor learning have different functional roles for sensorimotor integration.

Manipulation-specific cortical activity as mice handle food

Food handling offers unique yet largely unexplored opportunities to investigate how cortical activity relates to forelimb movements in a natural, ethologically essential, and kinematically rich form of manual dexterity. To determine these relationships, we recorded high-speed (1,000 fps) video and multi-channel electrophysiological cortical spiking activity while mice handled food. The high temporal resolution of the video allowed us to decompose active manipulation ("oromanual") events into characteristic submovements, enabling event-aligned analysis of cortical activity. Activity in forelimb M1 was strongly modulated during food handling, generally higher during oromanual events and lower during holding intervals. Optogenetic silencing and stimulation of forelimb M1 neurons partially affected food-handling movements, exerting suppressive and activating effects, respectively. We also extended the analysis to forelimb S1 and lateral M1, finding broadly similar oromanual-related activity across all three areas. However, each area's activity displayed a distinct timing and phasic/tonic temporal profile, which was further analyzed by non-negative matrix factorization and demonstrated to be attributable to area-specific composition of activity classes. Current or future forelimb position could be accurately predicted from activity in all three regions, indicating that the cortical activity in these areas contains high information content about forelimb movements during food handling. These results thus establish that cortical activity during food handling is manipulation specific, distributed, and broadly similar across multiple sensorimotor areas while also exhibiting area- and submovement-specific relationships with the fast kinematic hallmarks of this natural form of complex free-object-handling manual dexterity.

Learning-induced changes in the neural circuits underlying motor sequence execution

As the old adage goes: practice makes perfect. Yet, the neural mechanisms by which rote repetition transforms a halting behavior into a fluid, effortless, and "automatic" action are not well understood. Here we consider the possibility that well-practiced motor sequences, which initially rely on higher-level decision-making circuits, become wholly specified in lower-level control circuits. We review studies informing this idea, discuss the constraints on such shift in control, and suggest approaches to pinpoint circuit-level changes associated with motor sequence learning.

Markerless tracking enables distinction between strategic compensation and functional recovery after spinal cord injury

Injuries to the cervical spinal cord represent around 60% of all spinal cord injuries (SCIs). A major priority for patients with cervical SCIs is the recovery of any hand or arm function. The similarities between human and rodent "reach-to-eat movements" indicate that analyzing mouse forelimb reaching behavior may be a method of identifying clinically relevant treatments for people with cervical SCIs. One popular behavioral measure of forelimb functional recovery comprises the Single Pellet Retrieval Task (SPRT). The most common outcome measure for this task, however (percentage of pellets successfully retrieved), cannot readily distinguish between recovery of pre-injury motor patterns and strategic compensation. Our objective was to establish outcome measures for the SPRT that are readily adopted by different investigators and capable of measuring recovery of limb function after SCI. We used a simple semi-automated approach to high-speed tracking of mouse forepaw movements during pellet retrieval. DeepLabCut™, a machine learning based computer vision software package, was used to track individual features of the mouse forepaw, allowing a more detailed assessment of reaching behavior after SCI. Interestingly, kinematic analysis of movements pre- and post-injury illuminated persistent deficits in specific features of the reaching motor patterns, namely pronation and paw trajectory, that were poorly correlated with recovery of the ability to successfully retrieve pellets. Thus, we have developed an inexpensive method for detailed analysis of mouse reach-to-eat behavior following SCI. Further, our results suggest that binary success/fail outcome measures primarily assess an animal's ability to compensate rather than a restoration of normal function in the injured pathways and networks.

Cognitive experience alters cortical involvement in goal-directed navigation

Neural activity in the mammalian cortex has been studied extensively during decision tasks, and recent work aims to identify under what conditions cortex is actually necessary for these tasks. We discovered that mice with distinct cognitive experiences, beyond sensory and motor learning, use different cortical areas and neural activity patterns to solve the same navigation decision task, revealing past learning as a critical determinant of whether cortex is necessary for goal-directed navigation. We used optogenetics and calcium imaging to study the necessity and neural activity of multiple cortical areas in mice with different training histories. Posterior parietal cortex and retrosplenial cortex were mostly dispensable for accurate performance of a simple navigation task. In contrast, these areas were essential for the same simple task when mice were previously trained on complex tasks with delay periods or association switches. Multiarea calcium imaging showed that, in mice with complex-task experience, single-neuron activity had higher selectivity and neuron–neuron correlations were weaker, leading to codes with higher task information. Therefore, past experience is a key factor in determining whether cortical areas have a causal role in goal-directed navigation.

