Biology subjects

Pruszynski, J. A.

Publications and source records attributed to Pruszynski, J. A..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Neural correlates of online action preparation

When performing movements in rapid succession, the brain needs to coordinate preparation of an upcoming action with ongoing execution. Here we identify the processes and brain areas involved in this ability. Human participants performed pairs of single-finger presses or three-finger chords in settings where they had to prepare the second movement before or after the first response. Despite matched perceptual and movement requirements, 7T functional MRI revealed increased brain activity in regions along the intra-parietal sulcus and ventral visual stream when actions overlapped. Multivariate analyses suggested that these areas were involved in stimulus identification and action selection. In contrast, the dorsal premotor cortex, known to be involved in planning upcoming movements, showed no discernible signs of heightened activity. This observation suggests that the bottleneck during simultaneous action control and preparation arises at the level of stimulus identification and action selection, whereas movement planning in the premotor cortex can unfold concurrently with execution without interference between the two processes. SummaryThe brains ability to prepare for upcoming actions while controlling ongoing movements is a crucial evolutionary adaptation of the action system. However, the neural basis of online action preparation remains largely unknown. We found that superior-parietal and occipito-temporal areas exhibited heightened activation during online preparation. Surprisingly, the dorsal premotor cortex, known to be a crucial structure in motor planning, did not display additional activation for overlapping actions. These findings imply that while motor planning within the premotor cortex can occur in parallel with the execution of ongoing movement, the parallel stimulus identification and action selection in the posterior parietal cortex requires additional neural processes. HighlightsO_LIIndividuals prepare upcoming actions while simultaneously controlling ongoing movement C_LIO_LIWhen these two processes overlap, superior-parietal and occipito-temporal areas show increased activation C_LIO_LIMultivariate analysis suggests that increased activation arises to resolve simultaneous stimulus identification and action selection C_LIO_LIThe premotor cortex, known to be involved in motor planning, coordinates simultaneous planning and execution without extra neural engagement C_LI

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bioRxiv2023

Interaction of multiple future movement plans in sequential reaching

Real world actions often comprise of a series of movements that cannot be entirely planned before initiation. When these actions are executed rapidly, the planning of multiple future movements needs to occur simultaneously with the ongoing action. How the brain solves this task remains unknown. Here we address this question with a new sequential arm reaching paradigm that manipulates how many future reaches are available for planning while controlling execution of the ongoing reach. We show that participants plan at least two future reaches simultaneously with an ongoing reach. Further, the planning processes of the two future reaches are not independent of one another. Evidence that the planning processes interact is two-fold. First, correcting for a visual perturbation of the ongoing reach target is slower when more future reaches are planned. Second, the curvature of the current reach is modified based on the next reach only when their planning processes temporally overlap. These interactions between future planning processes may enable smooth production of sequential actions by linking individual segments of a long sequence at the level of motor planning.

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bioRxiv2023

Done in 65 ms: Express visuomotor responses in upper limb muscles in Rhesus Macaques

How rapidly can the brain transform vision into action? Work in humans has established that the transformation for visually-guided reaching can be remarkably rapid, with the first phase of upper limb muscle recruitment, the express visuomotor response, beginning within less than 100 ms of visual target presentation. Such short-latency responses limit the opportunities for extensive cortical processing, leading to the hypothesis that they are generated via the subcortical tectoreticulospinal pathway. Here, we examine if non-human primates (NHPs) exhibit express visuomotor responses. Two male macaques made visually-guided reaches in a behavioral paradigm known to elicit express visuomotor responses in humans, while we acquired intramuscular recordings from the deltoid muscle. Across several variants of this paradigm, express visuomotor responses began within 65 ms (range 48-91 ms) of target presentation. Although the timing of the express visuomotor response did not co-vary with reaction time, larger express visuomotor responses tended to precede shorter latency reaches. Finally, the magnitude of the express visuomotor response was muted on trials where NHPs withheld a reach to one stimulus in order to move to a stimulus appearing 34 ms later in the opposite direction. Overall, the response properties and contextual control of express visuomotor responses in NHPs resemble those in humans. Our results establish a new benchmark for visuomotor transformations underlying visually-guided reaches, setting the stage for experiments that can directly compare the role of cortical and subcortical areas in reaching when time is of the essence. Significance statementExpress visuomotor responses in upper limb muscles are brief periods of recruitment preceding visually-guided reaches. Such responses begin [~]90 ms after visual target presentation in humans, and potentially arise from signaling along the tecto-reticulo-spinal pathway. Here, we show that express visuomotor responses in macaques upper limb muscles resemble those in humans, excepting that they evolve [~]65 ms after target onset, consistent with shorter responses latencies in macaques versus humans. Our results clock the completion of the visuomotor transformation for rapid reaching, and set the stage for experiments to directly test the underlying substrates.

