Restoring the Sense of Touch Using a Sensorimotor Demultiplexing Neural Interface.

Paralyzed muscles can be reanimated following spinal cord injury (SCI) using a brain-computer interface (BCI) to enhance motor function alone. Importantly, the sense of touch is a key component of motor function. Here, we demonstrate that a human participant with a clinically complete SCI can use a BCI to simultaneously reanimate both motor function and the sense of touch, leveraging residual touch signaling from his own hand. In the primary motor cortex (M1), residual subperceptual hand touch signals are simultaneously demultiplexed from ongoing efferent motor intention, enabling intracortically controlled closed-loop sensory feedback. Using the closed-loop demultiplexing BCI almost fully restored the ability to detect object touch and significantly improved several sensorimotor functions. Afferent grip-intensity levels are also decoded from M1, enabling grip reanimation regulated by touch signaling. These results demonstrate that subperceptual neural signals can be decoded from the cortex and transformed into conscious perception, significantly augmenting function.

Glia Accumulate Evidence that Actions Are Futile and Suppress Unsuccessful Behavior.

When a behavior repeatedly fails to achieve its goal, animals often give up and become passive, which can be strategic for preserving energy or regrouping between attempts. It is unknown how the brain identifies behavioral failures and mediates this behavioral-state switch. In larval zebrafish swimming in virtual reality, visual feedback can be withheld so that swim attempts fail to trigger expected visual flow. After tens of seconds of such motor futility, animals became passive for similar durations. Whole-brain calcium imaging revealed noradrenergic neurons that responded specifically to failed swim attempts and radial astrocytes whose calcium levels accumulated with increasing numbers of failed attempts. Using cell ablation and optogenetic or chemogenetic activation, we found that noradrenergic neurons progressively activated brainstem radial astrocytes, which then suppressed swimming. Thus, radial astrocytes perform a computation critical for behavior: they accumulate evidence that current actions are ineffective and consequently drive changes in behavioral states. VIDEO ABSTRACT.

Visuomotor Coupling Shapes the Functional Development of Mouse Visual Cortex.

The emergence of sensory-guided behavior depends on sensorimotor coupling during development. How sensorimotor experience shapes neural processing is unclear. Here, we show that the coupling between motor output and visual feedback is necessary for the functional development of visual processing in layer 2/3 (L2/3) of primary visual cortex (V1) of the mouse. Using a virtual reality system, we reared mice in conditions of normal or random visuomotor coupling. We recorded the activity of identified excitatory and inhibitory L2/3 neurons in response to transient visuomotor mismatches in both groups of mice. Mismatch responses in excitatory neurons were strongly experience dependent and driven by a transient release from inhibition mediated by somatostatin-positive interneurons. These data are consistent with a model in which L2/3 of V1 computes a difference between an inhibitory visual input and an excitatory locomotion-related input, where the balance between these two inputs is finely tuned by visuomotor experience.

From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response.

Detailed descriptions of brain-scale sensorimotor circuits underlying vertebrate behavior remain elusive. Recent advances in zebrafish neuroscience offer new opportunities to dissect such circuits via whole-brain imaging, behavioral analysis, functional perturbations, and network modeling. Here, we harness these tools to generate a brain-scale circuit model of the optomotor response, an orienting behavior evoked by visual motion. We show that such motion is processed by diverse neural response types distributed across multiple brain regions. To transform sensory input into action, these regions sequentially integrate eye- and direction-specific sensory streams, refine representations via interhemispheric inhibition, and demix locomotor instructions to independently drive turning and forward swimming. While experiments revealed many neural response types throughout the brain, modeling identified the dimensions of functional connectivity most critical for the behavior. We thus reveal how distributed neurons collaborate to generate behavior and illustrate a paradigm for distilling functional circuit models from whole-brain data.

Competitive Disinhibition Mediates Behavioral Choice and Sequences in Drosophila.

Even a simple sensory stimulus can elicit distinct innate behaviors and sequences. During sensorimotor decisions, competitive interactions among neurons that promote distinct behaviors must ensure the selection and maintenance of one behavior, while suppressing others. The circuit implementation of these competitive interactions is still an open question. By combining comprehensive electron microscopy reconstruction of inhibitory interneuron networks, modeling, electrophysiology, and behavioral studies, we determined the circuit mechanisms that contribute to the Drosophila larval sensorimotor decision to startle, explore, or perform a sequence of the two in response to a mechanosensory stimulus. Together, these studies reveal that, early in sensory processing, (1) reciprocally connected feedforward inhibitory interneurons implement behavioral choice, (2) local feedback disinhibition provides positive feedback that consolidates and maintains the chosen behavior, and (3) lateral disinhibition promotes sequence transitions. The combination of these interconnected circuit motifs can implement both behavior selection and the serial organization of behaviors into a sequence.

