Circuit Mechanisms of Sensorimotor Learning

EEG connectivity and BDNF correlates of fast motor learning in laparoscopic surgery

This paper investigates the neural mechanisms underlying the early phase of motor learning in laparoscopic surgery training, using electroencephalography (EEG), brain-derived neurotrophic factor (BDNF) concentrations and subjective cognitive load recorded from n = 31 novice participants during laparoscopy training. Functional connectivity was quantified using inter-site phase clustering (ISPC) and subjective cognitive load was assessed using NASA-TLX scores. The study identified frequency-dependent connectivity patterns correlated with motor learning and BDNF expression. Gains in performance were associated with beta connectivity, particularly within prefrontal cortex and between visual and frontal areas, during task execution (r = - 0.73), and were predicted by delta connectivity during the initial rest episode (r = 0.83). The study also found correlations between connectivity and BDNF, with distinct topographic patterns emphasizing left temporal and visuo-frontal links. By highlighting the shifts in functional connectivity during early motor learning associated with learning, and linking them to brain plasticity mediated by BDNF, the multimodal findings could inform the development of more effective training methods and tailored interventions involving practice and feedback.

Visual experience orthogonalizes visual cortical stimulus responses via population code transformation

Sensory and behavioral experience can alter visual cortical stimulus coding, but the precise form of this plasticity is unclear. We measured orientation tuning in 4,000-neuron populations of mouse V1 before and after training on a visuomotor task. Changes to single-cell tuning curves appeared complex, including development of asymmetries and of multiple peaks. Nevertheless, these complex tuning curve transformations can be explained by a simple equation: a convex transformation suppressing responses to task stimuli specifically in cells responding at intermediate levels. The strength of the transformation varies across trials, suggesting a dynamic circuit mechanism rather than static synaptic plasticity. The transformation results in sparsening and orthogonalization of population codes for task stimuli. It cannot improve the performance of an optimal stimulus decoder, which is already perfect even for naive codes, but it improves the performance of a suboptimal decoder model with inductive bias as might be found in downstream readout circuits.

Language writ large: LLMs, ChatGPT, meaning, and understanding

Apart from what (little) OpenAI may be concealing from us, we all know (roughly) how Large Language Models (LLMs) such as ChatGPT work (their vast text databases, statistics, vector representations, and huge number of parameters, next-word training, etc.). However, none of us can say (hand on heart) that we are not surprised by what ChatGPT has proved to be able to do with these resources. This has even driven some of us to conclude that ChatGPT actually understands. It is not true that it understands. But it is also not true that we understand how it can do what it can do. I will suggest some hunches about benign "biases"-convergent constraints that emerge at the LLM scale that may be helping ChatGPT do so much better than we would have expected. These biases are inherent in the nature of language itself, at the LLM scale, and they are closely linked to what it is that ChatGPT lacks, which is direct sensorimotor grounding to connect its words to their referents and its propositions to their meanings. These convergent biases are related to (1) the parasitism of indirect verbal grounding on direct sensorimotor grounding, (2) the circularity of verbal definition, (3) the "mirroring" of language production and comprehension, (4) iconicity in propositions at LLM scale, (5) computational counterparts of human "categorical perception" in category learning by neural nets, and perhaps also (6) a conjecture by Chomsky about the laws of thought. The exposition will be in the form of a dialogue with ChatGPT-4.

Decoding the brain-machine interaction for upper limb assistive technologies: advances and challenges

Understanding how the brain encodes upper limb movements is crucial for developing control mechanisms in assistive technologies. Advances in assistive technologies, particularly Brain-machine Interfaces (BMIs), highlight the importance of decoding motor intentions and kinematics for effective control. EEG-based BMI systems show promise due to their non-invasive nature and potential for inducing neural plasticity, enhancing motor rehabilitation outcomes. While EEG-based BMIs show potential for decoding motor intention and kinematics, studies indicate inconsistent correlations with actual or planned movements, posing challenges for achieving precise and reliable prosthesis control. Further, the variability in predictive EEG patterns across individuals necessitates personalized tuning to improve BMI efficiency. Integrating multiple physiological signals could enhance BMI precision and reliability, paving the way for more effective motor rehabilitation strategies. Studies have shown that brain activity adapts to gravitational and inertial constraints during movement, highlighting the critical role of neural adaptation to biomechanical changes in creating control systems for assistive devices. This review aims to provide a comprehensive overview of recent progress in deciphering neural activity patterns associated with both physiological and assisted upper limb movements, highlighting avenues for future exploration in neurorehabilitation and brain-machine interface development.

Adaptation in the spinal cord after stroke: Implications for restoring cortical control over the final common pathway

Control of voluntary movement is predicated on integration between circuits in the brain and spinal cord. Although damage is often restricted to supraspinal or spinal circuits in cases of neurological injury, both spinal motor neurons and axons linking these cells to the cortical origins of descending motor commands begin showing changes soon after the brain is injured by stroke. The concept of 'transneuronal degeneration' is not new and has been documented in histological, imaging and electrophysiological studies dating back over a century. Taken together, evidence from these studies comports more with a system attempting to survive rather than one passively surrendering to degeneration. There tends to be at least some preservation of fibres at the brainstem origin and along the spinal course of the descending white matter tracts, even in severe cases. Myelin-associated proteins are observed in the spinal cord years after stroke onset. Spinal motor neurons remain morphometrically unaltered. Skeletal muscle fibres once innervated by neurons that lose their source of trophic input receive collaterals from adjacent neurons, causing spinal motor units to consolidate and increase in size. Although some level of excitability within the distributed brain network mediating voluntary movement is needed to facilitate recovery, minimal structural connectivity between cortical and spinal motor neurons can support meaningful distal limb function. Restoring access to the final common pathway via the descending input that remains in the spinal cord therefore represents a viable target for directed plasticity, particularly in light of recent advances in rehabilitation medicine.

VTA projections to M1 are essential for reorganization of layer 2-3 network dynamics underlying motor learning

The primary motor cortex (M1) is crucial for motor skill learning. Previous studies demonstrated that skill acquisition requires dopaminergic VTA (ventral-tegmental area) signaling in M1, however little is known regarding the effect of these inputs at the neuronal and network levels. Using dexterity task, calcium imaging, chemogenetic inhibiting, and geometric data analysis, we demonstrate VTA-dependent reorganization of M1 layer 2-3 during motor learning. While average activity and average functional connectivity of layer 2-3 network remain stable during learning, activity kinetics, correlational configuration of functional connectivity, and average connectivity strength of layer 2-3 neurons gradually transform towards an expert configuration. Additionally, sensory tone representation gradually shifts to success-failure outcome signaling. Inhibiting VTA dopaminergic inputs to M1 during learning, prevents all these changes. Our findings demonstrate dopaminergic VTA-dependent formation of outcome signaling and new connectivity configuration of the layer 2-3 network, supporting reorganization of the M1 network for storing new motor skills.

Aerobic Exercise Training for the Aging Brain: Effective Dosing and Vascular Mechanism

This article presents evidence supporting the hypothesis that starting aerobic exercise in early adulthood and continuing it throughout life leads to significant neurocognitive benefits compared with starting exercise later in life. Regular aerobic exercise at moderate-to-vigorous intensity during midlife is associated with significant improvement in cardiorespiratory fitness, which may create a favorable brain microenvironment promoting neuroplasticity through enhanced vascular function.

