Optimal feedback control and the neural basis of volitional motor control

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.

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.

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.

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).

Self-organizing recruitment of compensatory areas maximizes residual motor performance post-stroke

Whereas the orderly recruitment of compensatory motor cortical areas after stroke depends on the size of the motor cortex lesion affecting arm and hand movements, the mechanisms underlying this reorganization are unknown. Here, we hypothesized that the recruitment of compensatory areas results from the motor system's goal to optimize performance given the anatomical constraints before and after the lesion. This optimization is achieved through two complementary plastic processes: a homeostatic regulation process, which maximizes information transfer in sensory-motor networks, and a reinforcement learning process, which minimizes movement error and effort. To test this hypothesis, we developed a neuro-musculoskeletal model that controls a 7-muscle planar arm via a cortical network that includes a primary motor cortex and a premotor cortex that directly project to spinal motor neurons, and a contra-lesional primary motor cortex that projects to spinal motor neurons via the reticular formation. Synapses in the cortical areas are updated via reinforcement learning and the activity of spinal motor neurons is adjusted through homeostatic regulation. The model replicated neural, muscular, and behavioral outcomes in both non-lesioned and lesioned brains. With increasing lesion sizes, the model demonstrated systematic recruitment of the remaining primary motor cortex, premotor cortex, and contra-lesional cortex. The premotor cortex acted as a reserve area for fine motor control recovery, while the contra-lesional cortex helped avoid paralysis at the cost of poor joint control. Plasticity in spinal motor neurons enabled force generation after large cortical lesions despite weak corticospinal inputs. Compensatory activity in the premotor and contra-lesional motor cortex was more prominent in the early recovery period, gradually decreasing as the network minimized effort. Thus, the orderly recruitment of compensatory areas following strokes of varying sizes results from biologically plausible local plastic processes that maximize performance, whether the brain is intact or lesioned.

A sensory-motor theory of the neocortex

Recent neurophysiological and neuroanatomical studies suggest a close interaction between sensory and motor processes across the neocortex. Here, I propose that the neocortex implements active predictive coding (APC): each cortical area estimates both latent sensory states and actions (including potentially abstract actions internal to the cortex), and the cortex as a whole predicts the consequences of actions at multiple hierarchical levels. Feedback from higher areas modulates the dynamics of state and action networks in lower areas. I show how the same APC architecture can explain (1) how we recognize an object and its parts using eye movements, (2) why perception seems stable despite eye movements, (3) how we learn compositional representations, for example, part-whole hierarchies, (4) how complex actions can be planned using simpler actions, and (5) how we form episodic memories of sensory-motor experiences and learn abstract concepts such as a family tree. I postulate a mapping of the APC model to the laminar architecture of the cortex and suggest possible roles for cortico-cortical and cortico-subcortical pathways.

Quantifying how functional and structural personal factors influence biomechanical exposures in paramedic lifting tasks

It is unknown how structural (sex, stature, body mass) and functional (strength, flexibility) personal factors influence lifting strategy in paramedic work. We explored whether variance in peak low back forces and kinematic coordination patterns could be explained by structural and functional personal factors in paramedic lifting tasks. Seventy-two participants performed backboard and stretcher lifts. Peak low back forces normalised to body mass, as well as kinematic coordination patterns, were calculated as dependent variables. Being female, stronger, shorter, having higher body mass, and/or having greater lower body range of motion (ROM) were all independently associated with lower normalised low back forces across backboard and stretcher lifting. Females and stronger individuals seemed to define a movement objective to consistently minimise compressive forces, while individuals with greater hip ROM consistently minimised anteroposterior shear forces. The efficacy of improving strength and hip ROM to reduce low back forces in paramedic lifting should be investigated.Practitioner summary: Females, stronger individuals, and individuals with greater hip range of motion consistently exhibited lower normalised low back forces in paramedic lifting. Improving strength and hip range of motion via training is a potential proactive ergonomics approach to reduce peak low back forces in paramedic lifting tasks.

Learning Human Behavior in Shared Control: Adaptive Inverse Differential Game Approach

To enhance the collaborative intelligence of a machine, it is important for the machine to understand what behavior a human may adopt to interact with the machine when performing a task in shared control. In this study, an online behavior learning method is proposed for continuous-time linear human-in-the-loop shared control systems by using the system state data only. A two-player nonzero-sum linear quadratic dynamic game paradigm is used for modeling the control interaction between a human operator and an automation that actively compensates for human control action. In this game model, the cost function representing the human behavior is assumed to have an unknown weighting matrix. Here, we want to learn the human behavior or retrieve the weighting matrix by using the system state data only. Accordingly, a new adaptive inverse differential game (IDG) method, which integrates concurrent learning (CL) and linear matrix inequality (LMI) optimization, is proposed. First, a CL-based adaptive law and an interactive controller of the automation are developed to estimate the feedback gain matrix of the human online, and second, an LMI optimization problem is solved to determine the weighting matrix of the human cost function. Finally, simulation results on a cooperative shared control driver assistance system are provided to elucidate the feasibility of the developed method.

Visual sensitivity at the service of action control in posterior parietal cortex

The posterior parietal cortex (PPC) serves as a crucial hub for the integration of sensory with motor cues related to voluntary actions. Visual input is used in different ways along the dorsomedial and the dorsolateral visual pathways. Here we focus on the dorsomedial pathway and recognize a visual representation at the service of action control. Employing different experimental paradigms applied to behaving monkeys while single neural activity is recorded from the medial PPC (area V6A), we show how plastic visual representation can be, matching the different contexts in which the same object is proposed. We also present data on the exchange between vision and arm actions and highlight how this rich interplay can be used to weight different sensory inputs in order to monitor and correct arm actions online. Indeed, neural activity during reaching or reach-to-grasp actions can be excited or inhibited by visual information, suggesting that the visual perception of action, rather than object recognition, is the most effective factor for area V6A. Also, three-dimensional object shape is encoded dynamically by the neural population, according to the behavioral context of the monkey. Along this line, mirror neuron discharges in V6A indicate the plasticity of visual representation of the graspable objects, that changes according to the context and peaks when the object is the target of one's own action. In other words, object encoding in V6A is a visual encoding for action.