The science and engineering behind sensitized brain-controlled bionic hands.

Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain-machine interfaces (BMIs), which harness neural signals to reanimate the limbs via electrical activation of the muscles or to control extracorporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements with bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. Although the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and noninvasive strategies for sensory restoration.

The neuronal logic of how internal states control food choice.

When deciding what to eat, animals evaluate sensory information about food quality alongside multiple ongoing internal states1-10. How internal states interact to alter sensorimotor processing and shape decisions such as food choice remains poorly understood. Here we use pan-neuronal volumetric activity imaging in the brain of Drosophila melanogaster to investigate the neuronal basis of internal state-dependent nutrient appetites. We created a functional atlas of the ventral fly brain and find that metabolic state shapes sensorimotor processing across large sections of the neuropil. By contrast, reproductive state acts locally to define how sensory information is translated into feeding motor output. These two states thus synergistically modulate protein-specific food intake and food choice. Finally, using a novel computational strategy, we identify driver lines that label neurons innervating state-modulated brain regions and show that the newly identified 'borboleta' region is sufficient to direct food choice towards protein-rich food. We thus identify a generalizable principle by which distinct internal states are integrated to shape decision making and propose a strategy to uncover and functionally validate how internal states shape behaviour.

Optimal reaching subject to computational and physical constraints reveals structure of the sensorimotor control system.

Optimal feedback control provides an abstract framework describing the architecture of the sensorimotor system without prescribing implementation details such as what coordinate system to use, how feedback is incorporated, or how to accommodate changing task complexity. We investigate how such details are determined by computational and physical constraints by creating a model of the upper limb sensorimotor system in which all connection weights between neurons, feedback, and muscles are unknown. By optimizing these parameters with respect to an objective function, we find that the model exhibits a preference for an intrinsic (joint angle) coordinate representation of inputs and feedback and learns to calculate a weighted feedforward and feedback error. We further show that complex reaches around obstacles can be achieved by augmenting our model with a path-planner based on via points. The path-planner revealed "avoidance" neurons that encode directions to reach around obstacles and "placement" neurons that make fine-tuned adjustments to via point placement. Our results demonstrate the surprising capability of computationally constrained systems and highlight interesting characteristics of the sensorimotor system.

Visual guidance fine-tunes probing movements of an insect appendage.

Visually guided reaching, a regular feature of human life, comprises an intricate neural control task. It includes identifying the target's position in 3D space, passing the representation to the motor system that controls the respective appendages, and adjusting ongoing movements using visual and proprioceptive feedback. Given the complexity of the neural control task, invertebrates, with their numerically constrained central nervous systems, are often considered incapable of this level of visuomotor guidance. Here, we provide mechanistic insights into visual appendage guidance in insects by studying the probing movements of the hummingbird hawkmoth's proboscis as they search for a flower's nectary. We show that visually guided proboscis movements fine-tune the coarse control provided by body movements in flight. By impairing the animals' view of their proboscis, we demonstrate that continuous visual feedback is required and actively sought out to guide this appendage. In doing so, we establish an insect model for the study of neural strategies underlying eye-appendage control in a simple nervous system.

The human somatosensory cortex contributes to the encoding of newly learned movements.

Recent studies have indicated somatosensory cortex involvement in motor learning and retention. However, the nature of its contribution is unknown. One possibility is that the somatosensory cortex is transiently engaged during movement. Alternatively, there may be durable learning-related changes which would indicate sensory participation in the encoding of learned movements. These possibilities are dissociated by disrupting the somatosensory cortex following learning, thus targeting learning-related changes which may have occurred. If changes to the somatosensory cortex contribute to retention, which, in effect, means aspects of newly learned movements are encoded there, disruption of this area once learning is complete should lead to an impairment. Participants were trained to make movements while receiving rotated visual feedback. The primary motor cortex (M1) and the primary somatosensory cortex (S1) were targeted for continuous theta-burst stimulation, while stimulation over the occipital cortex served as a control. Retention was assessed using active movement reproduction, or recognition testing, which involved passive movements produced by a robot. Disruption of the somatosensory cortex resulted in impaired motor memory in both tests. Suppression of the motor cortex had no impact on retention as indicated by comparable retention levels in control and motor cortex conditions. The effects were learning specific. When stimulation was applied to S1 following training with unrotated feedback, movement direction, the main dependent variable, was unaltered. Thus, the somatosensory cortex is part of a circuit that contributes to retention, consistent with the idea that aspects of newly learned movements, possibly learning-updated sensory states (new sensory targets) which serve to guide movement, may be encoded there.