Learning binds new inputs into functional synaptic clusters via spinogenesis

Learning induces the formation of new excitatory synapses in the form of dendritic spines, but their functional properties remain unknown. Here, using longitudinal in vivo two-photon imaging and correlated electron microscopy of dendritic spines in the motor cortex of mice during motor learning, we describe a framework for the formation, survival and resulting function of new, learning-related spines. Specifically, our data indicate that the formation of new spines during learning is guided by the potentiation of functionally clustered preexisting spines exhibiting task-related activity during earlier sessions of learning. We present evidence that this clustered potentiation induces the local outgrowth of multiple filopodia from the nearby dendrite, locally sampling the adjacent neuropil for potential axonal partners, likely via targeting preexisting presynaptic boutons. Successful connections are then selected for survival based on co-activity with nearby task-related spines, ensuring that the new spine preserves functional clustering. The resulting locally coherent activity of new spines signals the learned movement. Furthermore, we found that a majority of new spines synapse with axons previously unrepresented in these dendritic domains. Thus, learning involves the binding of new information streams into functional synaptic clusters to subserve learned behaviors.

Movement-specific signaling is differentially distributed across motor cortex layer 5 projection neuron classes

Motor cortex generates descending output necessary for executing a wide range of limb movements. Although movement-related activity has been described throughout motor cortex, the spatiotemporal organization of movement-specific signaling in deep layers remains largely unknown. Here we record layer 5B population dynamics in the caudal forelimb area of motor cortex while mice perform a forelimb push/pull task and find that most neurons show movement-invariant responses, with a minority displaying movement specificity. Using cell-type-specific imaging, we identify that invariant responses dominate pyramidal tract (PT) neuron activity, with a small subpopulation representing movement type, whereas a larger proportion of intratelencephalic (IT) neurons display movement-type-specific signaling. The proportion of IT neurons decoding movement-type peaks prior to movement initiation, whereas for PT neurons, this occurs during movement execution. Our data suggest that layer 5B population dynamics largely reflect movement-invariant signaling, with information related to movement-type being routed through relatively small, distributed subpopulations of projection neurons.

Distinct roles for motor cortical and thalamic inputs to striatum during motor skill learning and execution

The acquisition and execution of motor skills are mediated by a distributed motor network, spanning cortical and subcortical brain areas. The sensorimotor striatum is an important cog in this network, yet the roles of its two main inputs, from motor cortex and thalamus, remain largely unknown. To address this, we silenced the inputs in rats trained on a task that results in highly stereotyped and idiosyncratic movement patterns. While striatal-projecting motor cortex neurons were critical for learning these skills, silencing this pathway after learning had no effect on performance. In contrast, silencing striatal-projecting thalamus neurons disrupted the execution of the learned skills, causing rats to revert to species-typical pressing behaviors and preventing them from relearning the task. These results show distinct roles for motor cortex and thalamus in the learning and execution of motor skills and suggest that their interaction in the striatum underlies experience-dependent changes in subcortical motor circuits.

Delays in the Reticulospinal System Are Associated With a Reduced Capacity to Learn a Simulated Feeding Task in Older Adults

Learning declines with age. Recent evidence indicates that the brainstem may play an important role in learning and motor skill acquisition. Our objective was to determine if delays in the reticular formation, measured via the startle reflex, correspond to age-related deficits in learning and retention. We hypothesized that delays in the startle reflex would be linearly correlated to learning and retention deficits in older adults. To determine if associations were unique to the reticulospinal system, we also evaluated corticospinal contributions with transcranial magnetic stimulation. Our results showed a linear relationship between startle onset latency and percent learning and retention but no relationship between active or passive motor-evoked potential onsets or peak-to-peak amplitude. These results lay the foundation for further study to evaluate if (1) the reticular formation is a subcortical facilitator of skill acquisition and (2) processing delays in the reticular formation contribute to age-related learning deficits.

The primary motor cortex: the hub of motor learning in rodents

The primary motor cortex, a dynamic center for overall motion control and decision making, undergoes significant alterations upon neural stimulation. Over the last few decades, data from numerous studies using rodent models have improved our understanding of the morphological and functional plasticity of the primary motor cortex. In particular, spatially specific formation of dendritic spines and their maintenance during distinct behaviors is considered crucial for motor learning. However, whether the modifications of specific synapses are associated with motor learning should be studied further. In this review, we summarized the findings of prior studies on the features and dynamics of the primary motor cortex in rodents.