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bioRxiv2023

MotorNet: a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networks

Artificial neural networks (ANNs) are a powerful class of computational models for unravelling neural mechanisms of brain function. However, for neural control of movement, they currently must be integrated with software simulating biomechanical effectors, leading to limiting impracticalities: (1) researchers must rely on two different platforms and (2) biomechanical effectors are not generally differentiable, constraining researchers to reinforcement learning algorithms despite the existence and potential biological relevance of faster training methods. To address these limitations, we developed MotorNet, an open-source Python toolbox for creating arbitrarily complex, differentiable, and biomechanically realistic effectors that can be trained on user-defined motor tasks using ANNs. MotorNet is designed to meet several goals: ease of installation, ease of use, a high-level user-friendly API, and a modular architecture to allow for flexibility in model building. MotorNet requires no dependencies outside Python, making it easy to get started with. For instance, it allows training ANNs on typically used motor control models such as a two joint, six muscle, planar arm within minutes on a typical desktop computer. MotorNet is built on PyTorch and therefore can implement any network architecture that is possible using the PyTorch framework. Consequently, it will immediately benefit from advances in artificial intelligence through PyTorch updates. Finally, it is open source, enabling users to create and share their own improvements, such as new effector and network architectures or custom task designs. MotorNets focus on higher order model and task design will alleviate overhead cost to initiate computational projects for new researchers by providing a standalone, ready-to-go framework, and speed up efforts of established computational teams by enabling a focus on concepts and ideas over implementation.

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bioRxiv2022

Reach corrections toward moving targets are faster than reach corrections toward jumping targets

Visually guided reaching is a common motor behavior that engages subcortical circuits to mediate rapid corrections. These circuits help us successfully grasp objects we see, even if those objects move during the reach. Although these neural mechanisms have evolved for interaction with the physical world, they are almost always studied in the context of reaching toward virtual targets displayed on a computer or projection screen. Virtual targets are unrealistic both because they cannot be physically grasped and because they generally move by "jumping" from one place to another instantaneously. Recent work has indicated that various aspects of "real" visual stimuli, including proximity and graspability, elicit distinct neural activity. In this study, we instructed participants to perform rapid reaches to physical objects. On some trials, these objects either moved realistically (continuously from one position to another) or unrealistically (jumping instantaneously to an equivalent position). Participants were consistently faster in correcting their reach trajectories when the object moved continuously.

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bioRxiv2022

Precise and stable edge orientation signaling by human first-order tactile neurons

