Optimal feedback control and the neural basis of volitional motor control

A session of transcutaneous electrical nerve stimulation changes the input-output function of motoneurons and alters the sense of force

Transcutaneous electrical nerve stimulation (TENS) is commonly used in research and clinical settings for pain management and augmenting somatosensory inputs for motor recovery. Besides its functional effect, TENS acutely alters kinesthesia and force steadiness. However, the short-term impact following a session of TENS on proprioception and motor unit behavior is unknown. We evaluated the effect of a session of TENS on the senses of force, joint position, touch, and discharge activity of motor units. Fifteen healthy participants underwent two experiments, each with two visits randomly administering TENS or sham-TENS. The sense of force (experiment 1) and position (experiment 2) were evaluated through matching trials by pinching a dial and rotating their wrist (ulnar deviation). Isometric pinch contractions were performed before and after the session of TENS or sham-TENS, in which electromyographic (EMG) signals were recorded from the first dorsal interosseus (FDI) and abductor pollicis brevis (APB). Results showed that TENS acutely altered the senses of force, position, and touch, but only the sense of force remained altered following TENS. Motor unit discharge rates increased in both FDI and APB muscles for the same force output following TENS. A positive correlation was also observed between changes in motor unit discharge rates and changes in errors in force perception. These findings suggest that a session of TENS may have short-term effects on the input/output function of motoneurons (5-10 min in this study), which in turn may alter the sense of force. However, the precise timeline for these short-term aftereffects is unknown.NEW & NOTEWORTHY It is often assumed that transcutaneous electrical nerve stimulation (TENS) has a transient effect on proprioception and motor control. We found that position, force, and touch perception were altered during TENS. However, the sense of force remained altered following TENS. As the discharge rate of motor units also increased in first dorsal interosseus (FDI) and abductor pollicis brevis (APB) muscles for the same force output following TENS, we suggest that their input-output function was altered, potentially causing a sustained decrease in performance in force-matching tasks.

Parietal cortex is recruited by frontal and cingulate areas to support action monitoring and updating during stopping

Recent evidence indicates that the intraparietal sulcus (IPS) may play a causal role in action stopping, potentially representing a novel neuromodulation target for inhibitory control dysfunctions. Here, we leverage intracranial recordings in human subjects to establish the timing and directionality of information flow between IPS and prefrontal and cingulate regions during action stopping. Prior to successful inhibition, information flows primarily from the inferior frontal gyrus (IFG), a critical inhibitory control node, to IPS. In contrast, during stopping errors the communication between IPS and IFG is lacking, and IPS is engaged by posterior cingulate cortex, an area outside of the classical inhibition network and typically associated with default mode. Anterior cingulate and orbitofrontal cortex also display performance-dependent connectivity with IPS. Our functional connectivity results provide direct electrophysiological evidence that IPS is recruited by frontal and anterior cingulate areas to support action plan monitoring and updating, and by posterior cingulate during control failures.

Internal sensory models allow for balance control using muscle spindle acceleration feedback

Motor control requires sensory feedback, and the nature of this feedback has implications for the tasks of the central nervous system (CNS): for an approximately linear mechanical system (e.g., a freely standing person, a rider on a bicycle), if the sensory feedback does not contain the state variables (i.e., joint position and velocity), then optimal control actions are based on an internal dynamical system that estimates these states from the available incomplete sensory feedback. Such a computational system can be implemented as a recurrent neural network (RNN), and it uses a sensory model to update the state estimates. This is highly relevant for muscle spindle primary afferents whose firing rates scale with acceleration: if fusimotor and skeletomotor control are perfectly coordinated, these firing rates scale with the exafferent joint acceleration component, and in the absence of fusimotor control, they scale with the total joint acceleration (exafferent plus reafferent). For both scenarios, a sensory model exists that expresses the exafferent joint acceleration as a function of the state variables, and for the second scenario, a sensory model exists that corrects for the reafferent joint acceleration. Simulations of standing and bicycle balance control under realistic conditions show that joint acceleration feedback is sufficient for balance control, but only if the reafferent acceleration component is either absent from the feedback or is corrected for in the computational system.

Sensorimotor delays constrain robust locomotion in a 3D kinematic model of fly walking

Walking animals must maintain stability in the presence of external perturbations, despite significant temporal delays in neural signaling and muscle actuation. Here, we develop a 3D kinematic model with a layered control architecture to investigate how sensorimotor delays constrain the robustness of walking behavior in the fruit fly, Drosophila. Motivated by the anatomical architecture of insect locomotor control circuits, our model consists of three component layers: a neural network that generates realistic 3D joint kinematics for each leg, an optimal controller that executes the joint kinematics while accounting for delays, and an inter-leg coordinator. The model generates realistic simulated walking that resembles real fly walking kinematics and sustains walking even when subjected to unexpected perturbations, generalizing beyond its training data. However, we found that the model's robustness to perturbations deteriorates when sensorimotor delay parameters exceed the physiological range. These results suggest that fly sensorimotor control circuits operate close to the temporal limit at which they can detect and respond to external perturbations. More broadly, we show how a modular, layered model architecture can be used to investigate physiological constraints on animal behavior.

Success-efficient/failure-safe strategy for hierarchical reinforcement motor learning

Our study explores how ecological aspects of motor learning enhance survival by improving movement efficiency and mitigating injury risks during task failures. Traditional motor control theories mainly address isolated body movements and often overlook these ecological factors. We introduce a novel computational motor control approach, incorporating ecological fitness and a strategy that alternates between success-driven movement efficiency and failure-driven safety, akin to win-stay/lose-shift tactics. In our experiments, participants performed squat-to-stand movements under novel force perturbations. They adapted effectively through various adaptive motor control mechanisms to avoid falls, reducing failure rates rapidly. The results indicate a high-level ecological controller in human motor learning that switches objectives between safety and movement efficiency, depending on failure or success. This approach is supported by policy learning, internal model adaptation, and adaptive feedback control. Our findings offer a comprehensive perspective on human motor control, integrating risk management in a hierarchical reinforcement learning framework for real-world environments.

Do people only adjust ongoing movements vigorously when it is advantageous to do so?

We previously found that arm movements towards a jittering target are constantly guided by the latest target position: the responses to target displacements became more vigorous as the movement proceeded, as required for the movement to reach the latest position smoothly within the remaining time. Here we examine whether this behaviour was a consequence of how that experiment was designed. We compared the vigour of adjustments in blocks of trials in which targets followed a random walk, as in our previous studies, with the vigour of adjustments in blocks of trials in which the target position varied at random with respect to a fixed position. For the random walk, the latest position is the best estimate of the final position, so neglecting earlier information can be useful. For random variability around a fixed position, the target's position at any instant is equally informative about the final position, so making vigorous adjustments in response to the latest information is pointless. In that case, the best estimate of the final position is the average of all the encountered positions. Some participants responded less vigorously in the latter case, but most did not. We discuss why tuning the adjustments to be complete within the remaining time may be a good strategy, even when the target does not follow a random walk.