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

Cerebellar patients exhibit a broad range of impairments when performing voluntary movements. However, the sequence of events leading to these deficits and the distinction between primary and compensatory processes remain unclear. We addressed this question by reversibly blocking cerebellar outflow in monkeys performing a planar reaching task. We found that the reduced hand velocity observed under cerebellar block is driven by a combination of a general decrease in muscle torque and a spatially tuned reduction in velocity, particularly pronounced in movements involving inter-joint interactions. The time course of these two processes was examined using repeated movements to the same target under cerebellar block. We found that the reduced velocity was driven by an acute onset of weakness superimposed on a gradually emergent strategy aimed to minimize passive inter-joint interactions. Finally, although the reduced velocity affected movements to all targets, it could not explain the enhanced motor noise observed under cerebellar block, which manifested as decomposed and variable trajectories. Our results suggest that cerebellar deficits lead to motor impairments through a loss of muscle strength and altered motor control strategy to compensate for the impaired control of limb dynamics. However, the loss of feedforward control also leads to increased motor noise, which cannot be strategically eliminated.

Significance Statement

Our study examined the impact of cerebellar dysfunction on motor control by reversibly blocking the cerebellar output in monkeys. Under cerebellar block, movements initially slowed due to acute-onset muscle weakness. Beyond this primary deficit, there was a secondary, seemingly strategic, slowing of movements aimed at mitigating inter-joint interactions associated with rapid, ballistic movements. Finally, during the cerebellar block we observed movement variability increased independently of the reduced velocity, likely reflecting errors in movement planning. Taken together, these findings highlight the role of cerebellar information in motor control and delineate the sequence of processes following cerebellar dysfunction that culminate in a broad range of motor impairments.

Inferring control objectives in a virtual balancing task in humans and monkeys

Natural behaviors have redundancy, which implies that humans and animals can achieve their goals with different strategies. Given only observations of behavior, is it possible to infer the control objective that the subject is employing? This challenge is particularly acute in animal behavior because we cannot ask or instruct the subject to use a particular strategy. This study presents a three-pronged approach to infer an animal's control objective from behavior. First, both humans and monkeys performed a virtual balancing task for which different control strategies could be utilized. Under matched experimental conditions, corresponding behaviors were observed in humans and monkeys. Second, a generative model was developed that represented two main control objectives to achieve the task goal. Model simulations were used to identify aspects of behavior that could distinguish which control objective was being used. Third, these behavioral signatures allowed us to infer the control objective used by human subjects who had been instructed to use one control objective or the other. Based on this validation, we could then infer objectives from animal subjects. Being able to positively identify a subject's control objective from observed behavior can provide a powerful tool to neurophysiologists as they seek the neural mechanisms of sensorimotor coordination.

Motor learning in multijoint virtual arm movements with novel kinematics

Humans move their hands toward precise positions, a skill supported by the coordination of multiple joint movements, even in the presence of inherent redundancy. However, it remains unclear how the central nervous system learns the relationship between redundant joint movements and hand positions when starting from scratch. To address this question, a virtual-arm reaching task was performed in which participants were required to move a cursor corresponding to the hand of a virtual arm to a target. The joint angles of the virtual arm were determined by the heights of the participants' fingers. The results demonstrated that the participants moved the cursor to the target straighter and faster in the late phase than they did in the initial phase of learning. This improvement was accompanied by a reduction in the amount of angular changes in the virtual limb joint, predominantly characterized by an increased reliance on the virtual shoulder joint as opposed to the virtual wrist joint. These findings suggest that the central nervous system selects a combination of multijoint movements that minimize motor effort while learning novel upper-limb kinematics.

Dynamic changes in somatosensory and cerebellar activity mediate temporal recalibration of self-touch

An organism's ability to accurately anticipate the sensations caused by its own actions is crucial for a wide range of behavioral, perceptual, and cognitive functions. Notably, the sensorimotor expectations produced when touching one's own body attenuate such sensations, making them feel weaker and less ticklish and rendering them easily distinguishable from potentially harmful touches of external origin. How the brain learns and keeps these action-related sensory expectations updated is unclear. Here we employ psychophysics and functional magnetic resonance imaging to pinpoint the behavioral and neural substrates of dynamic recalibration of expected temporal delays in self-touch. Our psychophysical results reveal that self-touches are less attenuated after systematic exposure to delayed self-generated touches, while responses in the contralateral somatosensory cortex that normally distinguish between delayed and nondelayed self-generated touches become indistinguishable. During the exposure, the ipsilateral anterior cerebellum shows increased activity, supporting its proposed role in recalibrating sensorimotor predictions. Moreover, responses in the cingulate areas gradually increase, suggesting that as delay adaptation progresses, the nondelayed self-touches trigger activity related to cognitive conflict. Together, our results show that sensorimotor predictions in the simplest act of touching one's own body are upheld by a sophisticated and flexible neural mechanism that maintains them accurate in time.

Exploration biases forelimb reaching strategies

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

Prediction error in implicit adaptation during visually- and memory-guided reaching tasks

Human movements are adjusted by motor adaptation in order to maintain their accuracy. There are two systems in motor adaptation, referred to as explicit or implicit adaptation. It has been suggested that the implicit adaptation is based on the prediction error and has been used in a number of motor adaptation studies. This study aimed to examine the effect of visual memory on prediction error in implicit visuomotor adaptation by comparing visually- and memory-guided reaching tasks. The visually-guided task is thought to be implicit learning based on prediction error, whereas the memory-guided task requires more cognitive processes. We observed the adaptation to visuomotor rotation feedback that is gradually rotated. We found that the adaptation and retention rates were higher in the visually-guided task than in the memory-guided task. Furthermore, the delta-band power obtained by electroencephalography (EEG) in the visually-guided task was increased immediately following the visual feedback, which indicates that the prediction error was larger in the visually-guided task. Our results show that the visuomotor adaptation is enhanced in the visually-guided task because the prediction error, which contributes update of the internal model, was more reliable than in the memory-guided task. Therefore, we suggest that the processing of the prediction error is affected by the task-type, which in turn affects the rate of the visuomotor adaptation.

