Objective:The notion of a somatotopically organized motor cortex, with movements of different body parts being controlled by spatially distinct areas of cortex, is well known. However, recent studies have challenged this notion and suggested a more distributed representation of movement control. This shift in perspective has significant implications, particularly when considering the implantation location of electrode arrays for intracortical brain-computer interfaces (iBCIs). We sought to evaluate whether the location of neural recordings from the precentral gyrus, and thus the underlying somatotopy, has any impact on the imagery strategies that can enable successful iBCI control.Approach:Three individuals with a spinal cord injury were enrolled in an ongoing clinical trial of an iBCI. Participants had two intracortical microelectrode arrays implanted in the arm and/or hand areas of the precentral gyrus based on presurgical functional imaging. Neural data were recorded while participants attempted to perform movements of the hand, wrist, elbow, and shoulder.Main results:We found that electrode arrays that were located more medially recorded significantly more activity during attempted proximal arm movements (elbow, shoulder) than did lateral arrays, which captured more activity related to attempted distal arm movements (hand, wrist). We also evaluated the relative contribution from the two arrays implanted in each participant to decoding accuracy during calibration of an iBCI decoder for translation and grasping tasks. For both task types, imagery strategy (e.g. reaching vs wrist movements) had a significant impact on the relative contributions of each array to decoding. Overall, we found some evidence of broad tuning to arm and hand movements; however, there was a clear bias in the amount of information accessible about each movement type in spatially distinct areas of cortex.Significance:These results demonstrate that classical concepts of somatotopy can have real consequences for iBCI use, and highlight the importance of considering somatotopy when planning iBCI implantation.

Intracortical microstimulation (ICMS) of somatosensory cortex evokes tactile sensations whose properties can be systematically manipulated by varying stimulation parameters. However, ICMS currently provides an imperfect sense of touch, limiting manual dexterity and tactile experience. Leveraging our understanding of how tactile features are encoded in the primary somatosensory cortex (S1), we sought to inform individuals with paralysis about local geometry and apparent motion of objects on their skin. We simultaneously delivered ICMS through electrodes with spatially patterned projected fields (PFs), evoking sensations of edges. We then created complex PFs that encode arbitrary tactile shapes and skin indentation patterns. By delivering spatiotemporally patterned ICMS, we evoked sensation of motion across the skin, the speed and direction of which could be controlled. Thus, we improved individuals’ tactile experience and use of brain-controlled bionic hands.

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.

Precise anatomical implantation of a microelectrode array is fundamental for successful brain-computer interface (BCI) surgery, ensuring high-quality, robust signal communication between the brain and the computer interface. Robotic neurosurgery can contribute to this goal, but its application in BCI surgery has been underexplored. Here, the authors present a novel robot-assisted surgical technique to implant rigid intracortical microelectrode arrays for the BCI. Using this technique, the authors performed surgery in a 31-year-old male with tetraplegia due to a traumatic C4 spinal cord injury that occurred a decade earlier. Each of the arrays was embedded into the parenchyma with a single insertion without complication. Postoperative imaging verified that the devices were placed as intended. With the motor cortex arrays, the participant successfully accomplished 2D control of a virtual arm and hand, with a success rate of 20 of 20 attempts, and recording quality was maintained at 100 and 200 days postimplantation. Intracortical microstimulation of the somatosensory cortex arrays elicited sensations in the fingers and palm. A robotic neurosurgery technique was successfully translated into BCI device implantation as part of an early feasibility trial with the long-term goal of restoring upper-limb function. The technique was demonstrated to be accurate and subsequently contributed to high-quality signal communication.

Objective. As brain-computer interface (BCI) research advances, many new applications are being developed. Tasks can be performed in different virtual environments, and whether a BCI user can switch environments seamlessly will influence the ultimate utility of a clinical device.Approach. Here we investigate the importance of the immersiveness of the virtual environment used to train BCI decoders on the resulting decoder and its generalizability between environments. Two participants who had intracortical electrodes implanted in their precentral gyrus used a BCI to control a virtual arm, both viewed immersively through virtual reality goggles and at a distance on a flat television monitor.Main results. Each participant performed better with a decoder trained and tested in the environment they had used the most prior to the study, one for each environment type. The neural tuning to the desired movement was minimally influenced by the immersiveness of the environment. Finally, in further testing with one of the participants, we found that decoders trained in one environment generalized well to the other environment, but the order in which the environments were experienced within a session mattered.Significance. Overall, experience with an environment was more influential on performance than the immersiveness of the environment, but BCI performance generalized well after accounting for experience.Clinical Trial: NCT01894802.