Proprioception: A New Era Set in Motion by Emerging Genetic and Bionic Strategies?

The generation of an internal body model and its continuous update is essential in sensorimotor control. Although known to rely on proprioceptive sensory feedback, the underlying mechanism that transforms this sensory feedback into a dynamic body percept remains poorly understood. However, advances in the development of genetic tools for proprioceptive circuit elements, including the sensory receptors, are beginning to offer new and unprecedented leverage to dissect the central pathways responsible for proprioceptive encoding. Simultaneously, new data derived through emerging bionic neural machine-interface technologies reveal clues regarding the relative importance of kinesthetic sensory feedback and insights into the functional proprioceptive substrates that underlie natural motor behaviors.

Ultra-sensitive and resilient compliant strain gauges for soft machines.

Soft machines are a promising design paradigm for human-centric devices1,2 and systems required to interact gently with their environment3,4. To enable soft machines to respond intelligently to their surroundings, compliant sensory feedback mechanisms are needed. Specifically, soft alternatives to strain gauges-with high resolution at low strain (less than 5 per cent)-could unlock promising new capabilities in soft systems. However, currently available sensing mechanisms typically possess either high strain sensitivity or high mechanical resilience, but not both. The scarcity of resilient and compliant ultra-sensitive sensing mechanisms has confined their operation to laboratory settings, inhibiting their widespread deployment. Here we present a versatile and compliant transduction mechanism for high-sensitivity strain detection with high mechanical resilience, based on strain-mediated contact in anisotropically resistive structures (SCARS). The mechanism relies upon changes in Ohmic contact between stiff, micro-structured, anisotropically conductive meanders encapsulated by stretchable films. The mechanism achieves high sensitivity, with gauge factors greater than 85,000, while being adaptable for use with high-strength conductors, thus producing sensors resilient to adverse loading conditions. The sensing mechanism also exhibits high linearity, as well as insensitivity to bending and twisting deformations-features that are important for soft device applications. To demonstrate the potential impact of our technology, we construct a sensor-integrated, lightweight, textile-based arm sleeve that can recognize gestures without encumbering the hand. We demonstrate predictive tracking and classification of discrete gestures and continuous hand motions via detection of small muscle movements in the arm. The sleeve demonstration shows the potential of the SCARS technology for the development of unobtrusive, wearable biomechanical feedback systems and human-computer interfaces.

Proprioceptive feedback amplification restores effective locomotion in a neuromechanical model of lampreys with spinal injuries.

Spinal injuries in many vertebrates can result in partial or complete loss of locomotor ability. While mammals often experience permanent loss, some nonmammals, such as lampreys, can regain swimming function, though the exact mechanism is not well understood. One hypothesis is that amplified proprioceptive (body-sensing) feedback can allow an injured lamprey to regain functional swimming even if the descending signal is lost. This study employs a multiscale, integrative, computational model of an anguilliform swimmer fully coupled to a viscous, incompressible fluid and examines the effects of amplified feedback on swimming behavior. This represents a model that analyzes spinal injury recovery by combining a closed-loop neuromechanical model with sensory feedback coupled to a full Navier-Stokes model. Our results show that in some cases, feedback amplification below a spinal lesion is sufficient to partially or entirely restore effective swimming behavior.

A proprioceptive feedback circuit drives Caenorhabditis elegans locomotor adaptation through dopamine signaling.

