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Background Paralysis and neuropathy, affecting millions of people worldwide, can be accompanied by significant loss of somatosensation. With tactile sensation being central to achieving dexterous movement, brain-computer interface (BCI) researchers have used intracortical and cortical surface electrical stimulation to restore somatotopically-relevant sensation to the hand. However, these approaches are restricted to stimulating the gyral areas of the brain. Since representation of distal regions of the hand extends into the sulcal regions of human primary somatosensory cortex (S1), it has been challenging to evoke sensory percepts localized to the fingertips. Objective/hypothesis Targeted stimulation of sulcal regions of S1, using stereoelectroencephalography (SEEG) depth electrodes, can evoke focal sensory percepts in the fingertips. Methods Two participants with intractable epilepsy received cortical stimulation both at the gyri via high-density electrocorticography (HD-ECoG) grids and in the sulci via SEEG depth electrode leads. We characterized the evoked sensory percepts localized to the hand. Results We show that highly focal percepts can be evoked in the fingertips of the hand through sulcal stimulation. fMRI, myelin content, and cortical thickness maps from the Human Connectome Project elucidated specific cortical areas and sub-regions within S1 that evoked these focal percepts. Within-participant comparisons showed that percepts evoked by sulcal stimulation via SEEG electrodes were significantly more focal (80% less area; p = 0.02) and localized to the fingertips more often, than by gyral stimulation via HD-ECoG electrodes. Finally, sulcal locations with consistent modulation of high-frequency neural activity during mechanical tactile stimulation of the fingertips showed the same somatotopic correspondence as cortical stimulation. Conclusions Our findings indicate minimally invasive sulcal stimulation via SEEG electrodes could be a clinically viable approach to restoring sensation.

In 2015, a group of neuroscientists and engineers assembled to watch a man play the video game Guitar Hero. He held the simplified guitar interface gingerly, using the fingers of his right hand to press down on the fret buttons and his left hand to hit the strum bar. What made this mundane bit of game play so extraordinary was the fact that the man had been paralyzed from the chest down for more than three years, without any use of his hands. Every time he moved his fingers to play a note, he was playing a song of restored autonomy.

Long-term recovery of limb function is a significant unmet need in people with paralysis. Targeted neuromodulation through epidural stimulation of the spinal cord, when paired with intense physical therapy, has shown promising results towards restoring volitional control of the upper limb in people with tetraplegia. Non-invasive neuromodulation of the cervical spinal cord using transcutaneous spinal cord stimulation (tSCS) has shown similar improvements in upper-limb motor control rehabilitation. However, the potential rehabilitative effects of activating specific cervical spinal segments in a targeted fashion using tSCS has largely remained unexplored. We show in two individuals with SCI that tSCS of the cervical spinal cord resulted in up to 1136% increase in exerted force, with minimal physical therapy. We also show, for the first time, the effectiveness of targeted tSCS in restoring strength (407% increase) and dexterity to the digits in an individual with paralysis of the hand due to a peripheral nerve injury. Furthermore, we believe this is the first study to document a 3-point improvement in sensation in people with SCI or peripheral injury after receiving tSCS. Lastly, participant gains persisted after a one-month period void of stimulation, suggesting tSCS may lead to lasting benefits. Non-invasive targeted spinal cord stimulation shows tremendous promise as a safe and effective therapeutic approach with broad applications.

Background Transcutaneous neuromuscular electrical stimulation is routinely used in physical rehabilitation and more recently in brain-computer interface applications for restoring movement in paralyzed limbs. Due to variable muscle responses to repeated or sustained stimulation, grasp force levels can change significantly over time. Here we develop and assess closed-loop methods to regulate individual finger forces to facilitate functional movement. We combined this approach with custom textile-based electrodes to form a light-weight, wearable device and evaluated in paralyzed study participants. Methods A textile-based electrode sleeve was developed by the study team and Myant, Corp. (Toronto, ON, Canada) and evaluated in a study involving three able-body participants and two participants with quadriplegia. A feedforward-feedback control structure was designed and implemented to accurately maintain finger force levels in a quadriplegic study participant. Results Individual finger flexion and extension movements, along with functional grasping, were evoked during neuromuscular electrical stimulation. Closed-loop control methods allowed accurate steady state performance ( Conclusions Textile-based electrodes were identified to be a feasible alternative to conventional electrodes and facilitated individual finger movement and functional grasping. Furthermore, closed-loop methods demonstrated accurate control of individual finger flexion force. This approach may be a viable solution for enabling grasp force regulation in quadriplegia. Trial registration NCT, NCT03385005. Registered Dec. 28, 2017

There is a broad and growing interest in Bioelectronic Medicine, a dynamic field that continues to generate new approaches in disease treatment. The fourth bioelectronic medicine summit “Technology targeting molecular mechanisms” took place on September 23 and 24, 2020. This virtual meeting was hosted by the Feinstein Institutes for Medical Research, Northwell Health. The summit called international attention to Bioelectronic Medicine as a platform for new developments in science, technology, and healthcare. The meeting was an arena for exchanging new ideas and seeding potential collaborations involving teams in academia and industry. The summit provided a forum for leaders in the field to discuss current progress, challenges, and future developments in Bioelectronic Medicine. The main topics discussed at the summit are outlined here.