The impact of task difficulty on neural modulation throughout a visuomotor multi-day practice training

The effectiveness of rehabilitation is contingent upon the motor recovery process which typically involves long-term motor skill re-acquisition. Given that the learning process can be modulated by task difficulty, elucidating the underlying neural mechanism is essential for optimizing rehabilitation prescription to suit different patient conditions. This study aimed to investigate the impact of task difficulty on cortical response during force-control training via electroencephalography (EEG). An 8-day visuomotor force-tracking training experiment was conducted. Healthy right-handed participants (N=33) were recruited and randomly assigned to 3 groups, and each group was tasked with a different level of difficulty. The task difficulty was manipulated by variation in force-production complexity and execution sequence assignments, with real-time visual feedback provided to participants for self-output adjustment. Behavioral performance was quantitatively assessed using a pre-defined score metric related to performance accuracy. The EEG signals were collected, and corresponding event-related desynchronization (ERD) and relative functional connectivity (FC) during the task execution were analyzed within the alpha- (8-13 Hz) and beta- (15-30 Hz) bands. A post-training experiment was also performed to evaluate the near-transfer capability of learning. Results showed all the behavioral performances improved during practice, while higher task difficulty level was affiliated with better post-training near-transfer ability. The dynamic neural response to training could be mediated by changes in difficulty level, where increased task complexity corresponded with the heightened activities in the beta-band priorly within the right dorsolateral prefrontal area. Additionally, stronger alpha-band functional connectivity was observed to be predominantly associated with the left motor area (LMA) during challenging tasks, and the intensification in connectivity persisted selectively post-training which appeared to be acritical factor for skill transfer performance improvement. These findings illustrated the dynamic neural mechanism through which task difficulty affects behavioral performance during long-term motor training with accurate force-control purpose. The selectively strengthened functional connectivity may contribute to facilitating new task execution after training interventions. Therefore, beneficial neural modulation can be expected to be feasible by well-designed task difficulty strategies for effective motor rehabilitation.

Immersive interfaces for clinical applications: current status and future perspective

Digital immersive technologies have become increasingly prominent in clinical research and practice, including medical communication and technical education, serious games for health, psychotherapy, and interfaces for neurorehabilitation. The worldwide enthusiasm for digital health and digital therapeutics has prompted the development and testing of numerous applications and interaction methods. Nevertheless, the lack of consistency in the approaches and the peculiarity of the constructed environments contribute to an increasing disparity between the eagerness for new immersive designs and the long-term clinical adoption of these technologies. Several challenges emerge in aligning the different priorities of virtual environment designers and clinicians. This article seeks to examine the utilization and mechanics of medical immersive interfaces based on extended reality and highlight specific design challenges. The transfer of skills from virtual to clinical environments is often confounded by perceptual and attractiveness factors. We argue that a multidisciplinary approach to development and testing, along with a comprehensive acknowledgement of the shared mechanisms that underlie immersive training, are essential for the sustainable integration of extended reality into clinical settings. The present review discusses the application of a multilevel sensory framework to extended reality design, with the aim of developing brain-centered immersive interfaces tailored for therapeutic and educational purposes. Such a framework must include broader design questions, such as the integration of digital technologies into psychosocial care models, clinical validation, and related ethical concerns. We propose that efforts to bridge the virtual gap should include mixed methodologies and neurodesign approaches, integrating user behavioral and physiological feedback into iterative design phases.

Kernels of Motor Memory Formation: Temporal Generalization in Bimanual Adaptation

In daily life, we coordinate both simultaneous and sequential bimanual movements to manipulate objects. Our ability to rapidly account for different object dynamics suggests there are neural mechanisms to quickly deal with them. Here we investigate how actions of one arm can serve as a contextual cue for the other arm and facilitate adaptation. Specifically, we examine the temporal characteristics that underlie motor memory formation and recall, by testing the contextual effects of prior, simultaneous, and post contralateral arm movements in both male and female human participants. To do so, we measure their temporal generalization in three bimanual interference tasks. Importantly, the timing context of the learned action plays a pivotal role in the temporal generalization. While motor memories trained with post adaptation contextual movements generalize broadly, motor memories trained with prior contextual movements exhibit limited generalization, and motor memories trained with simultaneous contextual movements do not generalize to prior or post contextual timings. This highlights temporal tuning in sensorimotor plasticity: different training conditions yield substantially different temporal generalization characteristics. Since these generalizations extend far beyond any variability in training times, we suggest that the observed differences may stem from inherent differences in the use of prior, current, and post adaptation contextual information in the generation of natural behavior. This would imply differences in the underlying neural circuitry involved in learning and executing the corresponding coordinated bimanual movements.

From animal models to human individuality: Integrative approaches to the study of brain plasticity

Plasticity allows organisms to form lasting adaptive changes in neural structures in response to interactions with the environment. It serves both species-general functions and individualized skill acquisition. To better understand human plasticity, we need to strengthen the dialogue between human research and animal models. Therefore, we propose to (1) enhance the interpretability of macroscopic methods used in human research by complementing molecular and fine-structural measures used in animals with such macroscopic methods, preferably applied to the same animals, to create macroscopic metrics common to both examined species; (2) launch dedicated cross-species research programs, using either well-controlled experimental paradigms, such as motor skill acquisition, or more naturalistic environments, where individuals of either species are observed in their habitats; and (3) develop conceptual and computational models linking molecular and fine-structural events to phenomena accessible by macroscopic methods. In concert, these three component strategies can foster new insights into the nature of plastic change.

Synaptic weight dynamics underlying memory consolidation: Implications for learning rules, circuit organization, and circuit function

Systems consolidation is a common feature of learning and memory systems, in which a long-term memory initially stored in one brain region becomes persistently stored in another region. We studied the dynamics of systems consolidation in simple circuit architectures with two sites of plasticity, one in an early-learning and one in a late-learning brain area. We show that the synaptic dynamics of the circuit during consolidation of an analog memory can be understood as a temporal integration process, by which transient changes in activity driven by plasticity in the early-learning area are accumulated into persistent synaptic changes at the late-learning site. This simple principle naturally leads to a speed-accuracy tradeoff in systems consolidation and provides insight into how the circuit mitigates the stability-plasticity dilemma of storing new memories while preserving core features of older ones. Furthermore, it imposes two constraints on the circuit. First, the plasticity rule at the late-learning site must stably support a continuum of possible outputs for a given input. We show that this is readily achieved by heterosynaptic but not standard Hebbian rules. Second, to turn off the consolidation process and prevent erroneous changes at the late-learning site, neural activity in the early-learning area must be reset to its baseline activity. We provide two biologically plausible implementations for this reset that propose functional roles in stabilizing consolidation for core elements of the cerebellar circuit.

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.