Designing for usability: development and evaluation of a portable minimally-actuated haptic hand and forearm trainer for unsupervised stroke rehabilitation

In stroke rehabilitation, simple robotic devices hold the potential to increase the training dosage in group therapies and to enable continued therapy at home after hospital discharge. However, we identified a lack of portable and cost-effective devices that not only focus on improving motor functions but also address sensory deficits. Thus, we designed a minimally-actuated hand training device that incorporates active grasping movements and passive pronosupination, complemented by a rehabilitative game with meaningful haptic feedback. Following a human-centered design approach, we conducted a usability study with 13 healthy participants, including three therapists. In a simulated unsupervised environment, the naive participants had to set up and use the device based on written instructions. Our mixed-methods approach included quantitative data from performance metrics, standardized questionnaires, and eye tracking, alongside qualitative feedback from semi-structured interviews. The study results highlighted the device's overall ease of setup and use, as well as its realistic haptic feedback. The eye-tracking analysis further suggested that participants felt safe during usage. Moreover, the study provided crucial insights for future improvements such as a more intuitive and comfortable wrist fixation, more natural pronosupination movements, and easier-to-follow instructions. Our research underscores the importance of continuous testing in the development process and offers significant contributions to the design of user-friendly, unsupervised neurorehabilitation technologies to improve sensorimotor stroke rehabilitation.

Optimal reaching subject to computational and physical constraints reveals structure of the sensorimotor control system

Optimal feedback control provides an abstract framework describing the architecture of the sensorimotor system without prescribing implementation details such as what coordinate system to use, how feedback is incorporated, or how to accommodate changing task complexity. We investigate how such details are determined by computational and physical constraints by creating a model of the upper limb sensorimotor system in which all connection weights between neurons, feedback, and muscles are unknown. By optimizing these parameters with respect to an objective function, we find that the model exhibits a preference for an intrinsic (joint angle) coordinate representation of inputs and feedback and learns to calculate a weighted feedforward and feedback error. We further show that complex reaches around obstacles can be achieved by augmenting our model with a path-planner based on via points. The path-planner revealed "avoidance" neurons that encode directions to reach around obstacles and "placement" neurons that make fine-tuned adjustments to via point placement. Our results demonstrate the surprising capability of computationally constrained systems and highlight interesting characteristics of the sensorimotor system.

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

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

Response Preparation Affects Cognitive Motor Control

OBJECTIVE

We investigated how the ability to control whether or not to inhibit an action is affected by the response preparation.

BACKGROUND

The ability to control actions is a central skill to properly behave in complex environments. Increased levels of response preparation are associated with reduced response times, but how they directly affect the ability to control actions is not well explored. We investigated how the response preparation affects the ability to control the generation of actions in the context of a stop selective task.

METHOD

Participants performed a visuo-motor stop selective task.

RESULTS

We found that an increased level of response preparation reduced the ability to control actions. In the condition with high preparation, we observed shorter response times and increased probability of wrong responses to a request to stop, compared to a condition with a lower level of preparation.

CONCLUSION

We demonstrated that high response preparation hinders action control.

APPLICATION

Understanding the cognitive factors that affect the ability to properly control actions is crucial to develop devices that can be exploited in different contexts such as the aviation, industrial, and military. We demonstrated that subjects' response preparation is a key factor influencing their ability to flexibly control their reaction to different stimuli. This study offers a suitable paradigm that can be used to investigate which system features in a controlled task promote an optimal balance between response speed and error rate.

Preparatory activity and the expansive null-space

The study of the cortical control of movement experienced a conceptual shift over recent decades, as the basic currency of understanding shifted from single-neuron tuning towards population-level factors and their dynamics. This transition was informed by a maturing understanding of recurrent networks, where mechanism is often characterized in terms of population-level factors. By estimating factors from data, experimenters could test network-inspired hypotheses. Central to such hypotheses are 'output-null' factors that do not directly drive motor outputs yet are essential to the overall computation. In this Review, we highlight how the hypothesis of output-null factors was motivated by the venerable observation that motor-cortex neurons are active during movement preparation, well before movement begins. We discuss how output-null factors then became similarly central to understanding neural activity during movement. We discuss how this conceptual framework provided key analysis tools, making it possible for experimenters to address long-standing questions regarding motor control. We highlight an intriguing trend: as experimental and theoretical discoveries accumulate, the range of computational roles hypothesized to be subserved by output-null factors continues to expand.

Task-driven neural network models predict neural dynamics of proprioception

Proprioception tells the brain the state of the body based on distributed sensory neurons. Yet, the principles that govern proprioceptive processing are poorly understood. Here, we employ a task-driven modeling approach to investigate the neural code of proprioceptive neurons in cuneate nucleus (CN) and somatosensory cortex area 2 (S1). We simulated muscle spindle signals through musculoskeletal modeling and generated a large-scale movement repertoire to train neural networks based on 16 hypotheses, each representing different computational goals. We found that the emerging, task-optimized internal representations generalize from synthetic data to predict neural dynamics in CN and S1 of primates. Computational tasks that aim to predict the limb position and velocity were the best at predicting the neural activity in both areas. Since task optimization develops representations that better predict neural activity during active than passive movements, we postulate that neural activity in the CN and S1 is top-down modulated during goal-directed movements.

Motor "laziness" constrains fixation selection in real-world tasks

Humans coordinate their eye, head, and body movements to gather information from a dynamic environment while maximizing reward and minimizing biomechanical and energetic costs. However, such natural behavior is not possible in traditional experiments employing head/body restraints and artificial, static stimuli. Therefore, it is unclear to what extent mechanisms of fixation selection discovered in lab studies, such as inhibition-of-return (IOR), influence everyday behavior. To address this gap, participants performed nine real-world tasks, including driving, visually searching for an item, and building a Lego set, while wearing a mobile eye tracker (169 recordings; 26.6 h). Surprisingly, in all tasks, participants most often returned to what they just viewed and saccade latencies were shorter preceding return than forward saccades, i.e., consistent with facilitation, rather than inhibition, of return. We hypothesize that conservation of eye and head motor effort ("laziness") contributes. Correspondingly, we observed center biases in fixation position and duration relative to the head's orientation. A model that generates scanpaths by randomly sampling these distributions reproduced all return phenomena we observed, including distinct 3-fixation sequences for forward versus return saccades. After controlling for orbital eccentricity, one task (building a Lego set) showed evidence for IOR. This, along with small discrepancies between model and data, indicates that the brain balances minimization of motor costs with maximization of rewards (e.g., accomplished by IOR and other mechanisms) and that the optimal balance varies according to task demands. Supporting this account, the orbital range of motion used in each task traded off lawfully with fixation duration.