Fast-adapting type 1 (FA-1) and slow-adapting type 1 (SA-1) first-order neurons in the human tactile system have distal axons that branch in the skin and form many transduction sites, yielding receptive fields with many highly sensitive zones or subfields. We previously demonstrated that this arrangement allows FA-1 and SA-1 neurons to signal the geometric features of touched objects, specifically the orientation of raised edges scanned with the fingertips. Here we show that such signaling operates for fine edge orientation differences (5-20{degrees}) and is stable across a broad range of scanning speeds (15-180 mm/s); that is, under conditions relevant for real-world hand use. We found that both FA-1 and SA-1 neurons weakly signal fine edge orientation differences via the intensity of their spiking responses and only when considering a single scanning speed. Both neuron types showed much stronger edge orientation signaling in the sequential structure of the evoked spike trains and FA-1 neurons performed better than SA-1 neurons. Represented in the spatial domain, the sequential structure was strikingly invariant across scanning speeds, especially those naturally used in tactile spatial discrimination tasks. This speed invariance suggests that neurons responses are structured via sequential stimulation of their subfields and thus links this capacity to their terminal organization in the skin. Indeed, the spatial precision of elicited action potentials rationally matched spatial acuity of subfield arrangements, which typically corresponds to the dimension of individual fingertip ridges. Significance StatementThe distal axons of human first-order tactile neurons branch and innervate many mechanosensitive end organs in the skin. For those neurons terminating in end organs associated with fingerprint ridges (Meissner and Merkel), this branching results in cutaneous receptive fields with multiple subfields spread across several ridges. Consequently, when a fingertip scans the surface of an object, the spatial coincidence between a neurons subfields and the tactile stimulus defines the sequential structure of the evoked spike train (i.e., the presence of action potential bursts and the gaps between them). Here we show that, for surfaces composed of oriented edges, this sequential structure signals information about edge orientation differences at the limit of what people can feel and that the spatial precision of the structuring is maintained across a broad range of speeds relevant for real-world hand use. We submit that, to be of human relevance, models of higher order tactile processing must consider the impact of multifocal receptive fields in the periphery. For example, the speed invariance of tactile fine-form/texture perception may arise simply because the same subsets of peripheral subfields in the population of first-order tactile neurons are stimulated together regardless of speed.

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bioRxiv2021

Mapping the integration of sensory information across fingers in human sensorimotor cortex

The integration of somatosensory signals across fingers is essential for dexterous object manipulation. Previous experiments suggest that this integration occurs in neural populations in the primary somatosensory cortex (S1). However, the integration process has not been fully characterized, as previous studies have mainly used two-finger stimulation paradigms. Here, we addressed this gap by stimulating all 31 single- and multi-finger combinations. We measured population-wide activity patterns evoked during finger stimulation in human S1 and primary motor cortex (M1) using 7T functional magnetic resonance imaging (fMRI) in female and male participants. Using multivariate fMRI analyses, we found clear evidence of unique non-linear interactions between fingers. In Brodmann area (BA) 3b, interactions predominantly occurred between pairs of neighbouring fingers. In BA 2, however, we found equally strong interactions between spatially distant fingers, as well as interactions between finger triplets and quadruplets. We additionally observed strong interactions in the hand area of M1. In both M1 and S1, these non-linear interactions did not reflect a general suppression of overall activity, suggesting instead that the interactions we observed reflect rich, non-linear integration of sensory inputs from the fingers. We suggest that this non-linear finger integration allows for a highly flexible mapping from finger sensory inputs to motor responses that facilitates dexterous object manipulation.

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bioRxiv2020

Motor planning brings human primary somatosensory cortex into movement-specific preparatory states

Motor planning plays a critical role in producing fast and accurate movement. Yet, the neural processes that occur in human primary motor and somatosensory cortex during planning, and how they relate to those during movement execution, remain poorly understood. Here we used 7T functional magnetic resonance imaging (fMRI) and a delayed movement paradigm to study single finger movement planning and execution. The inclusion of no-go trials and variable delays allowed us to separate what are typically overlapping planning and execution brain responses. Although our univariate results show widespread deactivation during finger planning, multivariate pattern analysis revealed finger-specific activity patterns in contralateral primary somatosensory cortex (S1), which predicted the planned finger action. Surprisingly, these activity patterns were as informative as those found in contralateral primary motor cortex (M1). Control analyses ruled out the possibility that the detected information was an artifact of subthreshold movements during the preparatory delay. Furthermore, we observed that finger-specific activity patterns during planning were highly correlated to those during execution. These findings reveal that motor planning activates the specific S1 and M1 circuits that are engaged during the execution of a finger press, while activity in both regions is overall suppressed. We propose that preparatory states in S1 may improve movement control through changes in sensory processing or via direct influence of spinal motor neurons. Significance statementMotor planning is important for good behavioral performance, yet it is unclear which neural processes underlie the preparation of the nervous system for an upcoming movement. Using high-resolution functional neuroimaging, we investigated how motor planning for finger presses changes the activity state in primary motor and primary somatosensory cortex, and how brain responses during planning and execution relate to each other. We show that planning leads to finger-specific activation in both M1 and S1, which is highly similar to the finger-specific activity patterns elicited during execution. Our findings suggest that S1 is being specifically prepared for an upcoming action, either to actively contribute to the outflowing motor command or to enable action-specific sensory gating.