Basal Forebrain Cholinergic Neurons Selectively Drive Coordinated Motor Learning in Mice

Motor control requires precise temporal and spatial encoding across distinct motor centers that is refined through the repetition of learning. The recruitment of motor regions requires modulatory input to shape circuit activity. Here, we identify a role for the baso-cortical cholinergic pathway in the acquisition of a coordinated motor skill in mice. Targeted depletion of basal forebrain cholinergic neurons results in significant impairments in training on the rotarod task of coordinated movement. Cholinergic neuromodulation is required during training sessions as chemogenetic inactivation of cholinergic neurons also impairs task acquisition. Rotarod learning is known to drive refinement of corticostriatal neurons arising in both medial prefrontal cortex (mPFC) and motor cortex, and we have found that cholinergic input to both motor regions is required for task acquisition. Critically, the effects of cholinergic neuromodulation are restricted to the acquisition stage, as depletion of basal forebrain cholinergic neurons after learning does not affect task execution. Our results indicate a critical role for cholinergic neuromodulation of distant cortical motor centers during coordinated motor learning.SIGNIFICANCE STATEMENT Acetylcholine release from basal forebrain cholinergic neuron terminals rapidly modulates neuronal excitability, circuit dynamics, and cortical coding; all processes required for processing complex sensory information, cognition, and attention. We found that depletion or transient silencing of cholinergic inputs to anatomically isolated motor areas, medial prefrontal cortex (mPFC) and motor cortex, selectively led to significant impairments on coordinated motor learning; disrupting this baso-cortical network after acquisition elicited no effect on task execution. Our results indicate a pivotal role for cholinergic neuromodulation of distant cortical motor centers during coordinated motor learning. These findings support the concept that cognitive components (such as attention) are indispensable in the adjustment of motor output and training-induced improvements in motor performance.

FARCI: Fast and Robust Connectome Inference

The inference of neuronal connectome from large-scale neuronal activity recordings, such as two-photon Calcium imaging, represents an active area of research in computational neuroscience. In this work, we developed FARCI (Fast and Robust Connectome Inference), a MATLAB package for neuronal connectome inference from high-dimensional two-photon Calcium fluorescence data. We employed partial correlations as a measure of the functional association strength between pairs of neurons to reconstruct a neuronal connectome. We demonstrated using in silico datasets from the Neural Connectomics Challenge (NCC) and those generated using the state-of-the-art simulator of Neural Anatomy and Optimal Microscopy (NAOMi) that FARCI provides an accurate connectome and its performance is robust to network sizes, missing neurons, and noise levels. Moreover, FARCI is computationally efficient and highly scalable to large networks. In comparison with the best performing connectome inference algorithm in the NCC, Generalized Transfer Entropy (GTE), and Fluorescence Single Neuron and Network Analysis Package (FluoroSNNAP), FARCI produces more accurate networks over different network sizes, while providing significantly better computational speed and scaling.

Disengagement of Motor Cortex during Long-Term Learning Tracks the Performance Level of Learned Movements

Not all movements require the motor cortex for execution. Intriguingly, dependence on motor cortex of a given movement is not fixed, but instead can dynamically change over the course of long-term learning. For instance, rodent forelimb movements that initially require motor cortex can become independent of the motor cortex after an extended period of training. However, it remains unclear whether long-term neural changes rendering the motor cortex dispensable are a simple function of the training length. To address this issue, we trained mice (both male and female) to perform two distinct forelimb movements, forward versus downward reaches with a joystick, concomitantly over several weeks, and then compared the involvement of the motor cortex between the two movements. Most mice achieved different levels of motor performance between the two movements after long-term training. Of the two movements, the one that achieved higher trial-to-trial consistency (i.e., consistent-direction movement) was significantly less affected by inactivation of motor cortex than the other (i.e., variable-direction movement). Two-photon calcium imaging of motor cortical neurons revealed that the consistent-direction movement activates fewer neurons, producing weaker and less consistent population activity than the variable-direction movement. Together, the motor cortex was less engaged and less necessary for learned movements that achieved higher levels of consistency. Thus, the long-term reorganization of neural circuits that frees the motor cortex from the learned movement is not a mere function of training length. Rather, this reorganization tracks the level of motor performance that the animal achieves during training.SIGNIFICANCE STATEMENT Long-term training of a movement reshapes motor circuits, disengaging motor cortex potentially for automatized execution of the learned movement. Acquiring new motor skills often involves learning of multiple movements (e.g., forehand and backhand strokes when learning tennis), but different movements do not always improve at the same time nor reach the same level of proficiency. Here we showed that the involvement of motor cortex after long-term training differs between similar yet distinct movements that reached different levels of expertise. Motor cortex was less engaged and less necessary for the more proficient movement. Thus, disengagement of motor cortex is not a simple function of training time, but instead tracks the level of expertise of a learned movement.

