Express Visuomotor Responses Reflect Knowledge of Both Target Locations and Contextual Rules during Reaches of Different Amplitudes.

When humans reach to visual targets, extremely rapid (∼90 ms) target-directed responses can be observed in task-relevant proximal muscles. Such express visuomotor responses are inflexibly locked in time and space to the target and have been proposed to reflect rapid visuomotor transformations conveyed subcortically via the tecto-reticulo-spinal pathway. Previously, we showed that express visuomotor responses are sensitive to explicit cue-driven information about the target, suggesting that the express pathway can be modulated by cortical signals affording contextual prestimulus expectations. Here, we show that the express visuomotor system incorporates information about the physical hand-to-target distance and contextual rules during visuospatial tasks requiring different movement amplitudes. In one experiment, we recorded the activity from two shoulder muscles as 14 participants (6 females) reached toward targets that appeared at different distances from the reaching hand. Increasing the reaching distance facilitated the generation of frequent and large express visuomotor responses. This suggests that both the direction and amplitude of veridical hand-to-target reaches are encoded along the putative subcortical express pathway. In a second experiment, we modulated the movement amplitude by asking 12 participants (4 females) to deliberately undershoot, overshoot, or stop (control) at the target. The overshoot and undershoot tasks impaired the generation of large and frequent express visuomotor responses, consistent with the inability of the express pathway to generate responses directed toward nonveridical targets as in the anti-reach task. Our findings appear to reflect strategic, cortically driven modulation of the express visuomotor circuit to facilitate rapid and effective response initiation during target-directed actions.SIGNIFICANCE STATEMENT Express (∼90 ms) arm muscle responses that are consistently tuned toward the location of visual stimuli suggest a subcortical contribution to target-directed visuomotor behavior in humans, potentially via the tecto-reticulo-spinal pathway. Here, we show that express muscle responses are modulated appropriately to reach targets at different distances, but generally suppressed when the task required nonveridical responses to overshoot/undershoot the real target. This suggests that the tecto-reticulo-spinal pathway can be exploited strategically by the cerebral cortex to facilitate rapid initiation of effective responses during a visuospatial task.

A hierarchical sensorimotor control framework for human-in-the-loop robotic hands.

Human manual dexterity relies critically on touch. Robotic and prosthetic hands are much less dexterous and make little use of the many tactile sensors available. We propose a framework modeled on the hierarchical sensorimotor controllers of the nervous system to link sensing to action in human-in-the-loop, haptically enabled, artificial hands.

Remembrance of things perceived: Adding thalamocortical function to artificial neural networks.

Recent research has illuminated the complexity and importance of the thalamocortical system but it has been difficult to identify what computational functions it performs. Meanwhile, deep-learning artificial neural networks (ANNs) based on bio-inspired models of purely cortical circuits have achieved surprising success solving sophisticated cognitive problems associated historically with human intelligence. Nevertheless, the limitations and shortcomings of artificial intelligence (AI) based on such ANNs are becoming increasingly clear. This review considers how the addition of thalamocortical connectivity and its putative functions related to cortical attention might address some of those shortcomings. Such bio-inspired models are now providing both testable theories of biological cognition and improved AI technology, much of which is happening outside the usual academic venues.

Developing Intelligent Robots that Grasp Affordance.