An animal adapts its motor behavior to navigate the external environment. This adaptation depends on proprioception, which provides feedback on an animal's body postures. How proprioception mechanisms interact with motor circuits and contribute to locomotor adaptation remains unclear. Here, we describe and characterize proprioception-mediated homeostatic control of undulatory movement in the roundworm Caenorhabditis elegans. We found that the worm responds to optogenetically or mechanically induced decreases in midbody bending amplitude by increasing its anterior amplitude. Conversely, it responds to increased midbody amplitude by decreasing the anterior amplitude. Using genetics, microfluidic and optogenetic perturbation response analyses, and optical neurophysiology, we elucidated the neural circuit underlying this compensatory postural response. The dopaminergic PDE neurons proprioceptively sense midbody bending and signal to AVK interneurons via the D2-like dopamine receptor DOP-3. The FMRFamide-like neuropeptide FLP-1, released by AVK, regulates SMB head motor neurons to modulate anterior bending. We propose that this homeostatic behavioral control optimizes locomotor efficiency. Our findings demonstrate a mechanism in which proprioception works with dopamine and neuropeptide signaling to mediate motor control, a motif that may be conserved in other animals.

A cortical network processes auditory error signals during human speech production to maintain fluency.

Hearing one's own voice is critical for fluent speech production as it allows for the detection and correction of vocalization errors in real time. This behavior known as the auditory feedback control of speech is impaired in various neurological disorders ranging from stuttering to aphasia; however, the underlying neural mechanisms are still poorly understood. Computational models of speech motor control suggest that, during speech production, the brain uses an efference copy of the motor command to generate an internal estimate of the speech output. When actual feedback differs from this internal estimate, an error signal is generated to correct the internal estimate and update necessary motor commands to produce intended speech. We were able to localize the auditory error signal using electrocorticographic recordings from neurosurgical participants during a delayed auditory feedback (DAF) paradigm. In this task, participants hear their voice with a time delay as they produced words and sentences (similar to an echo on a conference call), which is well known to disrupt fluency by causing slow and stutter-like speech in humans. We observed a significant response enhancement in auditory cortex that scaled with the duration of feedback delay, indicating an auditory speech error signal. Immediately following auditory cortex, dorsal precentral gyrus (dPreCG), a region that has not been implicated in auditory feedback processing before, exhibited a markedly similar response enhancement, suggesting a tight coupling between the 2 regions. Critically, response enhancement in dPreCG occurred only during articulation of long utterances due to a continuous mismatch between produced speech and reafferent feedback. These results suggest that dPreCG plays an essential role in processing auditory error signals during speech production to maintain fluency.

A high-throughput method to deliver targeted optogenetic stimulation to moving C. elegans populations.

We present a high-throughput optogenetic illumination system capable of simultaneous closed-loop light delivery to specified targets in populations of moving Caenorhabditis elegans. The instrument addresses three technical challenges: It delivers targeted illumination to specified regions of the animal's body such as its head or tail; it automatically delivers stimuli triggered upon the animal's behavior; and it achieves high throughput by targeting many animals simultaneously. The instrument was used to optogenetically probe the animal's behavioral response to competing mechanosensory stimuli in the the anterior and posterior gentle touch receptor neurons. Responses to more than 43,418 stimulus events from a range of anterior-posterior intensity combinations were measured. The animal's probability of sprinting forward in response to a mechanosensory stimulus depended on both the anterior and posterior stimulation intensity, while the probability of reversing depended primarily on the anterior stimulation intensity. We also probed the animal's response to mechanosensory stimulation during the onset of turning, a relatively rare behavioral event, by delivering stimuli automatically when the animal began to turn. Using this closed-loop approach, over 9,700 stimulus events were delivered during turning onset at a rate of 9.2 events per worm hour, a greater than 25-fold increase in throughput compared to previous investigations. These measurements validate with greater statistical power previous findings that turning acts to gate mechanosensory evoked reversals. Compared to previous approaches, the current system offers targeted optogenetic stimulation to specific body regions or behaviors with many fold increases in throughput to better constrain quantitative models of sensorimotor processing.

Precise cortical contributions to sensorimotor feedback control during reactive balance.