Intracortical brain-computer interfaces (iBCIs) have achieved remarkable progress in restoring arm and hand movement by inferring motor intent from neural signals in primary motor cortex (M1) and realizing the intended movements in a bionic limb. However, manual interactions with objects require not only restoration of movement but also the precise application of forces on objects, which implies a different mode of limb control that has been largely overlooked. One of the major obstacles in incorporating force control is the lack of understanding of how manual forces are encoded in M1 during object interactions. To fill this gap, we recorded the neural activity in M1 as monkeys grasped sensorized objects with varying levels of force. We found that static decoders could not reliably extract force information from M1 activity, suggesting a dynamic relationship between force and neural activity. Consistent with this hypothesis, a recurrent neural network could exploit these dynamics to accurately decode time-varying forces. Next, we applied the insights gleaned from our experiments with able-bodied macaques to build decoders of manual force in a human participant with tetraplegia. First, we found that the patterns of responses in human M1 during imagined force application were similar to those in monkey M1 during physical force application. We then applied recurrent neural networks to decode force from M1 activity and showed that these allow the participants to accurately exert forces with a (brain-controlled) virtual hand.

Vision and proprioception regulate motor output during reaching. To study the effects of sensory input on motor control, brain computer interfaces (BCIs) offer particular advantages. As part of a long-term clinical BCI trial, we implanted two 96-channel microelectrode arrays into M1 of a person who was completely paralyzed below the neck but retained intact somatosensation. Neural recordings from M1 were transformed into a 2-dimensional velocity control signal for a robotic arm using an optimal linear estimator decoder that was calibrated while the participant imagined performing movements demonstrated by a virtual arm. Once the decoder was calibrated, we asked the participant to move the robotic arm left and right past a pair of lines as many times as possible in one minute. We examined how visual and proprioceptive feedback were incorporated into BCI control during this task by providing the participant with either visual or proprioceptive feedback, both, or neither. Proprioceptive feedback was provided by moving the participant's own arm to match the movement of the robotic arm. Task performance with vision or proprioception alone was better than when neither were provided. However, providing proprioceptive feedback impaired performance relative to visual feedback alone, unless the decoder was calibrated with neural data collected while both visual and proprioceptive feedback were provided. Providing proprioceptive feedback during decoder calibration rescued performance because it better captured M1's neural activity during BCI control with proprioceptive feedback. In general, BCI performance was positively correlated with how well the decoder captured variance in neural activity during the task. In summary, we found that while the BCI participant was able to use proprioceptive feedback regardless of whether the decoder was trained with vision only or vision and proprioception, training the decoder with both visual and proprioceptive feedback made performance more robust to the addition or removal of visual or proprioceptive feedback. This was because training a decoder with proprioceptive feedback allows the decoder to take advantage of proprioception-driven activity in M1. Overall, we demonstrated that natural sensation can be effectively combined with BCI to improve performance in humans.### Competing Interest StatementThe authors have declared no competing interest.### Clinical TrialThis trial is registered on clinicaltrials.gov (http://clinicaltrials.gov/ct2/show/[NCT01364480][1]).### Funding StatementThis study was funded by the Defense Advanced Research Projects Agency's (Arlington, VA, USA) Revolutionizing Prosthetics program (contract number N66001-10-C-4056).### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:This study was conducted under an Investigational Device Exemption (IDE) granted by the US Food and Drug Administration and with approval from the Institutional Review Boards at the University of Pittsburgh and the Space and Naval Warfare Systems Center Pacific. This trial is registered on clinicaltrials.gov (http://clinicaltrials.gov/ct2/show/[NCT01364480][1]). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesAll data produced in the present study are available upon reasonable request to the authors [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT01364480&atom=%2Fmedrxiv%2Fearly%2F2024%2F02%2F28%2F2024.02.26.24303289.atom

Understanding how the body is represented in motor cortex is key to understanding how the brain controls movement. The precentral gyrus (PCG) has long been thought to contain largely distinct regions for the arm, leg and face (represented by the "motor homunculus"). However, mounting evidence has begun to reveal a more intermixed, interrelated and broadly tuned motor map. Here, we revisit the motor homunculus using microelectrode array recordings from 20 arrays that broadly sample PCG across 8 individuals, creating a comprehensive map of human motor cortex at single neuron resolution. We found whole-body representations throughout all sampled points of PCG, contradicting traditional leg/arm/face boundaries. We also found two speech-preferential areas with a broadly tuned, orofacial-dominant area in between them, previously unaccounted for by the homunculus. Throughout PCG, movement representations of the four limbs were interlinked, with homologous movements of different limbs (e.g., toe curl and hand close) having correlated representations. Our findings indicate that, while the classic homunculus aligns with each area's preferred body region at a coarse level, at a finer scale, PCG may be better described as a mosaic of functional zones, each with its own whole-body representation.

