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, in the non-overlap 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 intra-parietal 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.Significance Statement The brain's ability to select and plan 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 during online preparation. These findings imply that while motor planning within the premotor cortex can occur in parallel with the execution of ongoing movement, stimulus identification and action selection in the posterior parietal cortex constitute a bottleneck for online action preparation.

Myomatrix arrays for high-definition muscle recording

Neurons coordinate their activity to produce an astonishing variety of motor behaviors. Our present understanding of motor control has grown rapidly thanks to new methods for recording and analyzing populations of many individual neurons over time. In contrast, current methods for recording the nervous system's actual motor output - the activation of muscle fibers by motor neurons - typically cannot detect the individual electrical events produced by muscle fibers during natural behaviors and scale poorly across species and muscle groups. Here we present a novel class of electrode devices ('Myomatrix arrays') that record muscle activity at unprecedented resolution across muscles and behaviors. High-density, flexible electrode arrays allow for stable recordings from the muscle fibers activated by a single motor neuron, called a 'motor unit,' during natural behaviors in many species, including mice, rats, primates, songbirds, frogs, and insects. This technology therefore allows the nervous system's motor output to be monitored in unprecedented detail during complex behaviors across species and muscle morphologies. We anticipate that this technology will allow rapid advances in understanding the neural control of behavior and identifying pathologies of the motor system.

Myomatrix arrays for high-definition muscle recording

Neurons coordinate their activity to produce an astonishing variety of motor behaviors. Our present understanding of motor control has grown rapidly thanks to new methods for recording and analyzing populations of many individual neurons over time. In contrast, current methods for recording the nervous system's actual motor output - the activation of muscle fibers by motor neurons - typically cannot detect the individual electrical events produced by muscle fibers during natural behaviors and scale poorly across species and muscle groups. Here we present a novel class of electrode devices ("Myomatrix arrays") that record muscle activity at unprecedented resolution across muscles and behaviors. High-density, flexible electrode arrays allow for stable recordings from the muscle fibers activated by a single motor neuron, called a "motor unit", during natural behaviors in many species, including mice, rats, primates, songbirds, frogs, and insects. This technology therefore allows the nervous system's motor output to be monitored in unprecedented detail during complex behaviors across species and muscle morphologies. We anticipate that this technology will allow rapid advances in understanding the neural control of behavior and in identifying pathologies of the motor system.

Reach Corrections Toward Moving Objects are Faster Than Reach Corrections Toward Instantaneously Switching Targets

Visually guided reaching is a common motor behavior that engages subcortical circuits to mediate rapid corrections. Although these neural mechanisms have evolved for interacting with the physical world, they are often studied in the context of reaching toward virtual targets on a screen. These targets often change position by disappearing from one place reappearing in another instantaneously. In this study, we instructed participants to perform rapid reaches to physical objects that changed position in different ways. In one condition, the objects moved very rapidly from one place to another. In the other condition, illuminated targets instantaneously switched position by being extinguished in one position and illuminating in another. Participants were consistently faster in correcting their reach trajectories when the object moved continuously.

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 tecto-reticulo-spinal pathway. Here, we examine whether nonhuman 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. Further, we observed that the magnitude of the express visuomotor response could be muted by contextual context, although this effect was quite variable. Overall, the response properties 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.

Sensorimotor feedback loops are selectively sensitive to reward

Although it is well established that motivational factors such as earning more money for performing well improve motor performance, how the motor system implements this improvement remains unclear. For instance, feedback-based control, which uses sensory feedback from the body to correct for errors in movement, improves with greater reward. But feedback control encompasses many feedback loops with diverse characteristics such as the brain regions involved and their response time. Which specific loops drive these performance improvements with reward is unknown, even though their diversity makes it unlikely that they are contributing uniformly. We systematically tested the effect of reward on the latency (how long for a corrective response to arise?) and gain (how large is the corrective response?) of seven distinct sensorimotor feedback loops in humans. Only the fastest feedback loops were insensitive to reward, and the earliest reward-driven changes were consistently an increase in feedback gains, not a reduction in latency. Rather, a reduction of response latencies only tended to occur in slower feedback loops. These observations were similar across sensory modalities (vision and proprioception). Our results may have implications regarding feedback control performance in athletic coaching. For instance, coaching methodologies that rely on reinforcement or 'reward shaping' may need to specifically target aspects of movement that rely on reward-sensitive feedback responses.