1AbstractDevices interfacing with the brain through implantation in cortical or subcortical structures have great potential for restoration and rehabilitation in patients with sensory or motor dysfunction. Typical implantation surgeries are planned based on maps of brain activity generated from intact function. However, mapping brain activity for planning implantation surgeries is challenging in the target population due to abnormal residual function and, increasingly often, existing MRI-incompatible implanted hardware. Here, we present methods and results for mapping impaired somatosensory and motor function in an individual with paralysis and an existing brain-computer interface (BCI) device. Magnetoencephalography (MEG) was used to directly map the neural activity evoked during transcutaneous electrical stimulation and attempted movement of the impaired hand. Evoked fields were found to align with the expected anatomy and somatotopic organization. This approach may be valuable for guiding implants in other applications, such as cortical stimulation for pain and to improve implant targeting to help reduce the craniotomy size.

Millions of people worldwide suffer motor or sensory impairment due to stroke, spinal cord injury, multiple sclerosis, traumatic brain injury, diabetes, and motor neuron diseases such as ALS (amyotrophic lateral sclerosis). A brain-computer interface (BCI), which links the brain directly to a computer, offers a new way to study the brain and potentially restore these losses in patients living with debilitating conditions. One of the challenges currently facing BCI technology, however, is how to minimize surgical risk. Minimally invasive techniques, such as stereoelectroencephalography (SEEG) have become more widely used in clinical applications since they can lead to fewer complications. SEEG electrodes also give access to sulcal and white matter areas of the brain but have not been widely studied in brain-computer interfaces. We therefore investigated the viability of using SEEG electrodes in a BCI for recording and decoding neural signals related to movement and the sense of touch and compared its performance to electrocorticography electrodes (ECoG) placed on gyri. Initial poor decoding performance and the observation that most neural modulation patterns were highly variable trial-to-trial and transient (significantly shorter than the sustained finger movements studied), led to the development of a feature selection method based on a repeatability metric using temporal correlation. An algorithm based on temporal correlation was developed to isolate features that consistently repeated (required for accurate decoding) and possessed information content related to movement or touch-related stimuli. We subsequently used these features, along with deep learning methods, to automatically classify various motor and sensory events for individual fingers with high accuracy. Repeating features were found in sulcal, gyral, and white matter areas and were predominantly phasic or phasic-tonic across a wide frequency range for both HD (high density) ECoG and SEEG recordings. These findings motivated the use of long short-term memory (LSTM) recurrent neural networks (RNNs) which are well-suited to handling both transient and sustained input features. Combining temporal correlation-based feature selection with LSTM yielded decoding accuracies of up to 92.04 +/-1.51% for hand movements, up to 91.69 +/-0.49% for individual finger movements, and up to 80.64 +/-1.64% for focal tactile stimuli to the finger pads and palm while using a relatively small number of SEEG electrodes. These findings may lead to a new class of minimally invasive brain-computer interface systems in the future, increasing its applicability to a wider variety of conditions.

Paralysis and neuropathy, affecting millions of people worldwide, can be accompanied by a significant loss of somatosensation. With tactile sensation being central to achieving dexterous movement, brain-computer interface (BCI) researchers have explored the use of intracortical electrical stimulation to restore sensation to the hand. However, current approaches have been restricted to stimulating the gyral areas of the brain while functional imaging suggests that the representation of fingertips lie predominantly in the sulcal regions. Here we show, for the first time, highly focal percepts can be evoked in the fingertips of the hand through electrical stimulation of the sulcal areas of the brain. To this end, we mapped and compared sensations elicited in the hand by stimulating both gyral and sulcal areas of the human primary somatosensory cortex (S1). Two participants with intractable epilepsy were implanted with stereoelectroencephalography (SEEG) and high-density electrocorticography (HD-ECoG) electrodes in S1 guided by high-resolution functional imaging. Using myelin content and cortical thickness maps developed by the Human Connectome Project, we elucidated the specific sub-regions of S1 where focal percepts were evoked. Within-participant comparisons showed that sulcal stimulation using SEEG electrodes evoked percepts that are significantly more focal, with 80% less area of spread (p=0.02) and localized to the fingertips more often than in gyral stimulation via HD-ECoG electrodes. Finally, sulcal locations exhibiting repeated modulation patterns of high-frequency neural activity during mechanical tactile stimulation of the hand showed the same somatotopic correspondence as sulcal stimulation. These findings show that minimally-invasive sulcal stimulation could lead to a clinically viable approach to restoring sensation in those living with sensory impairment.SignificanceIntracortical or cortical surface stimulation of the primary somatosensory cortex (S1) offers the promise of restoring somatotopically-relevant sensation in people with sensory impairment. However, evoking percepts in the fingertips has been challenging as their representation has been shown to be predominantly located within sulcal regions of S1 – inaccessible by these stimulation approaches. We evoked highly focal percepts in the fingertips of the hand by stimulating the sulcal regions of S1 in people with intractable epilepsy using stereoelectroencephalography (SEEG) depth electrodes. Sensory percepts in the fingertips were more focal and more frequently evoked by SEEG electrodes than by high-density electrocorticography (HD-ECoG) grids evidenced by within-participant comparisons. Our results suggest that fingertip representations are more readily targeted within the sulcal regions. SEEG electrodes potentially offer a clinically viable approach to access the sulcal regions for sensory neuroprostheses that can aid dexterous motor control.