Disentangling acute motor deficits and adaptive responses evoked by the loss of cerebellar output

Patients with cerebellar damage experience various motor impairments, but the specific sequence of primary and compensatory processes that contribute to these deficits remains uncertain. To clarify this, we reversibly blocked cerebellar outflow in monkeys engaged in planar reaching tasks. This intervention led to a spatially selective reduction in hand velocity, primarily due to decreased muscle torque, especially in movements requiring high inter-joint torque coupling. When examining repeated reaches to the same target, we found that the reduced velocity resulted from both an immediate deficit and a gradually developing compensatory slowing to reduce passive inter-joint interactions. However, the slowed hand velocity did not account for the fragmented and variable movement trajectories observed during the cerebellar block. Our findings indicate that cerebellar impairment results in motor deficits due to both inadequate muscle torque and an altered compensatory control strategy for managing impaired limb dynamics. Additionally, impaired motor control elevates noise, which cannot be entirely mitigated through compensatory strategies.

Explaining human motor coordination via the synergy expansion hypothesis

The search for an answer to Bernstein's degrees of freedom problem has propelled a large portion of research studies in human motor control over the past six decades. Different theories have been developed to explain how humans might use their incredibly complex neuro-musculo-skeletal system with astonishing ease. Among these theories, motor synergies appeared as one possible explanation. In this work, the authors investigate the nature and role of synergies and propose a theoretical framework, namely the "expansion hypothesis," to answer Bernstein's problem. The expansion hypothesis is articulated in three propositions: mechanical, developmental, and behavioral. Each proposition addresses a different question on the nature of synergies: i) How many synergies can humans have? ii) How do we learn and develop synergies? iii) How do we use synergies? An example numerical simulation is presented and analyzed to clarify the hypothesis propositions. The expansion hypothesis is contextualized with respect to the existing literature on motor synergies both in healthy and impaired individuals, as well as other prominent theories in human motor control and development. The expansion hypothesis provides a framework to better comprehend and explain the nature, use, and evolution of human motor skills.

Selective direct motor cortical influence during naturalistic climbing in mice

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

BAND: Behavior-Aligned Neural Dynamics is all you need to capture motor corrections

Neural activity in motor cortical areas is well-explained by latent neural population dynamics: the motor preparation phase sets the initial condition for the movement while the dynamics that unfold during the motor execution phase orchestrate the sequence of muscle activations. While preparatory activity explains a large fraction of both neural and behavior variability during the execution of a planned movement, it cannot account for corrections and adjustments during movements as this requires sensory feedback not available during planning. Therefore, accounting for unplanned, sensory-guided movement requires knowledge of relevant inputs to the motor cortex from other brain areas. Here, we provide evidence that these inputs cause transient deviations from an autonomous neural population trajectory, and show that these dynamics cannot be found by unsupervised inference methods. We introduce the new Behavior-Aligned Neural Dynamics (BAND) model, which exploits semi-supervised learning to predict both planned and unplanned movements from neural activity in the motor cortex that can be missed by unsupervised inference methods. Our analysis using BAND suggests that 1) transient motor corrections are encoded in small neural variability; 2) motor corrections are encoded in a sparse sub-population of primary motor cortex neurons (M1); and 3) combining latent dynamical modeling with behavior supervision allows for capturing both the movement plan and corrections.

Effects of posterior parietal cortex anodal transcranial direct current stimulation on ankle tracking visuomotor control in healthy young adults

Ankle motor control is crucial for balance maintenance and fall prevention. Neurocomputational models of motor control suggest that the posterior parietal cortex (PPC) plays a critical role in estimating body and environmental states, a process fundamental to motor control. Anodal transcranial direct current stimulation (atDCS) has been shown to modulate cortical excitability and alter behaviors accordingly. This study explored the impact of atDCS over the PPC on ankle tracking visuomotor control using a motor adaptation research paradigm in healthy young adults. Thirty-eight participants were randomly assigned to either an atDCS or sham control group. All participants completed an ankle tracking experiment divided into three phases: pre-adaptation, adaptation, and re-adaptation, with each phase comprising eight blocks of five trials. During the experiment, each participant wore a sensor on the non-dominant foot and performed continuous dorsiflexion and plantarflexion movements to track a target cursor on a screen. Visual feedback of the foot position was provided, with a 1:1 feedback ratio in the pre- and re-adaptation phases and a 2.5:1 ratio in the adaptation phase to promote visual-motor remapping. The atDCS group received 20 min of 2 mA atDCS over the PPC during the adaptation phase. Tracking performance on each trial was measured as the root mean squared error (RMSE) between the target and actual movement trajectories. Both groups showed similar RMSEs in the pre-adaptation phase (p > 0.05). However, in the adaptation phase, the atDCS group demonstrated a significant reduction from block 1 to block 2 (p = 0.001, Cohen's d = 0.86) and maintained this improved performance in the following blocks, while the sham group showed no significant changes throughout this phase (p > 0.05). In the re-adaptation phase, both groups quickly returned to their pre-adaptation performance levels. These findings indicate that neither the atDCS nor the sham group adapted to the high visual feedback ratio. However, the early reduction in RMSE observed in the atDCS group suggests that atDCS over the PPC may transiently enhance ankle tracking visuomotor control under the heightened visual feedback ratio condition, resulting in short-term improvements. Future research is warranted to explore whether multiple atDCS sessions over the PPC could provide long-term benefits for lower extremity visuomotor control.

Contractions in human cerebellar-cortical manifold structure underlie motor reinforcement learning

How the brain learns new motor commands through reinforcement involves distributed neural circuits beyond known frontal-striatal pathways, yet a comprehensive understanding of this broader neural architecture remains elusive. Here, using human functional MRI (N = 46, 27 females) and manifold learning techniques, we identified a low-dimensional neural space that captured the dynamic changes in whole-brain functional organization during a reward-based trajectory learning task. By quantifying participants' learning rates through an Actor-Critic model, we discovered that periods of accelerated learning were characterized by significant manifold contractions across multiple brain regions, including areas of limbic and hippocampal cortex, as well as the cerebellum. This contraction reflected enhanced network integration, with notably stronger connectivity between several of these regions and the sensorimotor cerebellum correlating with higher learning rates. These findings challenge the traditional view of the cerebellum as solely involved in error-based learning, supporting the emerging view that it coordinates with other brain regions during reinforcement learning.Significance Statement This study reveals how distributed brain systems, including the cerebellum and hippocampus, alter their functional connectivity to support motor learning through reinforcement. Using advanced manifold learning techniques on functional MRI data, we examined changes in regional connectivity during reward-based learning and their relationship to learning rate. For several brain regions, we found that periods of heightened learning were associated with increased cerebellar connectivity, suggesting a key role for the cerebellum in reward-based motor learning. These findings challenge the traditional view of the cerebellum as solely involved in supervised (error-based) learning and add to a growing rodent literature supporting a role for cerebellar circuits in reward-driven learning.

The Role of Sensory Impairments on Recovery and Rehabilitation After Stroke

PURPOSE OF REVIEW

The current review aims to address critical gaps in the field of stroke rehabilitation related to sensory impairment. Here, we examine the role and importance of sensation throughout recovery of neural injury, potential clinical and experimental approaches for improving sensory function, and mechanism-based theories that may facilitate the design of sensory-based approaches for the rehabilitation of somatosensation.