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°) 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 corresponds to a spatial period similar to the dimensions of individual fingertip ridges.

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.

Motor planning brings human primary somatosensory cortex into action-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 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.

Stretch reflexes

Many of us know about stretch reflexes from the doctor's office, when a physician taps the tendon near our kneecap to elicit a quick knee extension. This procedure is used as a diagnostic tool to determine the integrity of the spinal cord and the extension response it elicits may seem otherwise useless. In fact, the tendon tap taps into one aspect of a critical building block of mammalian motor control, the stretch reflexes. Stretch reflexes are often thought to quickly resist unexpected changes in muscle length via a very simple circuit in the spinal cord, and this is one circuit that the tendon tap engages. It turns out, however, that stretch reflexes support a myriad of functions and are highly flexible. Under naturalistic conditions, stretch reflexes are shaped by peripheral physiology and engage neural circuits spanning the spinal cord, brainstem and cerebral cortex. In this Primer, we outline what is currently known about stretch reflex function and its underlying mechanisms, with a specific focus on how the cascade of nested responses collectively known as stretch reflexes interact with and build off of one another to support real-world motor behavior.

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.

The Planning Horizon for Movement Sequences

When performing a long chain of actions in rapid sequence, future movements need to be planned concurrently with ongoing action. However, how far ahead we plan, and whether this ability improves with practice, is currently unknown. Here, we designed an experiment in which healthy volunteers produced sequences of 14 finger presses quickly and accurately on a keyboard in response to numerical stimuli. On every trial, participants were only shown a fixed number of stimuli ahead of the current keypress. The size of this viewing window varied between 1 (next digit revealed with the pressing of the current key) and 14 (full view of the sequence). Participants practiced the task for 5 days, and their performance was continuously assessed on random sequences. Our results indicate that participants used the available visual information to plan multiple actions into the future, but that the planning horizon was limited: receiving information about more than three movements ahead did not result in faster sequence production. Over the course of practice, we found larger performance improvements for larger viewing windows and an expansion of the planning horizon. These findings suggest that the ability to plan future responses during ongoing movement constitutes an important aspect of skillful movement. Based on the results, we propose a framework to investigate the neuronal processes underlying simultaneous planning and execution.

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.

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.

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.

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 com bine 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.

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.

Learning New Feedforward Motor Commands Based on Feedback Responses

Learning a new motor task modifies feedforward (i.e., voluntary) motor commands and such learning also changes the sensitivity of feedback responses (i.e., reflexes) to mechanical perturbations [1-9]. For example, after people learn to generate straight reaching movements in the presence of an external force field or learn to reduce shoulder muscle activity when generating pure elbow movements with shoulder fixation, evoked stretch reflex responses to mechanical perturbations reflect the learning expressed during self-initiated reaching. Such a transfer from feedforward motor commands to feedback responses is thought to take place because of shared neural circuits at the level of the spinal cord, brainstem, and cerebral cortex [10-13]. The presence of shared neural resources also predicts the transfer from feedback responses to feedforward motor commands. Little is known about such a transfer presumably because it is relatively hard to elicit learning in reflexes without engaging associated voluntary responses following mechanical perturbations. Here, we demonstrate such transfer by leveraging two approaches to elicit stretch reflexes while minimizing engagement of voluntary motor responses in the learning process: applying very short mechanical perturbations [14-19] and instructing participants to not respond to them [20-26]. 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.

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.

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.

Maintaining arm control during self-triggered and unpredictable unloading perturbations

We often perform actions where we must break through some resistive force, but want to remain in control during this unpredictable transition; for example, when an object we are pushing on transitions from static to dynamic friction and begins to move. We designed a laboratory task to replicate this situation in which participants actively pushed against a robotic manipulandum until they exceeded an unpredictable threshold, at which point the manipulandum moved freely. Human participants were instructed to either stop the movement of the handle following this unloading perturbation, or to continue pushing. We found that participants were able to modulate their reflexes in response to this unpredictable and self-triggered unloading perturbation according to the instruction they were following, and that this reflex modulation could not be explained by pre-perturbation muscle state. However, in a second task, where participants reactively produced force during the pre-unloading phase in response to the robotic manipulandum to maintain a set hand position, they were unable to modulate their reflexes in the same task-dependent way. This occurred even though the forces they produced were matched to the first task and they had more time to prepare for the unloading event. We suggest this disparity occurs because of different neural circuits involved in posture and movement, meaning that participants in the first task did not require additional time to switch from postural to movement control.