Humans and robots operating in unstructured environments both need to classify objects through haptic exploration and use them in various tasks, but currently they differ greatly in their strategies for acquiring such capabilities. This review explores nascent technologies that promise more convergence. A novel form of artificial intelligence classifies objects according to sensory percepts during active exploration and decides on efficient sequences of exploratory actions to identify objects. Representing objects according to the collective experience of manipulating them provides a substrate for discovering causality and affordances. Such concepts that generalize beyond explicit training experiences are an important aspect of human intelligence that has eluded robots. For robots to acquire such knowledge, they will need an extended period of active exploration and manipulation similar to that employed by infants. The efficacy, efficiency and safety of such behaviors depends on achieving smooth transitions between movements that change quickly from exploratory to executive to reflexive. Animals achieve such smoothness by using a hierarchical control scheme that is fundamentally different from those of conventional robotics. The lowest level of that hierarchy, the spinal cord, starts to self-organize during spontaneous movements in the fetus. This allows its connectivity to reflect the mechanics of the musculoskeletal plant, a bio-inspired process that could be used to adapt spinal-like middleware for robots. Implementation of these extended and essential stages of fetal and infant development is impractical, however, for mechatronic hardware that does not heal and replace itself like biological tissues. Instead such development can now be accomplished in silico and then cloned into physical robots, a strategy that could transcend human performance.

Symbolic cues enhance express visuomotor responses in human arm muscles at the motor planning rather than the visuospatial processing stage.

Humans can produce "express" (∼100 ms) arm muscle responses that are inflexibly locked in time and space to visual target presentations, consistent with subcortical visuomotor transformations via the tecto-reticulo-spinal pathway. These express visuomotor responses are sensitive to explicit cue-driven expectations, but it is unclear at what stage of sensory-to-motor transformation such modulation occurs. Here, we recorded electromyographic activity from shoulder muscles as participants reached toward one of four virtual targets whose physical location was partially predictable from a symbolic cue. In an experiment in which targets could be veridically reached, express responses were inclusive of the biomechanical requirements for reaching the cued locations and not systematically modulated by cue validity. In a second experiment, movements were restricted to the horizontal plane so that the participants could perform only rightward or leftward reaches, irrespective of target position on the vertical axis. Express muscle responses were almost identical for targets that were validly cued in the horizontal direction, regardless of cue validity in the vertical dimension. Together, these findings suggest that the cue-induced enhancements of express responses are dominated by effects at the level of motor plans and not solely via facilitation of early visuospatial target processing. Notably, direct corticotectal and corticoreticular projections exist that are well-placed to modulate prestimulus motor preparation state in subcortical circuits. Our results could reflect a neural mechanism by which contextually relevant motor responses to compatible visual inputs are rapidly released via subcortical circuits that are sufficiently along the sensory-to-motor continuum.NEW & NOTEWORTHY Express arm muscle responses to suddenly appearing visual targets for reaching rapid have been attributed to the tecto-reticulo-spinal pathway in humans. We demonstrate that symbolic cues before target presentation can modulate such express arm muscle responses compatibly with the biomechanics of the cued reaching direction and the cue validity. This implies cortically mediated modulation of one or more sensorimotor transformation nodes of the subcortical express pathway.

A model for self-organization of sensorimotor function: spinal interneuronal integration.

Control of musculoskeletal systems depends on integration of voluntary commands and somatosensory feedback in the complex neural circuits of the spinal cord. It has been suggested that the various connectivity patterns that have been identified experimentally may result from the many transcriptional types that have been observed in spinal interneurons. We ask instead whether the muscle-specific details of observed connectivity patterns can arise as a consequence of Hebbian adaptation during early development, rather than being genetically ordained. We constructed an anatomically simplified model musculoskeletal system with realistic muscles and sensors and connected it to a recurrent, random neuronal network consisting of both excitatory and inhibitory neurons endowed with Hebbian learning rules. We then generated a wide set of randomized muscle twitches typical of those described during fetal development and allowed the network to learn. Multiple simulations consistently resulted in diverse and stable patterns of activity and connectivity that included subsets of the interneurons that were similar to "archetypical" interneurons described in the literature. We also found that such learning led to an increased degree of cooperativity between interneurons when performing larger limb movements on which it had not been trained. Hebbian learning gives rise to rich sets of diverse interneurons whose connectivity reflects the mechanical properties of the system. At least some of the transcriptomic diversity may reflect the effects of this process rather than the cause of the connectivity. Such a learning process seems better suited to respond to the musculoskeletal mutations that underlie the evolution of new species.NEW & NOTEWORTHY We present a model of a self-organizing early spinal cord circuitry, which is attached to a biologically realistic sensorized musculoskeletal system. Without any a priori-defined connectivity or organization, learning induced by spontaneous, fetal-like motor activity results in the emergence of a well-functioning spinal interneuronal circuit whose connectivity patterns resemble in many respects those observed in the adult mammalian spinal cord. Hence, our result questions the importance of genetically controlled wiring for spinal cord function.