Modulation of cortical representations of sensory and contextual information underlies aversive associative learning

Cortical neurons encode both sensory and contextual information, yet it remains unclear how experiences modulate these cortical representations. Here, we demonstrate that trace eyeblink conditioning (TEC), an aversive associative-learning paradigm linking conditioned (CS) with unconditioned stimuli (US), finely tunes cortical coding at both population and single-neuron levels. Initially, we show that the primary somatosensory cortex (S1) is necessary for TEC acquisition, as evidenced by local muscimol administration. At the population level, TEC enhances activity in a small subset (∼20%) of CS- or US-responsive primary neurons (rPNs) while diminishing activity in non-rPNs, including locomotion-tuned or unresponsive PNs. Crucially, TEC learning modulates the encoding of sensory versus contextual information in single rPNs: CS-responsive neurons become less responsive, while US-responsive neurons gain responses to CS. Moreover, we find that the cholinergic pathway, via nicotinic receptors, underlies TEC-induced modulations. These findings suggest that experiences dynamically tune cortical representations through cholinergic pathways.

Reward history guides focal attention in whisker somatosensory cortex

Prior reward is a potent cue for attentional capture, but the underlying neurobiology is largely unknown. In a novel whisker touch detection task, we show that mice flexibly shift attention between specific whiskers on a trial-by-trial timescale, guided by the recent history of stimulus-reward association. Two-photon calcium imaging and spike recordings revealed a robust neurobiological correlate of attention in the somatosensory cortex (S1), boosting sensory responses to the attended whisker in L2/3 and L5, but not L4. Attentional boosting in L2/3 pyramidal cells was topographically precise and whisker-specific, and shifted receptive fields toward the attended whisker. L2/3 VIP interneurons were broadly activated by whisker stimuli, motion, and arousal but did not carry a whisker-specific attentional signal, and thus did not mediate spatially focused tactile attention. Together, these findings establish a new model of focal attention in the mouse whisker tactile system, showing that the history of stimuli and rewards in the recent past can dynamically engage local modulation in cortical sensory maps to guide flexible shifts in ongoing behavior.

Pan-cortical cellular imaging in freely behaving mice using a miniaturized micro-camera array microscope (mini-MCAM)

Understanding how circuits in the brain simultaneously coordinate their activity to mediate complex ethnologically relevant behaviors requires recording neural activities from distributed populations of neurons in freely behaving animals. Current miniaturized imaging microscopes are typically limited to imaging a relatively small field of view, precluding the measurement of neural activities across multiple brain regions. Here we present a miniaturized micro-camera array microscope (mini-MCAM) that consists of four fluorescence imaging micro-cameras, each capable of capturing neural activity across a 4.5 mm x 2.55 mm field of view (FOV). Cumulatively, the mini-MCAM images over 30 mm 2 area of sparsely expressed GCaMP6s neurons distributed throughout the dorsal cortex, in regions including the primary and secondary motor, somatosensory, visual, retrosplenial, and association cortices across both hemispheres. We demonstrate cortex-wide cellular resolution in vivo Calcium (Ca 2+ ) imaging using the mini-MCAM in both head-fixed and freely behaving mice.

Synaptic weight dynamics underlying memory consolidation: implications for learning rules, circuit organization, and circuit function

Systems consolidation is a common feature of learning and memory systems, in which a long-term memory initially stored in one brain region becomes persistently stored in another region. We studied the dynamics of systems consolidation in simple circuit architectures with two sites of plasticity, one in an early-learning and one in a late-learning brain area. We show that the synaptic dynamics of the circuit during consolidation of an analog memory can be understood as a temporal integration process, by which transient changes in activity driven by plasticity in the early-learning area are accumulated into persistent synaptic changes at the late-learning site. This simple principle naturally leads to a speed-accuracy tradeoff in systems consolidation and provides insight into how the circuit mitigates the stability-plasticity dilemma of storing new memories while preserving core features of older ones. Furthermore, it imposes two constraints on the circuit. First, the plasticity rule at the late-learning site must stably support a continuum of possible outputs for a given input. We show that this is readily achieved by heterosynaptic but not standard Hebbian rules. Second, to turn off the consolidation process and prevent erroneous changes at the late-learning site, neural activity in the early-learning area must be reset to its baseline activity. We propose two biologically plausible implementations for this reset that suggest novel roles for core elements of the cerebellar circuit.

Significance Statement

How are memories transformed over time? We propose a simple organizing principle for how long term memories are moved from an initial to a final site of storage. We show that successful transfer occurs when the late site of memory storage is endowed with synaptic plasticity rules that stably accumulate changes in activity occurring at the early site of memory storage. We instantiate this principle in a simple computational model that is representative of brain circuits underlying a variety of behaviors. The model suggests how a neural circuit can store new memories while preserving core features of older ones, and suggests novel roles for core elements of the cerebellar circuit.

Activity-Dependent Remodeling of Corticostriatal Axonal Boutons During Motor Learning

Motor skill learning induces long-lasting synaptic plasticity at not only the inputs, such as dendritic spines1-4, but also at the outputs to the striatum of motor cortical neurons5,6. However, very little is known about the activity and structural plasticity of corticostriatal axons during learning in the adult brain. Here, we used longitudinal in vivo two-photon imaging to monitor the activity and structure of thousands of corticostriatal axonal boutons in the dorsolateral striatum in awake mice. We found that learning a new motor skill induces dynamic regulation of axonal boutons. The activities of motor corticostriatal axonal boutons exhibited selectivity for rewarded movements (RM) and un-rewarded movements (UM). Strikingly, boutons on the same axonal branches showed diverse responses during behavior. Motor learning significantly increased the fraction of RM boutons and reduced the heterogeneity of bouton activities. Moreover, motor learning-induced profound structural dynamism in boutons. By combining structural and functional imaging, we identified that newly formed axonal boutons are more likely to exhibit selectivity for RM and are stabilized during motor learning, while UM boutons are selectively eliminated. Our results highlight a novel form of plasticity at corticostriatal axons induced by motor learning, indicating that motor corticostriatal axonal boutons undergo dynamic reorganization that facilitates the acquisition and execution of motor skills.

Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance

Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circuits in hardware underpins engineering highly efficient neuromorphic chips, however, implementing a firing features-driven functional neural circuit is still an open question. In this work, inspired by avoidance neural circuits of crickets, we construct a spiking feature-driven sensorimotor control neural circuit consisting of three memristive Hodgkin-Huxley neurons. The ascending neurons exhibit mixed tonic spiking and bursting features, which are used for encoding sensing input. Additionally, we innovatively introduce a selective communication scheme in biology to decode mixed firing features using two descending neurons. We proceed to integrate such a neural circuit with a robot for avoidance control and achieve lower latency than conventional platforms. These results provide a foundation for implementing real brain-like systems driven by firing features with memristive neurons and put constructing high-order intelligent machines on the agenda.

Top-Down and Bottom-Up Mechanisms of Motor Recovery Poststroke

Stroke remains a leading cause of disability. Motor recovery requires the interaction of top-down and bottom-up mechanisms, which reinforce each other. Injury to the brain initiates a biphasic neuroimmune process, which opens a window for spontaneous recovery during which the brain is particularly sensitive to activity. Physical activity during this sensitive period can lead to rapid recovery by potentiating anti-inflammatory and neuroplastic processes. On the other hand, lack of physical activity can lead to early closure of the sensitive period and downstream changes in muscles, such as sarcopenia, muscle stiffness, and reduced cardiovascular capacity, and blood flow that impede recovery.