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.

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.

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.

Neural network models of the tactile system develop first-order units with spatially complex receptive fields

First-order tactile neurons have spatially complex receptive fields. Here we use machine-learning tools to show that such complexity arises for a wide range of training sets and network architectures. Moreover, we demonstrate that this complexity benefits network performance, especially on more difficult tasks and in the presence of noise. Our work suggests that spatially complex receptive fields are normatively good given the biological constraints of the tactile periphery.

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.

Fast and accurate edge orientation processing during object manipulation

Quickly and accurately extracting information about a touched object’s orientation is a critical aspect of dexterous object manipulation. However, the speed and acuity of tactile edge orientation processing with respect to the fingertips as reported in previous perceptual studies appear inadequate in these respects. Here we directly establish the tactile system’s capacity to process edge-orientation information during dexterous manipulation. Participants extracted tactile information about edge orientation very quickly, using it within 200 ms of first touching the object. Participants were also strikingly accurate. With edges spanning the entire fingertip, edge-orientation resolution was better than 3° in our object manipulation task, which is several times better than reported in previous perceptual studies. Performance remained impressive even with edges as short as 2 mm, consistent with our ability to precisely manipulate very small objects. Taken together, our results radically redefine the spatial processing capacity of the tactile system.

Putting on a necklace requires using your fingertips to hold open a clasp, which you then insert into a small ring. For you to do this, your nervous system must first work out which way the clasp and the ring are facing relative to one another. It then uses that information to coordinate the movements of your fingertips. If you fasten the necklace behind your head, your nervous system must perform these tasks without information from your eyes. Instead, it must use the way in which the edges of the clasp and the ring indent the skin on your fingertips to work out their orientation.

Earlier studies have examined this process by asking healthy volunteers to judge the orientation of objects – or more precisely edges – that an experimenter has pressed against their fingertips. But people perform worse than expected on this task given their manual dexterity. Pruszynski et al. wondered whether the task might underestimate the abilities of the volunteers because it involves passively perceiving objects, rather than actively manipulating them.

To test this idea, Pruszynski et al. designed a new experiment. Healthy volunteers were asked to use a fingertip to rotate a pointer on a dial to a target location. The participants could not see the dial, and so they had to use touch alone to determine which way the pointer was facing. They performed the task faster and more accurately than volunteers in the earlier passive experiments. Indeed, when the pointer was longer than a fingertip, the volunteers performed almost as well using touch alone as when allowed to look at the dial. Speed and accuracy remained impressive even when the pointer was only 2mm long.

The results of Pruszynski et al. show that we judge orientation more accurately when we manipulate objects than when we passively perceive them. In other words, we do better when we perform tasks in which being aware of orientation is vital. The results also suggest that the nervous system processes sensory information in different ways when it uses sensations to help control objects as opposed to just perceiving them. This could influence the development of new technology that aims to use brain activity to control computers or robotic limbs.

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.

Coordinating long-latency stretch responses across the shoulder, elbow, and wrist during goal-directed reaching

The long-latency stretch response (muscle activity 50-100 ms after a mechanical perturbation) can be coordinated across multiple joints to support goal-directed actions. Here we assessed the flexibility of such coordination and whether it serves to counteract intersegmental dynamics and exploit kinematic redundancy. In three experiments, participants made planar reaches to visual targets after elbow perturbations and we assessed the coordination of long-latency stretch responses across shoulder, elbow, and wrist muscles. Importantly, targets were placed such that elbow and wrist (but not shoulder) rotations could help transport the hand to the target-a simple form of kinematic redundancy. In experiment 1 we applied perturbations of different magnitudes to the elbow and found that long-latency stretch responses in shoulder, elbow, and wrist muscles scaled with perturbation magnitude. In experiment 2 we examined the trial-by-trial relationship between long-latency stretch responses at adjacent joints and found that the magnitudes of the responses in shoulder and elbow muscles, as well as elbow and wrist muscles, were positively correlated. In experiment 3 we explicitly instructed participants how to use their wrist to move their hand to the target after the perturbation. We found that long-latency stretch responses in wrist muscles were not sensitive to our instructions, despite the fact that participants incorporated these instructions into their voluntary behavior. Taken together, our results indicate that, during reaching, the coordination of long-latency stretch responses across multiple joints counteracts intersegmental dynamics but may not be able to exploit kinematic redundancy.