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.

Effect of binocular visual cue availability on fruit and insect grasping performance in two cheirogaleids: Implications for primate origins hypotheses

Forward-facing eyes with parallel optic axes, which provide a wide field of binocular vision and precise depth perception, are among the diagnostic features of crown primates; however, the adaptive significance of this feature remains contentious. Two of the most prominent primate-origins hypotheses propose that either foraging for fruit or nocturnal predation on insects created selective pressures that led to the evolution of diagnostic primate traits, including a wide binocular field. To determine whether either of these hypotheses provides a viable explanation for the evolution of primates' derived eye orientation, the importance of binocular depth cues for the two tasks invoked by these hypotheses was evaluated experimentally in Microcebus murinus and Cheirogaleus medius, cheirogaleids' considered reasonable living analogs of the earliest euprimates. Performance in grasping insects and fruit was evaluated when the animals made use of their full binocular visual field and when their binocular visual field was restricted using a helmet-mounted blinder. Restriction of the binocular field had no effect on fruit grasping performance; however, restriction of the binocular field resulted in a significant deficit in insect predation performance. Differences in behavioral variables also suggest that insect predation is a more visually demanding task than fruit foraging. These results support the role of insect predation, but not fruit foraging, in contributing to the selective pressures that led to the evolution of parallel optic axes and a wide binocular field in crown primates.

μSim: A goal-driven framework for elucidating the neural control of movement through musculoskeletal modeling

How does the motor cortex (MC) produce purposeful and generalizable movements from the complex musculoskeletal system in a dynamic environment? To elucidate the underlying neural dynamics, we use a goal-driven approach to model MC by considering its goal as a controller driving the musculoskeletal system through desired states to achieve movement. Specifically, we formulate the MC as a recurrent neural network (RNN) controller producing muscle commands while receiving sensory feedback from biologically accurate musculoskeletal models. Given this real-time simulated feedback implemented in advanced physics simulation engines, we use deep reinforcement learning to train the RNN to achieve desired movements under specified neural and musculoskeletal constraints. Activity of the trained model can accurately decode experimentally recorded neural population dynamics and single-unit MC activity, while generalizing well to testing conditions significantly different from training. Simultaneous goal- and data- driven modeling in which we use the recorded neural activity as observed states of the MC further enhances direct and generalizable single-unit decoding. Finally, we show that this framework elucidates computational principles of how neural dynamics enable flexible control of movement and make this framework easy-to-use for future experiments.

Modulations of stretch reflex by altering visuomotor contexts

Various functional modulations of the stretch reflex help to stabilize actions, but the computational mechanism behind its context-dependent tuning remains unclear. While many studies have demonstrated that motor contexts associated with the task goal cause functional modulation of the stretch reflex of upper limbs, it is not well understood how visual contexts independent of the task requirements affect the stretch reflex. To explore this issue, we conducted two experiments testing 20 healthy human participants (age range 20-45, average 31.3 ± 9.0), in which visual contexts were manipulated in a visually guided reaching task. During wrist flexion movements toward a visual target, a mechanical load was applied to the wrist joint to evoke stretch reflex of wrist flexor muscle (flexor carpi radialis). The first experiment (n = 10) examined the effect of altering the visuomotor transformation on the stretch reflex that was evaluated with surface electromyogram. We found that the amplitude of the stretch reflex decreased (p = 0.024) when a rotational transformation of 90° was introduced between the hand movement and the visual cursor, whereas the amplitude did not significantly change (p = 0.26) when the rotational transformation was accompanied by a head rotation so that the configuration of visual feedback was maintained in visual coordinates. The results suggest that the stretch reflex was regulated depending on whether the visuomotor mapping had already been acquired or not. In the second experiment (n = 10), we examined how uncertainty in the visual target or hand cursor affects the stretch reflex by removing these visual stimuli. We found that the reflex amplitude was reduced by the disappearance of the hand cursor (p = 0.039), but was not affected by removal of the visual target (p = 0.27), suggesting that the visual state of the body and target contribute differently to the reflex tuning. These findings support the idea that visual updating of the body state is crucial for regulation of quick motor control driven by proprioceptive signals.

Spontaneous Behavioural Recovery Following Stroke Relates to the Integrity of Parietal and Temporal Regions

Stroke is a devastating disease that results in neurological deficits and represents a leading cause of death and disability worldwide. Following a stroke, there is a degree of spontaneous recovery of function, the neural basis of which is of great interest among clinicians in their efforts to reduce disability following stroke and enhance rehabilitation. Conventionally, work on spontaneous recovery has tended to focus on the neural reorganization of motor cortical regions, with comparably little attention being paid to changes in non-motor regions and how these relate to recovery. Here we show, using structural neuroimaging in a macaque stroke model (N = 31) and by exploiting individual differences in spontaneous behavioural recovery, that the preservation of regions in the parietal and temporal cortices predict animal recovery. To characterize recovery, we performed a clustering analysis using Non-Human Primate Stroke Scale (NHPSS) scores and identified a good versus poor recovery group. By comparing the preservation of brain volumes in the two groups, we found that brain areas in integrity of brain areas in parietal, temporal and somatosensory cortex were associated with better recovery. In addition, a decoding approach performed across all subjects revealed that the preservation of specific brain regions in the parietal, somatosensory and medial frontal cortex predicted recovery. Together, these findings highlight the importance of parietal and temporal regions in spontaneous behavioural recovery.