A model for self-organization of sensorimotor function: the spinal monosynaptic loop.

Recent spinal cord literature abounds with descriptions of genetic preprogramming and the molecular control of circuit formation. In this paper, we explore to what extent circuit formation based on learning rather than preprogramming could explain the selective formation of the monosynaptic projections between muscle spindle primary afferents and homonymous motoneurons. We adjusted the initially randomized gains in the neural network according to a Hebbian plasticity rule while exercising the model system with spontaneous muscle activity patterns similar to those observed during early fetal development. Normal connectivity patterns developed only when we modeled ß motoneurons, which are known to innervate both intrafusal and extrafusal muscle fibers in vertebrate muscles but were not considered in previous literature regarding selective formation of these synapses in animals with paralyzed muscles. It was also helpful to correctly model the greatly reduced contractility of extrafusal muscle fibers during early development. Stronger and more coordinated muscle activity patterns such as observed later during neonatal locomotion impaired projection selectivity. These findings imply a generic functionality of a musculoskeletal system to imprint important aspects of its mechanical dynamics onto a neural network, without specific preprogramming other than setting a critical period for the formation and maturation of this general pattern of connectivity. Such functionality would facilitate the successful evolution of new species with altered musculoskeletal anatomy, and it may help to explain patterns of connectivity and associated reflexes that appear during abnormal development.NEW & NOTEWORTHY A novel model of self-organization of early spinal circuitry based on a biologically realistic plant, sensors, and neuronal plasticity in conjunction with empirical observations of fetal development. Without explicit need for guiding genetic rules, connection matrices emerge that support functional self-organization of the mature pattern of Ia to motoneuron connectivity in the spinal circuitry.

Turning Neural Prosthetics Into Viable Products.

Academic researchers concentrate on the scientific and technological feasibility of novel treatments. Investors and commercial partners, however, understand that success depends even more on strategies for regulatory approval, reimbursement, marketing, intellectual property protection and risk management. These considerations are critical for technologically complex and highly invasive treatments that entail substantial costs and risks in small and heterogeneous patient populations. Most implanted neural prosthetic devices for novel applications will be in FDA Device Class III, for which guidance documents have been issued recently. Less invasive devices may be eligible for the recently simplified "de novo" submission routes. We discuss typical timelines and strategies for integrating the regulatory path with approval for reimbursement, securing intellectual property and funding the enterprise, particularly as they might apply to implantable brain-computer interfaces for sensorimotor disabilities that do not yet have a track record of approved products.

insideOut: A Bio-Inspired Machine Learning Approach to Estimating Posture in Robots Driven by Compliant Tendons.

Estimates of limb posture are critical for controlling robotic systems. This is generally accomplished with angle sensors at individual joints that simplify control but can complicate mechanical design and robustness. Limb posture should be derivable from each joint's actuator shaft angle but this is problematic for compliant tendon-driven systems where (i) motors are not placed at the joints and (ii) nonlinear tendon stiffness decouples the relationship between motor and joint angles. Here we propose a novel machine learning algorithm to accurately estimate joint posture during dynamic tasks by limited training of an artificial neural network (ANN) receiving motor angles and tendon tensions, analogous to biological muscle and tendon mechanoreceptors. Simulating an inverted pendulum-antagonistically-driven by motors and nonlinearly-elastic tendons-we compare how accurately ANNs estimate joint angles when trained with different sets of non-collocated sensory information generated via random motor-babbling. Cross-validating with new movements, we find that ANNs trained with motor angles and tendon tension data predict joint angles more accurately than ANNs trained without tendon tension. Furthermore, these results are robust to changes in network/mechanical hyper-parameters. We conclude that regardless of the tendon properties, actuator behavior, or movement demands, tendon tension information invariably improves joint angle estimates from non-collocated sensory signals.