Distributed task-specific processing of somatosensory feedback for voluntary motor control

Corrective responses to limb disturbances are surprisingly complex, but the neural basis of these goal-directed responses is poorly understood. Here we show that somatosensory feedback is transmitted to many sensory and motor cortical regions within 25 ms of a mechanical disturbance applied to the monkey’s arm. When limb feedback was salient to an ongoing motor action (task engagement), neurons in parietal area 5 immediately (~25 ms) increased their response to limb disturbances, whereas neurons in other regions did not alter their response until 15 to 40 ms later. In contrast, initiation of a motor action elicited by a limb disturbance (target selection) altered neural responses in primary motor cortex ~65 ms after the limb disturbance, and then in dorsal premotor cortex, with no effect in parietal regions until 150 ms post-perturbation. Our findings highlight broad parietofrontal circuits that provide the neural substrate for goal-directed corrections, an essential aspect of highly skilled motor behaviors.

DOI:http://dx.doi.org/10.7554/eLife.13141.001

Humans and other animals can change a movement they are making in a split second, such as when a basketball player has to move around an unexpected opponent to shoot a ball through the hoop. These on-the-fly corrections rely on information about the movement that comes in from the senses. However, it was unclear how the brain and spinal cord process this sensory information to guide movement.

Omrani et al. have now recorded electrical activity from the brains of monkeys while the animals tried to keep their hand at a target. Each monkey wore a robotic exoskeleton that would occasionally move the monkey’s arm. Even if the monkey was not engaged in a motor task, a small nudge of the limb by the robot caused neural activity to spread rapidly throughout the sensory and motor regions of the cerebral cortex (the outer layer of the brain).

In some trials, when the monkey was actively trying to keep its hand at a target, the robot would again nudge the monkey’s arm slightly. Omrani et al. observed that within 25 milliseconds of this nudge, the activity in an area of the cortex called parietal area 5 responded even more, suggesting that this area was using information from the senses to actively deal with the change in arm position. This change in activity then spread to other parts of the brain.

In another set of trials, the monkey was trained to move to a second target if the robot nudged its arm. In this case, the activity in an area called the primary motor cortex increased even more, likely supporting the monkey’s ability to rapidly move to this second target. Overall, the study by Omrani et al. highlights the complex way that sensory feedback is processed in the cerebral cortex, supporting our ability to perform highly skilled motor actions.

DOI:http://dx.doi.org/10.7554/eLife.13141.002

Primary motor cortex neurons classified in a postural task predict muscle activation patterns in a reaching task

Primary motor cortex (M1) activity correlates with many motor variables, making it difficult to demonstrate how it participates in motor control. We developed a two-stage process to separate the process of classifying the motor field of M1 neurons from the process of predicting the spatiotemporal patterns of its motor field during reaching. We tested our approach with a neural network model that controlled a two-joint arm to show the statistical relationship between network connectivity and neural activity across different motor tasks. In rhesus monkeys, M1 neurons classified by this method showed preferred reaching directions similar to their associated muscle groups. Importantly, the neural population signals predicted the spatiotemporal dynamics of their associated muscle groups, although a subgroup of atypical neurons reversed their directional preference, suggesting a selective role in antagonist control. These results highlight that M1 provides important details on the spatiotemporal patterns of muscle activity during motor skills such as reaching.