RECENT FINDINGS

Recently, the field of neurorehabilitation has shifted to using more quantitative and sensitive measures to more accurately capture sensory function in stroke and other neurological populations. These approaches have laid the groundwork for understanding how sensory impairments impact overall function after stroke. However, there is less consensus on which interventions are effective for remediating sensory function, with approaches that vary from clinical re-training, robotics, and sensory stimulation interventions. Current evidence has found that sensory and motor systems are interdependent, but commonly have independent recovery trajectories after stroke. Therefore, it is imperative to assess somatosensory function in order to guide rehabilitation outcomes and trajectory. Overall, considerable work in the field still remains, as there is limited evidence for purported mechanisms of sensory recovery, promising early-stage work that focuses on sensory training, and a considerable evidence-practice gap related to clinical sensory rehabilitation.

Parietal cortex is recruited by frontal and cingulate areas to support action monitoring and updating during stopping

Recent evidence indicates that the intraparietal sulcus (IPS) may play a causal role in action stopping, potentially representing a novel neuromodulation target for inhibitory control dysfunctions. Here, we leverage intracranial recordings in human subjects to establish the timing and directionality of information flow between IPS and prefrontal and cingulate regions during action stopping. Prior to successful inhibition, information flows primarily from the inferior frontal gyrus (IFG), a critical inhibitory control node, to IPS. In contrast, during stopping errors the communication between IPS and IFG is lacking, and IPS is engaged by posterior cingulate cortex, an area outside of the classical inhibition network and typically associated with default mode. Anterior cingulate and orbitofrontal cortex also display performance-dependent connectivity with IPS. Our functional connectivity results provide direct electrophysiological evidence that IPS is recruited by frontal and anterior cingulate areas to support action plan monitoring/updating, and by posterior cingulate during control failures.

Predicting preferred motorcycle riding postures to support human factors/ergonomic trade-off analyses within a multi-objective optimisation-based digital human model

Digital human models (DHM) can predict how users might interact with new vehicle geometry during early-stage design, an important precursor to conducting trade-off analyses. However, predicting human postures requires assumptions about which performance criteria best predict realistic postures. Focusing on the design of motorcycles, we do not know what performance criteria drive preferred riding postures. Addressing this gap, we aimed to identify which performance criteria and corresponding weightings best predicted preferred motorcycle riding postures when using a DHM. To address our aim, we surveyed the literature to find experimental data specifying joint angles that correspond to preferred riding postures. We then deployed a response surface methodology to determine which performance criteria and weightings optimally predicted the preferred riding postures when using a DHM. Weighting the minimisation of the discomfort performance criteria (an aggregate of joint range of motion, displacement from neutral and joint torque) best predicted preferred motorcycle riding postures.

A neural implementation model of feedback-based motor learning

Animals use feedback to rapidly correct ongoing movements in the presence of a perturbation. Repeated exposure to a predictable perturbation leads to behavioural adaptation that compensates for its effects. Here, we tested the hypothesis that all the processes necessary for motor adaptation may emerge as properties of a controller that adaptively updates its policy. We trained a recurrent neural network to control its own output through an error-based feedback signal, which allowed it to rapidly counteract external perturbations. Implementing a biologically plausible plasticity rule based on this same feedback signal enabled the network to learn to compensate for persistent perturbations through a trial-by-trial process. The network activity changes during learning matched those from populations of neurons from monkey primary motor cortex - known to mediate both movement correction and motor adaptation - during the same task. Furthermore, our model natively reproduced several key aspects of behavioural studies in humans and monkeys. Thus, key features of trial-by-trial motor adaptation can arise from the internal properties of a recurrent neural circuit that adaptively controls its output based on ongoing feedback.

Conjoint specification of action by neocortex and striatum

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

Comparative Analysis of Motor Preparation Using Bereitschaftspotential With Two Methods of Wrist Extension

INTRODUCTION

Motor control for voluntary movement involves the orchestrated activity of motor cortical areas, which activate approximately one to two seconds before movement onset. This preparatory neural activity, termed Bereitschaftspotential (BP), reflects the readiness of cortical circuits for initiating movement. BP has been linked to parameters such as force and rate of contraction; however, the influence of movement precision on BP components is less understood. This study aims to investigate how movement precision affects BP characteristics, providing insights into the neural dynamics underlying motor preparation.

METHODS

Using a quasi-experimental design, BP characteristics were compared in 15 healthy, right-handed male participants (ages 40-60) under two conditions: precise wrist extensions (Method 1) and self-paced wrist extensions with relaxed precision (Method 2). BP was recorded at Cz, C3, and C4 electrodes, with electroencephalogram (EEG) data averaged from -3 seconds to +1 seconds relative to electromyogram (EMG) onset. Method 1 required controlled wrist extensions with visual feedback, while Method 2 permitted varied movement parameters. BP parameters analyzed included peak amplitude, early slope, late slope, and onset time, compared across the two methods using paired t-tests.

RESULTS

BP amplitude and slope were significantly greater during Method 1 than Method 2, particularly at the Cz and C3 sites. At Cz, all BP parameters were significantly higher in Method 1 compared to Method 2, including early slope (p = 0.0005), late slope (p = 0.0124), amplitude (p = 0.00003), and onset time (p = 0.0020). At C3, early slope (p = 0.0137), late slope (p = 0.0282), and amplitude (p = 0.0005) were significantly higher, while onset time showed no difference (p = 0.5823). At C4, only amplitude showed a significant difference (p = 0.034), with other parameters not reaching statistical significance. These findings indicate that Method 1, requiring precision, engaged motor cortical regions more intensively, particularly in preparatory neural activity.

CONCLUSION

This study highlights BP sensitivity to movement precision, with distinct BP features linked to precise motor tasks. BP monitoring could be valuable in neurorehabilitation, especially for conditions affecting motor preparation, such as Parkinson's disease and post-stroke patients. Future research may expand BP applications, emphasizing its potential as a biomarker for motor preparedness and precise motor control interventions.

Human-Aware Control for Physically Interacting Robots

This paper presents a novel model for predicting human movements and introduces a new control method for human-robot interaction based on this model. The developed predictive model of human movement is a holistic model that is based on well-supported neuroscientific and biomechanical theories of human motor control; it includes multiple levels of the human sensorimotor system hierarchy, including high-level decision-making based on internal models, muscle synergies, and physiological muscle mechanics. Therefore, this holistic model can predict arm kinematics and neuromuscular activities in a computationally efficient way. The computational efficiency of the model also makes it suitable for repetitive predictive simulations within a robot's control algorithm to predict the user's behavior in human-robot interactions. Therefore, based on this model and the nonlinear model predictive control framework, a human-aware control algorithm is implemented, which internally runs simulations to predict the user's interactive movement patterns in the future. Consequently, it can optimize the robot's motor torques to minimize an index, such as the user's neuromuscular effort. Simulation results of the holistic model and its utilization in the human-aware control of a two-link robot arm are presented. The holistic model is shown to replicate salient features of human movements. The human-aware controller's ability to predict and minimize the user's neuromuscular effort in a collaborative task is also demonstrated in simulations.