Background Cervical spinal cord injury severely affects grasping ability of its survivors. Fortunately, many individuals with tetraplegia retain residual arm movements that allow them to reach for objects. We propose a wearable technology that utilizes arm movement trajectory information and deep learning methods to determine grasp selection. Furthermore, we combined this approach with neuromuscular stimulation to determine if self-driven functional hand movement could be enabled in spinal cord injury participants. Methods Two cervical SCI participants performed arbitrary and natural reaching movements toward target objects in three-dimensional space, which were recorded using an inertial sensor worn on their wrist. Time series classifiers were trained to recognize the trajectories using either a Dynamic Time Warping (DTW) algorithm or a Long Short-Term Memory (LSTM) recurrent neural network. As an initial proof-of-concept, we demonstrate real-time classification of the arbitrary movements using DTW only (due to its implementation simplicity), which when used in combination with a high density neuromuscular stimulation sleeve with textile electrodes, enabled participants to perform functional grasping. Results Participants were able to consistently perform arbitrary two-dimensional and three-dimensional arm movements which could be classified with high accuracy. Furthermore, it was found that natural reaching trajectories for two different target objects (requiring two different grasp types) were distinct and also discriminable with high accuracy. In offline comparisons, LSTM (mean accuracies 99%) performed significantly better than DTW (mean accuracies 86 and 83%) for both arbitrary and natural reaching movements, respectively. Type I and II errors occurred more frequently for DTW (up to 60 and 15%, respectively), whereas it stayed under 5% for LSTM. Also, DTW achieved online accuracy of 79%. Conclusions We demonstrate the feasibility of utilizing arm trajectory information to determine grasp selection using a wearable inertial sensor along with DTW and deep learning methods. Importantly, this technology can be successfully used to control neuromuscular stimulation and restore functional independence to individuals living with paralysis. Trial registration NCT, NCT03385005. Registered September 26, 2017

Background: Cervical spinal cord injury severely affects grasping ability of its survivors. Fortunately, many individuals with tetraplegia retain residual arm movements that allow them to reach for objects. We propose a wearable technology that utilizes arm movement trajectory information and deep learning methods to determine grasp selection. Furthermore, we combined this approach with neuromuscular stimulation to determine if self-driven functional hand movement could be enabled in spinal cord injury participants.Methods: Two cervical SCI participants performed arbitrary and natural reaching movements toward target objects in three-dimensional space, which were recorded using an inertial sensor worn on their wrist. Time series classifiers were trained to recognize the trajectories using either a Dynamic Time Warping (DTW) algorithm or a Long Short-Term Memory (LSTM) recurrent neural network. As an initial proof-of-concept, we demonstrate real-time classification of the arbitrary movements using DTW only (due to its implementation simplicity), which when used in combination with a high density neuromuscular stimulation sleeve with textile electrodes, enabled participants to perform functional grasping.Results: Participants were able to consistently perform arbitrary two-dimensional and three-dimensional arm movements which could be classified with high accuracy. Furthermore, it was found that natural reaching trajectories for two different target objects (requiring two different grasp types) were distinct and also discriminable with high accuracy. In offline comparisons, LSTM (mean accuracies 99%) performed significantly better than DTW (mean accuracies 86% and 83%) for both arbitrary and natural reaching movements, respectively. Type I and II errors occurred more frequently for DTW (up to 60% and 15%, respectively), whereas it stayed under 5% for LSTM. Also, DTW achieved online accuracy of 79%. Conclusions: We demonstrate the feasibility of utilizing arm trajectory information to determine grasp selection using a wearable inertial sensor along with DTW and deep learning methods. Importantly, this technology can be successfully used to control neuromuscular stimulation and restore functional independence to individuals living with paralysis.

OBJECTIVE The vagus nerve has been implicated in a variety of immune responses, and the number of studies using mouse models to unravel key mechanisms has increased. However, as of yet, there is no electrode that can chronically record neural activity from the mouse vagus nerve due to its small diameter. Such recordings are critical to understand the role of these biomarkers for translational research. APPROACH In this study, we developed a methodology for surgically implanting the wrappable microwires onto the vagus nerve of mice. Similar to a cuff electrode, we wrapped de-insulated ends of microwires around the vagus nerve and re-insulated them on the nerve with Kwik-Sil. The recording fidelity of the wrappable microwire on the vagus nerve was validated in an acute, anesthetized model by comparing performance to commercially-available electrodes. A chronic, awake mouse model was then developed to record spontaneous compound action potentials (CAPs). MAIN RESULTS In an acute setting, the wrappable microwire successfully recorded spontaneous CAPs with similar signal-to-noise ratios (SNR) and peak-to-peak amplitude to commercially available electrodes. In chronic, awake recordings, viable SNRs were obtained from the wrappable microwires between 30 and 60 d (n = 8). Weekly impedance measurements showed no correlation with SNR or time, indicating device stability, and the electrodes recorded CAPs for the duration of the recording period. SIGNIFICANCE To the best of our knowledge, this is the first reported chronic, awake neural interface with the mouse vagus nerve. This approach can facilitate clinical translation for bioelectronic medicine in preclinical disease models of interest with the creation of more clinically relevant preclinical models.

