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.

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.

A somato-cognitive action network alternates with effector regions in motor cortex.

Motor cortex (M1) has been thought to form a continuous somatotopic homunculus extending down the precentral gyrus from foot to face representations1,2, despite evidence for concentric functional zones3 and maps of complex actions4. Here, using precision functional magnetic resonance imaging (fMRI) methods, we find that the classic homunculus is interrupted by regions with distinct connectivity, structure and function, alternating with effector-specific (foot, hand and mouth) areas. These inter-effector regions exhibit decreased cortical thickness and strong functional connectivity to each other, as well as to the cingulo-opercular network (CON), critical for action5 and physiological control6, arousal7, errors8 and pain9. This interdigitation of action control-linked and motor effector regions was verified in the three largest fMRI datasets. Macaque and pediatric (newborn, infant and child) precision fMRI suggested cross-species homologues and developmental precursors of the inter-effector system. A battery of motor and action fMRI tasks documented concentric effector somatotopies, separated by the CON-linked inter-effector regions. The inter-effectors lacked movement specificity and co-activated during action planning (coordination of hands and feet) and axial body movement (such as of the abdomen or eyebrows). These results, together with previous studies demonstrating stimulation-evoked complex actions4 and connectivity to internal organs10 such as the adrenal medulla, suggest that M1 is punctuated by a system for whole-body action planning, the somato-cognitive action network (SCAN). In M1, two parallel systems intertwine, forming an integrate-isolate pattern: effector-specific regions (foot, hand and mouth) for isolating fine motor control and the SCAN for integrating goals, physiology and body movement.

Anatomic position determines oncogenic specificity in melanoma.

Oncogenic alterations to DNA are not transforming in all cellular contexts1,2. This may be due to pre-existing transcriptional programmes in the cell of origin. Here we define anatomic position as a major determinant of why cells respond to specific oncogenes. Cutaneous melanoma arises throughout the body, whereas the acral subtype arises on the palms of the hands, soles of the feet or under the nails3. We sequenced the DNA of cutaneous and acral melanomas from a large cohort of human patients and found a specific enrichment for BRAF mutations in cutaneous melanoma and enrichment for CRKL amplifications in acral melanoma. We modelled these changes in transgenic zebrafish models and found that CRKL-driven tumours formed predominantly in the fins of the fish. The fins are the evolutionary precursors to tetrapod limbs, indicating that melanocytes in these acral locations may be uniquely susceptible to CRKL. RNA profiling of these fin and limb melanocytes, when compared with body melanocytes, revealed a positional identity gene programme typified by posterior HOX13 genes. This positional gene programme synergized with CRKL to amplify insulin-like growth factor (IGF) signalling and drive tumours at acral sites. Abrogation of this CRKL-driven programme eliminated the anatomic specificity of acral melanoma. These data suggest that the anatomic position of the cell of origin endows it with a unique transcriptional state that makes it susceptible to only certain oncogenic insults.

Dexterous Hand Movements and Their Recovery After Central Nervous System Injury.

Hand dexterity has uniquely developed in higher primates and is thought to rely on the direct corticomotoneuronal (CM) pathway. Recent studies have shown that rodents and carnivores lack the direct CM pathway but can control certain levels of dexterous hand movements through various indirect CM pathways. Some homologous pathways also exist in higher primates, and among them, propriospinal (PrS) neurons in the mid-cervical segments (C3-C4) are significantly involved in hand dexterity. When the direct CM pathway was lesioned caudal to the PrS and transmission of cortical commands to hand motoneurons via the PrS neurons remained intact, dexterous hand movements could be significantly recovered. This recovery model was intensively studied, and it was found that, in addition to the compensation by the PrS neurons, a large-scale reorganization in the bilateral cortical motor-related areas and mesolimbic structures contributed to recovery. Future therapeutic strategies should target these multihierarchical areas.

Pink noise promotes sooner state transitions during bimanual coordination.

The seemingly straightforward task of tying one's shoes requires a sophisticated interplay of joints, muscles, and neural pathways, posing a formidable challenge for researchers studying the intricacies of coordination. A widely accepted framework for measuring coordinated behavior is the Haken-Kelso-Bunz (HKB) model. However, a significant limitation of this model is its lack of accounting for the diverse variability structures inherent in the coordinated systems it frequently models. Variability is a pervasive phenomenon across various biological and physical systems, and it changes in healthy adults, older adults, and pathological populations. Here, we show, both empirically and with simulations, that manipulating the variability in coordinated movements significantly impacts the ability to change coordination patterns-a fundamental feature of the HKB model. Our results demonstrate that synchronized bimanual coordination, mirroring a state of healthy variability, instigates earlier transitions of coordinated movements compared to other variability conditions. This suggests a heightened adaptability when movements possess a healthy variability. We anticipate our study to show the necessity of adapting the HKB model to encompass variability, particularly in predictive applications such as neuroimaging, cognition, skill development, biomechanics, and beyond.

