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The primary motor (M1) and somatosensory (S1) cortices play critical roles in motor control but the signaling between these structures is poorly understood. To fill this gap, we recorded – in three participants in an ongoing human clinical trial (NCT01894802) for people with paralyzed hands – the responses evoked in the hand and arm representations of M1 during intracortical microstimulation (ICMS) in the hand representation of S1. We found that ICMS of S1 activated some M1 neurons at short, fixed latencies consistent with monosynaptic activation. Additionally, most of the ICMS-evoked responses in M1 were more variable in time, suggesting indirect effects of stimulation. The spatial pattern of M1 activation varied systematically: S1 electrodes that elicited percepts in a finger preferentially activated M1 neurons excited during that finger’s movement. Moreover, the indirect effects of S1 ICMS on M1 were context dependent, such that the magnitude and even sign relative to baseline varied across tasks. We tested the implications of these effects for brain-control of a virtual hand, in which ICMS conveyed tactile feedback. While ICMS-evoked activation of M1 disrupted decoder performance, this disruption was minimized using biomimetic stimulation, which emphasizes contact transients at the onset and offset of grasp, and reduces sustained stimulation. Here the authors record the responses evoked in the hand and arm representations of M1 during intracortical microstimulation in the hand representation of S1, and show somatotopically organized connections with motor cortex. The resulting interference with motor decoding is task dependent but can be alleviated by using biomimetic stimulation.

Tactile nerve fibers fall into a few classes that can be readily distinguished based on their spatiotemporal response properties. Because nerve fibers reflect local skin deformations, they individually carry ambiguous signals about object features. In contrast, cortical neurons exhibit heterogeneous response properties that reflect computations applied to convergent input from multiple classes of afferents, which confer to them a selectivity for behaviorally relevant features of objects. The conventional view is that these complex response properties arise within the cortex itself, implying that sensory signals are not processed to any significant extent in the two intervening structures—the cuneate nucleus (CN) and the thalamus. To test this hypothesis, we recorded the responses evoked in the CN to a battery of stimuli that have been extensively used to characterize tactile coding in both the periphery and cortex, including skin indentations, vibrations, random dot patterns, and scanned edges. We found that CN responses are more similar to their cortical counterparts than they are to their inputs: CN neurons receive input from multiple classes of nerve fibers, they have spatially complex receptive fields, and they exhibit selectivity for object features. Contrary to consensus, then, the CN plays a key role in processing tactile information.

Microstimulation of the somatosensory cortex can evoke tactile percepts in people with spinal cord injury, providing a means to restore touch. While location and intensity can be reliably conveyed, two issues that prevent creating more complex naturalistic sensations are a lack of methods to effectively scan the large stimulus parameter space and difficulties with assessing percept quality. Here, we address both challenges with an experimental paradigm that enables three male individuals with tetraplegia to control their stimulation parameters in a blinded fashion to create sensations for different virtual objects. Using this method, participants can reliably create object-specific sensations and report vivid object-appropriate characteristics. Moreover, both linear classifiers and participants can match stimulus profiles with their respective objects significantly above chance without any visual cues. Confusion between two sensations increases as the associated objects share more tactile characteristics. We conclude that while visual information contributes to the experience of the artificially evoked sensations, microstimulation in the somatosensory cortex itself can evoke intuitive percepts with a variety of tactile properties. This self-guided stimulation approach may be used to effectively characterize percepts from future stimulation paradigms. In this study, three individuals with tetraplegia designed vivid, reliable and object-appropriate sensations with a variety of tactile characteristics using self-selected stimulus parameters that stimulated their somatosensory cortex upon digital object contact

Populations of neurons produce activity with two central features. First, neuronal responses are very diverse - specific stimuli or behaviors prompt some neurons to emit many action potentials, while other neurons remain relatively silent. Second, the trial-to-trial fluctuations of neuronal response occupy a low-dimensional space due to correlated activity across the population. We link these two aspects of population response using a randomly coupled recurrent circuit model and derive the following relation: the more diverse the firing rates of neurons in a population, the lower the effective dimension of population trial-to-trial covariability. We tested our prediction using simultaneously recorded neuronal populations from numerous brain areas in mice, non-human primates, and in the motor cortex of human participants. Surprisingly, when populations are restricted to a single brain area our result holds, but when a population is composed from neurons spanning multiple brain areas the relation breaks down. This reflects the fact that the macroscopic connectivity structure at the multi-regional level is significantly more pronounced than the local wiring within a brain area. Finally, using our result we present a theory where a more diverse neuronal code leads to better fine discrimination performance from population activity. In line with this theory, we show that neuronal populations across the brain exhibit both more diverse mean responses and lower-dimensional fluctuations when the brain is in more heightened states of information processing. In sum, we present a key organizational principle of neuronal population response that is widely observed across the nervous system and acts to synergistically improve population representation.

