Neural Correlates of Online Action Preparation.

When performing movements in rapid succession, the brain needs to coordinate ongoing execution with the preparation of an upcoming action. Here we identify the processes and brain areas involved in this ability of online preparation. Human participants (both male and female) performed pairs of single-finger presses or three-finger chords in rapid succession, while 7T fMRI was recorded. In the overlap condition, they could prepare the second movement during the first response and in the nonoverlap condition only after the first response was completed. Despite matched perceptual and movement requirements, fMRI revealed increased brain activity in the overlap condition in regions along the intraparietal sulcus and ventral visual stream. Multivariate analyses suggested that these areas are 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 execution and preparation arises at the level of stimulus identification and action selection, whereas movement planning in the premotor cortex can unfold concurrently with the execution of a current action without requiring additional neural activity.

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 2-finger stimulation paradigms. Here, we addressed this gap by stimulating all 31 single- and multifinger combinations. We measured population-wide activity patterns evoked during finger stimulation in human S1 and primary motor cortex (M1) using 7T fMRI in female and male participants. Using multivariate fMRI analyses, we found clear evidence of unique nonlinear interactions between fingers. In Brodmann area (BA) 3b, interactions predominantly occurred between pairs of neighboring 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 nonlinear interactions did not reflect a general suppression of overall activity, suggesting instead that the interactions we observed reflect rich, nonlinear integration of sensory inputs from the fingers. We suggest that this nonlinear finger integration allows for a highly flexible mapping from finger sensory inputs to motor responses that facilitates dexterous object manipulation.SIGNIFICANCE STATEMENT Processing of somatosensory information in primary somatosensory cortex (S1) is essential for dexterous object manipulation. To successfully handle an object, the sensorimotor system needs to detect complex patterns of haptic information, which requires the nonlinear integration of sensory inputs across multiple fingers. Using multivariate fMRI analyses, we characterized brain activity patterns evoked by stimulating all single- and multifinger combinations. We report that progressively stronger multifinger interactions emerge in posterior S1 and in the primary motor cortex (M1), with interactions arising between inputs from neighboring and spatially distant fingers. Our results suggest that S1 and M1 provide the neural substrate necessary to support a flexible mapping from sensory inputs to motor responses of the hand.

Human Touch Receptors Are Sensitive to Spatial Details on the Scale of Single Fingerprint Ridges.

Fast-adapting type 1 (FA-1) and slowly-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 whose sensitivity topography includes many highly sensitive zones or "subfields." In experiments on humans of both sexes, using raised dots that tangentially scanned the receptive field we examined the spatial acuity of the subfields of FA-1 and SA-1 neurons and its constancy across scanning speed and direction. We report that the sensitivity of the subfield arrangement for both neuron types on average corresponds to a spatial period of ∼0.4 mm and provide evidence that a subfield's spatial selectivity arises because its associated receptor organ measures mechanical events limited to a single papillary ridge. Accordingly, the sensitivity topography of a neuron's 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 STATEMENT The branching of the distal axon of human 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 subfields' spatial selectivity typically corresponds to the dimension of the ridges (∼0.4 mm) and a neuron's subfield layout is well preserved across tangential movement speeds and directions representative of natural use of the fingertips. We submit that the receptor organs underlying subfields essentially measure mechanical events at individual ridges. 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.

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 were quite similar for flexion and extension of the same finger. In contrast, spiking patterns in monkeys were quite distinct for both fingers and directions, which is 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 STATEMENT The 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.

Sharing voxelwise neuroimaging results from rhesus monkeys and other species with Neurovault.