Trial-by-trial modulation of express visuomotor responses induced by symbolic or barely detectable cues.

Human cerebral cortex can produce visuomotor responses that are modulated by contextual and task-specific constraints. However, the distributed cortical network for visuomotor transformations limits the minimal response time of that pathway. Notably, humans can generate express visuomotor responses in arm muscles that are inflexibly tuned to the target location and occur 80-120 ms from stimulus presentation [stimulus-locked responses (SLRs)]. This suggests a subcortical pathway for visuomotor transformations that might involve the superior colliculus and its downstream reticulo-spinal projections. Here we investigated whether cognitive expectations can modulate the SLR. In one experiment, we recorded surface electromyogram (EMG) from shoulder muscles as participants reached toward a visual target whose location was unpredictable in control conditions and partially predictable in cue conditions by interpreting a symbolic cue (75% validity). Valid symbolic cues led to earlier and larger SLRs than control conditions; invalid symbolic cues produced later and smaller SLRs than control conditions. This is consistent with a cortical top-down modulation of the putative subcortical SLR network. In a second experiment, we presented high-contrast targets in isolation (control) or ∼24 ms after low-contrast stimuli, which could appear at the same (valid cue) or opposite (invalid cue) location as the target and with equal probability (50% cue validity). We observed earlier SLRs than control with the valid low-contrast cues, whereas the invalid cues led to the opposite results. These findings may reflect bottom-up attentional mechanisms, potentially evolving subcortically via the superior colliculus. Overall, our results support both top-down and bottom-up modulations of the putative subcortical SLR network in humans.NEW & NOTEWORTHY Express visuomotor responses in humans appear to reflect subcortical sensorimotor transformation of visual inputs, potentially conveyed via the tecto-reticulo-spinal pathway. Here we show that the express responses are influenced by both symbolic and barely detectable spatial cues about stimulus location. The symbolic cue-induced effects suggest cortical top-down modulation of the putative subcortical visuomotor network. The effects of barely detectable cues may reflect exogenous facilitation mechanisms of the tecto-reticulo-spinal pathway.

A Non-spiking Neuron Model With Dynamic Leak to Avoid Instability in Recurrent Networks.

Recurrent circuitry components are distributed widely within the brain, including both excitatory and inhibitory synaptic connections. Recurrent neuronal networks have potential stability problems, perhaps a predisposition to epilepsy. More generally, instability risks making internal representations of information unreliable. To assess the inherent stability properties of such recurrent networks, we tested a linear summation, non-spiking neuron model with and without a "dynamic leak", corresponding to the low-pass filtering of synaptic input current by the RC circuit of the biological membrane. We first show that the output of this neuron model, in either of its two forms, follows its input at a higher fidelity than a wide range of spiking neuron models across a range of input frequencies. Then we constructed fully connected recurrent networks with equal numbers of excitatory and inhibitory neurons and randomly distributed weights across all synapses. When the networks were driven by pseudorandom sensory inputs with varying frequency, the recurrent network activity tended to induce high frequency self-amplifying components, sometimes evident as distinct transients, which were not present in the input data. The addition of a dynamic leak based on known membrane properties consistently removed such spurious high frequency noise across all networks. Furthermore, we found that the neuron model with dynamic leak imparts a network stability that seamlessly scales with the size of the network, conduction delays, the input density of the sensory signal and a wide range of synaptic weight distributions. Our findings suggest that neuronal dynamic leak serves the beneficial function of protecting recurrent neuronal circuitry from the self-induction of spurious high frequency signals, thereby permitting the brain to utilize this architectural circuitry component regardless of network size or recurrency.