Tactile feedback from brain-controlled bionic hands can be partially restored via intracortical microstimulation (ICMS) of the primary somatosensory cortex. In ICMS, the location of percepts depends on the electrode's location and the percept intensity depends on the stimulation frequency and amplitude. Sensors on a bionic hand can thus be linked to somatotopically appropriate electrodes, and the contact force of each sensor can be used to determine the amplitude of a stimulus. Here we report a systematic investigation of the localization and intensity of ICMS-evoked percepts in three participants with cervical spinal cord injury. A retrospective analysis of projected fields showed that they were typically composed of a focal hotspot with diffuse borders, arrayed somatotopically in keeping with their underlying receptive fields and stable throughout the duration of the study. When testing the participants' ability to rapidly localize a single ICMS presentation, individual electrodes typically evoked only weak sensations, making object localization and discrimination difficult. However, overlapping projected fields from multiple electrodes produced more localizable and intense sensations and allowed for a more precise use of a bionic hand.

Intracortical microstimulation (ICMS) is a method for restoring sensation to people with paralysis as part of a bidirectional brain-computer interface to restore upper limb function. Evoking tactile sensations of the hand through ICMS requires precise targeting of implanted electrodes. Here we describe the presurgical imaging procedures used to generate functional maps of the hand area of the somatosensory cortex and subsequent planning that guided the implantation of intracortical microelectrode arrays. In five participants with cervical spinal cord injury, across two study locations, this procedure successfully enabled ICMS-evoked sensations localized to at least the first four digits of the hand. The imaging and planning procedures developed through this clinical trial provide a roadmap for other brain-computer interface studies to ensure successful placement of stimulation electrodes.

Intracortical microstimulation (ICMS) is a method for restoring sensation to people with paralysis as part of a bidirectional brain-computer interface (BCI) to restore upper limb function. Evoking tactile sensations of the hand through ICMS requires precise targeting of implanted electrodes. Here we describe the presurgical imaging procedures used to generate functional maps of the hand area of the somatosensory cortex and subsequent planning that guided the implantation of intracortical microelectrode arrays. In five participants with cervical spinal cord injury, across two study locations, this procedure successfully enabled ICMS-evoked sensations localized to at least the first four digits of the hand. The imaging and planning procedures developed through this clinical trial provide a roadmap for other BCI studies to ensure the successful placement of stimulation electrodes.

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.

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.

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.

This was an investigational device observational trial with the objective to evaluate the impact of distractions on intracortical brain-computer interface (BCI) performance. Two individuals with tetraplegia had microelectrode arrays implanted into their motor cortex for trials of intracortical BCI safety and performance. The primary task was moving a robotic arm between two targets as quickly as possible, performed alone and with various secondary distraction conditions. Primary outcomes included targets acquired, path efficiency, and subjective difficulty. There was no difference in the number of targets acquired for either subject with or without distractions. Median path efficiency was similar across all conditions (range: 0.766-0.846) except the motor distraction for Subject P2, where the median path efficiency dropped to 0.675 (p = 0.033, Mann-Whitney U test). Both subjects rated the overall difficulty of the task with and without distractions as low. Overall, intracortical BCI performance was robust to various distractions.

Objective. Intracortical microstimulation (ICMS) in somatosensory cortex can restore sensation to people with spinal cord injury. However, the recording quality from implanted microelectrodes can degrade over time and limitations in stimulation longevity have been considered a potential barrier to the clinical use of ICMS. Our objective was to evaluate recording stability of intracortical electrodes implanted in the motor and somatosensory cortex of one person. The electrodes in motor cortex had platinum tips and were not stimulated, while the electrodes in somatosensory cortex had sputtered iridium oxide film (SIROF) tips and were stimulated. Additionally, we measured how well ICMS was able to evoke sensations over time. Approach. We implanted microelectrode arrays with SIROF tips in the somatosensory cortex (SIROF-sensory) of a human participant with a cervical spinal cord injury. We regularly stimulated these electrodes to evoke tactile sensations on the hand. Here, we quantify the stability of these electrodes in comparison to non-stimulated platinum electrodes implanted in the motor cortex (platinum-motor) over 1500 days with recorded signal quality and electrode impedances. Additionally, we quantify the stability of ICMS-evoked sensations using detection thresholds. Main results. We found that recording quality, as assessed by the number of electrodes with high-amplitude waveforms (>100 µV peak-to-peak), peak-to-peak voltage, noise, and signal-to-noise ratio, decreased over time on SIROF-sensory and platinum-motor electrodes. However, SIROF-sensory electrodes were more likely to continue to record high-amplitude signals than platinum-motor electrodes. Interestingly, the detection thresholds for stimulus-evoked sensations decreased over time from a median of 31.5 μA at day 100–10.4 μA at day 1500, with the largest changes occurring between day 100 and 500. Significance. These results demonstrate that ICMS in human somatosensory cortex can be provided over long periods of time without deleterious effects on recording or stimulation capabilities. In fact, the sensitivity to stimulation improved over time.