Animal neuroimaging studies can provide unique insights into brain structure and function, and can be leveraged to bridge the gap between animal and human neuroscience. In part, this power comes from the ability to combine mechanistic interventions with brain-wide neuroimaging. Due to their phylogenetic proximity to humans, nonhuman primate neuroimaging holds particular promise. Because nonhuman primate neuroimaging studies are often underpowered, there is a great need to share data amongst translational researchers. Data sharing efforts have been limited, however, by the lack of standardized tools and repositories through which nonhuman neuroimaging data can easily be archived and accessed. Here, we provide an extension of the Neurovault framework to enable sharing of statistical maps and related voxelwise neuroimaging data from other species and template-spaces. Neurovault, which was previously limited to human neuroimaging data, now allows researchers to easily upload and share nonhuman primate neuroimaging results. This promises to facilitate open, integrative, cross-species science while affording researchers the increased statistical power provided by data aggregation. In addition, the Neurovault code-base now enables the addition of other species and template-spaces. Together, these advances promise to bring neuroimaging data sharing to research in other species, for supplemental data, location-based atlases, and data that would otherwise be relegated to a "file-drawer". As increasing numbers of researchers share their nonhuman neuroimaging data on Neurovault, this resource will enable novel, large-scale, cross-species comparisons that were previously impossible.

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 brainstem, 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 multijoint mechanical perturbations to participants' elbow and wrist as they began reaching toward 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 dependent on the hand's location relative to 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 arm's orientation diametrically altered how spinal reflexes in the elbow muscles were evoked, and in such a way that were again dependent on the hand's location relative to 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.NEW & NOTEWORTHY We have previously shown that spinal circuits can rapidly generate reflex responses that efficiently engage multiple joints to support postural hand control of the upper limb. Here, we show that spinal circuits can also rapidly generate such efficient responses during reaching actions.

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 arm's 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 arm's dynamics.

Orientation processing by synaptic integration across first-order tactile neurons.

Our ability to manipulate objects relies on tactile inputs from first-order tactile neurons that innervate the glabrous skin of the hand. The distal axon of these neurons branches in the skin and innervates many mechanoreceptors, yielding spatially-complex receptive fields. Here we show that synaptic integration across the complex signals from the first-order neuronal population could underlie human ability to accurately (< 3°) and rapidly process the orientation of edges moving across the fingertip. We first derive spiking models of human first-order tactile neurons that fit and predict responses to moving edges with high accuracy. We then use the model neurons in simulating the peripheral neuronal population that innervates a fingertip. We train classifiers performing synaptic integration across the neuronal population activity, and show that synaptic integration across first-order neurons can process edge orientations with high acuity and speed. In particular, our models suggest that integration of fast-decaying (AMPA-like) synaptic inputs within short timescales is critical for discriminating fine orientations, whereas integration of slow-decaying (NMDA-like) synaptic inputs supports discrimination of coarser orientations and maintains robustness over longer timescales. Taken together, our results provide new insight into the computations occurring in the earliest stages of the human tactile processing pathway and how they may be critical for supporting hand function.

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

People commonly hold and manipulate a variety of objects in everyday life, and these objects have different physical properties. 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 JA, Johansson RS, Flanagan JR. Curr Biol 26: 788-792, 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 BL, Lyon IN. Exp Brain Res 130: 159-168, 2000; Pisella L, Grea H, Tilikete C, Vighetto A, Desmurget M, Rode G, Boisson D, Rossetti Y. Nat Neurosci 3: 729-736, 2000). When participants had visual feedback, motor responses during the anti-reach task showed early automatic responses toward the moving target before later 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 <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, the circuits underlying responses show sharp distinctions in terms of their malleability.NEW & NOTEWORTHY When people reach toward a visual target that moves suddenly, they automatically correct their reach to follow the object; even when explicitly instructed not to follow a moving visual target, people exhibit an initial incorrect movement before moving in the correct direction. We show that when people use tactile feedback, they do not show an initial incorrect response, even though early muscle activity still occurs.

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 handheld 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 instructed to either follow the slip direction or move the arm in the opposite direction. Participants showed shoulder muscle 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. Shoulder 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 responses. Overall, the results demonstrate the existence of a rapid feedback system that stabilizes handheld objects.NEW & NOTEWORTHY We 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 handheld objects.