Goal-dependent modulation of the long-latency stretch response at the shoulder, elbow, and wrist

Many studies have demonstrated that muscle activity 50-100 ms after a mechanical perturbation (i.e., the long-latency stretch response) can be modulated in a manner that reflects voluntary motor control. These previous studies typically assessed modulation of the long-latency stretch response from individual muscles rather than how this response is concurrently modulated across multiple muscles. Here we investigated such concurrent modulation by having participants execute goal-directed reaches to visual targets after mechanical perturbations of the shoulder, elbow, or wrist while measuring activity from six muscles that articulate these joints. We found that shoulder, elbow, and wrist muscles displayed goal-dependent modulation of the long-latency stretch response, that the relative magnitude of participants' goal-dependent activity was similar across muscles, that the temporal onset of goal-dependent muscle activity was not reliably different across the three joints, and that shoulder muscles displayed goal-dependent activity appropriate for counteracting intersegmental dynamics. We also observed that the long-latency stretch response of wrist muscles displayed goal-dependent modulation after elbow perturbations and that the long-latency stretch response of elbow muscles displayed goal-dependent modulation after wrist perturbations. This pattern likely arises because motion at either joint could bring the hand to the visual target and suggests that the nervous system rapidly exploits such simple kinematic redundancy when processing sensory feedback to support goal-directed actions.

Perturbation-evoked responses in primary motor cortex are modulated by behavioral context

Corrective responses to external perturbations are sensitive to the behavioral task being performed. It is believed that primary motor cortex (M1) forms part of a transcortical pathway that contributes to this sensitivity. Previous work has identified two distinct phases in the perturbation response of M1 neurons, an initial response starting ∼20 ms after perturbation onset that does not depend on the intended motor action and a task-dependent response that begins ∼40 ms after perturbation onset. However, this invariant initial response may reflect ongoing postural control or a task-independent response to the perturbation. The present study tested these two possibilities by examining if being engaged in an ongoing postural task before perturbation onset modulated the initial perturbation response in M1. Specifically, mechanical perturbations were applied to the shoulder and/or elbow while the monkey maintained its hand at a central target or when it was watching a movie and not required to respond to the perturbation. As expected, corrective movements, muscle stretch responses, and M1 population activity in the late perturbation epoch were all significantly diminished in the movie task. Strikingly, initial perturbation responses (<40 ms postperturbation) remained the same across tasks, suggesting that the initial phase of M1 activity constitutes a task-independent response that is sensitive to the properties of the mechanical perturbation but not the goal of the ongoing motor task.

Primary motor cortex and fast feedback responses to mechanical perturbations: a primer on what we know now and some suggestions on what we should find out next

Many researchers have drawn a clear distinction between fast feedback responses to mechanical perturbations (e.g., stretch responses) and voluntary control processes. But this simple distinction is difficult to reconcile with growing evidence that long-latency stretch responses share most of the defining capabilities of voluntary control. My general view—and I believe a growing consensus—is that the functional similarities between long-latency stretch responses and voluntary control processes can be readily understood based on their shared neural circuitry, especially a transcortical pathway through primary motor cortex. Here I provide a very brief and selective account of the human and monkey studies linking a transcortical pathway through primary motor cortex to the generation and functional sophistication of the long-latency stretch response. I then lay out some of the notable issues that are ready to be answered.

Biting intentions modulate digastric reflex responses to sudden unloading of the jaw

Reflex responses in jaw-opening muscles can be evoked when a brittle object cracks between the teeth and suddenly unloads the jaw. We hypothesized that this reflex response is flexible and, as such, is modulated according to the instructed goal of biting through an object. Study participants performed two different biting tasks when holding a peanut half stacked on a chocolate piece between their incisors. In one task, they were asked to split the peanut half only (single-split task), and in the other task, they were asked to split both the peanut and the chocolate in one action (double-split task). In both tasks, the peanut split evoked a jaw-opening muscle response, quantified from electromyogram (EMG) recordings of the digastric muscle in a window 20-60 ms following peanut split. Consistent with our hypothesis, we found that the jaw-opening muscle response in the single-split trials was about twice the size of the jaw-opening muscle response in the double-split trials. A linear model that predicted the jaw-opening muscle response on a single-trial basis indicated that task settings played a significant role in this modulation but also that the presplit digastric muscle activity contributed to the modulation. These findings demonstrate that, like reflex responses to mechanical perturbations in limb muscles, reflex responses in jaw muscles not only show gain-scaling but also are modulated by subject intent.