Touch plays a key role in our perception of our body and shapes our interactions with the world, from the objects we manipulate to the people we touch. While the tactile sensibility of the hand has been extensively characterized, much less is known about touch on other parts of the body. Despite the important role of the breast in lactation as well as in affective and sexual touch, relatively little is known about its sensory properties. To fill this gap, we investigated the spatial acuity of the breast and compared it to that of the hand and back, body regions that span the range of tactile spatial acuity. First, we found that the tactile acuity of the breast was even lower than that of the back, heretofore the paragon of poor acuity. Second, acuity was lower for larger breasts, consistent with the hypothesis that innervation capacity does not scale with body size. Third, touches to different regions of the nipple were largely indistinguishable, suggesting that the nipple is a sensory unit. Fourth, localization errors were systematically biased toward the nipple.

Populations of neurons produce activity with two central features. First, neuronal responses are very diverse - specific stimuli or behaviors prompt some neurons to emit many action potentials, while other neurons remain relatively silent. Second, the trial-to-trial fluctuations of neuronal response occupy a low dimensional space, owing to significant correlations between the activity of neurons. These two features define the quality of neuronal representation. We link these two aspects of population response using a recurrent circuit model and derive the following relation: the more diverse the firing rates of neurons in a population, the lower the effective dimension of population trial-to-trial covariability. This surprising prediction is tested and validated using simultaneously recorded neuronal populations from numerous brain areas in mice, non-human primates, and in the motor cortex of human participants. Using our relation we present a theory where a more diverse neuronal code leads to better fine discrimination performance from population activity. In line with this theory, we show that neuronal populations across the brain exhibit both more diverse mean responses and lower-dimensional fluctuations when the brain is in more heightened states of information processing. In sum, we present a key organizational principle of neuronal population response that is widely observed across the nervous system and acts to synergistically improve population representation.Populations of neurons produce activity with two central features. First, neuronal responses are very diverse - specific stimuli or behaviors prompt some neurons to emit many action potentials, while other neurons remain relatively silent. Second, the trial-to-trial fluctuations of neuronal response occupy a low dimensional space, owing to significant correlations between the activity of neurons. These two features define the quality of neuronal representation. We link these two aspects of population response using a recurrent circuit model and derive the following relation: the more diverse the firing rates of neurons in a population, the lower the effective dimension of population trial-to-trial covariability. This surprising prediction is tested and validated using simultaneously recorded neuronal populations from numerous brain areas in mice, non-human primates, and in the motor cortex of human participants. Using our relation we present a theory where a more diverse neuronal code leads to better fine discrimination performance from population activity. In line with this theory, we show that neuronal populations across the brain exhibit both more diverse mean responses and lower-dimensional fluctuations when the brain is in more heightened states of information processing. In sum, we present a key organizational principle of neuronal population response that is widely observed across the nervous system and acts to synergistically improve population representation.

Manual interactions with objects are supported by tactile signals from the hand. This tactile feedback can be restored in brain-controlled bionic hands via intracortical microstimulation (ICMS) of somatosensory cortex (S1). In ICMS-based tactile feedback, contact force can be signaled by modulating the stimulation intensity based on the output of force sensors on the bionic hand, which in turn modulates the perceived magnitude of the sensation. In the present study, we gauged the dynamic range and precision of ICMS-based force feedback in three human participants implanted with arrays of microelectrodes in S1. To this end, we measured the increases in sensation magnitude resulting from increases in ICMS amplitude and participant's ability to distinguish between different intensity levels. We then assessed whether we could improve the fidelity of this feedback by implementing \"biomimetic\" ICMS-trains, designed to evoke patterns of neuronal activity that more closely mimic those in natural touch, and by delivering ICMS through multiple channels at once. We found that multi-channel biomimetic ICMS gives rise to stronger and more distinguishable sensations than does its single-channel counterpart. Finally, we implemented biomimetic multi-channel feedback in a bionic hand and had the participant perform a compliance discrimination task. We found that biomimetic multi-channel tactile feedback yielded improved discrimination over its single-channel linear counterpart. We conclude that multi-channel biomimetic ICMS conveys finely graded force feedback that more closely approximates the sensitivity conferred by natural touch.