ObjectivesMusculoskeletal pain and fatigue are common features in systemic lupus erythematosus (SLE). The cholinergic anti-inflammatory pathway is a physiological mechanism diminishing inflammation, engaged by stimulating the vagus nerve. We evaluated the effects of non-invasive vagus nerve stimulation in patients with SLE and with musculoskeletal pain.Methods18 patients with SLE and with musculoskeletal pain ≥4 on a 10 cm Visual Analogue Scale were randomised (2:1) in this double-blind study to receive transcutaneous auricular vagus nerve stimulation (taVNS) or sham stimulation (SS) for 4 consecutive days. Evaluations at baseline, day 5 and day 12 included patient assessments of pain, disease activity (PtGA) and fatigue. Tender and swollen joint counts and the Physician Global Assessment (PGA) were completed by a physician blinded to the patient’s therapy. Potential biomarkers were evaluated.ResultstaVNS and SS were well tolerated. Subjects receiving taVNS had a significant decrease in pain and fatigue compared with SS and were more likely (OR=25, p=0.02) to experience a clinically significant reduction in pain. PtGA, joint counts and PGA also improved. Pain reduction and improvement of fatigue correlated with the cumulative current received. In general, responses were maintained through day 12. Plasma levels of substance P were significantly reduced at day 5 compared with baseline following taVNS but other neuropeptides, serum and whole blood-stimulated inflammatory mediators, and kynurenine metabolites showed no significant change at days 5 or 12 compared with baseline.ConclusiontaVNS resulted in significantly reduced pain, fatigue and joint scores in SLE. Additional studies evaluating this intervention and its mechanisms are warranted.

BCI (brain-computer interface) and functional electrical stimulation (FES) technologies have advanced significantly over the last several decades. Recent efforts have involved the integration of these technologies with the goal of restoring functional movement in paralyzed patients. Implantable BCIs have provided neural recordings with increased spatial resolution and have been combined with sophisticated neural decoding algorithms and increasingly capable FES systems to advance efforts toward this goal. This chapter reviews historical developments that have occurred as the exciting fields of BCI and FES have evolved and now overlapped to allow new breakthroughs in medicine, targeting restoration of movement and lost function in users with disabilities.

Background Electrical stimulation of peripheral nerves is a widely used technique to treat a variety of conditions including chronic pain, motor impairment, headaches, and epilepsy. Nerve stimulation to achieve efficacious symptomatic relief depends on the proper selection of electrical stimulation parameters to recruit the appropriate fibers within a nerve. Recently, electrical stimulation of the vagus nerve has shown promise for controlling inflammation and clinical trials have demonstrated efficacy for the treatment of inflammatory disorders. This application of vagus nerve stimulation activates the inflammatory reflex, reducing levels of inflammatory cytokines during inflammation. Methods Here, we wanted to test whether altering the parameters of electrical vagus nerve stimulation would change circulating cytokine levels of normal healthy animals in the absence of increased inflammation. To examine this, we systematically tested a set of electrical stimulation parameters and measured serum cytokine levels in healthy mice. Results Surprisingly, we found that specific combinations of pulse width, pulse amplitude, and frequency produced significant increases of the pro-inflammatory cytokine tumor necrosis factor (TNF), while other parameters selectively lowered serum TNF levels, as compared to sham-stimulated mice. In addition, serum levels of the anti-inflammatory cytokine interleukin-10 (IL-10) were significantly increased by select parameters of electrical stimulation but remained unchanged with others. Conclusions These results indicate that electrical stimulation parameter selection is critically important for the modulation of cytokines via the cervical vagus nerve and that specific cytokines can be increased by electrical stimulation in the absence of inflammation. As the next generation of bioelectronic therapies and devices are developed to capitalize on the neural regulation of inflammation, the selection of nerve stimulation parameters will be a critically important variable for achieving cytokine-specific changes.

Electrical stimulation of peripheral nerves is a widely used technique to treat a variety of conditions including chronic pain, motor impairment, headaches, and epilepsy. Nerve stimulation to achieve efficacious symptomatic relief depends on the proper selection of electrical stimulation parameters to recruit the appropriate fibers within a nerve. Recently, electrical stimulation of the vagus nerve has shown promise for controlling inflammation and clinical trials have demonstrated efficacy for the treatment of inflammatory disorders. This application of vagus nerve stimulation activates the inflammatory reflex, reducing levels of inflammatory cytokines during inflammation. Here, we wanted to test whether altering the parameters of electrical vagus nerve stimulation would change circulating cytokine levels of normal healthy animals in the absence of increased inflammation. To examine this, we systematically tested a set of electrical stimulation parameters and measured serum cytokine levels in healthy mice. Surprisingly, we found that specific combinations of pulse width, pulse amplitude, and frequency produced significant increases of the pro-inflammatory cytokine tumor necrosis factor alpha (TNFα), while other parameters selectively lowered serum TNFα levels, as compared to sham-stimulated mice. In addition, serum levels of the anti-inflammatory cytokine interleukin-10 (IL-10) were significantly increased by select parameters of electrical stimulation but remained unchanged with others. These results indicate that electrical stimulation parameter selection is critically important for the modulation of cytokines via the cervical vagus nerve and that specific cytokines can be increased by electrical stimulation in the absence of inflammation. As the next generation of bioelectronic therapies and devices are developed to capitalize on the neural regulation of inflammation, the selection of nerve stimulation parameters will be a critically important variable for achieving cytokine-specific changes.