The neural mechanisms of manual dexterity.

The hand endows us with unparalleled precision and versatility in our interactions with objects, from mundane activities such as grasping to extraordinary ones such as virtuoso pianism. The complex anatomy of the human hand combined with expansive and specialized neuronal control circuits allows a wide range of precise manual behaviours. To support these behaviours, an exquisite sensory apparatus, spanning the modalities of touch and proprioception, conveys detailed and timely information about our interactions with objects and about the objects themselves. The study of manual dexterity provides a unique lens into the sensorimotor mechanisms that endow the nervous system with the ability to flexibly generate complex behaviour.

Cardiac activity impacts cortical motor excitability.

Human cognition and action can be influenced by internal bodily processes such as heartbeats. For instance, somatosensory perception is impaired both during the systolic phase of the cardiac cycle and when heartbeats evoke stronger cortical responses. Here, we test whether these cardiac effects originate from overall changes in cortical excitability. Cortical and corticospinal excitability were assessed using electroencephalographic and electromyographic responses to transcranial magnetic stimulation while concurrently monitoring cardiac activity with electrocardiography. Cortical and corticospinal excitability were found to be highest during systole and following stronger neural responses to heartbeats. Furthermore, in a motor task, hand-muscle activity and the associated desynchronization of sensorimotor oscillations were stronger during systole. These results suggest that systolic cardiac signals have a facilitatory effect on motor excitability-in contrast to sensory attenuation that was previously reported for somatosensory perception. Thus, it is possible that distinct time windows exist across the cardiac cycle, optimizing either perception or action.

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.

The hands' default location guides tactile spatial selectivity.

Our skin is a two-dimensional sheet that can be folded into a multitude of configurations due to the mobility of our body parts. Parts of the human tactile system might account for this flexibility by being tuned to locations in the world rather than on the skin. Using adaptation, we scrutinized the spatial selectivity of two tactile perceptual mechanisms for which the visual equivalents have been reported to be selective in world coordinates: tactile motion and the duration of tactile events. Participants' hand position-uncrossed or crossed-as well as the stimulated hand varied independently across adaptation and test phases. This design distinguished among somatotopic selectivity for locations on the skin and spatiotopic selectivity for locations in the environment, but also tested spatial selectivity that fits neither of these classical reference frames and is based on the default position of the hands. For both features, adaptation consistently affected subsequent tactile perception at the adapted hand, reflecting skin-bound spatial selectivity. Yet, tactile motion and temporal adaptation also transferred across hands but only if the hands were crossed during the adaptation phase, that is, when one hand was placed at the other hand's typical location. Thus, selectivity for locations in the world was based on default rather than online sensory information about the location of the hands. These results challenge the prevalent dichotomy of somatotopic and spatiotopic selectivity and suggest that prior information about the hands' default position -right hand at the right side-is embedded deep in the tactile sensory system.

Regressive changes in sizes of somatosensory cuneate nucleus after sensory loss in primates.

Neurons in the early stages of processing sensory information suffer transneuronal atrophy when deprived of their activating inputs. For over 40 y, members of our laboratory have studied the reorganization of the somatosensory cortex during and after recovering from different types of sensory loss. Here, we took advantage of the preserved histological material from these studies of the cortical effects of sensory loss to evaluate the histological consequences in the cuneate nucleus of the lower brainstem and the adjoining spinal cord. The neurons in the cuneate nucleus are activated by touch on the hand and arm, and relay this activation to the contralateral thalamus, and from the thalamus to the primary somatosensory cortex. Neurons deprived of activating inputs tend to shrink and sometimes die. We considered the effects of differences in species, type and extent of sensory loss, recovery time after injury, and age at the time of injury on the histology of the cuneate nucleus. The results indicate that all injuries that deprived part or all of the cuneate nucleus of sensory activation result in some atrophy of neurons as reflected by a decrease in nucleus size. The extent of the atrophy is greater with greater sensory loss and with longer recovery times. Based on supporting research, atrophy appears to involve a reduction in neuron size and neuropil, with little or no neuron loss. Thus, the potential exists for restoring the hand to cortex pathway with brain-machine interfaces, for bionic prosthetics, or biologically with hand replacement surgery.

Adaptive control is reversed between hands after left hemisphere stroke and lost following right hemisphere stroke.

