The central nervous system (CNS) can effectively control body movements despite environmental changes. While much is known about adaptation to external environmental changes, less is known about responses to internal bodily changes. This study investigates how the CNS adapts to long-term alterations in the musculoskeletal system using a tendon transfer model in nonhuman primates (Macaca fuscata). We surgically relocated finger flexor and extensor muscles to examine how the CNS adapts its strategy for finger movement control by measuring muscle activities during grasping tasks. Two months post-surgery, the monkeys demonstrated significant recovery of grasping function despite the initial disruption. Our findings suggest a two-phase CNS adaptation process: an initial phase enabling function with the transferred muscles, followed by a later phase abandoning this enabled function and restoring a control strategy that, while potentially less conflicted than the maladaptive state, resembled the original pattern, possibly representing a 'good enough' solution. These results highlight a multi-phase CNS adaptation process with distinct time constants in response to sudden bodily changes, offering potential insights into understanding and treating movement disorders.

Dexterous hand movements are uniquely developed in primates and indispensable for their daily activities. Traditionally, they were thought to depend primarily on the evolutionarily "newer" direct corticomotoneuronal (CM) pathway. However, recent studies suggest that the "older" indirect corticospinal pathways, mediated by spinal premotor interneurons (PreM-INs), also contribute, highlighting the need to clarify their functional differences. Here, we recorded neuronal activity from PreM-INs and CM cells in macaques during a precision grip task to compare their roles in generating hand muscle activity. Our results show that PreM-INs exert stronger facilitation across a broader set of muscles, promoting synergistic coactivation, whereas CM cells provide more selective facilitation, enabling control of relatively individual muscles. Decomposition analysis further revealed that these systems correspond to different control modes-synergy-based and individual-based control-balancing stability and flexibility. These findings redefine our understanding of primate dexterous hand control as emerging from the cooperative integration of evolutionarily distinct premotoneuronal systems.

Neural dynamics are thought to reflect computations that relay and transform information in the brain. Previous studies have identified the neural population dynamics in many individual brain regions as a trajectory geometry, preserving a common computational motif. However, whether these populations share particular geometric patterns across brain-wide neural populations remains unclear. Here, by mapping neural dynamics widely across temporal/frontal/limbic regions in the cortical and subcortical structures of monkeys, we show that 10 neural populations, including 2,500 neurons, propagate visual item information in a stochastic manner. We found that visual inputs predominantly evoked rotational dynamics in the higher-order visual area, TE, and its downstream striatum tail, while curvy/straight dynamics appeared frequently downstream in the orbitofrontal/hippocampal network. These geometric changes were not deterministic but rather stochastic according to their respective emergence rates. Our meta-analysis results indicate that visual information propagates as a heterogeneous mixture of stochastic neural population signals in the brain.

Primate hands house an array of mechanoreceptors and proprioceptors, which are essential for tactile and kinematic information crucial for daily motor action. While the regulation of these somatosensory signals is essential for hand movements, the specific central nervous system (CNS) location and mechanism remain unclear. Our study demonstrates the attenuation of somatosensory signals in the cuneate nucleus during voluntary movement, suggesting significant modulation at this initial relay station in the CNS. The attenuation is comparable to the cerebral cortex but more pronounced than in the spinal cord, indicating the cuneate nuclei’s role in somatosensory perception modulation during movement. Moreover, our findings suggest that the descending motor tract may regulate somatosensory transmission in the cuneate nucleus, enhancing relevant signals and suppressing unnecessary ones for the regulation of movement. This process of recurrent somatosensory modulation between cortical and subcortical areas could be a basic mechanism for modulating somatosensory signals to achieve active perception.