Human arm redundancy: a new approach for the inverse kinematics problem

The inverse kinematics (IK) problem addresses how both humans and robotic systems coordinate movement to resolve redundancy, as in the case of arm reaching where more degrees of freedom are available at the joint versus hand level. This work focuses on which coordinate frames best represent human movements, enabling the motor system to solve the IK problem in the presence of kinematic redundancies. We used a multi-dimensional sparse source separation method to derive sets of basis (or source) functions for both the task and joint spaces, with joint space represented by either absolute or anatomical joint angles. We assessed the similarities between joint and task sources in each of these joint representations, finding that the time-dependent profiles of the absolute reference frame's sources show greater similarity to corresponding sources in the task space. This result was found to be statistically significant. Our analysis suggests that the nervous system represents multi-joint arm movements using a limited number of basis functions, allowing for simple transformations between task and joint spaces. Additionally, joint space seems to be represented in an absolute reference frame to simplify the IK transformations, given redundancies. Further studies will assess this finding's generalizability and implications for neural control of movement.

Exploring Optimal Objective Function Weightings to Predict Lifting Postures Under Unfatigued and Fatigued States

OBJECTIVE

To explore whether the optimal objective function weightings change when using a digital human model (DHM) to predict origin and destination lifting postures under unfatigued and fatigued states.

BACKGROUND

The ability to predict human postures can depend on state-based influences (e.g., fatigue). Altering objective function weightings within a predictive DHM could improve the ability to predict tasks specific lifting postures under unique fatigue states.

METHOD

A multi-objective optimization-based DHM was used to predict origin and destination lifting postures for ten anthropometrically scaled avatars by using different objective functions weighting combinations. Predicted and measured postures were compared to determine the root mean squared error. A response surface methodology was used to identify the optimal objective function weightings, which was found by generating the posture that minimized error between measured and predicted lifting postures. The resultant weightings were compared to determine if the optimal objective function weightings changed for different lifting postures or fatigue states.

RESULTS

Discomfort and total joint torque weightings were affected by posture (origin/destination) and fatigue state (unfatigued/fatigued); however, post-hoc differences between fatigue states and lifting postures were not sufficiently large to be detected. Weighting the discomfort objective function alone tended to predict postures that generalized well to both postures and fatigue states.

CONCLUSION

Lift postures were optimal predicted using the minimization of discomfort objective function regardless of fatigue state.

APPLICATION

Weighting the discomfort objective can predict unfatigued postures, but more research is needed to understand the optimal objective function weightings to predict postures during a fatigued state.

Out of control: computational dynamic control dysfunction in stress- and anxiety-related disorders

Control theory, which has played a central role in technological progress over the last 150 years, has also yielded critical insights into biology and neuroscience. Recently, there has been a surging interest in integrating control theory with computational psychiatry. Here, we review the state of the field of using control theory approaches in computational psychiatry and show that recent research has mapped a neural control circuit consisting of frontal cortex, parietal cortex, and the cerebellum. This basic feedback control circuit is modulated by estimates of reward and cost via the basal ganglia as well as by arousal states coordinated by the insula, dorsal anterior cingulate cortex, amygdala, and locus coeruleus. One major approach within the broader field of control theory, known as proportion-integral-derivative (PID) control, has shown promise as a model of human behavior which enables precise and reliable estimates of underlying control parameters at the individual level. These control parameters correlate with self-reported fear and with both structural and functional variation in affect-related brain regions. This suggests that dysfunctional engagement of stress and arousal systems may suboptimally modulate parameters of domain-general goal-directed control algorithms, impairing performance in complex tasks involving movement, cognition, and affect. Future directions include clarifying the causal role of control deficits in stress- and anxiety-related disorders and developing clinically useful tools based on insights from control theory.

Brain and muscle activity during fatiguing maximum-speed knee movement

Although the underlying mechanisms behind upper limb (e.g., finger) motor slowing during movements performed at the maximum voluntary rate have been explored, the same cannot be said for the lower limb. This is especially relevant considering the lower limb's larger joints and different functional patterns. Despite the similar motor control base, previously found differences in movement patterns and segment inertia may lead to distinct central and peripheral manifestations of fatigue in larger joint movement. Therefore, we aimed to explore these manifestations in a fatiguing knee maximum movement rate task by measuring brain and muscle activity, as well as brain-muscle coupling using corticomuscular coherence, during this task. A significant decrease in knee movement rate up to half the task duration was observed. After an early peak, brain activity showed a generalized decrease during the first half of the task, followed by a plateau, whereas knee flexor muscle activity showed a continuous decline. A similar decline was also seen in corticomuscular coherence but for both flexor and extensor muscles. The electrophysiological manifestations associated with knee motor slowing therefore showed some common and some distinct aspects compared with smaller joint tasks. Both central and peripheral manifestations of fatigue were observed; the changes seen in both EEG and electromyographic (EMG) variables suggest that multiple mechanisms were involved in exercise regulation and fatigue development.NEW & NOTEWORTHY The loss of knee movement rate with acute fatigue induced by high-speed movement is associated with both central and peripheral electrophysiological changes, such as a decrease in EEG power, increased agonist-antagonist cocontraction, and impaired brain-muscle coupling. These findings had not previously been reported for the knee joint, which shows functional and physiological differences compared with the existing findings for smaller upper limb joints.

Basis of movement control in dystonia and why botulinum toxin should influence it?

Dystonia is a network disorder involving multiple brain regions, such as the motor cortex, sensory cortex, basal ganglia, and cerebellum. Botulinum toxin (BoNT) is the first-line therapy for treating focal dystonia and is a potent molecule that blocks the release of acetylcholine at the peripheral neuromuscular junction. However, the clinical benefits of BoNT are not solely related to peripheral muscle relaxation or modulation of afferent input from the muscle spindle. An increasing body of evidence, albeit in smaller cohorts, has shown that BoNT leads to distant modulation of the pathological brain substrates implicated in dystonia. A single treatment session of BoNT has been observed to reduce excessive motor excitability and improve sensory processing. Furthermore, owing to plasticity effects that are induced by botulinum, neural reorganization of pathological networks occurs, presumably leading to defective motor programs of dystonia replaced with normal movement patterns. However, longitudinal studies investigating the effects of multiple treatment sessions in large, well-characterized homogenous cohorts of dystonia will provide further compelling evidence supporting central botulinum mechanisms.