BCI (brain-computer interface) and functional electrical stimulation (FES) technologies have advanced significantly over the last several decades. Recent efforts have involved the integration of these technologies with the goal of restoring functional movement in paralyzed patients. Implantable BCIs have provided neural recordings with increased spatial resolution and have been combined with sophisticated neural decoding algorithms and increasingly capable FES systems to advance efforts toward this goal. This chapter reviews historical developments that have occurred as the exciting fields of BCI and FES have evolved and now overlapped to allow new breakthroughs in medicine, targeting restoration of movement and lost function in users with disabilities.

The human nervous system is a vast network carrying not only sensory and movement information, but also information to and from our organs, intimately linking it to our overall health. Scientists and engineers have been working for decades to tap into this network and 'crack the neural code' by decoding neural signals and learning how to 'speak' the language of the nervous system. Progress has been made in developing neural decoding methods to decipher brain activity and bioelectronic technologies to treat rheumatoid arthritis, paralysis, epilepsy and for diagnosing brain-related diseases such as Parkinson's and Alzheimer's disease. In a recent first-in-human study involving paralysis, a paralysed male study participant regained movement in his hand, years after his injury, through the use of a bioelectronic neural bypass. This work combined neural decoding and neurostimulation methods to translate and re-route signals around damaged neural pathways within the central nervous system. By extending these methods to decipher neural messages in the peripheral nervous system, status information from our bodily functions and specific organs could be gained. This, one day, could allow real-time diagnostics to be performed to give us a deeper insight into a patient's condition, or potentially even predict disease or allow early diagnosis. The future of bioelectronic medicine is extremely bright and is wide open as new diagnostic and treatment options are developed for patients around the world.

Bioelectronic medicine is a rapidly growing field that explores targeted neuromodulation in new treatment options addressing both disease and injury. New bioelectronic methods are being developed to monitor and modulate neural activity directly. The therapeutic benefit of these approaches has been validated in recent clinical studies in various conditions, including paralysis. By using decoding and modulation strategies together, it is possible to restore lost function to those living with paralysis and other debilitating conditions by interpreting and rerouting signals around the affected portion of the nervous system. This, in effect, creates a bioelectronic "neural bypass" to serve the function of the damaged/degenerated network. By learning the language of neurons and using neural interface technology to tap into critical networks, new approaches to repairing or restoring function in areas impacted by disease or injury may become a reality.

Background Glucose is a crucial energy source. In humans, it is the primary sugar for high energy demanding cells in brain, muscle and peripheral neurons. Deviations of blood glucose levels from normal levels for an extended period of time is dangerous or even fatal, so regulation of blood glucose levels is a biological imperative. The vagus nerve, comprised of sensory and motor fibres, provides a major anatomical substrate for regulating metabolism. While prior studies have implicated the vagus nerve in the neurometabolic interface, its specific role in either the afferent or efferent arc of this reflex remains elusive. Methods Here we use recently developed methods to isolate and decode specific neural signals acquired from the surface of the vagus nerve in BALB/c wild type mice to identify those that respond robustly to hypoglycemia. We also attempted to decode neural signals related to hyperglycemia. In addition to wild type mice, we analyzed the responses to acute hypo- and hyperglycemia in transient receptor potential cation channel subfamily V member 1 (TRPV1) cell depleted mice. The decoding algorithm uses neural signals as input and reconstructs blood glucose levels. Results Our algorithm was able to reconstruct the blood glucose levels with high accuracy (median error 18.6 mg/dl). Hyperglycemia did not induce robust vagus nerve responses, and deletion of TRPV1 nociceptors attenuated the hypoglycemia-dependent vagus nerve signals. Conclusion These results provide insight to the sensory vagal signaling that encodes hypoglycemic states and suggest a method to measure blood glucose levels by decoding nerve signals. Trial registration Not applicable.

The axons of the sensory, or afferent, vagus nerve transmit action potentials to the central nervous system in response to changes in the body’s metabolic and physiological status. Recent advances in identifying neural circuits that regulate immune responses to infection, inflammation and injury have revealed that vagus nerve signals regulate the release of cytokines and other factors produced by macrophages. Here we record compound action potentials in the cervical vagus nerve of adult mice and reveal the specific activity that occurs following administration of the proinflammatory cytokines tumor necrosis factor (TNF) and interleukin 1β (IL-1β). Importantly, the afferent vagus neurograms generated by TNF exposure are abolished in double knockout mice lacking TNF receptors 1 and 2 (TNF-R1/2KO), whereas IL-1 β-specific neurograms are eliminated in knockout mice lacking IL-1 β receptor (IL-1RKO). Conversely, TNF neurograms are preserved in IL-1RKO mice, and IL-1 β neurograms are unchanged in TNF-R1/2KO mice. Analysis of the temporal dynamics and power spectral characteristics of afferent vagus neurograms for TNF and IL-1β reveals cytokine-selective signals. The nodose ganglion contains the cell bodies of the sensory neurons whose axons run through the vagus nerve. The nodose neurons express receptors for TNF and IL-1β, and we show that exposing them to TNF and IL-1β significantly stimulates their calcium uptake. Together these results indicate that afferent vagus signals in response to cytokines provide a basic model of nervous system sensing of immune responses.