Generalizing movement patterns following shoulder fixation.

Generalizing newly learned movement patterns beyond the training context is challenging for most motor learning situations. Here we tested whether learning of a new physical property of the arm during self-initiated reaching generalizes to new arm configurations. Human participants performed a single-joint elbow reaching task and/or countered mechanical perturbations that created pure elbow motion with the shoulder joint free to rotate or locked by the manipulandum. With the shoulder free, we found activation of shoulder extensor muscles for pure elbow extension trials, appropriate for countering torques that arise at the shoulder due to forearm rotation. After locking the shoulder joint, we found a partial reduction in shoulder muscle activity, appropriate because locking the shoulder joint cancels the torques that arise at the shoulder due to forearm rotation. In our first three experiments, we tested whether and to what extent this partial reduction in shoulder muscle activity generalizes when reaching in different situations: 1) different initial shoulder orientation, 2) different initial elbow orientation, and 3) different reach distance/speed. We found generalization for the different shoulder orientation and reach distance/speed as measured by a reliable reduction in shoulder activity in these situations but no generalization for the different elbow orientation. In our fourth experiment, we found that generalization is also transferred to feedback control by applying mechanical perturbations and observing reflex responses in a distinct shoulder orientation. These results indicate that partial learning of new intersegmental dynamics is not sufficient for modifying a general internal model of arm dynamics.NEW & NOTEWORTHY Here we show that partially learning to reduce shoulder muscle activity following shoulder fixation generalizes to other movement conditions, but it does not generalize globally. These findings suggest that the partial learning of new intersegmental dynamics is not sufficient for modifying a general internal model of the arm's dynamics.

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 postmortem analyses. Here, we demonstrate the feasibility for getting high-quality and reproducible somatosensory 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 a topographic body representation with the leg representation in the most medial part, the face representation in the most lateral part, and the arm representation between leg and face representation within areas 3a, 3b, and 1/2. A similar sequence from leg to face from caudal to rostral sites was identified in areas S2 and PV. By generating functional connectivity maps of seeds defined in the primary and second somatosensory regions, we identified two clusters of tactile representation within the posterior and midcingulate cortex. However, unlike humans and macaques, no clear somatotopic maps were observed. At the subcortical level, we found a somatotopic body representation in the thalamus and, for the first time in marmosets, 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 noninvasive way in awake marmosets.NEW & NOTEWORTHY We used somatosensory stimulation combined with functional MRI (fMRI) in awake marmosets to reveal the topographic body representation in areas S1, S2, thalamus, and putamen. We showed the existence of a body representation organization within the thalamus and the cingulate cortex by computing functional connectivity maps from seeds defined in S1/S2, using resting-state fMRI data. This noninvasive approach will be essential for chronic studies by guiding invasive recording and manipulation techniques.

Feedforward and Feedback Control Share an Internal Model of the Arm's Dynamics.

Recent work has shown that, when countering external forces, the nervous system adjusts not only predictive (i.e., feedforward) control of reaching but also reflex (i.e., feedback) responses to mechanical perturbations. Here we show that altering the physical properties of the arm (i.e., intersegmental dynamics) causes the nervous system to adjust feedforward control and that this learning transfers to feedback responses even though the latter were never directly trained. Forty-five human participants (30 females) performed a single-joint elbow reaching task and countered mechanical perturbations that created pure elbow motion. In our first experiment, we altered intersegmental dynamics by asking participants to generate pure elbow movements when the shoulder joint was either free to rotate or locked by the robotic manipulandum. With the shoulder unlocked, we found robust activation of shoulder flexor muscles for pure elbow flexion trials, as required to counter the interaction torques that arise at the shoulder because of forearm rotation. After locking the shoulder joint, which cancels these interaction torques, we found a substantial reduction in shoulder muscle activity over many trials. In our second experiment, we tested whether such learning transfers to feedback control. Mechanical perturbations applied to the arm with the shoulder unlocked revealed that feedback responses also account for intersegmental dynamics. After locking the shoulder joint, we found a substantial reduction in shoulder feedback responses, as appropriate for the altered intersegmental dynamics. Our work suggests that feedforward and feedback control share an internal model of the arm's dynamics.SIGNIFICANCE STATEMENT Here we show that altering the physical properties of the arm causes people to learn new motor commands and that this learning transfers to their reflex responses to unexpected mechanical perturbations, even though the reflex responses were never directly trained. Our results suggest that feedforward motor commands and reflex responses share an internal model of the arm's dynamics.