Cross-hemispheric communication: Insights on lateralized brain functions

On the surface, the two hemispheres of vertebrate brains look almost perfectly symmetrical, but several motor, sensory, and cognitive systems show a deeply lateralized organization. Importantly, the two hemispheres are connected by various commissures, white matter tracts that cross the brain's midline and enable cross-hemispheric communication. Cross-hemispheric communication has been suggested to play an important role in the emergence of lateralized brain functions. Here, we review current advances in understanding cross-hemispheric communication that have been made using modern neuroscientific tools in rodents and other model species, such as genetic labeling, large-scale recordings of neuronal activity, spatiotemporally precise perturbation, and quantitative behavior analyses. These findings suggest that the emergence of lateralized brain functions cannot be fully explained by largely static factors such as genetic variation and differences in structural brain asymmetries. In addition, learning-dependent asymmetric interactions between the left and right hemispheres shape lateralized brain functions.

Visuo-frontal interactions during social learning in freely moving macaques

Social interactions represent a ubiquitous aspect of our everyday life that we acquire by interpreting and responding to visual cues from conspecifics1. However, despite the general acceptance of this view, how visual information is used to guide the decision to cooperate is unknown. Here, we wirelessly recorded the spiking activity of populations of neurons in the visual and prefrontal cortex in conjunction with wireless recordings of oculomotor events while freely moving macaques engaged in social cooperation. As animals learned to cooperate, visual and executive areas refined the representation of social variables, such as the conspecific or reward, by distributing socially relevant information among neurons in each area. Decoding population activity showed that viewing social cues influences the decision to cooperate. Learning social events increased coordinated spiking between visual and prefrontal cortical neurons, which was associated with improved accuracy of neural populations to encode social cues and the decision to cooperate. These results indicate that the visual-frontal cortical network prioritizes relevant sensory information to facilitate learning social interactions while freely moving macaques interact in a naturalistic environment.

Active perceptual learning involves motor exploration and adaptation of predictive sensory integration

Our ability to perceive both externally generated and self-generated sensory stimuli can be enhanced through training, known as passive and active perceptual learning (APL). Here, we sought to explore the mechanisms underlying APL by using active haptic training (AHT), which has been demonstrated to enhance the somatosensory perception of a finger in a trained motor skill. In total 120 pianists participated in this study. First, AHT reorganized the muscular coordination during the piano keystroke. Second, AHT increased the relative reliance on afferent sensory information relative to predicted one, in contrast to no increment of overall perceptual sensitivity. Finally, AHT improved feedback movement control of keystrokes. These results suggest that APL involves active exploration and adaptation of predictive sensory integration, which underlies the co-enhancement of active perception and feedback control of movements of well-trained individuals.

Cell-Type Specific Connectivity of Whisker-Related Sensory and Motor Cortical Input to Dorsal Striatum

The anterior dorsolateral striatum (DLS) is heavily innervated by convergent excitatory projections from the primary motor (M1) and sensory cortex (S1) and considered an important site of sensorimotor integration. M1 and S1 corticostriatal synapses have functional differences in their connection strength with striatal spiny projection neurons (SPNs) and fast-spiking interneurons (FSIs) in the DLS and, as a result, exert distinct influences on sensory-guided behaviors. In the present study, we tested whether M1 and S1 inputs exhibit differences in the subcellular anatomical distribution of striatal neurons. We injected adeno-associated viral vectors encoding spaghetti monster fluorescent proteins (sm.FPs) into M1 and S1 in male and female mice and used confocal microscopy to generate 3D reconstructions of corticostriatal inputs to single identified SPNs and FSIs obtained through ex vivo patch clamp electrophysiology. We found that M1 and S1 dually innervate SPNs and FSIs; however, there is a consistent bias towards the M1 input in SPNs that is not found in FSIs. In addition, M1 and S1 inputs were distributed similarly across the proximal, medial, and distal regions of SPN and FSI dendrites. Notably, closely localized M1 and S1 clusters of inputs were more prevalent in SPNs than FSIs, suggesting that cortical inputs are integrated through cell-type specific mechanisms. Our results suggest that the stronger functional connectivity from M1 to SPNs compared to S1, as previously observed, is due to a higher quantity of synaptic inputs. Our results have implications for how sensorimotor integration is performed in the striatum through cell-specific differences in corticostriatal connections.

Action observation plus motor imagery and somatosensory discrimination training are effective non-motor approaches to improve manual dexterity

BACKGROUND

Action observation plus motor imagery (AOMI) and somatosensory discrimination training (SSDT) represent sensory input-based approaches to train the motor system without necessarily asking subjects to perform active movements.

PURPOSE

To investigate AOMI and SSDT effects compared to no intervention on manual dexterity in healthy subjects.

STUDY DESIGN

Randomized controlled study.

METHODS

Sixty healthy right-handed participants were randomized into AOMI, SSDT or Control (CTRL) groups. AOMI observed video-clips including right-hand dexterity tasks and concurrently performed motor imagery, SSDT performed surfaces recognition and 2-point distance discrimination tasks with the right hand, whereas CTRL underwent no intervention. A blinded physiotherapist assessed participants for manual dexterity using the Purdue Pegboard Test (Right hand-R, Left hand-L, Both hands-B, R+L+B and assembly tasks) at baseline (T0) and training end (T1). A mixed-design Analysis of Variance with Time as within-subject factor and Group as between-subject factor was used to investigate between-group differences over time.

RESULTS

A Time by Group interaction and Time effect were found for R task, which increased from T0 to T1 in all groups with very large effect sizes for SSDT (d = 1.8, CI95 2.4-1.0, P < .001) and AOMI (d = 1.7, CI95 2.5-1.0, P < .001) and medium effect size for CTRL (d = 0.6, CI95 1.2-0.2, P < .001). Between-group post-hoc comparison for deltas (T1-T0) showed large effect size (d = 1.0, CI95 1.6-0.3, P = .003) in favor of SSDT and medium effect size (d = 0.7, CI95 1.4-0.1, P = .026) in favor of AOMI compared to CTRL. Time effects were found for L, B, R + L + B and assembly tasks (P < .001).

CONCLUSIONS

AOMI and SSDT induced greater manual dexterity improvements than no intervention. These findings supported the role of visual and somatosensory stimuli in building a motor plan and enhancing the accuracy of hand movements. These non-motor approaches may enhance motor performance in job or hobbies requiring marked manual dexterity.

Cortico-spinal modularity in the parieto-frontal system: A new perspective on action control

Classical neurophysiology suggests that the motor cortex (MI) has a unique role in action control. In contrast, this review presents evidence for multiple parieto-frontal spinal command modules that can bypass MI. Five observations support this modular perspective: (i) the statistics of cortical connectivity demonstrate functionally-related clusters of cortical areas, defining functional modules in the premotor, cingulate, and parietal cortices; (ii) different corticospinal pathways originate from the above areas, each with a distinct range of conduction velocities; (iii) the activation time of each module varies depending on task, and different modules can be activated simultaneously; (iv) a modular architecture with direct motor output is faster and less metabolically expensive than an architecture that relies on MI, given the slow connections between MI and other cortical areas; (v) lesions of the areas composing parieto-frontal modules have different effects from lesions of MI. Here we provide examples of six cortico-spinal modules and functions they subserve: module 1) arm reaching, tool use and object construction; module 2) spatial navigation and locomotion; module 3) grasping and observation of hand and mouth actions; module 4) action initiation, motor sequences, time encoding; module 5) conditional motor association and learning, action plan switching and action inhibition; module 6) planning defensive actions. These modules can serve as a library of tools to be recombined when faced with novel tasks, and MI might serve as a recombinatory hub. In conclusion, the availability of locally-stored information and multiple outflow paths supports the physiological plausibility of the proposed modular perspective.