Bioelectronic medicine requires the ability to monitor and modulate nerve activity in awake patients over time. The vagus nerve is a promising stimulation target, and preclinical models often use mice. However, an awake, chronic mouse vagus nerve interface has yet to be demonstrated. Here, we developed a functional wrappable microwire electrode to chronically interface with the small diameter mouse cervical vagus nerve (∼100 μm). In an acute setting, the wrappable microwire had similar recording performance to commercially available electrodes. A chronic, awake mouse model was then developed to record spontaneous compound action potentials (CAPs). Viable signal-to-noise ratios (SNRs) were obtained from the wrappable microwires between 30 and 60 days (n = 8). Weekly impedance measurements showed no correlation between SNR or time. The wrappable microwires successfully interfaced with small diameter nerves and has been validated in a chronic, awake preclinical model, which can better facilitate clinical translation for bioelectronic medicine.

Background Understanding the long-term behavior of intracortically-recorded signals is essential for improving the performance of Brain Computer Interfaces. However, few studies have systematically investigated chronic neural recordings from an implanted microelectrode array in the human brain. Methods In this study, we show the applicability of wavelet decomposition method to extract and demonstrate the utility of long-term stable features in neural signals obtained from a microelectrode array implanted in the motor cortex of a human with tetraplegia. Wavelet decomposition was applied to the raw voltage data to generate mean wavelet power (MWP) features, which were further divided into three sub-frequency bands, low-frequency MWP (lf-MWP, 0–234 Hz), mid-frequency MWP (mf-MWP, 234 Hz–3.75 kHz) and high-frequency MWP (hf-MWP, >3.75 kHz). We analyzed these features using data collected from two experiments that were repeated over the course of about 3 years and compared their signal stability and decoding performance with the more standard threshold crossings, local field potentials (LFP), multi-unit activity (MUA) features obtained from the raw voltage recordings. Results All neural features could stably track neural information for over 3 years post-implantation and were less prone to signal degradation compared to threshold crossings. Furthermore, when used as an input to support vector machine based decoding algorithms, the mf-MWP and MUA demonstrated significantly better performance, respectively, in classifying imagined motor tasks than using the lf-MWP, hf-MWP, LFP, or threshold crossings. Conclusions Our results suggest that using MWP features in the appropriate frequency bands can provide an effective neural feature for brain computer interface intended for chronic applications. Trial registration This study was approved by the U.S. Food and Drug Administration (Investigational Device Exemption) and the Ohio State University Medical Center Institutional Review Board (Columbus, Ohio). The study conformed to institutional requirements for the conduct of human subjects and was filed on ClinicalTrials.gov (Identifier NCT01997125). Electronic supplementary material The online version of this article (10.1186/s42234-018-0011-x) contains supplementary material, which is available to authorized users.

The nervous system maintains physiological homeostasis through reflex pathways that modulate organ function. This process begins when changes in the internal milieu (e.g., blood pressure, temperature, or pH) activate visceral sensory neurons that transmit action potentials along the vagus nerve to the brainstem. IL-1β and TNF, inflammatory cytokines produced by immune cells during infection and injury, and other inflammatory mediators have been implicated in activating sensory action potentials in the vagus nerve. However, it remains unclear whether neural responses encode cytokine-specific information. Here we develop methods to isolate and decode specific neural signals to discriminate between two different cytokines. Nerve impulses recorded from the vagus nerve of mice exposed to IL-1β and TNF were sorted into groups based on their shape and amplitude, and their respective firing rates were computed. This revealed sensory neural groups responding specifically to TNF and IL-1β in a dose-dependent manner. These cytokine-mediated responses were subsequently decoded using a Naive Bayes algorithm that discriminated between no exposure and exposures to IL-1β and TNF (mean successful identification rate 82.9 ± 17.8%, chance level 33%). Recordings obtained in IL-1 receptor-KO mice were devoid of IL-1β-related signals but retained their responses to TNF. Genetic ablation of TRPV1 neurons attenuated the vagus neural signals mediated by IL-1β, and distal lidocaine nerve block attenuated all vagus neural signals recorded. The results obtained in this study using the methodological framework suggest that cytokine-specific information is present in sensory neural signals within the vagus nerve.