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bioRxiv2020

Whole brain mapping of somatosensory responses in awake marmosets investigated with ultra-high field fMRI

The common marmoset (Callithrix jacchus) is a small-bodied New World primate that is becoming an important model to study brain functions. Despite several studies exploring the somatosensory system of marmosets, all results have come from anesthetized animals using invasive techniques and post-mortem analyses. Here we demonstrate the feasibility for getting high-quality and reproduceable sensorimotor mapping in awake marmosets with functional magnetic resonance imaging (fMRI). We acquired fMRI sequences in four animals while they received tactile stimulation (via air-puffs), delivered to the face, arm or leg. We found that the body representation progressed medially from the leg to the face in areas 3a, 3b, 1/2, and from caudal to rostral sites in areas S2 and PV. SI and SII exhibited a body representation in their functional connectivity pattern within the posterior and midcingulate and the thalamus. Interestingly, we also found a somatotopic body representation in two subcortical areas: the thalamus and, for the first time, in the putamen. These maps have similar organizations as those previously found in Old World macaque monkeys and humans, suggesting that these subcortical somatotopic organizations were already established before Old and New World primates diverged. Our results show the first whole brain mapping of somatosensory responses acquired in a non-invasive way in awake marmosets. Significant statementHere we used somatosensory stimulation combined with functional magnetic resonance imaging to map whole brain activation in awake marmosets. We used light tactile stimulation, consisting of air-puffs, delivered on the face, arm or leg. We found a topographic body representation in primary (SI) and secondary (SII) somatosensory regions, thalamus and putamen. We also revealed the existence of a body representation organization within the thalamus and the cingulate cortex by computing functional connectivity maps from seeds defined in SI/SII for face, arm and leg using resting-state fMRI data. This non-invasive approach will be essential for chronic studies by guiding invasive recording and manipulation techniques.

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bioRxiv2020

Human first-order tactile neurons can resolve spatial details on the scale of single fingerprint ridges

Fast-adapting type 1 (FA-1) and slow-adapting type 1 (SA-1) first-order tactile neurons provide detailed spatiotemporal tactile information when we touch objects with fingertips. The distal axon of these neuron types branches in the skin and innervates many receptor organs associated with fingerprint ridges (Meissner corpuscles and Merkel cell neurite complexes, respectively), resulting in heterogeneous receptive fields that include many highly sensitive zones or subfields. Using raised dots that tangentially scanned a neurons receptive field, here we examined the spatial resolution capacity of FA-1 and SA-1 neurons afforded by their heterogeneous receptive fields and its constancy across scanning speed and direction. We report that the resolution of both neuron types on average corresponds to a spatial period of [~]0.4 mm and provide evidence that a subfields spatial selectivity arises because its associated receptor organ measures mechanical events limited to a single fingerprint ridge. Accordingly, the sensitivity topography of a neurons receptive fields is quite stable over repeated mappings and over scanning speeds representative of real-world hand use. The sensitivity topography is substantially conserved also for different scanning directions, but the subfields can be relatively displaced by direction-dependent shear deformations of the skin surface. Significance StatementThe branching of the distal axon of first-order tactile neurons with receptor-organs associated with fingerprint ridges (Meissner and Merkel end-organs) results in cutaneous receptive fields composed of several distinct subfields spread across multiple ridges. We show that the spatial selectivity of the subfields typically corresponds to the dimension of the ridges ([~]0.4 mm) and that neurons subfield layout is well preserved across tangential movement speeds and directions representative of natural use of the fingertips. We submit that the receptor-organ underlying a subfield essentially measures mechanical events at an individual ridge. That neurons receive convergent input from multiple subfields does not preclude the possibility that spatial details can be resolved on the scale of single fingerprint ridges by a population code.