When we interact with objects, we rely on signals from the hand that convey information about the object and our interaction with it. A basic feature of these interactions, the locations of contacts between the hand and object, is often only available via the sense of touch. Information about locations of contact between a brain-controlled bionic hand and an object can be signaled via intracortical microstimulation (ICMS) of somatosensory cortex (S1), which evokes touch sensations that are localized to a specific patch of skin. To provide intuitive location information, tactile sensors on the robotic hand drive ICMS through electrodes that evoke sensations at skin locations matching sensor locations. This approach requires that ICMS-evoked sensations be focal, stable, and distributed over the hand. To systematically investigate the localization of ICMS-evoked sensations, we analyzed the projected fields (PFs) of ICMS-evoked sensations - their location and spatial extent - from reports obtained over multiple years from three participants implanted with microelectrode arrays in S1. First, we found that PFs vary widely in their size across electrodes, are highly stable within electrode, are distributed over large swaths of each participant's hand, and increase in size as the amplitude or frequency of ICMS increases. Second, while PF locations match the locations of the receptive fields (RFs) of the neurons near the stimulating electrode, PFs tend to be subsumed by the corresponding RFs. Third, multi-channel stimulation gives rise to a PF that reflects the conjunction of the PFs of the component channels. By stimulating through electrodes with largely overlapping PFs, then, we can evoke a sensation that is experienced primarily at the intersection of the component PFs. To assess the functional consequence of this phenomenon, we implemented multichannel ICMS-based feedback in a bionic hand and demonstrated that the resulting sensations are more localizable than are those evoked via single-channel ICMS.

Stereognosis, the sense of the 3-dimensional shape of objects held in hand, requires the integration of somatosensory signals about local features -such as edges and surface curvature- with proprioceptive signals about the conformation of the fingers on the object. However, the mechanism of this integration remains unknown. Here, we investigated the spatial model that is used to integrate information about the global shape of the object with information about its local features at each point of contact. To this end, human observers judged the dissimilarity of pairs of objects that differed in their global shape, their local features, or both. We then compared the dissimilarity ratings when both global shape and local features changed to ratings when only global shape or only local features changed. We tested this with object sets of different levels of complexity, including spheres of different sizes and surface features to more varied shapes and features. For all object sets, we found that ratings when both global shape and local features changed was approximately an additive combination of the ratings when only global shape or only local features changed. For the majority of subjects, a city-block spatial model best explained their responses. Our results suggest that information about global shape is encoded independently from that about local features during interactions with objects. This implies that the neural representations of object shape and local features, though integrated, are separable.Competing Interest StatementThe authors have declared no competing interest.

Tactile nerve fibers fall into a few classes that can be readily distinguished based on their spatiotemporal response properties. Because nerve fibers reflect local skin deformations, they individually carry ambiguous signals about object features. In contrast, cortical neurons exhibit heterogeneous response properties that reflect computations applied to convergent input from multiple classes of afferents, which confer to them a selectivity for behaviorally relevant features of objects. The conventional view is that these complex response properties arise within the cortex itself, implying that sensory signals are not processed to any significant extent in the two intervening structures - the cuneate nucleus (CN) and the thalamus. To test this hypothesis, we recorded the responses evoked in CN to a battery of stimuli that have been extensively used to characterize tactile coding in both the periphery and cortex, including skin indentations, vibrations, random dot patterns, and scanned edges. We found that CN responses are more similar to their cortical counterparts than they are to their inputs: CN neurons receive input from multiple classes of nerve fibers, they have spatially complex receptive fields, and they exhibit selectivity for object features. Contrary to consensus, then, CN plays a key role in processing tactile information. Competing Interest Statement The authors have declared no competing interest.