Stability of representational geometry across a wide range of fMRI activity levels.

Fine-grained activity patterns, as measured with functional magnetic resonance imaging (fMRI), are thought to reflect underlying neural representations. Multivariate analysis techniques, such as representational similarity analysis (RSA), can be used to test models of brain representation by quantifying the representational geometry (the collection of pair-wise dissimilarities between activity patterns). One important caveat, however, is that non-linearities in the coupling between neural activity and the fMRI signal may lead to significant distortions in the representational geometry estimated from fMRI activity patterns. Here we tested the stability of representational dissimilarity measures in primary sensory-motor (S1 and M1) and early visual regions (V1/V2) across a large range of activation levels. Participants were visually cued with different letters to perform single finger presses with one of the 5 fingers at a rate of 0.3-2.6 Hz. For each stimulation frequency, we quantified the difference between the 5 activity patterns in M1, S1, and V1/V2. We found that the representational geometry remained relatively stable, even though the average activity increased over a large dynamic range. These results indicate that the representational geometry of fMRI activity patterns can be reliably assessed, largely independent of the average activity in the region. This has important methodological implications for RSA and other multivariate analysis approaches that use the representational geometry to make inferences about brain representations.

A rapid visuomotor response on the human upper limb is selectively influenced by implicit motor learning.

How do humans learn to adapt their motor actions to achieve task success? Recent behavioral and patient studies have challenged the classic notion that motor learning arises solely from the errors produced during a task, suggesting instead that explicit cognitive strategies can act in concert with the implicit, error-based, motor learning component. In this study, we show that the earliest wave of directionally tuned neuromuscular activity that begins within ~100 ms of peripheral visual stimulus onset is selectively influenced by the implicit component of motor learning. In contrast, the voluntary neuromuscular activity associated with reach initiation, which evolves ~100-200 ms later, is influenced by both the implicit and explicit components of motor learning. The selective influence of the implicit, but not explicit, component of motor learning on the directional tuning of the earliest cascade of neuromuscular activity supports the notion that these components of motor learning can differentially influence descending motor pathways. NEW & NOTEWORTHY Motor learning can be driven both by an implicit error-based component and an explicit strategic component, but the influence of these components on the descending pathways that contribute to motor control is unknown. In this study, we show that the implicit component selectively influences a reflexive circuit that rapidly generates a visuomotor response on the human upper limb. Our results show that the substrates mediating implicit and explicit motor learning exert distinct influences on descending motor pathways.

Concentric radiofrequency arrays to increase the statistical power of resting-state maps in monkeys.