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bioRxiv2020

Shared internal models for feedforward and feedback control of arm dynamics in non-human primates

Previous work has shown that humans account for and learn novel properties or the arms dynamics, and that such learning causes changes in both the predictive (i.e., feedforward) control of reaching and reflex (i.e., feedback) responses to mechanical perturbations. Here we show that similar observations hold in old-world monkeys (macaca fascicularis). Two monkeys were trained to use an exoskeleton to perform a single-joint elbow reaching and to respond to mechanical perturbations that created pure elbow motion. Both of these tasks engaged robust shoulder muscle activity as required to account for the torques that typically arise at the shoulder when the forearm rotates around the elbow joint (i.e., intersegmental dynamics). We altered these intersegmental arm dynamics by having the monkeys generate the same elbow movements with the shoulder joint either free to rotate, as normal, or fixed by the robotic manipulandum, which eliminates the shoulder torques caused by forearm rotation. After fixing the shoulder joint, we found a systematic reduction in shoulder muscle activity. In addition, after releasing the shoulder joint again, we found evidence of kinematic aftereffects (i.e., reach errors) in the direction predicted if failing to compensate for normal arm dynamics. We also tested whether such learning transfers to feedback responses evoked by mechanical perturbations and found a reduction in shoulder feedback responses, as appropriate for these altered arm intersegmental dynamics. Demonstrating this learning and transfer in non-human primates will allow the investigation of the neural mechanisms involved in feedforward and feedback control of the arms dynamics.

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bioRxiv2020

Voluntary modification of rapid tactile-motor responses during reaching differs from its visuo-motor counterpart

People commonly hold and manipulate a variety of objects in everyday life, and these objects have different physical properties. In order to successfully control this wide range of objects, people must associate new patterns of tactile stimuli with appropriate motor outputs. We performed a series of experiments investigating the extent to which people can voluntarily modify tactile-motor associations in the context of a rapid tactile-motor response guiding the hand to a moving target (previously described in Pruszynski et al. 2016) by using an anti-reach paradigm in which participants were instructed to move their hands in the opposite direction of a target jump. We compared performance to that observed when people make visually guided reaches to a moving target (cf. Day & Lyon 2000, Pisella et al. 2000). When participants had visual feedback, motor responses during the anti-reach task showed early automatic responses toward the moving target before voluntary modification to move in the instructed direction. When the same participants had only tactile feedback, however, they were able to suppress this early phase of the motor response, which occurs less than 100 ms after the target jump. Our results indicate that, while the tactile motor and visual motor systems both support rapid responses that appear similar under some conditions, these responses show sharp distinctions in terms of their malleability.

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bioRxiv2020

Explicit feedback and instruction do not change shoulder muscle activity reduction after shoulder fixation

Generating pure elbow rotation requires contracting muscles at both the shoulder and elbow joints to counter torques that arise at the shoulder when the forearm rotates (i.e., intersegmental dynamics). Previous work has shown that human participants learn to reduce their shoulder muscle activity if the same elbow movement is performed after the shoulder joint is mechanically locked, which is appropriate because locking the shoulder joint eliminates the torques that arise at the shoulder when the forearm rotates. However, this learning is slow (i.e., it unfolds over hundreds of trials) and incomplete (i.e., shoulder activity is not fully eliminated). Here we investigated whether and how the addition of explicit strategies and biofeedback modulate this type of learning. Three groups of human participants (N = 55) performed voluntary pure elbow rotations using a robotic exoskeleton that permits shoulder and elbow rotation in a horizontal plane. Participants did the task with the shoulder free to move (baseline), then with the shoulder joint locked by the robotic manipulandum (adaptation), and then with the shoulder free to move again (post-adaptation). The first group of participants performed this protocol and received no instructions about what to do after their shoulder was locked. The second group of participants received visual feedback about their shoulder muscle activity after each movement and was instructed to reduce their shoulder activity to zero. The third group of participants also received visual biofeedback, but it was removed part way through the experiment. We found that, although all groups learned, the rate and magnitude of learning was not reliably different across the groups. Taken together, our results suggest that learning new arm dynamics, unlike other motor learning paradigms, unfolds independent of explicit instructions, biofeedback and task instructions.