Human motor adaptability is of utmost utility after neurologic injury such as unilateral stroke. For successful adaptive control of movements, the nervous system must learn to correctly identify the source of a movement error and predictively compensate for this error. The current understanding is that in bimanual tasks, this process is flexible such that errors are assigned to, and compensated for, by the limb that is more likely to produce those errors. Here, we tested the flexibility of the error assignment process in right-handed chronic stroke survivors using a bimanual reaching task in which the hands jointly controlled a single cursor. We predicted that the nondominant left hand in neurotypical adults and the paretic hand in chronic stroke survivors will be more responsible for cursor errors and will compensate more within a trial and learn more from trial to trial. We found that in neurotypical adults, the nondominant left hand does compensate more than the right hand within a trial but learns less trial-to-trial. After a left hemisphere stroke, the paretic right hand compensates more than the nonparetic left hand within-trial but learns less trial-to-trial. After a right hemisphere stroke, the paretic left hand neither corrects more within-trial nor learns more trial-to-trial. Thus, adaptive control of visually guided bimanual reaching movements is reversed between hands after the left hemisphere stroke and lost following the right hemisphere stroke. These results indicate that responsibility assignment is not fully flexible but depends on a central mechanism that is lateralized to the right hemisphere.

Does Ipsilateral Remapping Following Hand Loss Impact Motor Control of the Intact Hand?

What happens once a cortical territory becomes functionally redundant? We studied changes in brain function and behavior for the remaining hand in humans (male and female) with either a missing hand from birth (one-handers) or due to amputation. Previous studies reported that amputees, but not one-handers, show increased ipsilateral activity in the somatosensory territory of the missing hand (i.e., remapping). We used a complex finger task to explore whether this observed remapping in amputees involves recruiting more neural resources to support the intact hand to meet greater motor control demands. Using basic fMRI analysis, we found that only amputees had more ipsilateral activity when motor demand increased; however, this did not match any noticeable improvement in their behavioral task performance. More advanced multivariate fMRI analyses showed that amputees had stronger and more typical representation-relative to controls' contralateral hand representation-compared with one-handers. This suggests that in amputees, both hand areas work together more collaboratively, potentially reflecting the intact hand's efference copy. One-handers struggled to learn difficult finger configurations, but this did not translate to differences in univariate or multivariate activity relative to controls. Additional white matter analysis provided conclusive evidence that the structural connectivity between the two hand areas did not vary across groups. Together, our results suggest that enhanced activity in the missing hand territory may not reflect intact hand function. Instead, we suggest that plasticity is more restricted than generally assumed and may depend on the availability of homologous pathways acquired early in life.

Chronic Adaptations in the Dorsal Horn Following a Cervical Spinal Cord Injury in Primates.

Spinal cord injury (SCI) is devastating, with limited treatment options and variable outcomes. Most in vivo SCI research has focused on the acute and early post-injury periods, and the promotion of axonal growth, so little is understood about the clinically stable chronic state, axonal growth over time, and what plasticity endures. Here, we followed animals into the chronic phase following SCI, to address this gap. Male macaques received targeted deafferentation, affecting three digits of one hand, and were divided into short (4-6 months) or long-term (11-12 months) groups, based on post-injury survival times. Monkeys were assessed behaviorally, where possible, and all exhibited an initial post-injury deficit in manual dexterity, with gradual functional recovery over 2 months. We previously reported extensive sprouting of somatosensory corticospinal (S1 CST) fibers in the dorsal horn in the first five post-injury months. Here, we show that by 1 year, the S1 CST sprouting is pruned, with the terminal territory resembling control animals. This was reflected in the number of putatively "functional" synapses observed, which increased over the first 4-5 months, and then returned to baseline by 1 year. Microglia density also increased in the affected dorsal horn at 4-6 months and then decreased, but did not return to baseline by 1 year, suggesting refinement continues beyond this time. Overall, there is a long period of reorganization and consolidation of adaptive circuitry in the dorsal horn, extending well beyond the initial behavioral recovery. This provides a potential window to target therapeutic opportunities during the chronic phase.

Encoding Manual Dexterity through Modulation of Intrinsic α Band Connectivity.

The human hand possesses both consolidated motor skills and remarkable flexibility in adapting to ongoing task demands. However, the underlying mechanisms by which the brain balances stability and flexibility remain unknown. In the absence of external input or behavior, spontaneous (intrinsic) brain connectivity is thought to represent a prior of stored memories. In this study, we investigated how manual dexterity modulates spontaneous functional connectivity in the motor cortex during hand movement. Using magnetoencephalography, in 47 human participants (both sexes), we examined connectivity modulations in the α and ß frequency bands at rest and during two motor tasks (i.e., finger tapping or toe squeezing). The flexibility and stability of such modulations allowed us to identify two groups of participants with different levels of performance (high and low performers) on the nine-hole peg test, a test of manual dexterity. In the α band, participants with higher manual dexterity showed distributed decreases of connectivity, specifically in the motor cortex, increased segregation, and reduced nodal centrality. Participants with lower manual dexterity showed an opposite pattern. Notably, these patterns from the brain to behavior are mirrored by results from behavior to the brain. Indeed, when participants were divided using the median split of the dexterity score, we found the same connectivity patterns. In summary, this experiment shows that a long-term motor skill-manual dexterity-influences the way the motor systems respond during movements.