The role of the cortex in shaping automatic whole-body motor behaviors such as walking and balance is poorly understood. Gait and balance are typically mediated through subcortical circuits, with the cortex becoming engaged as needed on an individual basis by task difficulty and complexity. However, we lack a mechanistic understanding of how increased cortical contribution to whole-body movements shapes motor output. Here we use reactive balance recovery as a paradigm to identify relationships between hierarchical control mechanisms and their engagement across balance tasks of increasing difficulty in young adults. We hypothesize that parallel sensorimotor feedback loops engaging subcortical and cortical circuits contribute to balance-correcting muscle activity, and that the involvement of cortical circuits increases with balance challenge. We decomposed balance-correcting muscle activity based on hypothesized subcortically- and cortically-mediated feedback components driven by similar sensory information, but with different loop delays. The initial balance-correcting muscle activity was engaged at all levels of balance difficulty. Its onset latency was consistent with subcortical sensorimotor loops observed in the lower limb. An even later, presumed, cortically-mediated burst of muscle activity became additionally engaged as balance task difficulty increased, at latencies consistent with longer transcortical sensorimotor loops. We further demonstrate that evoked cortical activity in central midline areas measured using electroencephalography (EEG) can be explained by a similar sensory transformation as muscle activity but at a delay consistent with its role in a transcortical loop driving later cortical contributions to balance-correcting muscle activity. These results demonstrate that a neuromechanical model of muscle activity can be used to infer cortical contributions to muscle activity without recording brain activity. Our model may provide a useful framework for evaluating changes in cortical contributions to balance that are associated with falls in older adults and in neurological disorders such as Parkinson's disease.

Sensory experience remodels genome architecture in neural circuit to drive motor learning.

Neuronal-activity-dependent transcription couples sensory experience to adaptive responses of the brain including learning and memory. Mechanisms of activity-dependent gene expression including alterations of the epigenome have been characterized1-8. However, the fundamental question of whether sensory experience remodels chromatin architecture in the adult brain in vivo to induce neural code transformations and learning and memory remains to be addressed. Here we use in vivo calcium imaging, optogenetics and pharmacological approaches to show that granule neuron activation in the anterior dorsal cerebellar vermis has a crucial role in a delay tactile startle learning paradigm in mice. Of note, using large-scale transcriptome and chromatin profiling, we show that activation of the motor-learning-linked granule neuron circuit reorganizes neuronal chromatin including through long-distance enhancer-promoter and transcriptionally active compartment interactions to orchestrate distinct granule neuron gene expression modules. Conditional CRISPR knockout of the chromatin architecture regulator cohesin in anterior dorsal cerebellar vermis granule neurons in adult mice disrupts enhancer-promoter interactions, activity-dependent transcription and motor learning. These findings define how sensory experience patterns chromatin architecture and neural circuit coding in the brain to drive motor learning.

Dorsomedial frontal cortex damage impairs error-based, but not reinforcement-based motor learning in humans.

We adapt our movements to new and changing environments through multiple processes. Sensory error-based learning counteracts environmental perturbations that affect the sensory consequences of movements. Sensory errors also cause the upregulation of reflexes and muscle co-contraction. Reinforcement-based learning enhances the selection of movements that produce rewarding outcomes. Although some findings have identified dissociable neural substrates of sensory error- and reinforcement-based learning, correlative methods have implicated dorsomedial frontal cortex in both. Here, we tested the causal contributions of dorsomedial frontal to adaptive motor control, studying people with chronic damage to this region. Seven human participants with focal brain lesions affecting the dorsomedial frontal and 20 controls performed a battery of arm movement tasks. Three experiments tested: (i) the upregulation of visuomotor reflexes and muscle co-contraction in response to unpredictable mechanical perturbations, (ii) sensory error-based learning in which participants learned to compensate predictively for mechanical force-field perturbations, and (iii) reinforcement-based motor learning based on binary feedback in the absence of sensory error feedback. Participants with dorsomedial frontal damage were impaired in the early stages of force field adaptation, but performed similarly to controls in all other measures. These results provide evidence for a specific and selective causal role for the dorsomedial frontal in sensory error-based learning.

Tactile suppression stems from specific sensorimotor predictions.

The ability to sample sensory information with our hands is crucial for smooth and efficient interactions with the world. Despite this important role of touch, tactile sensations on a moving hand are perceived weaker than when presented on the same but stationary hand. This phenomenon of tactile suppression has been explained by predictive mechanisms, such as internal forward models, that estimate future sensory states of the body on the basis of the motor command and suppress the associated predicted sensory feedback. The origins of tactile suppression have sparked a lot of debate, with contemporary accounts claiming that suppression is independent of sensorimotor predictions and is instead due to an unspecific mechanism. Here, we target this debate and provide evidence for specific tactile suppression due to precise sensorimotor predictions. Participants stroked with their finger over textured objects that caused predictable vibrotactile feedback signals on that finger. Shortly before touching the texture, we probed tactile suppression by applying external vibrotactile probes on the moving finger that either matched or mismatched the frequency generated by the stroking movement along the texture. We found stronger suppression of the probes that matched the predicted sensory feedback. These results show that tactile suppression is specifically tuned to the predicted sensory states of a movement.