Alterations in orbitofrontal cortex communication relate to suicidal attempts in patients with major depressive disorder

BACKGROUND

Investigating how the interaction between the orbitofrontal cortex (OFC) and various brain regions/functional networks in major depressive disorder (MDD) patients with a history of suicide attempt (SA) holds importance for understanding the neurobiology of this population.

METHODS

We employed resting-state functional magnetic resonance imaging (rs-fMRI) to analyze the OFC's functional segregation in 586 healthy individuals. A network analysis framework was then applied to rs-fMRI data from 86 MDD-SA patients and 85 MDD-Control patients, utilizing seed mappings of OFC subregions and a multi-connectivity-indicator strategy involving cross-correlation, total interdependencies, Granger causality, and machine learning.

RESULTS

Four functional subregions of left and right OFC, were designated as seed regions of interest. Relative to the MDD-Control group, the MDD-SA group exhibited enhanced functional connectivity (FC) and attenuated interaction between the OFC and the sensorimotor network, imbalanced communication between the OFC and the default mode network, enhanced FC and interaction between the OFC and the ventral attention network, enhanced interaction between the OFC and the salience network, and attenuated FC between the OFC and the frontoparietal network.

LIMITATIONS

The medication and treatment condition of patients with MDD was not controlled, so the medication effect on the alteration model cannot be affirmed.

CONCLUSION

The findings suggest an imbalanced interaction pattern between the OFC subregions and a set of cognition- and emotion-related functional networks/regions in the MDD-SA group.

Hypergravity is more challenging than microgravity for the human sensorimotor system

The importance of gravity for human motor control is well established, but it remains unclear how the central nervous system accounts for gravitational changes to perform complex motor skills. We tested the hypothesis that microgravity and hypergravity have distinct effects on the neuromuscular control of reaching movements compared to normogravity. To test the influence of gravity levels on sensorimotor planning and control, participants (n = 9) had to reach toward visual targets during parabolic flights. Whole-body kinematics and muscular activity were adjusted in microgravity, allowing arm reaching to be as accurate as in normogravity. However, we observed in hypergravity a systematic undershooting, which likely resulted from a lack of reorganization of muscle activations. While new studies are necessary to clarify whether hypergravity impairs the internal model of limb dynamics, our findings provide new evidence that hypergravity creates a challenge that the human sensorimotor system is unable to solve in the short term.

A control policy can be adapted to task demands during both motor execution and motor planning

Movement planning consists of several processes related to the preparation of a movement such as decision making, target selection, application of task demands, action selection, and specification of movement kinematics. These numerous processes are reflected in the reaction time, which is the time that it takes to start executing the movement. However, not all the processes that lead to motor planning increase reaction time. In this paper, we wanted to test whether tuning the control policy to task demands contributes to reaction time. Taking into account that the tuning of the control policy differs for narrow and wide targets, we used a timed response paradigm to track the amount of time needed to tune the control policy appropriately to task demands. We discovered that it does not take any time during motor planning and even that it can occur indistinguishably during motor planning or during motor execution. That is, the tuning the control policy was equally good when the narrow or wide target was displayed before than when it was displayed after the start of the movement. These results suggest that the frontier between motor planning and execution is not as clear cut as it is often depicted.NEW & NOTEWORTHY Movement preparation consists of different processes such as target selection and movement parameters selection. We investigate the time that it takes to tune movement parameters to task demands. We found that the brain does this instantaneously and that this can even happen during movement. Therefore, this suggests that there exists an overlap during movement planning and execution.

Long-term effects of concussion on attention, sensory gating and motor learning

The current work aimed to understand the behavioral manifestations that result from disruptions to the selective facilitation of task-relevant sensory information at early cortical processing stages in those with a history of concussion. A total of 40 participants were recruited to participate in this study, with 25 in the concussion history group (Hx) and 15 in the control group (No-Hx). Somatosensory-evoked potentials (SEPs) were elicited via median nerve stimulation while subjects performed a task that manipulated their focus of attention toward or away from proprioceptive cues. Participants also completed an implicit motor sequence learning task relying solely on proprioceptive cues, as well as a visual attentional blink (AB) task to understand the effect of concussion on rapid shifts in attention. The Hx SEP data replicated past work showing an absence of relevancy-based facilitation at early cortical processing stages (N20-P27) that emerged at later processing stages. Our Hx showed evidence of relevancy-based facilitation at either the P50-N70 or the N70-P100. Performance on the learning task was not significantly different between the Hx and No-Hx. Performance on the AB task revealed greater AB magnitude in the Hx compared to the No-Hx. Collectively, these results suggest a compensatory strategy in the Hx that enables them to learn to the same degree as controls. However, when the attentional system is taxed with high temporal demands there are decrements in performance. These results are of particular importance given that these individuals are at an increased risk of sustaining subsequent concussions, and musculoskeletal injuries.

Future spinal reflex is embedded in primary motor cortex output

Mammals can execute intended limb movements despite the fact that spinal reflexes involuntarily modulate muscle activity. To generate appropriate muscle activity, the cortical descending motor output must coordinate with spinal reflexes, yet the underlying neural mechanism remains unclear. We simultaneously recorded activities in motor-related cortical areas, afferent neurons, and forelimb muscles of monkeys performing reaching movements. Motor-related cortical areas, predominantly primary motor cortex (M1), encode subsequent afferent activities attributed to forelimb movement. M1 also encodes a subcomponent of muscle activity evoked by these afferent activities, corresponding to spinal reflexes. Furthermore, selective disruption of the afferent pathway specifically reduced this subcomponent of muscle activity, suggesting that M1 output drives muscle activity not only through direct descending pathways but also through the "transafferent" pathway composed of descending plus subsequent spinal reflex pathways. Thus, M1 provides optimal motor output based on an internal forward model that prospectively computes future spinal reflexes.

Muscle spindles provide flexible sensory feedback for movement sequences

Sensory feedback is essential for motor performance and must adapt to task demands. Muscle spindle afferents (MSAs) are a major primary source of feedback about movement, and their responses are readily modulated online by gain-controller fusimotor neurons and other mechanisms. They are therefore a powerful site for implementing flexible sensorimotor control. We recorded from MSAs innervating the jaw musculature during performance of a directed lick sequence task. Jaw MSAs encoded complex jaw-tongue kinematics. However, kinematic encoding alone accounted for less than half of MSA spiking variability. MSA coding of kinematics changed based on sequence progression (beginning, middle, or end of the sequence, or reward consumption), suggesting that MSAs are flexibly tuned across the task. Dynamic control of incoming feedback signals from MSAs may be a strategy for adaptable sensorimotor control during performance of complex behaviors.