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bioRxiv2020

Structure of population activity in primary motor cortex for single finger flexion and extension

How is the primary motor cortex (M1) organized to control fine finger movements? We investigated the population activity in M1 for single finger flexion and extension, using 7T functional magnetic resonance imaging (fMRI) in female and male human participants, and compared these results to the neural spiking patterns recorded in two male monkeys performing the identical task. fMRI activity patterns were distinct for movements of different fingers, but quite similar for flexion and extension of the same finger. In contrast, spiking patterns in monkeys were quite distinct for both fingers and directions, similar to what was found for muscular activity patterns. The discrepancy between fMRI and electrophysiological measurements can be explained by two (non-mutually exclusive) characteristics of the organization of finger flexion and extension movements. Given that fMRI reflects predominantly input and recurrent activity, the results can be explained by an architecture in which neural populations that control flexion or extension of the same finger produce distinct outputs, but interact tightly with each other and receive similar inputs. Additionally, neurons tuned to different movement directions for the same finger (or combination of fingers) may cluster closely together, while neurons that control different finger combinations may be more spatially separated. When measuring this organization with fMRI at a coarse spatial scale, the activity patterns for flexion and extension of the same finger would appear very similar. Overall, we suggest that the discrepancy between fMRI and electrophysiological measurements provides new insights into the general organization of fine finger movements in M1. Significance statementThe primary motor cortex (M1) is important for producing individuated finger movements. Recent evidence shows that movements that commonly co-occur are associated with more similar activity patterns in M1. Flexion and extension of the same finger, which never co-occur, should therefore be associated with distinct representations. However, using carefully controlled experiments and multivariate analyses, we demonstrate that human fMRI activity patterns for flexion or extension of the same finger are highly similar. In contrast, spiking patterns measured in monkey M1 are clearly distinct. This suggests that populations controlling opposite movements of the same finger, while producing distinct outputs, may cluster together and share inputs and local processing. These results provide testable hypotheses about the organization of hand control in M1.

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bioRxiv2020

Spinal stretch reflexes support efficient control of reaching

Efficiently controlling the movement of our hand requires coordinating the motion of multiple joints of the arm. Although it is widely assumed that this type of efficient control is implemented by processing that occurs in the cerebral cortex and brain stem, recent work has shown that spinal circuits can generate efficient motor output that supports keeping the hand in a static location. Here, we show that a spinal pathway can also efficiently control the hand during reaching. In our first experiment we applied multi-joint mechanical perturbations to participants elbow and wrist as they began reaching towards a target. We found that spinal stretch reflexes evoked in elbow muscles were not proportional to how much the elbow muscles were stretched but instead were efficiently scaled to the hands distance from the target. In our second experiment we applied the same elbow and wrist perturbations but had participants change how they grasped the manipulandum, diametrically altering how the same wrist perturbation moved the hand relative to the reach target. We found that changing the arms orientation diametrically altered how spinal reflexes in the elbow muscles were evoked, and in such a way that were again efficiently scaled to the hands distance from the target. These findings demonstrate that spinal circuits can help efficiently control the hand during dynamic reaching actions, and show that efficient and flexible motor control is not exclusively dependent on processing that occurs within supraspinal regions of the nervous system.