Skilled hand movements are an evolutionary advance in primates. Phylogenetically distinct corticospinal pathways are involved in hand control: “newer” direct corticomotoneuronal (CM) pathways and “older” indirect corticospinal pathways mediated by spinal premotor interneurons (PreM-INs). However, the functional differences of these pathways remain unclear. Here we show that CM cells and PreM-INs have distinct physiological properties for the activation of hand muscles and provide different types of movement control signals while monkeys perform a precision grip task. Spike-triggered averaging of electromyographic activity indicated that PreM-INs coactivate a larger number of muscles, whereas CM cells more selectively control fewer muscles. The firing activity of PreM-INs was tightly correlated with their target muscle activity and had a greater contribution to generating hand muscle activity. In contrast, CM cell activity diverged temporally from the target muscle activity and had a smaller contribution to its generation. On the basis of these results, we hypothesize that PreM-INs produce gross muscle activity by activating synergistic muscles, whereas CM cells fine-tune target muscle activity. This idea was supported by dimensional reduction analyses of hand muscle activity, as PreM-IN activity was specifically correlated with lower dimensional control of muscle activity, and CM cell activity was correlated with higher dimensional control. These results indicate that the two pathways have distinct functions, synergistic control and fine tuning of hand muscle activity, both of which are essential for the development of dexterous hand movement in primates. ### Competing Interest Statement The authors have declared no competing interest.

Neural population dynamics provide a key computational framework for understanding information processing in the sensory, cognitive, and motor functions of the brain. They systematically depict complex neural population activity, dominated by strong temporal dynamics as trajectory geometry in a low-dimensional neural space. However, neural population dynamics are poorly related to the conventional analytical framework of single-neuron activity, the rate-coding regime that analyzes firing rate modulations using task parameters. To link the rate-coding and dynamic models, we developed a variant of state-space analysis in the regression subspace, which describes the temporal structures of neural modulations using continuous and categorical task parameters. In macaque monkeys, using two neural population datasets containing either of two standard task parameters, continuous and categorical, we revealed that neural modulation structures are reliably captured by these task parameters in the regression subspace as trajectory geometry in a lower dimension. Furthermore, we combined the classical optimal-stimulus response analysis (usually used in rate-coding analysis) with the dynamic model and found that the most prominent modulation dynamics in the lower dimension were derived from these optimal responses. Using those analyses, we successfully extracted geometries for both task parameters that formed a straight geometry, suggesting that their functional relevance is characterized as a unidimensional feature in their neural modulation dynamics. Collectively, our approach bridges neural modulation in the rate-coding model and the dynamic system, and provides researchers with a significant advantage in exploring the temporal structure of neural modulations for pre-existing datasets.

Neurons coordinate their activity to produce an astonishing variety of motor behaviors. Our present understanding of motor control has grown rapidly thanks to new methods for recording and analyzing populations of many individual neurons over time. In contrast, current methods for recording the nervous system’s actual motor output – the activation of muscle fibers by motor neurons – typically cannot detect the individual electrical events produced by muscle fibers during natural behaviors and scale poorly across species and muscle groups. Here we present a novel class of electrode devices (‘Myomatrix arrays’) that record muscle activity at unprecedented resolution across muscles and behaviors. High-density, flexible electrode arrays allow for stable recordings from the muscle fibers activated by a single motor neuron, called a ‘motor unit,’ during natural behaviors in many species, including mice, rats, primates, songbirds, frogs, and insects. This technology therefore allows the nervous system’s motor output to be monitored in unprecedented detail during complex behaviors across species and muscle morphologies. We anticipate that this technology will allow rapid advances in understanding the neural control of behavior and identifying pathologies of the motor system.

Summary Successful extraction of tactile and kinematic information is crucial for appropriate motor actions in daily life. To achieve this objective, sensory signals should be effectively regulated during motor actions. This regulation is understood both empirically and conceptually, but it is not well known where and how it is implemented in the central nervous system. Here, we show that, during voluntary movement, sensory signals are already attenuated in the primate cuneate nucleus, an early processing site in the ascending lemniscus pathway. The degree of suppression was comparable with the one reported in the cortex, suggesting that psychological attenuation of somatosensation could be ascribed to the cuneate. The results also revealed that this sensory attenuation was of descending origin, suggesting that cortical sensory prediction signals could regulate cuneate sensory transmission for extracting meaningful, and attenuate unnecessary, signals for movement regulation. This recurrent sensory modulation mechanism between cortical and subcortical areas may generalize to other sensory modalities and cognitive processes.