Objective. Microelectrode arrays are standard tools for conducting chronic electrophysiological experiments, allowing researchers to simultaneously record from large numbers of neurons. Specifically, Utah electrode arrays (UEAs) have been utilized by scientists in many species, including rodents, rhesus macaques, marmosets, and human participants. The field of clinical human brain-computer interfaces currently relies on the UEA as a number of research groups have clearance from the United States Federal Drug Administration (FDA) for this device through the investigational device exemption pathway. Despite its widespread usage in systems neuroscience, few studies have comprehensively evaluated the reliability and signal quality of the Utah array over long periods of time in a large dataset. Approach. We collected and analyzed over 6000 recorded datasets from various cortical areas spanning almost nine years of experiments, totaling 17 rhesus macaques (Macaca mulatta) and 2 human subjects, and 55 separate microelectrode Utah arrays. The scale of this dataset allowed us to evaluate the average life of these arrays, based primarily on the signal-to-noise ratio of each electrode over time. Main results. Using implants in primary motor, premotor, prefrontal, and somatosensory cortices, we found that the average lifespan of available recordings from UEAs was 622 days, although we provide several examples of these UEAs lasting over 1000 days and one up to 9 years; human implants were also shown to last longer than non-human primate implants. We also found that electrode length did not affect longevity and quality, but iridium oxide metallization on the electrode tip exhibited superior yield as compared to platinum metallization. Significance. Understanding longevity and reliability of microelectrode array recordings allows researchers to set expectations and plan experiments accordingly and maximize the amount of high-quality data gathered. Our results suggest that one can expect chronic unit recordings to last at least two years, with the possibility for arrays to last the better part of a decade.

Prosthetic arms controlled by a brain-computer interface can enable people with tetraplegia to perform functional movements. However, vision provides limited feedback because information about grasping objects is best relayed through tactile feedback. We supplemented vision with tactile percepts evoked using a bidirectional brain-computer interface that records neural activity from the motor cortex and generates tactile sensations through intracortical microstimulation of the somatosensory cortex. This enabled a person with tetraplegia to substantially improve performance with a robotic limb; trial times on a clinical upper-limb assessment were reduced by half, from a median time of 20.9 to 10.2 seconds. Faster times were primarily due to less time spent attempting to grasp objects, revealing that mimicking known biological control principles results in task performance that is closer to able-bodied human abilities.

The dexterous manipulation of objects relies not only on a sophisticated motor system to move the arm and hand but also on a barrage of sensory signals that convey information about the consequences of those movements. To develop a dexterous bionic hand thus requires the restoration of both the control and the sensory signals. In this chapter, we discuss efforts to create artificial sensory feedback through electrical stimulation of the nerves for amputees or electrical stimulation of the brain for tetraplegic patients. We describe technologies developed to sense the state of the hand and contact with objects and those designed to interface with the peripheral and central nervous systems. Artificial sensory feedback, while still early in its development, already confers greater dexterity to bionic hands, but ongoing efforts to increase the naturalism of the artificial sensations show promise for further improvements.

Motor commands for the arm and hand generally arise from the contralateral motor cortex, where most of the relevant corticospinal tract originates. However, the ipsilateral motor cortex shows activity related to arm movement despite the lack of direct connections. The extent to which the activity related to ipsilateral movement is independent from that related to contralateral movement is unclear based on conflicting conclusions in prior work. Here we investigate bilateral arm and hand movement tasks completed by two human subjects with intracortical microelectrode arrays implanted in the left hand and arm area of the motor cortex. Neural activity was recorded while they attempted to perform arm and hand movements in a virtual environment. This enabled us to quantify the strength and independence of motor cortical activity related to continuous movements of each arm. We also investigated the subjects' ability to control both arms through a brain-computer interface. Through a number of experiments, we found that ipsilateral arm movement was represented independently of, but more weakly than, contralateral arm movement. However, the representation of grasping was correlated between the two hands. This difference between hand and arm representation was unexpected and poses new questions about the different ways the motor cortex controls the hands and arms.