Force variability is mostly not motor noise: Theoretical implications for motor control.

Variability in muscle force is a hallmark of healthy and pathological human behavior. Predominant theories of sensorimotor control assume 'motor noise' leads to force variability and its 'signal dependence' (variability in muscle force whose amplitude increases with intensity of neural drive). Here, we demonstrate that the two proposed mechanisms for motor noise (i.e. the stochastic nature of motor unit discharge and unfused tetanic contraction) cannot account for the majority of force variability nor for its signal dependence. We do so by considering three previously underappreciated but physiologically important features of a population of motor units: 1) fusion of motor unit twitches, 2) coupling among motoneuron discharge rate, cross-bridge dynamics, and muscle mechanics, and 3) a series-elastic element to account for the aponeurosis and tendon. These results argue strongly against the idea that force variability and the resulting kinematic variability are generated primarily by 'motor noise.' Rather, they underscore the importance of variability arising from properties of control strategies embodied through distributed sensorimotor systems. As such, our study provides a critical path toward developing theories and models of sensorimotor control that provide a physiologically valid and clinically useful understanding of healthy and pathologic force variability.

A new approach to medical diagnostic decision support.

Data mining is a powerful tool to reduce costs and mitigate errors in the diagnostic analysis and repair of complex engineered system, but it has yet to be applied systematically to the most complex and socially expensive system - the human body. The currently available approaches of knowledge-based and pattern-based artificial intelligence are unsuited to the iterative and often subjective nature of clinician-patient interactions. Furthermore, current electronic health records generally have poor design and low quality for such data mining. Bayesian methods have been developed to suggest multiple possible diagnoses given a set of clinical findings, but the larger problem is advising the physician on useful next steps. A new approach based on inverting Bayesian inference allows identification of the diagnostic actions that are most likely to disambiguate a differential diagnosis at each point in a patient's work-up. This can be combined with personalized cost information to suggest a cost-effective path to the clinician. Because the software is tracking the clinician's decision-making process, it can provide salient suggestions for both diagnoses and diagnostic tests in standard, coded formats that need only to be selected. This would reduce the need to type in free text, which is prone to ambiguities, omissions and errors. As the database of high-quality records grows, the scope, utility and acceptance of the system should also grow automatically, without requiring expert updating or correction.

Learning to use Muscles.

The human musculoskeletal system is highly complex mechanically. Its neural control must deal successfully with this complexity to perform the diverse, efficient, robust and usually graceful behaviors of which humans are capable. Most of those behaviors might be performed by many different subsets of its myriad possible states, so how does the nervous system decide which subset to use? One solution that has received much attention over the past 50 years would be for the nervous system to be fundamentally limited in the patterns of muscle activation that it can access, a concept known as muscle synergies or movement primitives. Another solution, based on engineering control methodology, is for the nervous system to compute the single optimal pattern of muscle activation for each task according to a cost function. This review points out why neither appears to be the solution used by humans. There is a third solution that is based on trial-and-error learning, recall and interpolation of sensorimotor programs that are good-enough rather than limited or optimal. The solution set acquired by an individual during the protracted development of motor skills starting in infancy forms the basis of motor habits, which are inherently low-dimensional. Such habits give rise to muscle usage patterns that are consistent with synergies but do not reflect fundamental limitations of the nervous system and can be shaped by training or disability. This habit-based strategy provides a robust substrate for the control of new musculoskeletal structures during evolution as well as for efficient learning, athletic training and rehabilitation therapy.

Shoulder kinematics plus contextual target information enable control of multiple distal joints of a simulated prosthetic arm and hand.