A novel perspective on the role of nucleus accumbens neurons in encoding associative learning

The nucleus accumbens (NAc) has been considered a key brain region for encoding reward/aversion and cue-outcome associations. These processes are encoded by medium spiny neurons that express either dopamine receptor D1 (D1-MSNs) or D2 (D2-MSNs). Despite the well-established role of NAc neurons in encoding reward/aversion, the underlying processing by D1-/D2-MSNs remains largely unknown. Recent electrophysiological, optogenetic and calcium imaging studies provided insight on the complex role of D1- and D2-MSNs in these behaviours and helped to clarify their involvement in associative learning. Here, we critically discuss findings supporting an intricate and complementary role of NAc D1- and D2-MSNs in associative learning, emphasizing the need for additional studies in order to fully understand the role of these neurons in behaviour.

Approach-avoidance behavior and motor-specific modulation towards smoking-related cues in smokers

AIMS

By performing three transcranial magnetic stimulation (TMS) experiments, we measured the motor-specific modulatory mechanisms in the primary motor cortex (M1) at both the intercortical and intracortical levels when smokers actively approach or avoid smoking-related cues.

DESIGN, SETTING AND PARTICIPANTS

For all experiments, the design was group (smokers versus non-smokers) × action (approach versus avoidance) × image type (neutral versus smoking-related). The study was conducted at the Shanghai University of Sport, CHN, TMS Laboratory. For experiment 1, 30 non-smokers and 30 smokers; for experiment 2, 16 non-smokers and 16 smokers; for experiment 3, 16 non-smokers and 16 smokers.

MEASUREMENTS

For all experiments, the reaction times were measured using the smoking stimulus-response compatibility task. While performing the task, single-pulse TMS was applied to the M1 in experiment 1 to measure the excitability of the corticospinal pathways, and paired-pulse TMS was applied to the M1 in experiments 2 and 3 to measure the activity of intracortical facilitation (ICF) and short-interval intracortical inhibition (SICI) circuits, respectively.

FINDINGS

Smokers had faster responses when approaching smoking-related cues (F1,58  = 36.660, P < 0.001, η p 2  = 0.387), accompanied by higher excitability of the corticospinal pathways (F1,58  = 10.980, P = 0.002, η p 2  = 0.159) and ICF circuits (F1,30  = 22.187, P < 0.001, η p 2  = 0.425), while stronger SICI effects were observed when they avoided these cues (F1,30  = 10.672, P = 0.003, η p 2  = 0.262).

CONCLUSIONS

Smokers appear to have shorter reaction times, higher motor-evoked potentials and stronger intracortical facilitation effects when performing approach responses to smoking-related cues and longer reaction times, a lower primary motor cortex descending pathway excitability and a stronger short-interval intracortical inhibition effect when avoiding them.

Learning induced neuronal identity switch in the superficial layers of the primary somatosensory cortex

During learning, multi-dimensional inputs are integrated within the sensory cortices. However, the strategies by which the sensory cortex employs to achieve learning remains poorly understood. We studied the sensory cortical neuronal coding of trace eyeblink conditioning (TEC) in head-fixed, freely running mice, where whisker deflection was used as a conditioned stimulus (CS) and an air puff to the cornea delivered after an interval was used as unconditioned stimulus (US). After training, mice learned the task with a set of stereotypical behavioral changes, most prominent ones include prolonged closure of eyelids, and increased reverse running between CS and US onset. The local blockade of the primary somatosensory cortex (S1) activities with muscimol abolished the behavior learning suggesting that S1 is required for the TEC. In naive animals, based on the response properties to the CS and US, identities of the small proportion (~20%) of responsive primary neurons (PNs) were divided into two subtypes: CR (i.e. CS-responsive) and UR neurons (i.e. US-responsive). After animals learned the task, identity of CR and UR neurons changed: while the CR neurons are less responsive to CS, UR neurons gain responsiveness to CS, a new phenomenon we defined as 'learning induced neuronal identity switch (LINIS)'. To explore the potential mechanisms underlying LINIS, we found that systemic and local (i.e. in S1) administration of the nicotinic receptor antagonist during TEC training blocked the LINIS, and concomitantly disrupted the behavior learning. Additionally, we monitored responses of two types of cortical interneurons (INs) and observed that the responses of the somatostatin-expressing (SST), but not parvalbumin-expressing (PV) INs are negatively correlated with the learning performance, suggesting that SST-INs contribute to the LINIS. Thus, we conclude that L2/3 PNs in S1 encode perceptual learning by LINIS like mechanisms, and cholinergic pathways and cortical SST interneurons are involved in the formation of LINIS.

Emergence of preparatory dynamics in VIP interneurons during motor learning

To determine what actions to perform in each context, animals must learn how to execute motor programs in response to sensory cues. In rodents, the interface between sensory processing and motor planning occurs in the secondary motor cortex (M2). Here, we investigate dynamics in vasointestinal peptide (VIP) and somatostatin (SST) interneurons in M2 during acquisition of a cue-based, reach-to-grasp (RTG) task in mice. We observe the emergence of preparatory activity consisting of sensory responses and ramping activation in a subset of VIP interneurons during motor learning. We show that preparatory and movement activities in VIP neurons exhibit compartmentalized dynamics, with principal component 1 (PC1) and PC2 reflecting primarily movement and preparatory activity, respectively. In contrast, we observe later and more synchronous activation of SST neurons during the movement epoch with learning. Our results reveal how VIP population dynamics might support sensorimotor learning and compartmentalization of sensory processing and movement execution.

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

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

Orexin recruits non-selective cationic conductance and endocannabinoid to dynamically modulate firing of caudal pontine reticular nuclear neurones

The neuropeptide orexin is involved in motor circuit function. However, its modulation on neuronal activities of motor structures, integrating orexin's diverse downstream molecular cascades, remains elusive. By combining whole-cell patch-clamp recordings and neuropharmacological methods, we revealed that both non-selective cationic conductance (NSCC) and endocannabinoids (eCBs) are recruited by orexin signalling on reticulospinal neurones in the caudal pontine reticular nucleus (PnC). The orexin-NSCC cascade provides a depolarizing force that proportionally enhances the firing-responsive gain of these neurones. Meanwhile, the orexin-eCB cascade selectively attenuates excitatory synaptic strength in these neurones by activating presynaptic cannabinoid receptor type 1. This cascade restrains the firing response of the PnC reticulospinal neurones to excitatory inputs. Intriguingly, non-linear or linear interactions between orexin postsynaptic excitation and presynaptic inhibition can influence the firing responses of PnC reticulospinal neurones in different directions. When presynaptic inhibition is in the lead, non-linear interactions can prominently downregulate or even gate the firing response. Conversely, linear interactions occur to promote the firing response, and these linear interactions can be considered a proportional reduction in the contribution of depolarization to firing by presynaptic inhibition. Through the dynamic employment of these interactions, adaptive modulation may be achieved by orexin to restrain or even gate the firing output of the PnC to weak/irrelevant input signals and facilitate those to salient signals. KEY POINTS: This study investigated the effects of orexin on the firing activity of PnC reticulospinal neurones, a key element of central motor control. We found that orexin recruited both the non-selective cationic conductances (NSCCs) and endocannabinoid (eCB)-cannabinoid receptor type 1 (CB1R) system to pontine reticular nucleus (PnC) reticulospinal neurones. The orexin-NSCC cascade exerts a postsynaptic excitation that enhances the firing response, whereas the orexin-eCB-CB1R cascade selectively attenuates excitatory synaptic strength that restrains the firing response. The postsynaptic and presynaptic actions of orexins occur in an overlapping time window and interact to dynamically modulate firings in PnC reticulospinal neurones. Non-linear interactions occur when presynaptic inhibition of orexin is in the lead, and these interactions can prominently downregulate or even gate firing responses in PnC reticulospinal neurones. Linear interactions occur when postsynaptic excitation of orexin is in the lead, and these interactions can promote the firing response. These linear interactions can be considered a proportional reduction of the contribution of depolarization to firing by presynaptic inhibition.