Dynamic causal modelling highlights the importance of decreased self-inhibition of the sensorimotor cortex in motor fatigability

Motor fatigability emerges when challenging motor tasks must be maintained over an extended period of time. It is frequently observed in everyday life and affects patients as well as healthy individuals. Motor fatigability can be measured using simple tasks like finger tapping at maximum speed for 30 s. This typically results in a rapid decrease of tapping frequency, a phenomenon called motor slowing. In a previous study (Bächinger et al, eLife, 8 (September), https://doi.org/10.7554/eLife.46750 , 2019), we showed that motor slowing goes hand in hand with a gradual increase in blood oxygen level dependent signal in the primary sensorimotor cortex (SM1), supplementary motor area (SMA), and dorsal premotor cortex (PMd). It is unclear what drives the activity increase in SM1 caused by motor slowing and whether motor fatigability affects the dynamic interactions between SM1, SMA, and PMd. Here, we performed dynamic causal modelling (DCM) on data of 24 healthy young participants collected during functional magnetic resonance imaging to answer this question. The regions of interest (ROI) were defined based on the peak activation within SM1, SMA, and PMd. The model space consisted of bilateral connections between all ROI, with intrinsic self-modulation as inhibitory, and driving inputs set to premotor areas. Our findings revealed that motor slowing was associated with a significant reduction in SM1 self-inhibition, as uncovered by testing the maximum à posteriori against 0 (t(23)=-4.51, p < 0.001). Additionally, the model revealed a significant decrease in the driving input to premotor areas (t(23) > 2.71, p < 0.05) suggesting that structures other than cortical motor areas may contribute to motor fatigability.

Distinct patterns of connectivity with the motor cortex reflect different components of sensorimotor learning

Sensorimotor learning is supported by multiple competing processes that operate concurrently, making it a challenge to elucidate their neural underpinnings. Here, using human functional MRI, we identify 3 distinct axes of connectivity between the motor cortex and other brain regions during sensorimotor adaptation. These 3 axes uniquely correspond to subjects' degree of implicit learning, performance errors and explicit strategy use, and involve different brain networks situated at increasing levels of the cortical hierarchy. We test the generalizability of these neural axes to a separate form of motor learning known to rely mainly on explicit processes and show that it is only the Explicit neural axis, composed of higher-order areas in transmodal cortex, that predicts learning in this task. Together, our study uncovers multiple distinct patterns of functional connectivity with motor cortex during sensorimotor adaptation, the component processes that these patterns support, and how they generalize to other forms of motor learning.

Implicit motor adaptation patterns in a redundant motor task manipulating a stick with both hands

The remarkable ability of the motor system to adapt to novel environments has traditionally been investigated using kinematically non-redundant tasks, such as planar reaching movements. This limitation prevents the study of how the motor system achieves adaptation by altering the movement patterns of our redundant body. To address this issue, we developed a redundant motor task in which participants reached for targets with the tip of a virtual stick held with both hands. Despite the redundancy of the task, participants consistently employed a stereotypical strategy of flexibly changing the tilt angle of the stick depending on the direction of tip movement. Thus, this baseline relationship between tip-movement direction and stick-tilt angle constrained both the physical and visual movement patterns of the redundant system. Our task allowed us to systematically investigate how the motor system implicitly changed both the tip-movement direction and the stick-tilt angle in response to imposed visual perturbations. Both types of perturbations, whether directly affecting the task (tip-movement direction) or not (stick-tilt angle around the tip), drove adaptation, and the patterns of implicit adaptation were guided by the baseline relationship. Consequently, tip-movement adaptation was associated with changes in stick-tilt angle, and intriguingly, even seemingly ignorable stick-tilt perturbations significantly influenced tip-movement adaptation, leading to tip-movement direction errors. These findings provide a new understanding that the baseline relationship plays a crucial role not only in how the motor system controls movement of the redundant system, but also in how it implicitly adapts to modify movement patterns.

Does pre-speech auditory modulation reflect processes related to feedback monitoring or speech movement planning?

Previous studies have revealed that auditory processing is modulated during the planning phase immediately prior to speech onset. To date, the functional relevance of this pre-speech auditory modulation (PSAM) remains unknown. Here, we investigated whether PSAM reflects neuronal processes that are associated with preparing auditory cortex for optimized feedback monitoring as reflected in online speech corrections. Combining electroencephalographic PSAM data from a previous data set with new acoustic measures of the same participants' speech, we asked whether individual speakers' extent of PSAM is correlated with the implementation of within-vowel articulatory adjustments during /b/-vowel-/d/ word productions. Online articulatory adjustments were quantified as the extent of change in inter-trial formant variability from vowel onset to vowel midpoint (a phenomenon known as centering). This approach allowed us to also consider inter-trial variability in formant production, and its possible relation to PSAM, at vowel onset and midpoint separately. Results showed that inter-trial formant variability was significantly smaller at vowel midpoint than at vowel onset. PSAM was not significantly correlated with this amount of change in variability as an index of within-vowel adjustments. Surprisingly, PSAM was negatively correlated with inter-trial formant variability not only in the middle but also at the very onset of the vowels. Thus, speakers with more PSAM produced formants that were already less variable at vowel onset. Findings suggest that PSAM may reflect processes that influence speech acoustics as early as vowel onset and, thus, that are directly involved in motor command preparation (feedforward control) rather than output monitoring (feedback control).

Increased muscle coactivation is linked with fast feedback control when reaching in unpredictable visual environments

Humans encounter unpredictable disturbances in daily activities and sports. When encountering unpredictable physical disturbances, healthy participants increase the peak velocity of their reaching movements, muscle coactivation, and responses to sensory feedback. Emerging evidence suggests that muscle coactivation may facilitate responses to sensory feedback and may not solely increase stiffness to resist displacements. We tested this idea by examining how healthy participants alter the control of reaching movements and responses to sensory feedback when encountering variable visuomotor rotations. The rotations changed amplitude and direction between movements, creating unpredictable errors that required fast online corrections. Participants increased the peak velocity of their movements, muscle coactivation, and responses to visual and proprioceptive feedback with the variability of the visuomotor rotations. The findings highlight an increase in neural responsiveness to sensory feedback and suggest that muscle coactivation may prime the nervous system for fast responses to sensory feedback that accommodate properties of unpredictable visual environments.

Heuristic satisficing inferential decision making in human and robot active perception

Inferential decision-making algorithms typically assume that an underlying probabilistic model of decision alternatives and outcomes may be learned a priori or online. Furthermore, when applied to robots in real-world settings they often perform unsatisfactorily or fail to accomplish the necessary tasks because this assumption is violated and/or because they experience unanticipated external pressures and constraints. Cognitive studies presented in this and other papers show that humans cope with complex and unknown settings by modulating between near-optimal and satisficing solutions, including heuristics, by leveraging information value of available environmental cues that are possibly redundant. Using the benchmark inferential decision problem known as "treasure hunt", this paper develops a general approach for investigating and modeling active perception solutions under pressure. By simulating treasure hunt problems in virtual worlds, our approach learns generalizable strategies from high performers that, when applied to robots, allow them to modulate between optimal and heuristic solutions on the basis of external pressures and probabilistic models, if and when available. The result is a suite of active perception algorithms for camera-equipped robots that outperform treasure-hunt solutions obtained via cell decomposition, information roadmap, and information potential algorithms, in both high-fidelity numerical simulations and physical experiments. The effectiveness of the new active perception strategies is demonstrated under a broad range of unanticipated conditions that cause existing algorithms to fail to complete the search for treasures, such as unmodelled time constraints, resource constraints, and adverse weather (fog).

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.

Closing the sensory feedback loop is necessary for effective neurorehabilitation

Recent advances in neurotechnology enable somatosensory feedback restoration in disabled individuals. This Perspective discusses how closing the sensory feedback loop with brain implants and nerve electrodes for stimulation may improve rehabilitation and assistive systems for patients.