Neural population dynamics, presumably fundamental computational units in the brain, provide a key framework for understanding information processing in the sensory, cognitive, and motor functions. However, neural population dynamics is not explicitly related to the conventional analytic framework for single-neuron activity, i.e., representational models that analyze neuronal modulations associated with cognitive and motor parameters. In this study, we applied a recently developed state-space analysis to incorporate the representational models into the dynamic model in combination with these parameters. We compared neural population dynamics between continuous and categorical task parameters during two visual recognition tasks, using the datasets originally designed for a single-neuron approach. We successfully extracted neural population dynamics in the regression subspace, which represent modulation dynamics for both continuous and categorical task parameters with reasonable temporal characteristics. Furthermore, we combined the classical optimal-stimulus analysis paradigm for the single-neuron approach (i.e., stimulus identified as maximum neural responses) into the dynamic model, and found that the most prominent modulation dynamics at the lower dimension were derived from these optimal responses. Thus, our approach provides a unified framework for incorporating knowledge acquired with the single-neuron approach into the dynamic model as a standard procedure for describing neural modulation dynamics in the brain.

The Fugl-Meyer Assessment is widely used to test motor function in stroke survivors. In the Fugl-Meyer Assessment, stroke survivors perform several movement tasks and clinicians subjectively rate the performance of each task item. The individual task items in the Fugl-Meyer Assessment are selected on the basis of clinical experience, and their physiological relevance has not yet been evaluated. In the present study, we aimed to objectively rate the performance of task items by measuring the muscle activity of 41 muscles from the upper body while stroke survivors and healthy participants performed 37 Fugl-Meyer Assessment upper extremity task items. We used muscle synergy analysis to compare muscle activity between subjects and found that 13 muscle synergies in the healthy participants (which we defined as standard synergies) were able to reconstruct all of the muscle activity in the Fugl-Meyer Assessment. Among the standard synergies, synergies involving the upper arms, forearms and fingers were activated to varying degrees during different task items. In contrast, synergies involving posterior trunk muscles were activated during all tasks, which suggests the importance of posterior trunk muscle synergies throughout all sequences. Furthermore, we noted the inactivation of posterior trunk muscle synergies in stroke survivors with severe but not mild impairments, suggesting that lower trunk stability and the underlying activity of posterior trunk muscle synergies may have a strong influence on stroke severity and recovery. By comparing the synergies of stroke survivors with standard synergies, we also revealed that some synergies in stroke survivors corresponded to merged standard synergies; the merging rate increased with the impairment of stroke survivors. Moreover, the degrees of severity-dependent changes in the merging rate (the merging rate-severity relationship) were different among different task items. This relationship was significant for 26 task items only and not for the other 11 task items. Because muscle synergy analysis evaluates coordinated muscle activities, this different dependency suggests that these 26 task items are appropriate for evaluating muscle coordination and the extent of its impairment in stroke survivors. Overall, we conclude that the Fugl-Meyer Assessment reflects physiological function and muscle coordination impairment and suggest that it could be performed using a subset of the 37 task items.

Neural population dynamics provide a key computational framework for understanding information processing in the brain. However, they are not related to the standard conventional analytical framework of single-neuron activity, which involves rate coding regime. To link the rate-coding and dynamical models, we developed a variant of state-space analysis in the regression subspace that describes neural population dynamics for describing activity modulations by continuous and categorical task parameters during two cognitive tasks. Using datasets originally designed for the rate-coding analysis, neural modulation structures extracted from the orbitofrontal cortex and hippocampal neurons were captured as a trajectory geometry in a lower dimension. The extracted geometries for both parameters formed a straight geometry, indicating that both neural populations have unidimensional feature during a memory access. Thus, our versatile procedure for describing neural modulation dynamics provides researchers with a significant advantage to rethink the modulation dynamics in a multi-dimensional subspace for pre-existing data sets.Funding Information: This research was supported by JSPS KAKENHI (Grant Number JP:15H05374, 21H02797), the Takeda Science Foundation, the Research Foundation for the Electrotechnology of Chubu (H.Y.), and the National Natural Science Foundation of China (Grant 31871139) (Y.N.).Conflict of Interests: The authors declare no competing of interests.Ethical Approval: All experimental procedures were approved by the Animal Care and Use Committee of the University of Tsukuba (Exp. 1, protocol no. H30.336), as well as Institutional Animal Care and Use of Laboratory Animals, approved at Peking University (Exp. 2, project number Psych-YujiNaya-1), and were performed in compliance with the US Public Health Service’s Guide for the Care and Use of Laboratory Animals.

