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.

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.

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.

Hand-Jaw Coordination as Mice Handle Food Is Organized around Intrinsic Structure-Function Relationships.

Rodent jaws evolved structurally to support dual functionality, for either biting or chewing food. Rodent hands also function dually during food handling, for actively manipulating or statically holding food. How are these oral and manual functions coordinated? We combined electrophysiological recording of muscle activity and kilohertz kinematic tracking to analyze masseter and hand actions as mice of both sexes handled food. Masseter activity was organized into two modes synchronized to hand movement modes. In holding/chewing mode, mastication occurred as rhythmic (∼5 Hz) masseter activity while the hands held food below the mouth. In oromanual/ingestion mode, bites occurred as lower-amplitude aperiodic masseter events that were precisely timed to follow regrips (by ∼200 ms). Thus, jaw and hand movements are flexibly coordinated during food handling: uncoupled in holding/chewing mode and tightly coordinated in oromanual/ingestion mode as regrip-bite sequences. Key features of this coordination were captured in a simple model of hierarchically orchestrated mode-switching and intramode action sequencing. We serendipitously detected an additional masseter-related action, tooth sharpening, identified as bouts of higher-frequency (∼13 Hz) rhythmic masseter activity, which was accompanied by eye displacement, including rhythmic proptosis, attributable to masseter contractions. Collectively, the findings demonstrate how a natural, complex, and goal-oriented activity is organized as an assemblage of distinct modes and complex actions, adapted for the divisions of function arising from anatomical structure. These results reveal intricate, high-speed coordination of disparate effectors and show how natural forms of dexterity can serve as a model for understanding the behavioral neurobiology of multi-body-part coordination.

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.

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.

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.

Cortical activations associated with spatial remapping of finger touch using EEG.

The spatial coding of tactile information is functionally essential for touch-based shape perception and motor control. However, the spatiotemporal dynamics of how tactile information is remapped from the somatotopic reference frame in the primary somatosensory cortex to the spatiotopic reference frame remains unclear. This study investigated how hand position in space or posture influences cortical somatosensory processing. Twenty-two healthy subjects received electrical stimulation to the right thumb (D1) or little finger (D5) in three position conditions: palm down on right side of the body (baseline), hand crossing the body midline (effect of position), and palm up (effect of posture). Somatosensory-evoked potentials (SEPs) were recorded using electroencephalography. One early-, two mid-, and two late-latency neurophysiological components were identified for both fingers: P50, P1, N125, P200, and N250. D1 and D5 showed different cortical activation patterns: compared with baseline, the crossing condition showed significant clustering at P1 for D1, and at P50 and N125 for D5; the change in posture showed a significant cluster at N125 for D5. Clusters predominated at centro-parietal electrodes. These results suggest that tactile remapping of fingers after electrical stimulation occurs around 100-125 ms in the parietal cortex.

Differential contribution of sensorimotor cortex and subthalamic nucleus to unimanual and bimanual hand movements.

Why does unilateral deep brain stimulation improve motor function bilaterally? To address this clinical observation, we collected parallel neural recordings from sensorimotor cortex (SMC) and the subthalamic nucleus (STN) during repetitive ipsilateral, contralateral, and bilateral hand movements in patients with Parkinson's disease. We used a cross-validated electrode-wise encoding model to map electromyography data to the neural signals. Electrodes in the STN encoded movement at a comparable level for both hands, whereas SMC electrodes displayed a strong contralateral bias. To examine representational overlap across the two hands, we trained the model with data from one condition (contralateral hand) and used the trained weights to predict neural activity for movements produced with the other hand (ipsilateral hand). Overall, between-hand generalization was poor, and this limitation was evident in both regions. A similar method was used to probe representational overlap across different task contexts (unimanual vs. bimanual). Task context was more important for the STN compared to the SMC indicating that neural activity in the STN showed greater divergence between the unimanual and bimanual conditions. These results indicate that SMC activity is strongly lateralized and relatively context-free, whereas the STN integrates contextual information with the ongoing behavior.

Simultaneous cortical, subcortical, and brainstem mapping of sensory activation.

Nonpainful tactile sensory stimuli are processed in the cortex, subcortex, and brainstem. Recent functional magnetic resonance imaging studies have highlighted the value of whole-brain, systems-level investigation for examining sensory processing. However, whole-brain functional magnetic resonance imaging studies are uncommon, in part due to challenges with signal to noise when studying the brainstem. Furthermore, differentiation of small sensory brainstem structures such as the cuneate and gracile nuclei necessitates high-resolution imaging. To address this gap in systems-level sensory investigation, we employed a whole-brain, multi-echo functional magnetic resonance imaging acquisition at 3T with multi-echo independent component analysis denoising and brainstem-specific modeling to enable detection of activation across the entire sensory system. In healthy participants, we examined patterns of activity in response to nonpainful brushing of the right hand, left hand, and right foot (n = 10 per location), and found the expected lateralization, with distinct cortical and subcortical responses for upper and lower limb stimulation. At the brainstem level, we differentiated the adjacent cuneate and gracile nuclei, corresponding to hand and foot stimulation respectively. Our findings demonstrate that simultaneous cortical, subcortical, and brainstem mapping at 3T could be a key tool to understand the sensory system in both healthy individuals and clinical cohorts with sensory deficits.

Gray matter volume of functionally relevant primary motor cortex is causally related to learning a hand motor task.