BACKGROUND: Prosthetic restoration of reach and grasp function after a trans-humeral amputation requires control of multiple distal degrees of freedom in elbow, wrist and fingers. However, such a high level of amputation reduces the amount of available myoelectric and kinematic information from the residual limb. METHODS: To overcome these limits, we added contextual information about the target's location and orientation such as can now be extracted from gaze tracking by computer vision tools. For the task of picking and placing a bottle in various positions and orientations in a 3D virtual scene, we trained artificial neural networks to predict postures of an intact subject's elbow, forearm and wrist (4 degrees of freedom) either solely from shoulder kinematics or with additional knowledge of the movement goal. Subjects then performed the same tasks in the virtual scene with distal joints predicted from the context-aware network. RESULTS: Average movement times of 1.22s were only slightly longer than the naturally controlled movements (0.82 s). When using a kinematic-only network, movement times were much longer (2.31s) and compensatory movements from trunk and shoulder were much larger. Integrating contextual information also gave rise to motor synergies closer to natural joint coordination. CONCLUSIONS: Although notable challenges remain before applying the proposed control scheme to a real-world prosthesis, our study shows that adding contextual information to command signals greatly improves prediction of distal joint angles for prosthetic control.

The influence of temporal predictability on express visuomotor responses.

Humans are able to generate target-directed visuomotor responses in less than 100 ms after stimulus onset. These "express" responses have been termed stimulus-locked responses (SLRs) and are proposed to be modulated by visuomotor transformations performed subcortically via the superior colliculus. Unfortunately, these responses have proven difficult to detect consistently across individuals. The recent report of an effective paradigm for generating SLRs in 100% of participants appears to change this. The task required the interception of a target moving at a constant velocity that emerged from behind a barrier. Here, we aimed to reproduce the efficacy of this paradigm for eliciting SLRs and to test the hypothesis that its effectiveness derives from the predictability of target onset time as opposed to target motion per se. In one experiment, we recorded surface electromyogram (EMG) from shoulder muscles as participants made reaches to intercept temporally predictable or unpredictable targets. Consistent with our hypothesis, predictably timed targets produced more frequent and stronger SLRs than unpredictably timed targets. In a second experiment, we compared different temporally predictable stimuli and observed that transiently presented targets produced larger and earlier SLRs than sustained moving targets. Our results suggest that target motion is not critical for facilitating the SLR expression and that timing predictability does not rely on extrapolation of a physically plausible motion trajectory. These findings provide support for a mechanism whereby an internal timer, probably located in cerebral cortex, primes the processing of both visual input and motor output within the superior colliculus to produce SLRs.NEW & NOTEWORTHY Express stimulus-driven responses in humans have been proposed to be originated subcortically via the superior colliculus. These short-latency responses are facilitated by the presentation of dynamic visual stimuli. Here, we show that this facilitation is related to the predictable target timing, regardless of its kinematic attributes. We propose that the superior colliculus can be primed to generate express stimulus-driven motor responses via cortical top-down projection.

Prenatal diagnosis and management of congenital complete heart block.

Congenital complete heart block (CCHB) is a life-threatening medical condition in the unborn fetus with insufficiently validated prenatal interventions. Maternal administration of medications aimed at decreasing the immune response in the fetus and beta-agonists intended to increase fetal cardiac output have shown only marginal benefits. Anti-inflammatory therapies cannot reverse CCHB, but may decrease myocarditis and improve heart function. Advances in prenatal diagnosis and use of strict surveillance protocols for delivery timing have demonstrated small improvements in morbidity and mortality. Ambulatory surveillance programs and wearable fetal heart rate monitors may afford early identification of evolving fetal heart block allowing for emergent treatment. There is also preliminary data suggesting a roll for prevention of CCHB with hydroxychloroquine, but the efficacy and safety is still being studied. To date, intrauterine fetal pacing has not been successful due to the high-risk invasive placement techniques and potential problems with lead dislodgement. The development of a fully implantable micropacemaker via a minimally invasive approach has the potential to pace fetal patients with CCHB and thus delay delivery and allow fetal hydrops to resolve. The challenge remains to establish accepted prenatal interventions capable of successfully managing CCHB in utero until postnatal pacemaker placement is successfully achieved.