Objective To demonstrate naturalistic motor control speed, coordinated grasp, and carryover from trained to novel objects by an individual with tetraplegia using a brain-computer interface (BCI)-controlled neuroprosthetic. Design Phase I trial for an intracortical BCI integrated with forearm functional electrical stimulation (FES). Data reported span postimplant days 137 to 1478. Setting Tertiary care outpatient rehabilitation center. Participant A 27-year-old man with C5 class A (on the American Spinal Injury Association Impairment Scale) traumatic spinal cord injury Interventions After array implantation in his left (dominant) motor cortex, the participant trained with BCI-FES to control dynamic, coordinated forearm, wrist, and hand movements. Main Outcome Measures Performance on standardized tests of arm motor ability (Graded Redefined Assessment of Strength, Sensibility, and Prehension [GRASSP], Action Research Arm Test [ARAT], Grasp and Release Test [GRT], Box and Block Test), grip myometry, and functional activity measures (Capabilities of Upper Extremity Test [CUE-T], Quadriplegia Index of Function-Short Form [QIF-SF], Spinal Cord Independence Measure–Self-Report [SCIM-SR]) with and without the BCI-FES. Results With BCI-FES, scores improved from baseline on the following: Grip force (2.9 kg); ARAT cup, cylinders, ball, bar, and blocks; GRT can, fork, peg, weight, and tape; GRASSP strength and prehension (unscrewing lids, pouring from a bottle, transferring pegs); and CUE-T wrist and hand skills. QIF-SF and SCIM-SR eating, grooming, and toileting activities were expected to improve with home use of BCI-FES. Pincer grips and mobility were unaffected. BCI-FES grip skills enabled the participant to play an adapted “Battleship” game and manipulate household objects. Conclusions Using BCI-FES, the participant performed skillful and coordinated grasps and made clinically significant gains in tests of upper limb function. Practice generalized from training objects to household items and leisure activities. Motor ability improved for palmar, lateral, and tip-to-tip grips. The expects eventual home use to confer greater independence for activities of daily living, consistent with observed neurologic level gains from C5-6 to C7-T1. This marks a critical translational step toward clinical viability for BCI neuroprosthetics.

Background The vagus nerve plays an important role in the regulation of organ function, including reflex pathways that regulate immunity and inflammation. Recent studies using genetically modified mice have improved our understanding of molecular mechanisms in the neural control of immunity. However, mapping neural signals transmitted in the vagus nerve in mice has been limited by technical challenges. Here, we have standardized an experimental protocol to record compound action potentials transmitted in the vagus nerve. Methods The vagus nerve was isolated in Balb/c and B6.129S mice, and placed either on a hook or cuff electrode. The electrical signals from the vagus nerve were digitized using either a Neuralynx or Plexon data acquisition system. Changes in the vagus nerve activity in response to anesthesia, feeding and administration of bacterial endotoxin were analyzed. Results We have developed an electrophysiological recording system to record compound action potentials from the cervical vagus nerve in mice. Cuff electrodes significantly reduce background noise and increase the signal to noise ratio as compared to hook electrodes. Baseline vagus nerve activity varies in response to anesthesia depth and food intake. Analysis of vagus neurograms in different mouse strains (Balb/c and C57BL/6) reveal no significant differences in baseline activity. Importantly, vagus neurogramactivity in wild type and TLR4 receptor knock out mice exhibits receptor dependency of endotoxin mediated signals. Conclusions These methods for recording vagus neurogram in mice provide a useful tool to further delineate the role of vagus neural pathways in a standardized murine disease model.

Many advances have been made in the field of invasive brain–computer interface (BCI) technology in recent years, including improved implantable microelectrodes, better-performing neural decoding algorithms, and the development of invasive BCI systems that have helped restore functional movement in paralyzed clinical study participants. This chapter describes historical developments that have paved the way for recent achievements in the invasive BCI field. Critical technology components of an invasive BCI system are described, and future applications and directions for the field are discussed.

Many neuromodulation devices used to treat disease and injury are “open-loop” in nature and lack sensing and the algorithms required to automatically adjust or provide stimulation based on a measured input. This chapter describes new sensing, neural decoding, and closed-loop technologies that can provide responsive or adaptive stimulation. Furthermore, neural signals in the human brain, and in the peripheral nervous system, carry a tremendous amount of information regarding current health status and can be used for diagnostic purposes. Current and potential neural sensing/decoding methods that could be used for advanced and real-time diagnostics are also discussed.

Introducing neural sensing and decoding to open-loop neurostimulation technologies has the potential to significantly improve the diagnosis and treatment of a wide variety of diseases treated through bioelectronic medicine. Chronically implanted multi-electrode arrays (MEA) can be used for such neural sensing and are critical for obtaining data of high spatial and temporal resolution to provide accurate decoding. Signals recorded from these arrays include local field potentials (LFP), and multiunit (MU) and single-unit (SU) activity. LFP offer signal stability over time, but at the expense of decreased spatial resolution. SU activity, on the other hand, offers better spatial resolution, but is considered less stable in chronic applications. MU activity, which represents an aggregate spiking activity of a population of neurons on the order of several hundred microns away from the recording tip, is considered a signal that can offer a compromise between the two signals. Here we used a wavelet decomposition method to extract and characterize the LFP, MU and SU signals obtained from a 96-channel MEA implanted in the motor cortex of a nonhuman primate over a 7.5-month period. We observed that not only are the MU signals more stable over time compared with SU activity, but that they are also significantly less correlated among electrodes compared with LFP over the spatial scale of the implanted array. Histological analysis of tissue sections also revealed a 51% reduction in the number of neuronal cell bodies within a radius around the electrode tips of the implanted tissue compared with control tissue. Our results indicate that MU activity offers long-term signal stability with less correlated signals, potentially providing an effective signal for neural sensing in bioelectronic medicine.