Striatal Synaptic Changes and Behavior in Adult mouse Upon Prenatal Exposure to Valproic Acid

Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders characterized by persistent deficits in social communication and social interaction. Altered synaptogenesis and aberrant connectivity responsible for social behavior and communication have been reported in autism pathogenesis. Autism has a strong genetic and heritable component; however, environmental factors including toxins, pesticides, infection and in utero exposure to drugs such as VPA have also been implicated in ASD. Administration of VPA during pregnancy has been used as a rodent model to study pathophysiological mechanisms involved in ASD, and in this study, we used the mouse model of prenatal exposure to VPA to assess the effects on striatal and dorsal hippocampus function in adult mice. Alterations in repetitive behaviors and shift habits were observed in mice prenatally exposed to VPA. In particular, such mice presented a better performance in learned motor skills and cognitive deficits in Y-maze learning frequently associated with striatal and hippocampal function. These behavioral changes were associated with a decreased level of proteins involved in the formation and maintenance of excitatory synapses, such as Nlgn-1 and PSD-95. In conclusion, motor skill abilities, repetitive behaviors, and impaired flexibility to shift habits are associated with reduced striatal excitatory synaptic function in the adult mouse prenatally exposed to VPA.

Neural Plasticity in Sensorimotor Brain-Machine Interfaces

Brain-machine interfaces (BMIs) aim to treat sensorimotor neurological disorders by creating artificial motor and/or sensory pathways. Introducing artificial pathways creates new relationships between sensory input and motor output, which the brain must learn to gain dexterous control. This review highlights the role of learning in BMIs to restore movement and sensation, and discusses how BMI design may influence neural plasticity and performance. The close integration of plasticity in sensory and motor function influences the design of both artificial pathways and will be an essential consideration for bidirectional devices that restore both sensory and motor function.

Somatosensory Information in Skilled Motor Performance: A Narrative Review

Historically, research aimed at improving motor performance has largely focused on the neural processes involved in motor execution due to their role in muscle activation. However, accompanying somatosensory and proprioceptive sensory information is also vitally involved in performing motor skills. Here we review research from interdisciplinary fields to provide a description for how somatosensation informs the successful performance of motor skills as well as emphasize the need for careful selection of study methods to isolate the neural processes involved in somatosensory perception. We also discuss upcoming strategies of intervention that have been used to improve performance via somatosensory targets. We believe that a greater appreciation for somatosensation's role in motor learning and control will enable researchers and practitioners to develop and apply methods for the enhancement of human performance that will benefit clinical, healthy, and elite populations alike.

Motor cortical functional connectivity changes due to short-term immobilization of upper limb: an fNIRS case report

Introduction

A short-term immobilization of one hand affects musculoskeletal functions, and the associated brain network adapts to the alterations happening to the body due to injuries. It was hypothesized that the injury-associated temporary disuse of the upper limb would alter the functional interactions of the motor cortical processes and will produce long-term changes throughout the immobilization and post-immobilization period.

Methods

The case participant (male, 12 years old, right arm immobilized for clavicle fracture) was scanned using optical imaging technology of fNIRS over immobilization and post-immobilization. Pre-task data was collected for 3 min for RSFC analysis, processed, and analyzed using the Brain AnalyzIR toolbox. Connectivity was measured using Pearson correlation coefficients (R) from NIRS Toolbox's connectivity module.

Results

The non-affected hand task presented an increased ipsilateral response during the immobilization period, which then decreased over the follow-up visits. The right-hand task showed a bilateral activation pattern following immobilization, but the contralateral activation pattern was restored during the 1-year follow-up visit. Significant differences in the average connection strength over the study period were observed. The average Connection strength decreased from the third week of immobilization and continued to be lower than the baseline value. Global network efficiency decreased in weeks two and three, while the network settled into a higher efficient state during the follow-up periods after post-immobilization.

Discussion

Short-term immobilization of the upper limb is shown to have cortical changes in terms of activations of brain regions as well as connectivity. The short-term dis-use of the upper limb has shifted the unilateral activation pattern to the bilateral coactivation of the motor cortex from both hemispheres. Resting-state data reveals a disruption in the motor cortical network during the immobilization phase, and the network is reorganized into an efficient network over 1 year after the injury. Understanding such cortical reorganization could be informative for studying the recovery from neurological disorders affecting motor control in the future.

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

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

Cell-Type Specific Connectivity of Whisker-Related Sensory and Motor Cortical Input to Dorsal Striatum

The anterior dorsolateral striatum (DLS) is heavily innervated by convergent excitatory projections from the primary motor (M1) and sensory cortex (S1) and is considered an important site of sensorimotor integration. M1 and S1 corticostriatal synapses have functional differences in the strength of their connections with striatal spiny projection neurons (SPNs) and fast-spiking interneurons (FSIs) in the DLS, and as a result exert an opposing influence on sensory-guided behaviors. In the present study, we tested whether M1 and S1 inputs exhibit differences in the subcellular anatomical distribution onto striatal neurons. We injected adeno-associated viral vectors encoding spaghetti monster fluorescent proteins (sm.FPs) into M1 and S1, and used confocal microscopy to generate 3D reconstructions of corticostriatal inputs to single identified SPNs and FSIs obtained through ex-vivo patch-clamp electrophysiology. We found that SPNs are less innervated by S1 compared to M1, but FSIs receive a similar number of inputs from both M1 and S1. In addition, M1 and S1 inputs were distributed similarly across the proximal, medial, and distal regions of SPNs and FSIs. Notably, clusters of inputs were prevalent in SPNs but not FSIs. Our results suggest that SPNs have stronger functional connectivity to M1 compared to S1 due to a higher density of synaptic inputs. The clustering of M1 and S1 inputs onto SPNs but not FSIs suggest that cortical inputs are integrated through cell-type specific mechanisms and more generally have implications for how sensorimotor integration is performed in the striatum.