Adaptive Inverse Nonlinear Optimal Control Based on Finite-Time Concurrent Learning and Semidefinite Programming

This article investigates the problem of inverse optimal control (IOC) for a class of nonlinear affine systems. An adaptive IOC approach is proposed to recover the cost functional using only the system state data, which integrates the finite-time concurrent learning (FTCL) technique and the semidefinite programming (SDP) technique. First, an identifier neural network (NN) is employed to approximate the unknown nonlinear control policy, and an FTCL-based update law is proposed to estimate the weights of the identifier NN online, which removes the traditional persistent excitation (PE) condition. Moreover, the finite-time convergence as well as the uniformly ultimately boundness (UUB) of estimation error of the identifier NN weights are analysed according to whether or not there exists the identifier NN approximation error. Then, with the help of a value NN for approximating the value function, an SDP problem with a quadratic objective function can be set up for determining the weighting matrices of the cost functional. Finally, simulation results are presented to validate the proposed method.

Enhancing stroke rehabilitation with whole-hand haptic rendering: development and clinical usability evaluation of a novel upper-limb rehabilitation device

INTRODUCTION

There is currently a lack of easy-to-use and effective robotic devices for upper-limb rehabilitation after stroke. Importantly, most current systems lack the provision of somatosensory information that is congruent with the virtual training task. This paper introduces a novel haptic robotic system designed for upper-limb rehabilitation, focusing on enhancing sensorimotor rehabilitation through comprehensive haptic rendering.

METHODS

We developed a novel haptic rehabilitation device with a unique combination of degrees of freedom that allows the virtual training of functional reach and grasp tasks, where we use a physics engine-based haptic rendering method to render whole-hand interactions between the patients' hands and virtual tangible objects. To evaluate the feasibility of our system, we performed a clinical mixed-method usability study with seven patients and seven therapists working in neurorehabilitation. We employed standardized questionnaires to gather quantitative data and performed semi-structured interviews with all participants to gain qualitative insights into the perceived usability and usefulness of our technological solution.

RESULTS

The device demonstrated ease of use and adaptability to various hand sizes without extensive setup. Therapists and patients reported high satisfaction levels, with the system facilitating engaging and meaningful rehabilitation exercises. Participants provided notably positive feedback, particularly emphasizing the system's available degrees of freedom and its haptic rendering capabilities. Therapists expressed confidence in the transferability of sensorimotor skills learned with our system to activities of daily living, although further investigation is needed to confirm this.

CONCLUSION

The novel haptic robotic system effectively supports upper-limb rehabilitation post-stroke, offering high-fidelity haptic feedback and engaging training tasks. Its clinical usability, combined with positive feedback from both therapists and patients, underscores its potential to enhance robotic neurorehabilitation.

Task-dependent coarticulation of movement sequences

Combining individual actions into sequences is a hallmark of everyday activities. Classical theories propose that the motor system forms a single specification of the sequence as a whole, leading to the coarticulation of the different elements. In contrast, recent neural recordings challenge this idea and suggest independent execution of each element specified separately. Here, we show that separate or coarticulated sequences can result from the same task-dependent controller, without implying different representations in the brain. Simulations show that planning for multiple reaches simultaneously allows separate or coarticulated sequences depending on instructions about intermediate goals. Human experiments in a two-reach sequence task validated this model. Furthermore, in co-articulated sequences, the second goal influenced long-latency stretch responses to external loads applied during the first reach, demonstrating the involvement of the sensorimotor network supporting fast feedback control. Overall, our study establishes a computational framework for sequence production that highlights the importance of feedback control in this essential motor skill.

Imbalanced optimal feedback motor control system in spinocerebellar ataxia type 3

BACKGROUND AND PURPOSE

Human motor planning and control depend highly on optimal feedback control systems, such as the neocortex-cerebellum circuit. Here, diffusion tensor imaging was used to verify the disruption of the neocortex-cerebellum circuit in spinocerebellar ataxia type 3 (SCA3), and the circuit's disruption correlation with SCA3 motor dysfunction was investigated.

METHODS

This study included 45 patients with familial SCA3, aged 17-67 years, and 49 age- and sex-matched healthy controls, aged 21-64 years. Tract-based spatial statistics and probabilistic tractography was conducted using magnetic resonance images of the patients and controls. The correlation between the local probability of probabilistic tractography traced from the cerebellum and clinical symptoms measured using specified symptom scales was also calculated.

RESULTS

The cerebellum-originated probabilistic tractography analysis showed that structural connectivity, mainly in the subcortical cerebellar-thalamo-cortical tract, was significantly reduced and the cortico-ponto-cerebellar tract was significantly stronger in the SCA3 group than in the control group. The enhanced tract was extended to the right lateral parietal region and the right primary motor cortex. The enhanced neocortex-cerebellum connections were highly associated with disease progression, including duration and symptomatic deterioration. Tractography probabilities from the cerebellar to parietal and sensorimotor areas were significantly negatively correlated with motor abilities in patients with SCA3.

CONCLUSION

To our knowledge, this study is the first to reveal that disrupting the neocortex-cerebellum loop can cause SCA3-induced motor dysfunctions. The specific interaction between the cerebellar-thalamo-cortical and cortico-ponto-cerebellar pathways in patients with SCA3 and its relationship with ataxia symptoms provides a new direction for future research.

Motor cortical inactivation impairs corrective submovements in mice performing a hold-still center-out reach task

Holding still and aiming reaches to spatial targets may depend on distinct neural circuits. Using automated homecage training and a sensitive joystick, we trained freely moving mice to contact a joystick, hold their forelimb still, and then reach to rewarded target locations. Mice learned the task by initiating forelimb sequences with clearly resolved submillimeter-scale micromovements followed by millimeter-scale reaches to learned spatial targets. Hundreds of thousands of trajectories were decomposed into millions of kinematic submovements, while photoinhibition was used to test roles of motor cortical areas. Inactivation of both caudal and rostral forelimb areas preserved the ability to produce aimed reaches, but reduced reach speed. Inactivation specifically of contralateral caudal forelimb area (CFA) additionally impaired the ability to aim corrective submovements to remembered locations following target undershoots. Our findings show that motor cortical inactivations reduce the gain of forelimb movements but that inactivation specifically of contralateral CFA impairs corrective movements important for reaching a target location.NEW & NOTEWORTHY To test the role of different cortical areas in holding still and reaching to targets, this study combined home-cage training with optogenetic silencing as mice engaged in a learned center-out-reach task. Inactivation specifically of contralateral caudal forelimb area (CFA) impaired corrective movements necessary to reach spatial targets to earn reward.

An output-null signature of inertial load in motor cortex

Coordinated movement requires the nervous system to continuously compensate for changes in mechanical load across different conditions. For voluntary movements like reaching, the motor cortex is a critical hub that generates commands to move the limbs and counteract loads. How does cortex contribute to load compensation when rhythmic movements are sequenced by a spinal pattern generator? Here, we address this question by manipulating the mass of the forelimb in unrestrained mice during locomotion. While load produces changes in motor output that are robust to inactivation of motor cortex, it also induces a profound shift in cortical dynamics. This shift is minimally affected by cerebellar perturbation and significantly larger than the load response in the spinal motoneuron population. This latent representation may enable motor cortex to generate appropriate commands when a voluntary movement must be integrated with an ongoing, spinally-generated rhythm.