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bioRxiv2019

Motor learning and transfer: from feedback to feedforward control

Previous work has demonstrated that when learning a new motor task, the nervous system modifies feedforward (ie. voluntary) motor commands and that such learning transfers to fast feedback (ie. reflex) responses evoked by mechanical perturbations. Here we show the inverse, that learning new feedback responses transfers to feedforward motor commands. Sixty human participants (34 females) used a robotic exoskeleton and either 1) received short duration mechanical perturbations (20 ms) that created pure elbow rotation or 2) generated self-initiated pure elbow rotations. They did so with the shoulder joint free to rotate (normal arm dynamics) or locked (altered arm dynamics) by the robotic manipulandum. With the shoulder unlocked, the perturbation evoked clear shoulder muscle activity in the long-latency stretch reflex epoch (50-100ms post-perturbation), as required for countering the imposed joint torques, but little muscle activity thereafter in the so-called voluntary response. After locking the shoulder joint, which alters the required joint torques to counter pure elbow rotation, we found a reliable reduction in the long-latency stretch reflex over many trials. This reduction transferred to feedforward control as we observed 1) a reduction in shoulder muscle activity during self-initiated pure elbow rotation trials and 2) kinematic errors (ie. aftereffects) in the direction predicted when failing to compensate for normal arm dynamics, even though participants never practiced self-initiated movements with the shoulder locked. Taken together, our work shows that transfer between feedforward and feedback control is bidirectional, furthering the notion that these processes share common neural circuits that underlie motor learning and transfer.

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bioRxiv2019

Sensory information from a slipping object elicits a rapid and automatic shoulder response

Humans have the remarkable ability to hold, grasp, and manipulate objects. Previous work has reported rapid and coordinated reactions in hand and shoulder muscles in response to external perturbations to the arm during object manipulation; however, little is known about how somatosensory feedback of an object slipping in the hand influences responses of the arm. We built a hand-held device to stimulate the sensation of slipping at all five fingertips. The device was integrated into an exoskeleton robot that supported it against gravity. The setup allowed us to decouple somatosensory stimulation in the fingers from forces applied to the arm-- two variables that are highly interdependent in real-world scenarios. Fourteen participants performed three experiments in which we measured their arm feedback responses during slip stimulation. Slip stimulations were applied horizontally, in one of two directions, and participants were either instructed to follow the slip direction, or to move the arm in the opposite direction. Participants showed responses within [~]67 ms of slip onset when following the direction of slip, but significantly slower responses when instructed to move in the opposite direction. Arm responses were modulated by the speed but not the distance of the slip. Finally, when slip stimulation was combined with mechanical perturbations to the arm, we found that sensory information from the fingertips significantly modulated the shoulder feedback response. Overall, the results demonstrate the existence of a rapid feedback system that stabilizes hand-held objects.\n\nNEW & NOTHEWORTHYWe tested whether the sensation of an object slipping from the fingers modulates shoulder feedback responses. We found rapid shoulder feedback responses when participants were instructed to follow the slip direction with the arm. Shoulder responses following mechanical joint perturbations were also potentiated when combined with slipping. These results demonstrate the existence of fast and automatic feedback responses in the arm in reaction to sensory input to the fingertips that maintain grip on hand-held objects.

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bioRxiv2019

Generalizing movement patterns following shoulder fixation

A common goal of motor learning is generalizing newly learned movement patterns beyond the training context. Here we tested whether learning a new physical property of the arm during self-initiated reaching generalizes to new arm configurations. One hundred human participants performed a single-joint elbow reaching task and/or countered mechanical perturbations that created pure elbow motion. Participants did so with the shoulder joint either free to rotate or locked by the robotic manipulandum. With the shoulder free, we found activation of shoulder extensor muscles for pure elbow extension trials, as required to counter the interaction torques that arise at the shoulder due to forearm rotation. After locking the shoulder joint, we found a substantial reduction in shoulder muscle activity that developed slowly over many trials. This reduction is appropriate because locking the shoulder joint cancels the interaction torques that arise at the shoulder to do forearm rotation and thus removes the need to activate shoulder muscles. In our first three experiments, we tested whether this reduction generalizes when reaching is self-initiated in (1) a different initial shoulder orientation, (2) a different initial elbow orientation and (3) for a different reach distance/speed. We found reliable generalization across initial shoulder orientation and reach distance/speed but not for initial elbow orientation. In our fourth experiment, we tested whether generalization is also transferred to feedback control by applying mechanical perturbations and observing reflex responses in a distinct shoulder orientation. We found robust transfer to feedback control.\n\nNew & NoteworthyHere we show that learning to reduce shoulder muscles activity following shoulder fixation generalizes to other movement conditions but does not generalize globally, indicating that the nervous system does not implement such learning by modifying a general internal model of arm dynamics.\n\nDisclosuresThe authors declare no conflict of interest.

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