Significance Statement

The dorsolateral striatum (DLS) is a key brain area involved in sensorimotor integration due to its dense innervation by the primary motor (M1) and sensory cortex (S1). However, the quantity and anatomical distribution of these inputs to the striatal cell population has not been well characterized. In this study we demonstrate that corticostriatal projections from M1 and S1 differentially innervate spiny projection neurons (SPNs) and fast-spiking interneurons (FSIs) in the DLS. S1 inputs innervate SPNs less than M1 and are likely to form synaptic clusters in SPNs but not in FSIs. These findings suggest that sensorimotor integration is partly achieved by differences in the synaptic organization of corticostriatal inputs to local striatal microcircuits.

Analysis of changes in brain morphological structure of taekwondo athletes by diffusion tensor imaging

OBJECTIVE

Taekwondo, which is the most preferred sport among the martial arts, is known to improve individuals physically, spiritually and mentally. The aim of this study is to reveal the effect of teakwondo sport on the brain and brain structures. DESIGN;: 30 taekwondo athletes and 15 control groups were included in this study. Diffusion tensor MR images of each participant were taken. The information was obtained by the self-declaration of the athletes, whether they were sports years, amateur or elite.

METHOD

Total brain volume and volumes of white matter, gray matter, frontal lobe, precentral gyrus, corticospinal tract, basal nuclei, postcentral gyrus, hippocampus and amigdala and the ratio of these volumes to total brain volume were evaluated statistically between the groups using MriCloud software and ROIEditor program.

RESULTS

An increase in total brain volume, gray matter, frontal lobe and precentral gyrus volume in athletes was associated with taekwondo training. When the ratio of brain parts to total brain volume was examined, it was determined that there was a difference in the ratio of gray matter, white matter volumes in amateur athletes, right frontal lobe, left corticospinal tract, right postcentral gyrus volumes in elite athletes, and left postcentral gyrus volumes of both athletes compared to sedentary individuals.

CONCLUSIONS

The increase in the volume of gray matter, frontal lobe, postcentral gyrus and corticospinal tract together with the brain volume shows that taekwondo exercise contributes to physical, spiritual and mental development.

State-dependent and region-specific alterations of cerebellar connectivity across stable human wakefulness and NREM sleep states

Sleep regulation and functioning may rely on systematic coordination throughout the whole brain, including the cerebellum. However, whether and how interactions between the cerebellum and other brain regions vary across sleep stages remain poorly understood. Here, using simultaneous EEG-fMRI recordings captured from 73 participants during wakefulness and non-rapid eye movement (NREM) sleep, we constructed cerebellar connectivity among intrinsic functional networks with intra-cerebellar, neocortical and subcortical regions. We uncovered that cerebellar connectivity exhibited sleep-dependent alterations: slight differences between wakefulness and N1/N2 sleep and greater changes in N3 sleep than other states. Region-specific cerebellar connectivity changes between N2 sleep and N3 sleep were also revealed: general breakdown of intra-cerebellar connectivity, enhancement of limbic-cerebellar connectivity and alterations of cerebellar connectivity with spatially specific neocortices. Further correlation analysis showed that functional connectivity between the cerebellar Control II network and regions (including the insula, hippocampus, and amygdala) correlated with delta power during N3 and beta power during N2 sleep. These findings systematically reveal altered cerebellar connectivity among intrinsic networks from wakefulness to deep sleep and highlight the potential role of the cerebellum in sleep regulation and functioning.

Receptor and Ionic Mechanism of Histamine on Mouse Dorsolateral Striatal Neurons

The dorsolateral striatum (DLS) is the critical neural substrate that plays a role in motor control and motor learning. Our past study revealed a direct histaminergic projection from the tuberomammillary nucleus (TMN) of the hypothalamus to the rat striatum. However, the afferent of histaminergic fibers in the mouse DLS, the effect of histamine on DLS neurons, and the underlying receptor and ionic mechanisms remain unclear. Here, we demonstrated a direct histaminergic innervation from the TMN in the mouse DLS, and histamine excited both the direct-pathway spiny projection neurons (d-SPNs) and the indirect-pathway spiny projection neurons (i-SPNs) of DLS via activation of postsynaptic H1R and H2R, albeit activation of presynaptic H3R suppressed neuronal activity by inhibiting glutamatergic synaptic transmission on d-SPNs and i-SPNs in DLS. Moreover, sodium-calcium exchanger 3 (NCX3), potassium-leak channels linked to H1R, and hyperpolarization-activated cyclic nucleotide-gated channel 2 (HCN2) coupled to H2R co-mediated the excitatory effect induced by histamine on d-SPNs and i-SPNs in DLS. These results demonstrated the pre- and postsynaptic receptors and their downstream multiple ionic mechanisms underlying the inhibitory and excitatory effects of histamine on d-SPNs and i-SPNs in DLS, suggesting a potential modulatory effect of the central histaminergic system on the DLS as well as its related motor control and motor learning.

Precise movement-based predictions in the mouse auditory cortex

Many of the sensations experienced by an organism are caused by their own actions, and accurately anticipating both the sensory features and timing of self-generated stimuli is crucial to a variety of behaviors. In the auditory cortex, neural responses to self-generated sounds exhibit frequency-specific suppression, suggesting that movement-based predictions may be implemented early in sensory processing. However, it remains unknown whether this modulation results from a behaviorally specific and temporally precise prediction, nor is it known whether corresponding expectation signals are present locally in the auditory cortex. To address these questions, we trained mice to expect the precise acoustic outcome of a forelimb movement using a closed-loop sound-generating lever. Dense neuronal recordings in the auditory cortex revealed suppression of responses to self-generated sounds that was specific to the expected acoustic features, to a precise position within the movement, and to the movement that was coupled to sound during training. Prediction-based suppression was concentrated in L2/3 and L5, where deviations from expectation also recruited a population of prediction-error neurons that was otherwise unresponsive. Recording in the absence of sound revealed abundant movement signals in deep layers that were biased toward neurons tuned to the expected sound, as well as expectation signals that were present throughout the cortex and peaked at the time of expected auditory feedback. Together, these findings identify distinct populations of auditory cortical neurons with movement, expectation, and error signals consistent with a learned internal model linking an action to its specific acoustic outcome.

Thalamocortical contribution to flexible learning in neural systems

Animal brains evolved to optimize behavior in dynamic environments, flexibly selecting actions that maximize future rewards in different contexts. A large body of experimental work indicates that such optimization changes the wiring of neural circuits, appropriately mapping environmental input onto behavioral outputs. A major unsolved scientific question is how optimal wiring adjustments, which must target the connections responsible for rewards, can be accomplished when the relation between sensory inputs, action taken, and environmental context with rewards is ambiguous. The credit assignment problem can be categorized into context-independent structural credit assignment and context-dependent continual learning. In this perspective, we survey prior approaches to these two problems and advance the notion that the brain's specialized neural architectures provide efficient solutions. Within this framework, the thalamus with its cortical and basal ganglia interactions serves as a systems-level solution to credit assignment. Specifically, we propose that thalamocortical interaction is the locus of meta-learning where the thalamus provides cortical control functions that parametrize the cortical activity association space. By selecting among these control functions, the basal ganglia hierarchically guide thalamocortical plasticity across two timescales to enable meta-learning. The faster timescale establishes contextual associations to enable behavioral flexibility, while the slower one enables generalization to new contexts.