Facilitated adaptation via structural learning increases bimanual interference

Bimanual coordination is an essential feature of the motor system, yet interactions between the limbs during independent control remain poorly understood. Interference between the two hands, or the assimilation of movement characteristics between the two effectors, can be induced by perturbing one arm (e.g., via visuomotor rotation) and then measuring the effects in the contralateral limb. In this study, we sought to further determine the role adaptation plays in bimanual interference using a structural learning paradigm to alter feedback regulation in reaching. We trained healthy participants to counter 60 unique random rotations in right hand visual feedback over 240 reaches. Following this, we assessed feedforward and feedback measures of interference in a bimanual reaching task where the right hand was exposed to a fixed visual feedback rotation while the left hand reached without visual feedback. We found that participants who had been exposed to the structural training task in the right hand showed increased left hand interference during the first 20 trials of the test task. Moreover, interference was greater in feedback, rather than feedforward control parameters. The results further suggest that structural learning enhances bimanual interference via sensory feedback upregulation.

Behavioral Motor Performance

The human sensorimotor control system has exceptional abilities to perform skillful actions. We easily switch between strenuous tasks that involve brute force, such as lifting a heavy sewing machine, and delicate movements such as threading a needle in the same machine. Using a structure with different control architectures, the motor system is capable of updating its ability to perform through our daily interaction with the fluctuating environment. However, there are issues that make this a difficult computational problem for the brain to solve. The brain needs to control a nonlinear, nonstationary neuromuscular system, with redundant and occasionally undesired degrees of freedom, in an uncertain environment using a body in which information transmission is subject to delays and noise. To gain insight into the mechanisms of motor control, here we survey movement laws and invariances that shape our everyday motion. We then examine the major solutions to each of these problems in the three parts of the sensorimotor control system, sensing, planning, and acting. We focus on how the sensory system, the control architectures, and the structure and operation of the muscles serve as complementary mechanisms to overcome deviations and disturbances to motor behavior and give rise to skillful motor performance. We conclude with possible future research directions based on suggested links between the operation of the sensorimotor system across the movement stages. © 2024 American Physiological Society. Compr Physiol 14:5179-5224, 2024.

Action does not enhance but attenuates predicted touch

Dominant motor control theories propose that the brain predicts and attenuates the somatosensory consequences of actions, referred to as somatosensory attenuation. Support comes from psychophysical and neuroimaging studies showing that touch applied on a passive hand elicits attenuated perceptual and neural responses if it is actively generated by one's other hand, compared to an identical touch from an external origin. However, recent experimental findings have challenged this view by providing psychophysical evidence that the perceived intensity of touch on the passive hand is enhanced if the active hand does not receive touch simultaneously with the passive hand (somatosensory enhancement) and by further attributing attenuation to the double tactile stimulation of the hands upon contact. Here, we directly contrasted the hypotheses of the attenuation and enhancement models regarding how action influences somatosensory perception by manipulating whether the active hand contacts the passive hand. We further assessed somatosensory perception in the absence of any predictive cues in a condition that turned out to be essential for interpreting the experimental findings. In three pre-registered experiments, we demonstrate that action does not enhance the predicted touch (Experiment 1), that the previously reported 'enhancement' effects are driven by the reference condition used (Experiment 2), and that self-generated touch is robustly attenuated regardless of whether the two hands make contact (Experiment 3). Our results provide conclusive evidence that action does not enhance but attenuates predicted touch and prompt a reappraisal of recent experimental findings upon which theoretical frameworks proposing a perceptual enhancement by action prediction are based.

Fast Feedback Responses to Categorical Sensorimotor Errors That Do Not Indicate Error Magnitude Are Optimized Based on Short- and Long-Term Memory

Skilled motor performance depends critically on rapid corrective responses that act to preserve the goal of the movement in the face of perturbations. Although it is well established that the gain of corrective responses elicited while reaching toward objects adapts to different contexts, little is known about the adaptability of corrective responses supporting the manipulation of objects after they are grasped. Here, we investigated the adaptability of the corrective response elicited when an object being lifted is heavier than expected and fails to lift off when predicted. This response involves a monotonic increase in vertical load force triggered, within ∼90 ms, by the absence of expected sensory feedback signaling lift off and terminated when actual lift off occurs. Critically, because the actual weight of the object cannot be directly sensed at the moment the object fails to lift off, any adaptation of the corrective response would have to be based on memory from previous lifts. We show that when humans, including men and women, repeatedly lift an object that on occasional catch trials increases from a baseline weight to a fixed heavier weight, they scale the gain of the response (i.e., the rate of force increase) to the heavier weight within two to three catch trials. We also show that the gain of the response scales, on the first catch trial, with the baseline weight of the object. Thus, the gain of the lifting response can be adapted by both short- and long-term experience. Finally, we demonstrate that this adaptation preserves the efficacy of the response across contexts.SIGNIFICANCE STATEMENT Here, we present the first investigation of the adaptability of the corrective lifting response elicited when an object is heavier than expected and fails to lift off when predicted. A striking feature of the response, which is driven by a sensory prediction error arising from the absence of expected sensory feedback, is that the magnitude of the error is unknown. That is, the motor system only receives a categorical error indicating that the object is heavier than expected but not its actual weight. Although the error magnitude is not known at the moment the response is elicited, we show that the response can be scaled to predictions of error magnitude based on both recent and long-term memories.

Effectiveness of Interventions Aimed at Changing Movement Patterns in People With Patellofemoral Pain: A Systematic Review With Network Meta-analysis

OBJECTIVE: To summarize the effectiveness of interventions for changing movement during weight-bearing functional tasks in people with patellofemoral pain (PFP). DESIGN: Systematic review with network meta-analysis (NMA). LITERATURE SEARCH: Medline, Embase, CINAHL, SPORTDiscus, and Cochrane Library were searched from inception up to May 2023. STUDY SELECTION CRITERIA: Randomized controlled trials involving people with PFP and nonsurgical, nonpharmacological interventions on task kinematics were included. DATA SYNTHESIS: NMA was conducted for frontal knee movement data, and pairwise meta-analysis was used to pool data when NMA was not possible. Reduced movements were those changes that indicated movements occurring with less amplitude. The GRADE approach was used to grade the certainty of the evidence. RESULTS: Thirty-seven trials were included (n = 1235 participants). Combining knee/hip exercises with internal feedback had the strongest effect on reducing frontal knee movements (standardized mean difference [SMD] from NMA = -2.66; GRADE: moderate evidence). On pairwise comparisons, the same combination of interventions reduced frontal hip movements (SMD = -0.47; GRADE: moderate evidence) and increased sagittal knee movements (SMD = 1.03; GRADE: moderate evidence), with no effects on sagittal hip movements (GRADE: very low evidence), compared to knee/hip exercises alone. There was no effect for single applications of braces on the frontal knee movement (GRADE: very low evidence) and taping on movements of the knee, hip, and ankle (GRADE: very low to low evidence) compared to no intervention. CONCLUSION: Knee/hip exercises combined with internal feedback techniques may change knee and hip movements in people with PFP. The combination of these interventions can reduce frontal knee and hip movements, and can increase sagittal knee movements. J Orthop Sports Phys Ther 2023;53(12):1-13. Epub 14 September 2023. doi:10.2519/jospt.2023.11956.