Effect of Phasic Experimental Pain Applied during Motor Preparation or Execution on Motor Performance and Adaptation in a Reaching Task: A Randomized Trial

Musculoskeletal conditions often involve pain related to specific movements. However, most studies on the impact of experimental pain on motor performance and learning have used tonic pain models. This study aimed to evaluate the effect of experimental phasic pain during the preparation or execution of a reaching task on the acquisition and retention of sensorimotor adaptation. Participants were divided into three groups: no pain, pain during motor preparation, and pain during motor execution. Pain was induced over the scapula with a laser while participants performed a force field adaptation task over two days. To assess the effect of pain on motor performance, two baseline conditions (with or without pain) involving unperturbed pointing movements were also conducted. The results indicated that the timing of the nociceptive stimulus differently affected baseline movement performance. Pain during motor preparation shortened reaction time, while pain during movement execution decreased task performance. However, when these baseline effects were accounted for, no impact of pain on motor adaptation or retention was observed. All groups showed significant improvements in all motor variables for both adaptation and retention. In conclusion, while acute phasic pain during motor preparation or execution can affect the movement itself, it does not interfere with motor acquisition or retention during a motor adaptation task.

A computational study of how an α- to γ-motoneurone collateral can mitigate velocity-dependent stretch reflexes during voluntary movement

The primary motor cortex does not uniquely or directly produce alpha motoneurone (α-MN) drive to muscles during voluntary movement. Rather, α-MN drive emerges from the synthesis and competition among excitatory and inhibitory inputs from multiple descending tracts, spinal interneurons, sensory inputs, and proprioceptive afferents. One such fundamental input is velocity-dependent stretch reflexes in lengthening muscles, which should be inhibited to enable voluntary movement. It remains an open question, however, the extent to which unmodulated stretch reflexes disrupt voluntary movement, and whether and how they are inhibited in limbs with numerous multiarticular muscles. We used a computational model of a Rhesus Macaque arm to simulate movements with feedforward α-MN commands only, and with added velocity-dependent stretch reflex feedback. We found that velocity-dependent stretch reflex caused movement-specific, typically large and variable disruptions to arm movements. These disruptions were greatly reduced when modulating velocity-dependent stretch reflex feedback (i) as per the commonly proposed (but yet to be clarified) idealized alpha-gamma (α-γ) coactivation or (ii) an alternative α-MN collateral projection to homonymous γ-MNs. We conclude that such α-MN collaterals are a physiologically tenable propriospinal circuit in the mammalian fusimotor system. These collaterals could still collaborate with α-γ coactivation, and the few skeletofusimotor fibers (β-MNs) in mammals, to create a flexible fusimotor ecosystem to enable voluntary movement. By locally and automatically regulating the highly nonlinear neuro-musculo-skeletal mechanics of the limb, these collaterals could be a critical low-level enabler of learning, adaptation, and performance via higher-level brainstem, cerebellar, and cortical mechanisms.

Audiomotor prediction errors drive speech adaptation even in the absence of overt movement

Observed outcomes of our movements sometimes differ from our expectations. These sensory prediction errors recalibrate the brain's internal models for motor control, reflected in alterations to subsequent movements that counteract these errors (motor adaptation). While leading theories suggest that all forms of motor adaptation are driven by learning from sensory prediction errors, dominant models of speech adaptation argue that adaptation results from integrating time-advanced copies of corrective feedback commands into feedforward motor programs. Here, we tested these competing theories of speech adaptation by inducing planned, but not executed, speech. Human speakers (male and female) were prompted to speak a word and, on a subset of trials, were rapidly cued to withhold the prompted speech. On standard trials, speakers were exposed to real-time playback of their own speech with an auditory perturbation of the first formant to induce single-trial speech adaptation. Speakers experienced a similar sensory error on movement cancelation trials, hearing a perturbation applied to a recording of their speech from a previous trial at the time they would have spoken. Speakers adapted to auditory prediction errors in both contexts, altering the spectral content of spoken vowels to counteract formant perturbations even when no actual movement coincided with the perturbed feedback. These results build upon recent findings in reaching, and suggest that prediction errors, rather than corrective motor commands, drive adaptation in speech.

Reach-dependent reorientation of rotational dynamics in motor cortex

During reaching, neurons in motor cortex exhibit complex, time-varying activity patterns. Though single-neuron activity correlates with movement parameters, movement correlations explain neural activity only partially. Neural responses also reflect population-level dynamics thought to generate outputs. These dynamics have previously been described as "rotational," such that activity orbits in neural state space. Here, we reanalyze reaching datasets from male Rhesus macaques and find two essential features that cannot be accounted for with standard dynamics models. First, the planes in which rotations occur differ for different reaches. Second, this variation in planes reflects the overall location of activity in neural state space. Our "location-dependent rotations" model fits nearly all motor cortex activity during reaching, and high-quality decoding of reach kinematics reveals a quasilinear relationship with spiking. Varying rotational planes allows motor cortex to produce richer outputs than possible under previous models. Finally, our model links representational and dynamical ideas: representation is present in the state space location, which dynamics then convert into time-varying command signals.

Exploring motor skill acquisition in bimanual coordination: insights from navigating a novel maze task

In this study, we introduce a novel maze task designed to investigate naturalistic motor learning in bimanual coordination. We developed and validated an extended set of movement primitives tailored to capture the full spectrum of scenarios encountered in a maze game. Over a 3-day training period, we evaluated participants' performance using these primitives and a custom-developed software, enabling precise quantification of performance. Our methodology integrated the primitives with in-depth kinematic analyses and thorough thumb pressure assessments, charting the trajectory of participants' progression from novice to proficient stages. Results demonstrated consistent improvement in maze performance and significant adaptive changes in joint behaviors and strategic recalibrations in thumb pressure distribution. These findings highlight the central nervous system's adaptability in orchestrating sophisticated motor strategies and the crucial role of tactile feedback in precision tasks. The maze platform and setup emerge as a valuable foundation for future experiments, providing a tool for the exploration of motor learning and coordination dynamics. This research underscores the complexity of bimanual motor learning in naturalistic environments, enhancing our understanding of skill acquisition and task efficiency while emphasizing the necessity for further exploration and deeper investigation into these adaptive mechanisms.

A posture subspace in primary motor cortex

To generate movements, the brain must combine information about movement goal and body posture. Motor cortex (M1) is a key node for the convergence of these information streams. How are posture and goal information organized within M1's activity to permit the flexible generation of movement commands? To answer this question, we recorded M1 activity while monkeys performed a variety of tasks with the forearm in a range of postures. We found that posture- and goal-related components of neural population activity were separable and resided in nearly orthogonal subspaces. The posture subspace was stable across tasks. Within each task, neural trajectories for each goal had similar shapes across postures. Our results reveal a simpler organization of posture information in M1 than previously recognized. The compartmentalization of posture and goal information might allow the two to be flexibly combined in the service of our broad repertoire of actions.