Dynamic reorganization of the cortico-basal ganglia-thalamo-cortical network during task learning

Adaptive behavior is coordinated by neuronal networks that are distributed across multiple brain regions such as in the cortico-basal ganglia-thalamo-cortical (CBGTC) network. Here, we ask how cross-regional interactions within such mesoscale circuits reorganize when an animal learns a new task. We apply multi-fiber photometry to chronically record simultaneous activity in 12 or 48 brain regions of mice trained in a tactile discrimination task. With improving task performance, most regions shift their peak activity from the time of reward-related action to the reward-predicting stimulus. By estimating cross-regional interactions using transfer entropy, we reveal that functional networks encompassing basal ganglia, thalamus, neocortex, and hippocampus grow and stabilize upon learning, especially at stimulus presentation time. The internal globus pallidus, ventromedial thalamus, and several regions in the frontal cortex emerge as salient hub regions. Our results highlight the learning-related dynamic reorganization that brain networks undergo when task-appropriate mesoscale network dynamics are established for goal-oriented behavior.

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Multi-fiber photometry reveals brain network adaptations during learning

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Activity in most regions temporally shifts from reward to predictive stimulus

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Cross-regional interactions in the CBGTC network increase and stabilize with learning

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Internal pallidum, VM thalamus, and prefrontal cortex regions emerge as hubs

The learning-relative hemodynamic modulation of cortical plasticity induced by a force-control motor training

Background

Novel motor skills are generally acquired through repetitive practices which are believed to be strongly related to neural plasticity mechanisms. This study aimed to investigate the learning-relative hemodynamic modulation of cortical plasticity induced by long-term motor training.

Methods

An 8-day participation-control program was conducted. Eighteen right-handed healthy participants were recruited and randomly assigned into the training (12) and control groups (6). The training group were arranged to undergo the 8-day block-designed motor training which required to repeat a visuomotor force-control task. The functional near-infrared spectroscopy (fNIRS) was used to continuously monitor the cortical hemodynamic response during training. Two transcranial magnetic stimulation (TMS) measurements were performed before and after training to evaluate the cortical excitability changes. The transfer effects of learning were also investigated.

Results

The behavior performance was quantified via score execution accuracy to illustrate the fast/slow learning stages as experience cumulated. The cortical hemodynamic activations mapped by fNIRS exhibited a temporal evolution trends that agreed the expansion–renormalization model, which assumed the brain modulation against skill acquisition includes complex mechanisms of neural expansion, selection, and renormalization. Functional connectivity (FC) analysis showed the FC strength was maintained, while the measured homodynamic activation returned to baseline after certain level of skill acquisition. Furthermore, the TMS results demonstrated a significant increase of motor evoked potential (MEP) on the targeted muscle for the trained participants, who significantly outperformed the untrained subjects in learning transfer investigation.

Conclusion

The study illustrated the expansion–renormalization trends during continuous motor training, and relative analysis showed the functional connectivity enhancement may be maintained after amplitude renormalization of cortical hemodynamic activations. The TMS findings further gave an implication of neural facilitations on the descending motor pathway when brain activation returned to renormalization status after certain level of learning stages was achieved, and the learning can transfer to enhance the performance while encountering similar tasks.

Applying the Properties of Neurons in Machine Learning: A Brain-like Neural Model with Interactive Stimulation for Data Classification

Some neural models achieve outstanding results in image recognition, semantic segmentation and natural language processing. However, their classification performance on structured and small-scale datasets that do not involve feature extraction is worse than that of traditional algorithms, although they require more time to train. In this paper, we propose a brain-like neural model with interactive stimulation (NMIS) that focuses on data classification. It consists of a primary neural field and a senior neural field that play different cognitive roles. The former is used to correspond to real instances in the feature space, and the latter stores the category pattern. Neurons in the primary field exchange information through interactive stimulation and their activation is transmitted to the senior field via inter-field interaction, simulating the mechanisms of neuronal interaction and synaptic plasticity, respectively. The proposed NMIS is biologically plausible and does not involve complex optimization processes. Therefore, it exhibits better learning ability on small-scale and structured datasets than traditional BP neural networks. For large-scale data classification, a nearest neighbor NMIS (NN_NMIS), an optimized version of NMIS, is proposed to improve computational efficiency. Numerical experiments performed on some UCI datasets show that the proposed NMIS and NN_NMIS are significantly superior to some classification algorithms that are widely used in machine learning.

Impact of Sensory Afferences in Postural Control Quantified by Force Platform: A Protocol for Systematic Review

Older adults’ postural balance is a critical domain of research as balance deficit is an important risk factor for falls that can lead to severe injuries and death. Considering the effects of ageing on sensory systems, we propose that posturographic evaluation with a force platform exploring the effect of sensory deprivation or perturbation on balance could help understand postural control alterations in the elderly. The aim of the future systematic review and meta-analysis described in this protocol is to explore the capacity of older adults to maintain their balance during sensory perturbations, and compare the effect of perturbation between the sensory channels contributing to balance. Seven databases will be searched for studies evaluating older adults’ balance under various sensory conditions. After evaluating the studies’ risk of bias, results from similar studies (i.e., similar experimental conditions and posturographic markers) will be aggregated. This protocol describes a future review that is expected to provide a better understanding of changes in sensory systems of balance due to ageing, and therefore perspectives on fall assessment, prevention, and rehabilitation.

Human Variation in Error-Based and Reinforcement Motor Learning Is Associated With Entorhinal Volume

Error-based and reward-based processes are critical for motor learning and are thought to be mediated via distinct neural pathways. However, recent behavioral work in humans suggests that both learning processes can be bolstered by the use of cognitive strategies, which may mediate individual differences in motor learning ability. It has been speculated that medial temporal lobe regions, which have been shown to support motor sequence learning, also support the use of cognitive strategies in error-based and reinforcement motor learning. However, direct evidence in support of this idea remains sparse. Here we first show that better overall learning during error-based visuomotor adaptation is associated with better overall learning during the reward-based shaping of reaching movements. Given the cognitive contribution to learning in both of these tasks, these results support the notion that strategic processes, associated with better performance, drive intersubject variation in both error-based and reinforcement motor learning. Furthermore, we show that entorhinal cortex volume is larger in better learning individuals-characterized across both motor learning tasks-compared with their poorer learning counterparts. These results suggest that individual differences in learning performance during error and reinforcement learning are related to neuroanatomical differences in entorhinal cortex.

Effect of cortical extracellular GABA on motor response

To elucidate how the flattening of sensory tuning due to a deficit in tonic inhibition slows motor responses, we simulated a neural network model in which a sensory cortical network ([Formula: see text]) and a motor cortical network ([Formula: see text]) are reciprocally connected, and the [Formula: see text] projects to spinal motoneurons (Mns). The [Formula: see text] was presented with a feature stimulus and the reaction time of Mns was measured. The flattening of sensory tuning in [Formula: see text] caused by decreasing the concentration of gamma-aminobutyric acid (GABA) in extracellular space resulted in a decrease in the stimulus-sensitive [Formula: see text] pyramidal cell activity while increasing the stimulus-insensitive [Formula: see text] pyramidal cell activity, thereby prolonging the reaction time of Mns to the applied feature stimulus. We suggest that a reduction in extracellular GABA concentration in sensory cortex may interfere with selective activation in motor cortex, leading to slowing the activation of spinal motoneurons and therefore to slowing motor responses.