The close homology of monkeys and humans has increased the prevalence of non-human-primate models in functional MRI studies of brain connectivity. To improve upon the attainable resolution in functional MRI studies, a commensurate increase in the sensitivity of the radiofrequency receiver coil is required to avoid a reduction in the statistical power of the analysis. Most receive coils are comprised of multiple loops distributed equidistantly over a surface to produce spatially independent sensitivity profiles. A larger number of smaller elements will in turn provide a higher signal-to-noise ratio (SNR) over the same field of view. As the loops become physically smaller, noise originating from the sample is reduced relative to noise originating from the coil. In this coil-noise-dominated regime, coil elements can have overlapping sensitivity profiles, yet still possess only mildly correlated noise. In this manuscript, we demonstrate that inductively decoupled, concentric coil arrays can improve temporal SNR when operating in the coil-noise-dominated regime-in contrast to what is expected for the more ubiquitous sample-noise-dominated array. A small, thin, 7-channel flexible coil is developed and operated in conjunction with an existing whole-head monkey coil. The mean and maximum noise correlation between the two arrays was 5% and 23%, respectively. When the flex coil was placed over the sensorimotor cortex, the temporal SNR improved by up to 2.3-fold in the peripheral cortex and up to 1.3-fold at a 2- to 3-cm depth within the brain. When the flex coil was placed over the frontal eye fields, resting-state maps showed substantially elevated sensitivity to correlations in the prefrontal cortex (54%), supplementary eye fields (39%), and anterior cingulate cortex (41%). The concentric-coil topology provided a pragmatic and robust means to significantly improve local temporal SNR and the statistical power of functional connectivity maps.

Edge orientation perception during active touch.

Previous studies investigating the perceptual attributes of tactile edge orientation processing have applied their stimuli to an immobilized fingertip. Here we tested the perceptual attributes of edge orientation processing when participants actively touched the stimulus. Our participants moved their finger over two pairs of edges, one pair parallel and the other nonparallel to varying degrees, and were asked to identify which of the two pairs was nonparallel. In addition to the psychophysical estimates of edge orientation acuity, we measured the speed at which participants moved their finger and the forces they exerted when moving their finger over the stimulus. We report four main findings. First, edge orientation acuity during active touch averaged 12.4°, similar to that previously reported during passive touch. Second, on average, participants moved their finger over the stimuli at ~20 mm/s and exerted contact forces of ~0.3 N. Third, there was no clear relationship between how people moved their finger or how they pressed on the stimulus and their edge orientation acuity. Fourth, consistent with previous work testing tactile spatial acuity, we found a significant correlation between fingertip size and orientation acuity such that people with smaller fingertips tended to have better orientation acuity. NEW & NOTEWORTHY Edge orientation acuity expressed by the motor system during manipulation is many times better than edge orientation acuity assessed in psychophysical studies where stimuli are applied to a passive fingertip. Here we show that this advantage is not because of movement per se because edge orientation acuity assessed in a psychophysical task, where participants actively move their finger over the stimuli, yields results similar to previous passive psychophysical studies.

Done in 100 ms: path-dependent visuomotor transformation in the human upper limb.

A core assumption underlying mental chronometry is that more complex tasks increase cortical processing, prolonging reaction times. In this study we show that increases in task complexity alter the magnitude, rather than the latency, of the output for a circuit that rapidly transforms visual information into motor actions. We quantified visual stimulus-locked responses (SLRs), which are changes in upper limb muscle recruitment that evolve at a fixed latency ~100 ms after novel visual stimulus onset. First, we studied the underlying reference frame of the SLR by dissociating the initial eye and hand position. Despite its quick latency, we found that the SLR was expressed in a hand-centric reference frame, suggesting that the circuit mediating the SLR integrated retinotopic visual information with body configuration. Next, we studied the influence of planned movement trajectory, requiring participants to prepare and generate either curved or straight reaches in the presence of obstacles to attain the same visual stimulus location. We found that SLR magnitude was influenced by the planned movement trajectory to the same visual stimulus. On the basis of these results, we suggest that the circuit mediating the SLR lies in parallel to other well-studied corticospinal pathways. Although the fixed latency of the SLR precludes extensive cortical processing, inputs conveying information relating to task complexity, such as body configuration and planned movement trajectory, can preset nodes within the circuit underlying the SLR to modulate its magnitude. NEW & NOTEWORTHY We studied stimulus-locked responses (SLRs), which are changes in human upper limb muscle recruitment that evolve at a fixed latency ~100 ms after novel visual stimulus onset. We showed that despite its quick latency, the circuitry mediating the SLR transformed a retinotopic visual signal into a hand-centric motor command that is modulated by the planned movement trajectory. We suggest that the circuit generating the SLR is mediated through a tectoreticulospinal, rather than a corticospinal, pathway.