Wavelet Coherence Analysis of Post-Stroke Intermuscular Coupling Modulated by Myoelectric-Controlled Interfaces

Intermuscular coupling reflects the corticospinal interaction associated with the control of muscles. Nevertheless, the deterioration of intermuscular coupling caused by stroke has not received much attention. The purpose of this study was to investigate the effect of myoelectric-controlled interface (MCI) dimensionality on the intermuscular coupling after stroke. In total, ten age-matched controls and eight stroke patients were recruited and executed elbow tracking tasks within 1D or 2D MCI. Movement performance was quantified using the root mean square error (RMSE). Wavelet coherence was used to analyze the intermuscular coupling in alpha band (8-12 Hz) and beta band (15-35 Hz). The results found that smaller RMSE of antagonist muscles was observed in both groups within 2D MCI compared to 1D MCI. The alpha-band wavelet coherence was significantly lower in the patients compared to the controls during elbow extension. Furthermore, a decreased alpha-band and beta-band wavelet coherence was observed in the controls and stroke patients, as the dimensionality of MCI increased. These results may suggest that stroke-related neural impairments deteriorate the motor performance and intermuscular coordination pattern, and, further, that MCI holds promise as a novel effective tool for rehabilitation through the direct modulation of muscle activation pattern.

Initial and corrective submovement encoding differences within primary motor cortex during precision reaching

Precision reaching often requires corrective submovements to obtain the desired goal. Most studies of reaching have focused on single initial movements, and implied the cortical encoding model was the same for all submovements. However, corrective submovements may show different encoding patterns from the initial submovement with distinct patterns of activation across the population. Two rhesus macaques performed a precision center-out-task with small targets. Neural activity from single units in the primary motor cortex and associated behavioral data were recorded to evaluate movement characteristics. Neural population data and individual neuronal firing rates identified with a peak finding algorithm to identify peaks in hand speed were examined for encoding differences between initial and corrective submovements. Individual neurons were fitted with a regression model that included the reach vector, position, and speed to predict firing rate. For both initial and corrective submovements, the largest effect remained movement direction. We observed a large subset changed their preferred direction greater than 45° between initial and corrective submovements. Neuronal depth of modulation also showed considerable variation when adjusted for movement speed. By using principal component analysis, neural trajectories of initial and corrective submovements progressed through different neural subspaces. These findings all suggest that different neural encoding patterns exist for initial and corrective submovements within the cortex. We hypothesize that this variation in how neurons change to encode small, corrective submovements might allow for a larger portion of the neural space being used to encode a greater range of movements with varying amplitudes and levels of precision.NEW & NOTEWORTHY Neuronal recordings matched with kinematic behavior were collected in a precision center-out task that often required corrective movements. We reveal large differences in preferred direction and depth of modulation between initial and corrective submovements across the neural population. We then present a model of the neural population describing how these shifts in tuning create different subspaces for signaling initial and corrective movements likely to improve motor precision.

Force reserve predicts compensation in reaching movement with induced shoulder strength deficit

Following events such as fatigue or stroke, individuals often move their trunks forward during reaching, leveraging a broader muscle group even when only arm movement would suffice. In previous work, we showed the existence of a "force reserve": a phenomenon where individuals, when challenged with a heavy weight, adjusted their motor coordination to preserve approximately 40% of their shoulder's force. Here, we investigated if such reserve can predict hip, shoulder, and elbow movements and torques resulting from an induced shoulder strength deficit. We engaged 20 healthy participants in a reaching task with incrementally heavier dumbbells, analyzing arm and trunk movements via motion capture and joint torques through inverse dynamics. We simulated these movements using an optimal control model of a 3-degree-of-freedom upper body, contrasting three cost functions: traditional sum of squared torques, a force reserve function incorporating a nonlinear penalty, and a normalized torque function. Our results demonstrate a clear increase in trunk movement correlated with heavier dumbbell weights, with participants employing compensatory movements to maintain a shoulder force reserve of approximately 40% of maximum torque. Simulations showed that while traditional and reserve functions accurately predicted trunk compensation, only the reserve function effectively predicted joint torques under heavier weights. These findings suggest that compensatory movements are strategically employed to minimize shoulder effort and distribute load across multiple joints in response to weakness. We discuss the implications of the force reserve cost function in the context of optimal control of human movements and its relevance for understanding compensatory movements poststroke.NEW & NOTEWORTHY Our study reveals key findings on compensatory movements during upper limb reaching tasks under shoulder strength deficits, as observed poststroke. Using heavy dumbbells with healthy volunteers, we demonstrate how forward trunk displacement conserves around 40% of shoulder strength reserve during reaching. We show that an optimal controller employing a cost function combining squared motor torque and a nonlinear penalty for excessive muscle activation outperforms traditional controllers in predicting torques and compensatory movements in these scenarios.

Human-in-the-Loop Behavior Modeling via an Integral Concurrent Adaptive Inverse Reinforcement Learning

One goal of artificial intelligence (AI) research is to teach machines how to learn from humans, such that they can perform a certain task in a natural human-like way. In this article, an online adaptive inverse reinforcement learning (IRL) approach to human behavior modeling is proposed to enhance machine intelligence for a class of linear human-in-the-loop (HiTL) systems using the state data only, where the human behavior is described by a linear quadratic optimal control model with an unknown weighting matrix for the quadratic cost function. First, an integral concurrent adaptive law is developed to learn the human feedback gain matrix online using the demonstrated state data only, which removes the persistent excitation (PE) conditions required by traditional adaptive estimation approaches and thus is more in line with real applications. Then, with the learned feedback gain matrix, the IRL problem is formulated as a linear matrix inequality (LMI) optimization problem, which can be efficiently solved to retrieve the weighting matrix of the human cost function. Finally, a simulation example is provided to illustrate the effectiveness of the proposed approach.

Context-dependence of deterministic and nondeterministic contributions to closed-loop steering control

In natural circumstances, sensory systems operate in a closed loop with motor output, whereby actions shape subsequent sensory experiences. A prime example of this is the sensorimotor processing required to align one's direction of travel, or heading, with one's goal, a behavior we refer to as steering. In steering, motor outputs work to eliminate errors between the direction of heading and the goal, modifying subsequent errors in the process. The closed-loop nature of the behavior makes it challenging to determine how deterministic and nondeterministic processes contribute to behavior. We overcome this by applying a nonparametric, linear kernel-based analysis to behavioral data of monkeys steering through a virtual environment in two experimental contexts. In a given context, the results were consistent with previous work that described the transformation as a second-order linear system. Classically, the parameters of such second-order models are associated with physical properties of the limb such as viscosity and stiffness that are commonly assumed to be approximately constant. By contrast, we found that the fit kernels differed strongly across tasks in these and other parameters, suggesting context-dependent changes in neural and biomechanical processes. We additionally fit residuals to a simple noise model and found that the form of the noise was highly conserved across both contexts and animals. Strikingly, the fitted noise also closely matched that found previously in a human steering task. Altogether, this work presents a kernel-based analysis that characterizes the context-dependence of deterministic and non-deterministic components of a closed-loop sensorimotor task.