Volitional limb motor control involves dynamic and static muscle actions. It remains elusive how such distinct actions are controlled through separated or shared neural circuits. Here we explored the potential separation for dynamic and static controls in primate hand actions, by investigating the neuronal coherence between local field potentials (LFPs) of the spinal cord and the forelimb electromyographic activity (EMGs), and LFPs of the motor cortex and the EMGs during the performance of a precision grip in macaque monkeys. We observed the emergence of beta-range coherence with EMGs at spinal cord and motor cortex in the separated phases; spinal coherence during the grip phase and cortical coherence during the hold phase. Further, both of the coherences were influenced by bidirectional interactions with reasonable latencies as beta oscillatory cycles. These results indicate that dedicated feedback circuits comprising spinal and cortical structures underlie dynamic and static controls of dexterous hand actions.

The use of focused ultrasound for brain therapy has been widely researched. Transcranial focused ultrasound (tFUS) in conjunction with microbubbles has great potential use for blood-brain barrier (BBB) opening and neuromodulation. However, focal aberration due to the skull is a major obstacle to the application of tFUS to brain therapy. A phased array transducer with adaptive differently phased ultrasound waves emitted from each element of the array is widely used to correct distortions due to the skull. In this study, the phase distribution of the array was estimated by the three-dimensional finite differential time domain (FDTD) method using a model based on computed tomography (CT) images. The focal controllability using a 64-channel-array transducer through the skull was examined by both experiments and numerical simulation. The results show that focal aberration was effectively corrected at 500 kHz and 1.7 MHz. Moreover, we demonstrated that the simplest method considering only the time difference of flight is applicable to focal control at low frequencies. It can markedly decrease computational time.

Grasping is a highly complex movement that requires the coordination of multiple hand joints and muscles. Muscle synergies have been proposed to be the functional building blocks that coordinate such complex motor behaviors, but little is known about how they are implemented in the central nervous system. Here we demonstrate that premotor interneurons (PreM-INs) in the primate cervical spinal cord underlie the spatiotemporal patterns of hand muscle synergies during a voluntary grasping task. Using spike-triggered averaging of hand muscle activity, we found that the muscle fields of PreM-INs were not uniformly distributed across hand muscles but rather distributed as clusters corresponding to muscle synergies. Moreover, although individual PreM-INs have divergent activation patterns, the population activity of PreM-INs reflects the temporal activation of muscle synergies. These findings demonstrate that spinal PreM-INs underlie the muscle coordination required for voluntary hand movements in primates. Given the evolution of neural control of primate hand functions, we suggest that spinal premotor circuits provide the fundamental coordination of multiple joints and muscles upon which more fractionated control is achieved by superimposed, phylogenetically newer, pathways.

The limitations of conventional surface electromyography (sEMG) cause it to be unsuitable for use with the deep and compact muscles of the forearm. However, while source separation and localization techniques have been extensively explored to identify active sources in the brain using electroencephalography (EEG) signals, these techniques have not been adapted for identifying active sources in muscles using sEMG signals, despite being of a similar premise. Here, we perform an experiment to explore the capabilities of conventional EEG single-dipole localization techniques to localize the extensor digitorum and extensor indicis when selectively activated. The localization methodology consists of separating the raw sEMG signals using independent component analysis (ICA), estimating a physics-based forward model, and then correlating the obtained lead-field matrix with the ICA mixing matrix. The results show that single-dipole localization is not suitable for describing the active sources of muscles.