Small Enhancement of Bimanual Typing Performance after 20 Sessions of tDCS in Healthy Young Adults

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that may improve motor learning. However, the long-term effects of tDCS have not been explored, and the ecological validity of the evaluated tasks was limited. To determine whether 20 sessions of tDCS over the primary motor cortex (M1) would enhance the performance of a complex life motor skill, i.e., typing, in healthy young adults. Healthy young adults (n = 60) were semi-randomly assigned to three groups: the tDCS group (n = 20) received anodal tDCS over M1; the SHAM group (n = 20) received sham tDCS, both while performing a typing task; and the Control group (CON, n = 20) only performed the typing task. Typing speed and errors at maximum (mTT) and submaximal (iTT) speeds were measured before training, and after 10 and 20 sessions of tDCS. Every subject increased maximum typing speed after 10 and 20 tDCS sessions, with no significant differences (p > 0.05) between the groups. The number of errors at submaximal rates decreased significantly (p < 0.05) by 4% after 10 tDCS sessions compared with the 3% increase in the SHAM and the 2% increase in the CON groups. Between the 10th and 20th tDCS sessions, the number of typing errors increased significantly in all groups. While anodal tDCS reduced typing errors marginally, such performance-enhancing effects plateaued after 10 sessions without any further improvements in typing speed. These findings suggest that long-term tDCS may not have functionally relevant effects on healthy young adults' typing performance.

What to train first: Balance or explosive strength? Impact on performance and intracortical inhibition

Explosive strength and balance training are commonly applied to enhance explosive strength and balance performance. Even though both training methods are frequently implemented, ordering effects have largely been neglected. Therefore, the present study aimed to investigate ordering effects of balance and explosive strength training on explosive strength and balance performance as well as changes in short-interval intracortical inhibition (SICI). Two groups of subjects either participated in 4 weeks of balance training followed by 4 weeks of explosive strength training (BT-ET) or vice versa (ET-BT). Before, after 4 and 8 weeks, balance performance, as well as explosive strength, was tested. Additionally, SICI was tested during rest as well as during balance perturbations and explosive contractions. The results show a training specific increase in performance with an increase in balance control followed by an increase in explosive strength in the BT-ET, while the ET-BT increased its balance and explosive strength in the opposite order. There were no significant ordering effects. Both groups showed a significant decrease in SICI during the explosive contractions after the eight weeks of training. When SICI was tested during the balance perturbations, SICI initially increased after the first 4 weeks of training but returned to baseline until the end of the eight weeks. It is suggested that the decrease in SICI with prolonged training might show a disengagement of the motor cortex during the balance task. During the explosive contractions, the low SICI levels are beneficial to provide the necessary level of excitatory cortical drive.

Remembrance of things practiced with fast and slow learning in cortical and subcortical pathways

The learning of motor skills unfolds over multiple timescales, with rapid initial gains in performance followed by a longer period in which the behavior becomes more refined, habitual, and automatized. While recent lesion and inactivation experiments have provided hints about how various brain areas might contribute to such learning, their precise roles and the neural mechanisms underlying them are not well understood. In this work, we propose neural- and circuit-level mechanisms by which motor cortex, thalamus, and striatum support motor learning. In this model, the combination of fast cortical learning and slow subcortical learning gives rise to a covert learning process through which control of behavior is gradually transferred from cortical to subcortical circuits, while protecting learned behaviors that are practiced repeatedly against overwriting by future learning. Together, these results point to a new computational role for thalamus in motor learning and, more broadly, provide a framework for understanding the neural basis of habit formation and the automatization of behavior through practice.

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.