Inferring control objectives in a virtual balancing task in humans and monkeys

Natural behaviors have redundancy, which implies that humans and animals can achieve their goals with different control objectives. Given only observations of behavior, is it possible to infer the control strategy that the subject is employing? This challenge is particularly acute in animal behavior because we cannot ask or instruct the subject to use a particular control strategy. This study presents a threepronged approach to infer an animal's control strategy from behavior. First, both humans and monkeys performed a virtual balancing task for which different control objectives could be utilized. Under matched experimental conditions, corresponding behaviors were observed in humans and monkeys. Second, a generative model was developed that represented two main control strategies to achieve the task goal. Model simulations were used to identify aspects of behavior that could distinguish which control objective was being used. Third, these behavioral signatures allowed us to infer the control objective used by human subjects who had been instructed to use one control objective or the other. Based on this validation, we could then infer strategies from animal subjects. Being able to positively identify a subject's control objective from behavior can provide a powerful tool to neurophysiologists as they seek the neural mechanisms of sensorimotor coordination.

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 contexts. 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 clocked 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, which is minimally affected by cerebellar perturbation and significantly larger than the 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.

Playing with your ears: Audio-motor skill learning is sensitive to the lateral relationship between trained hand and ear

A salient feature of motor and sensory circuits in the brain is their contralateral hemispheric bias-a feature that might play a role in integration and learning of sensorimotor skills. In the current behavioral study, we examined whether the lateral configuration between sound-producing hand and feedback-receiving ear affects performance and learning of an audio-motor skill. Right-handed participants (n = 117) trained to play a piano sequence using their right or left hand while auditory feedback was presented monaurally, either to the right or left ear. Participants receiving auditory feedback to the contralateral ear during training performed better than participants receiving ipsilateral feedback (with respect to the training hand). Furthermore, in the Left-Hand training groups, the contralateral training advantage persisted in a generalization task. Our results demonstrate that audio-motor learning is sensitive to the lateral configuration between motor and sensory circuits and suggest that integration of neural activity across hemispheres facilitates such learning.

Decoding hand kinetics and kinematics using somatosensory cortex activity in active and passive movement

Area 2 of the primary somatosensory cortex (S1), encodes proprioceptive information of limbs. Several studies investigated the encoding of movement parameters in this area. However, the single-trial decoding of these parameters, which can provide additional knowledge about the amount of information available in sub-regions of this area about instantaneous limb movement, has not been well investigated. We decoded kinematic and kinetic parameters of active and passive hand movement during center-out task using conventional and state-based decoders. Our results show that this area can be used to accurately decode position, velocity, force, moment, and joint angles of hand. Kinematics had higher accuracies compared to kinetics and active trials were decoded more accurately than passive trials. Although the state-based decoder outperformed the conventional decoder in the active task, it was the opposite in the passive task. These results can be used in intracortical micro-stimulation procedures to provide proprioceptive feedback to BCI subjects.

Modeling Human Suboptimal Control: A Review

This review paper provides an overview of the approaches to model neuromuscular control, focusing on methods to identify nonoptimal control strategies typical of populations with neuromuscular disorders or children. Where possible, the authors tightened the description of the methods to the mechanisms behind the underlying biomechanical and physiological rationale. They start by describing the first and most simplified approach, the reductionist approach, which splits the role of the nervous and musculoskeletal systems. Static optimization and dynamic optimization methods and electromyography-based approaches are summarized to highlight their limitations and understand (the need for) their developments over time. Then, the authors look at the more recent stochastic approach, introduced to explore the space of plausible neural solutions, thus implementing the uncontrolled manifold theory, according to which the central nervous system only controls specific motions and tasks to limit energy consumption while allowing for some degree of adaptability to perturbations. Finally, they explore the literature covering the explicit modeling of the coupling between the nervous system (acting as controller) and the musculoskeletal system (the actuator), which may be employed to overcome the split characterizing the reductionist approach.

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

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

Head movement kinematics are differentially altered for extended versus short duration gait exercises in individuals with vestibular loss

Head kinematics are altered in individuals with vestibular schwannoma (VS) during short duration gait tasks [i.e., Functional Gait Assessment (FGA)], both before and after surgery, yet whether these differences extend to longer duration gait exercises is currently unknown. Here we examined the effects of vestibular loss and subsequent compensation on head kinematics in individuals with VS during gait exercises of relatively extended versus short duration (< 10 versus 30 s), compared to age-matched controls. Six-dimensional head movements were recorded during extended and short duration gait exercises before and then 6 weeks after sectioning of the involved vestibular nerve (vestibular neurectomy). Standard functional, physiological, and subjective clinical assessments were also performed at each time point. Kinematics were differentially altered in individuals with vestibular loss at both time points during extended versus short duration exercises. Range of motion was significantly reduced in extended tasks. In contrast, movement variability predominately differed for the short duration exercises. Overall, our results indicate that quantifying head kinematics during longer duration gait tasks can provide novel information about how VS individuals compensate for vestibular loss, and suggest that measurements of range of motion versus variability can provide information regarding the different strategies deployed to maintain functional locomotion.