Rapid feedback responses are flexibly coordinated across arm muscles to support goal-directed reaching.

A transcortical pathway helps support goal-directed reaching by processing somatosensory information to produce rapid feedback responses across multiple joints and muscles. Here, we tested whether such feedback responses can account for changes in arm configuration and for arbitrary visuomotor transformations-two manipulations that alter how muscles at the elbow and wrist need to be coordinated to achieve task success. Participants used a planar three degree-of-freedom exoskeleton robot to move a cursor to a target following a mechanical perturbation that flexed the elbow. In our first experiment, the cursor was mapped to the veridical position of the robot handle, but participants grasped the handle with two different hand orientations (thumb pointing upward or thumb pointing downward). We found that large rapid feedback responses were evoked in wrist extensor muscles when wrist extension helped move the cursor to the target (i.e., thumb upward), and in wrist flexor muscles when wrist flexion helped move the cursor to the target (i.e., thumb downward). In our second experiment, participants grasped the robot handle with their thumb pointing upward, but the cursor's movement was either veridical or was mirrored such that flexing the wrist moved the cursor as if the participant extended their wrist, and vice versa. After extensive practice, we found that rapid feedback responses were appropriately tuned to the wrist muscles that supported moving the cursor to the target when the cursor was mapped to the mirrored movement of the wrist, but were not tuned to the appropriate wrist muscles when the cursor was remapped to the wrist's veridical movement. NEW & NOTEWORTHY We show that rapid feedback responses were evoked in different wrist muscles depending on the arm's orientation, and this muscle activity was appropriate to generate the wrist motion that supported a reaching action. Notably, we also show that these rapid feedback responses can be evoked in wrist muscles that are detrimental to a reaching action if a nonveridical mapping between wrist and hand motion is extensively learned.

Compensating for intersegmental dynamics across the shoulder, elbow, and wrist joints during feedforward and feedback control.

Moving the arm is complicated by mechanical interactions that arise between limb segments. Such intersegmental dynamics cause torques applied at one joint to produce movement at multiple joints, and in turn, the only way to create single joint movement is by applying torques at multiple joints. We investigated whether the nervous system accounts for intersegmental limb dynamics across the shoulder, elbow, and wrist joints during self-initiated planar reaching and when countering external mechanical perturbations. Our first experiment tested whether the timing and amplitude of shoulder muscle activity account for interaction torques produced during single-joint elbow movements from different elbow initial orientations and over a range of movement speeds. We found that shoulder muscle activity reliably preceded movement onset and elbow agonist activity, and was scaled to compensate for the magnitude of interaction torques arising because of forearm rotation. Our second experiment tested whether elbow muscles compensate for interaction torques introduced by single-joint wrist movements. We found that elbow muscle activity preceded movement onset and wrist agonist muscle activity, and thus the nervous system predicted interaction torques arising because of hand rotation. Our third and fourth experiments tested whether shoulder muscles compensate for interaction torques introduced by different hand orientations during self-initiated elbow movements and to counter mechanical perturbations that caused pure elbow motion. We found that the nervous system predicted the amplitude and direction of interaction torques, appropriately scaling the amplitude of shoulder muscle activity during self-initiated elbow movements and rapid feedback control. Taken together, our results demonstrate that the nervous system robustly accounts for intersegmental dynamics and that the process is similar across the proximal to distal musculature of the arm as well as between feedforward (i.e., self-initiated) and feedback (i.e., reflexive) control.NEW & NOTEWORTHY Intersegmental dynamics complicate the mapping between applied joint torques and the resulting joint motions. We provide evidence that the nervous system robustly predicts these intersegmental limb dynamics across the shoulder, elbow, and wrist joints during reaching and when countering external perturbations.