Differential Modulation of Local Field Potentials in the Primary and Premotor Cortices during Ipsilateral and Contralateral Reach to Grasp in Macaque Monkeys.

Hand movements are associated with modulations of neuronal activity across several interconnected cortical areas, including the primary motor cortex (M1) and the dorsal and ventral premotor cortices (PMd and PMv). Local field potentials (LFPs) provide a link between neuronal discharges and synaptic inputs. Our current understanding of how LFPs vary in M1, PMd, and PMv during contralateral and ipsilateral movements is incomplete. To help reveal unique features in the pattern of modulations, we simultaneously recorded LFPs in these areas in two macaque monkeys performing reach and grasp movements with either the right or left hand. The greatest effector-dependent differences were seen in M1, at low (≤13 Hz) and γ frequencies. In premotor areas, differences related to hand use were only present in low frequencies. PMv exhibited the greatest increase in low frequencies during instruction cues and the smallest effector-dependent modulation during movement execution. In PMd, δ oscillations were greater during contralateral reach and grasp, and ß activity increased during contralateral grasp. In contrast, ß oscillations decreased in M1 and PMv. These results suggest that while M1 primarily exhibits effector-specific LFP activity, premotor areas compute more effector-independent aspects of the task requirements, particularly during movement preparation for PMv and production for PMd. The generation of precise hand movements likely relies on the combination of complementary information contained in the unique pattern of neural modulations contained in each cortical area. Accordingly, integrating LFPs from premotor areas and M1 could enhance the performance and robustness of brain-machine interfaces.

Subcortical contributions to the sense of body ownership.

The sense of body ownership (i.e. the feeling that our body or its parts belong to us) plays a key role in bodily self-consciousness and is believed to stem from multisensory integration. Experimental paradigms such as the rubber hand illusion have been developed to allow the controlled manipulation of body ownership in laboratory settings, providing effective tools for investigating malleability in the sense of body ownership and the boundaries that distinguish self from other. Neuroimaging studies of body ownership converge on the involvement of several cortical regions, including the premotor cortex and posterior parietal cortex. However, relatively less attention has been paid to subcortical structures that may also contribute to body ownership perception, such as the cerebellum and putamen. Here, on the basis of neuroimaging and neuropsychological observations, we provide an overview of relevant subcortical regions and consider their potential role in generating and maintaining a sense of ownership over the body. We also suggest novel avenues for future research targeting the role of subcortical regions in making sense of the body as our own.

Learning the signatures of the human grasp using a scalable tactile glove.

Humans can feel, weigh and grasp diverse objects, and simultaneously infer their material properties while applying the right amount of force-a challenging set of tasks for a modern robot1. Mechanoreceptor networks that provide sensory feedback and enable the dexterity of the human grasp2 remain difficult to replicate in robots. Whereas computer-vision-based robot grasping strategies3-5 have progressed substantially with the abundance of visual data and emerging machine-learning tools, there are as yet no equivalent sensing platforms and large-scale datasets with which to probe the use of the tactile information that humans rely on when grasping objects. Studying the mechanics of how humans grasp objects will complement vision-based robotic object handling. Importantly, the inability to record and analyse tactile signals currently limits our understanding of the role of tactile information in the human grasp itself-for example, how tactile maps are used to identify objects and infer their properties is unknown6. Here we use a scalable tactile glove and deep convolutional neural networks to show that sensors uniformly distributed over the hand can be used to identify individual objects, estimate their weight and explore the typical tactile patterns that emerge while grasping objects. The sensor array (548 sensors) is assembled on a knitted glove, and consists of a piezoresistive film connected by a network of conductive thread electrodes that are passively probed. Using a low-cost (about US$10) scalable tactile glove sensor array, we record a large-scale tactile dataset with 135,000 frames, each covering the full hand, while interacting with 26 different objects. This set of interactions with different objects reveals the key correspondences between different regions of a human hand while it is manipulating objects. Insights from the tactile signatures of the human grasp-through the lens of an artificial analogue of the natural mechanoreceptor network-can thus aid the future design of prosthetics7, robot grasping tools and human-robot interactions1,8-10.