Neural Prosthetics:A Review of Empirical vs. Systems Engineering Strategies.

Implantable electrical interfaces with the nervous system were first enabled by cardiac pacemaker technology over 50 years ago and have since diverged into almost all of the physiological functions controlled by the nervous system. There have been a few major clinical and commercial successes, many contentious claims, and some outright failures. These tend to be reviewed within each clinical subspecialty, obscuring the many commonalities of neural control, biophysics, interface materials, electronic technologies, and medical device regulation that they share. This review cites a selection of foundational and recent journal articles and reviews for all major applications of neural prosthetic interfaces in clinical use, trials, or development. The hard-won knowledge and experience across all of these fields can now be amalgamated and distilled into more systematic processes for development of clinical products instead of the often empirical (trial and error) approaches to date. These include a frank assessment of a specific clinical problem, the state of its underlying science, the identification of feasible targets, the availability of suitable technologies, and the path to regulatory and reimbursement approval. Increasing commercial interest and investment facilitates this systematic approach, but it also motivates projects and products whose claims are dubious.

Minimally Invasive Implantation of a Micropacemaker Into the Pericardial Space.

BACKGROUND: Permanent cardiac pacemakers require invasive procedures with complications often related to long pacemaker leads. We are developing a percutaneous pacemaker for implantation of an entire pacing system into the pericardial space. METHODS: Percutaneous micropacemaker implantations were performed in 6 pigs (27.4-34.1 kg) using subxyphoid access to the pericardial space. Modifications in the implantation methods and hardware were made after each experiment as the insertion method was optimized. In the first 5 animals, nonfunctional pacemaker devices were studied. In the final animal, a functional pacemaker was implanted. RESULTS: Successful placement of the entire nonfunctional pacing system into the pericardial space was demonstrated in 2 of the first 5 animals, and successful implantation and capture was achieved using a functional system in the last animal. A sheath was developed that allows retractable features to secure positioning within the pericardial space. In addition, a miniaturized camera with fiberoptic illumination allowed visualization of the implantation site before electrode insertion into myocardium. All animals studied during follow-up survived without symptoms after the initial postoperative period. CONCLUSIONS: A novel micropacemaker system allows cardiac pacing without entering the vascular space or surgical exposure of the heart. This pericardial pacemaker system may be an option for a large number of patients currently requiring transvenous pacemakers but is particularly relevant for patients with restricted vascular access, young children, or those with congenital heart disease who require epicardial access.

Relationships between full-day arm movement characteristics and developmental status in infants with typical development as they learn to reach: An observational study.

Background: Advances in wearable sensor technology now allow us to quantify the number, type and kinematic characteristics of bouts of infant arm movement made across a full day in the natural environment. Our aim here was to determine whether the amount and kinematic characteristics of arm movements made across the day in the natural environment were related to developmental status in infants with typical development as they learned to reach for objects using their arms. Methods: We used wearable sensors to measure arm movement across days and months as infants developed arm reaching skills. In total, 22 infants with typical development participated, aged between 38 and 203 days. Of the participants, 2 infants were measured once and the other 20 infants were measured once per month for 3 to 6 visits. The Bayley Scales of Infant Development was used to measure developmental level. Results: Our main findings were: 1) infant arm movement characteristics as measured by full-day wearable sensor data were related to Bayley motor, cognitive and language scores, indicating a relationship between daily movement characteristics and developmental status; 2) infants who moved more had larger increases in language and cognitive scores across visits; and 3) larger changes in movement characteristics across visits were related to higher motor scores. Conclusions: This was a preliminary, exploratory, small study of the potential importance of infant arm movement characteristics as measured by full-day wearable sensor data. Our results support full-day arm movement activity as an area of interest for future study as a biomarker of neurodevelopmental status and as a target for early intervention.