Continuous evaluation of cost-to-go for flexible reaching control and online decisions

Humans consider the parameters linked to movement goal during reaching to adjust their control strategy online. Indeed, rapid changes in target structure or disturbances interfering with their initial plan elicit rapid changes in behavior. Here, we hypothesize that these changes could result from the continuous use of a decision variable combining motor and cognitive components. We combine an optimal feedback controller with a real-time evaluation of the expected cost-to-go, which considers target- and movement-related costs, in a common theoretical framework. This model reproduces human behaviors in presence of changes in the target structure occurring during movement and of online decisions to flexibly change target following external perturbations. It also predicts that the time taken to decide to select a novel goal after a perturbation depends on the amplitude of the disturbance and on the rewards of the different options, which is a direct result of the continuous monitoring of the cost-to-go. We show that this result was present in our previously collected dataset. Together our developments point towards a continuous evaluation of the cost-to-go during reaching to update control online and make efficient decisions about movement goal.

Physics-based character animation and human motor control

Motor neuroscience and physics-based character animation (PBCA) approach human and humanoid control from different perspectives. The primary goal of PBCA is to control the movement of a ragdoll (humanoid or animal) applying forces and torques within a physical simulation. The primary goal of motor neuroscience is to understand the contribution of different parts of the nervous system to generate coordinated movements. We review the functional principles and the functional anatomy of human motor control and the main strategies used in PBCA. We then explore common research points by discussing the functional anatomy and ongoing debates in motor neuroscience from the perspective of PBCA. We also suggest there are several benefits to be found in studying sensorimotor integration and human-character coordination through closer collaboration between these two fields.

Spatio-temporal evolution of human neural activity during visually cued hand movements

Making hand movements in response to visual cues is common in daily life. It has been well known that this process activates multiple areas in the brain, but how these neural activations progress across space and time remains largely unknown. Taking advantage of intracranial electroencephalographic (iEEG) recordings using depth and subdural electrodes from 36 human subjects using the same task, we applied single-trial and cross-trial analyses to high-frequency iEEG activity. The results show that the neural activation was widely distributed across the human brain both within and on the surface of the brain, and focused specifically on certain areas in the parietal, frontal, and occipital lobes, where parietal lobes present significant left lateralization on the activation. We also demonstrate temporal differences across these brain regions. Finally, we evaluated the degree to which the timing of activity within these regions was related to sensory or motor function. The findings of this study promote the understanding of task-related neural processing of the human brain, and may provide important insights for translational applications.

Neuromechanical Strategies for Obstacle Negotiation during Overground Locomotion following Incomplete Spinal Cord Injury in Adult Cats

Following incomplete spinal cord injury in animals, including humans, substantial locomotor recovery can occur. However, functional aspects of locomotion, such as negotiating obstacles, remains challenging. We collected kinematic and electromyography data in 10 adult cats (5 males, 5 females) before and at weeks 1-2 and 7-8 after a lateral mid-thoracic hemisection on the right side of the cord while they negotiated obstacles of three different heights. Intact cats always cleared obstacles without contact. At weeks 1-2 after hemisection, the ipsilesional right hindlimb contacted obstacles in ∼50% of trials, triggering a stumbling corrective reaction or absent responses, which we termed Other. When complete clearance occurred, we observed exaggerated ipsilesional hindlimb flexion when crossing the obstacle with contralesional Left limbs leading. At weeks 7-8 after hemisection, the proportion of complete clearance increased, Other responses decreased, and stumbling corrective reactions remained relatively unchanged. We found redistribution of weight support after hemisection, with reduced diagonal supports and increased homolateral supports, particularly on the left contralesional side. The main neural strategy for complete clearance in intact cats consisted of increased knee flexor activation. After hemisection, ipsilesional knee flexor activation remained, but it was insufficient or more variable as the limb approached the obstacle. Intact cats also increased their speed when stepping over an obstacle, an increase that disappeared after hemisection. The increase in complete clearance over time after hemisection paralleled the recovery of muscle activation patterns or new strategies. Our results suggest partial recovery of anticipatory control through neuroplastic changes in the locomotor control system.SIGNIFICANCE STATEMENT Most spinal cord injuries (SCIs) are incomplete and people can recover some walking functions. However, the main challenge for people with SCIs that do recover a high level of function is to produce a gait that can adjust to everyday occurrences, such as turning, stepping over an obstacle, etc. Here, we use the cat model to answer two basic questions: How does an animal negotiate an obstacle after an incomplete SCI and why does it fail to safely clear it? We show that the inability to clear an obstacle is because of improper activation of muscles that flex the knee. Animals recover a certain amount of function thanks to new strategies and changes within the nervous system.

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

Precision reaching tasks often require corrective submovements for successful completion. Most studies of reaching have focused on single initial movements, and the cortical encoding model was implied to be 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 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 utilizing 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 and 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.

Neuromuscular control: from a biomechanist's perspective

Understanding neural control of movement necessitates a collaborative approach between many disciplines, including biomechanics, neuroscience, and motor control. Biomechanics grounds us to the laws of physics that our musculoskeletal system must obey. Neuroscience reveals the inner workings of our nervous system that functions to control our body. Motor control investigates the coordinated motor behaviours we display when interacting with our environment. The combined efforts across the many disciplines aimed at understanding human movement has resulted in a rich and rapidly growing body of literature overflowing with theories, models, and experimental paradigms. As a result, gathering knowledge and drawing connections between the overlapping but seemingly disparate fields can be an overwhelming endeavour. This review paper evolved as a need for us to learn of the diverse perspectives underlying current understanding of neuromuscular control. The purpose of our review paper is to integrate ideas from biomechanics, neuroscience, and motor control to better understand how we voluntarily control our muscles. As biomechanists, we approach this paper starting from a biomechanical modelling framework. We first define the theoretical solutions (i.e., muscle activity patterns) that an individual could feasibly use to complete a motor task. The theoretical solutions will be compared to experimental findings and reveal that individuals display structured muscle activity patterns that do not span the entire theoretical solution space. Prevalent neuromuscular control theories will be discussed in length, highlighting optimality, probabilistic principles, and neuromechanical constraints, that may guide individuals to families of muscle activity solutions within what is theoretically possible. Our intention is for this paper to serve as a primer for the neuromuscular control scientific community by introducing and integrating many of the ideas common across disciplines today, as well as inspire future work to improve the representation of neural control in biomechanical models.