Variability in brain structure is associated with the capacity for behavioral change. However, a causal link between specific brain areas and behavioral change (such as motor learning) has not been demonstrated. We hypothesized that greater gray matter volume of a primary motor cortex (M1) area active during a hand motor learning task is positively correlated with subsequent learning of the task, and that the disruption of this area blocks learning of the task. Healthy participants underwent structural MRI before learning a skilled hand motor task. Next, participants performed this learning task during fMRI to determine M1 areas functionally active during this task. This functional ROI was anatomically constrained with M1 boundaries to create a group-level "Active-M1" ROI used to measure gray matter volume in each participant. Greater gray matter volume in the left hemisphere Active-M1 ROI was related to greater motor learning in the corresponding right hand. When M1 hand area was disrupted with repetitive transcranial stimulation (rTMS), learning of the motor task was blocked, confirming its causal link to motor learning. Our combined imaging and rTMS approach revealed greater cortical volume in a task-relevant M1 area is causally related to learning of a hand motor task in healthy humans.

Visuomotor prediction during action planning in the human frontoparietal cortex and cerebellum.

The concept of forward models in the brain, classically applied to describing on-line motor control, can in principle be extended to action planning, i.e. assuming forward sensory predictions are issued during the mere preparation of movements. To test this idea, we combined a delayed movement task with a virtual reality based manipulation of visuomotor congruence during functional magnetic resonance imaging. Participants executed simple hand movements after a delay. During the delay, two aspects of the upcoming movement could be cued: the movement type and the visuomotor mapping (i.e. congruence of executed hand movements and visual movement feedback by a glove-controlled virtual hand). Frontoparietal areas showed increased delay period activity when preparing pre-specified movements (cued > uncued). The cerebellum showed increased activity during the preparation for incongruent > congruent visuomotor mappings. The left anterior intraparietal sulcus showed an interaction effect, responding most strongly when a pre-specified (cued) movement was prepared under expected visuomotor incongruence. These results suggest that motor planning entails a forward prediction of visual body movement feedback, which can be adjusted in anticipation of nonstandard visuomotor mappings, and which is likely computed by the cerebellum and integrated with state estimates for (planned) control in the anterior intraparietal sulcus.

Phylogenetic differences in the morphology and shape of the central sulcus in great apes and humans: implications for the evolution of motor functions.

The central sulcus divides the primary motor and somatosensory cortices in many anthropoid primate brains. Differences exist in the surface area and depth of the central sulcus along the dorso-ventral plane in great apes and humans compared to other primate species. Within hominid species, there are variations in the depth and aspect of their hand motor area, or knob, within the precentral gyrus. In this study, we used post-image analyses on magnetic resonance images to characterize the central sulcus shape of humans, chimpanzees (Pan troglodytes), gorillas (Gorilla gorilla), and orangutans (Pongo pygmaeus and Pongo abelii). Using these data, we examined the morphological variability of central sulcus in hominids, focusing on the hand region, a significant change in human evolution. We show that the central sulcus shape differs between great ape species, but all show similar variations in the location of their hand knob. However, the prevalence of the knob location along the dorso-ventral plane and lateralization differs between species and the presence of a second ventral motor knob seems to be unique to humans. Humans and orangutans exhibit the most similar and complex central sulcus shapes. However, their similarities may reflect divergent evolutionary processes related to selection for different positional and habitual locomotor functions.

Proactive Interhemispheric Disinhibition Supports Response Preparation during Selective Stopping.

Response inhibition is essential for terminating inappropriate actions. A substantial response delay may occur in the nonstopped effector when only part of a multieffector action is terminated. This stopping-interference effect has been attributed to nonselective response inhibition processes and can be reduced with proactive cuing. This study aimed to elucidate the role of interhemispheric primary motor cortex (M1-M1) influences during selective stopping with proactive cuing. We hypothesized that stopping-interference would be reduced as stopping certainty increased because of proactive recruitment of interhemispheric facilitation or inhibition when cued to respond or stop, respectively. Twenty-three healthy human participants of either sex performed a bimanual anticipatory response inhibition paradigm with cues signaling the likelihood of a stop-signal occurring. Dual-coil transcranial magnetic stimulation was used to determine corticomotor excitability (CME), interhemispheric inhibition (IHI), and interhemispheric facilitation (IHF) in the left hand at rest and during response preparation. Response times slowed and stopping-interference decreased with increased stopping certainty. Proactive response inhibition was marked by a reduced rate of rise and faster cancel time in electromyographical bursts during stopping. There was a nonselective release of IHI but not CME from rest to in-task response preparation, whereas IHF was not observed in either context. An effector-specific reduction in CME but no reinstatement of IHI was observed when the left hand was cued to stop. These findings indicate that stopping speed and selectivity are better with proactive cueing and that interhemispheric M1-M1 channels modulate inhibitory tone during response preparation to support going but not proactive response inhibition.SIGNIFICANCE STATEMENT Response inhibition is essential for terminating inappropriate actions and, in some cases, may be required for only part of a multieffector action. The present study examined interhemispheric influences between the primary motor cortices during selective stopping with proactive cuing. Stopping selectivity was greater with increased stopping certainty and was marked by proactive adjustments to the hand cued to stop and hand cued to respond separately. Inhibitory interhemispheric influences were released during response preparation but were not directly involved in proactive response inhibition. These findings indicate that between-hand stopping can be selective with proactive cuing, but cue-related improvements are unlikely to reflect the advance engagement of interhemispheric influences between primary motor cortices.