BOLD responses to tactile stimuli in visual and auditory cortex depend on the frequency content of stimulation

Although some brain areas preferentially process information from a particular sensory modality, these areas can also respond to other modalities. Here we used fMRI to show that such responsiveness to tactile stimuli depends on the temporal frequency of stimulation. Participants performed a tactile threshold-tracking task where the tip of either their left or right middle finger was stimulated at 3, 20, or 100 Hz. Whole-brain analysis revealed an effect of stimulus frequency in two regions: the auditory cortex and the visual cortex. The BOLD response in the auditory cortex was stronger during stimulation at hearable frequencies (20 and 100 Hz) whereas the response in the visual cortex was suppressed at infrasonic frequencies (3 Hz). Regardless of which hand was stimulated, the frequency-dependent effects were lateralized to the left auditory cortex and the right visual cortex. Furthermore, the frequency-dependent effects in both areas were abolished when the participants performed a visual task while receiving identical tactile stimulation as in the tactile threshold-tracking task. We interpret these findings in the context of the metamodal theory of brain function, which posits that brain areas contribute to sensory processing by performing specific computations regardless of input modality.

Optimal feedback control and the long-latency stretch response

There has traditionally been a separation between voluntary control processes and the fast feedback responses which follow mechanical perturbations (i.e., stretch "reflexes"). However, a recent theory of motor control, based on optimal control, suggests that voluntary motor behavior involves the sophisticated manipulation of sensory feedback. We have recently proposed that one implication of this theory is that the long-latency stretch "reflex", like voluntary control, should support a rich assortment of behaviors because these two processes are intimately linked through shared neural circuitry including primary motor cortex. In this review, we first describe the basic principles of optimal feedback control related to voluntary motor behavior. We then explore the functional properties of upper-limb stretch responses, with a focus on how the sophistication of the long-latency stretch response rivals voluntary control. And last, we describe the neural circuitry that underlies the long-latency stretch response and detail the evidence that primary motor cortex participates in sophisticated feedback responses to mechanical perturbations.

Primary motor cortex underlies multi-joint integration for fast feedback control

A basic difficulty for the nervous system is integrating locally ambiguous sensory information to form accurate perceptions about the outside world^1–4. This local-to-global problem is also fundamental to motor control of the arm since complex mechanical interactions between the shoulder and elbow allow a particular amount of motion at one joint to arise from an infinite combination of shoulder and elbow torques⁵ (Fig. 1a). Here we show that a transcortical pathway through primary motor cortex (M1) resolves this ambiguity during fast feedback control. We demonstrate that single M1 neurons of behaving monkeys can integrate shoulder and elbow motion information into motor commands which appropriately counter the underlying torque within ~50 ms of a mechanical perturbation. Moreover, we reveal a causal link between M1 processing and multi-joint integration in humans by showing that shoulder muscle responses occurring ~50 ms after pure elbow displacement can be potentiated by transcranial magnetic stimulation. Our results show that M1 underlies multi-joint integration during fast feedback control, demonstrating that transcortical processing permits feedback responses to express a level of sophistication previously reserved for voluntary control and providing neurophysiological support for influential theories positing that voluntary movement is generated by the intelligent manipulation of sensory feedback^6,7.

The long-latency reflex is composed of at least two functionally independent processes

The nervous system counters mechanical perturbations applied to the arm with a stereotypical sequence of muscle activity, starting with the short-latency stretch reflex and ending with a voluntary response. Occurring between these two events is the enigmatic long-latency reflex. Although researchers have been fascinated by the long-latency reflex for over 60 years, some of the most basic questions about this response remain unresolved and often debated. In the present study we help resolve one such question by providing clear evidence that the human long-latency reflex during a naturalistic motor task is not a single functional response; rather, it appears to reflect the output of (at least) two functionally independent processes that overlap in time and sum linearly. One of these functional components shares an important attribute of the short-latency reflex (i.e., automatic gain scaling, sensitivity to background load), and the other shares a defining feature of voluntary control (i.e., task dependency, sensitivity to goal target position). We further show that the task-dependent component of long-latency activity reflects a feedback control process rather than the simplest triggered reaction to a mechanical stimulus.