Neuroprosthetics that combine a brain computer interface (BCI) with functional electrical stimulation (FES) can restore voluntary control of a patients’ own paralyzed limbs. To date, human studies have demonstrated an “all-or-none” type of control for a fixed number of pre-determined states, like hand-open and hand-closed. To be practical for everyday use, a BCI-FES system should enable smooth control of limb movements through a continuum of states and generate situationally appropriate, graded muscle contractions. Crucially, this functionality will allow users of BCI-FES neuroprosthetics to manipulate objects of different sizes and weights without dropping or crushing them. In this study, we present the first evidence that using a BCI-FES system, a human with tetraplegia can regain volitional, graded control of muscle contraction in his paralyzed limb. In addition, we show the critical ability of the system to generalize beyond training states and accurately generate wrist flexion states that are intermediate to training levels. These innovations provide the groundwork for enabling enhanced and more natural fine motor control of paralyzed limbs by BCI-FES neuroprosthetics.

Following traumatic brain injury (TBI), ischemia and hypoxia play a major role in further worsening of the damage, a process referred to as ‘secondary injury’. Protecting neurons from causative factors of secondary injury has been the guiding principle of modern TBI management. Stimulation of trigeminal nerve induces pressor response and improves cerebral blood flow (CBF) by activating the rostral ventrolateral medulla. Moreover, it causes cerebrovasodilation through the trigemino-cerebrovascular system and trigemino-parasympathetic reflex. These effects are capable of increasing cerebral perfusion, making trigeminal nerve stimulation (TNS) a promising strategy for TBI management. Here, we investigated the use of electrical TNS for improving CBF and brain oxygen tension (PbrO2), with the goal of decreasing secondary injury. Severe TBI was produced using controlled cortical impact (CCI) in a rat model, and TNS treatment was delivered for the first hour after CCI. In comparison to TBI group, TBI animals with TNS treatment demonstrated significantly increased systemic blood pressure, CBF and PbrO2 at the hyperacute phase of TBI. Furthermore, rats in TNS-treatment group showed significantly reduced brain edema, blood-brain barrier disruption, lesion volume, and brain cortical levels of TNF-α and IL-6. These data provide strong early evidence that TNS could be an effective neuroprotective strategy.

Multimodal brain monitoring is an important approach to gain insight into brain function, modulation, and pathology. We have developed a unique micromachined neural probe capable of real-time continuous monitoring of multiple physiological, biochemical and electrophysiological variables. However, to date, it has only been used in anesthetized animals due to a lack of an appropriate interface for awake animals. We have developed a versatile headstage for recording the small neural signal and bridging the sensors to the remote sensing units for multimodal brain monitoring in awake rats. The developed system has been successfully validated in awake rats by simultaneously measuring four cerebral variables: electrocorticography, oxygen tension, temperature and cerebral blood flow. Reliable signal recordings were obtained with minimal artifacts from movement and environmental noise. For the first time, multiple variables of cerebral function and metabolism were simultaneously recorded from awake rats using a single neural probe. The system is envisioned for studying the effects of pharmacologic treatments, mapping the development of central nervous system diseases, and better understanding normal cerebral physiology.

Recent advances in Brain Computer Interfaces (BCIs) have created hope that one day paralyzed patients will be able to regain control of their paralyzed limbs. As part of an ongoing clinical study, we have implanted a 96-electrode Utah array in the motor cortex of a paralyzed human. The array generates almost 3 million data points from the brain every second. This presents several big data challenges towards developing algorithms that should not only process the data in real-time (for the BCI to be responsive) but are also robust to temporal variations and non-stationarities in the sensor data. We demonstrate an algorithmic approach to analyze such data and present a novel method to evaluate such algorithms. We present our methodology with examples of decoding human brain data in real-time to inform a BCI.

Neuroprosthetic technology has been used to restore cortical control of discrete (non-rhythmic) hand movements in a paralyzed person. However, cortical control of rhythmic movements which originate in the brain but are coordinated by Central Pattern Generator (CPG) neural networks in the spinal cord has not been demonstrated previously. Here we show a demonstration of an artificial neural bypass technology that decodes cortical activity and emulates spinal cord CPG function allowing volitional rhythmic hand movement. The technology uses a combination of signals recorded from the brain, machine-learning algorithms to decode the signals, a numerical model of CPG network, and a neuromuscular electrical stimulation system to evoke rhythmic movements. Using the neural bypass, a quadriplegic participant was able to initiate, sustain, and switch between rhythmic and discrete finger movements, using his thoughts alone. These results have implications in advancing neuroprosthetic technology to restore complex movements in people living with paralysis.

A new method for early detection of extravasations during contrast-enhanced computed tomographic procedures is evaluated. The new method uses radio frequency permittivity-based sensing to detect the onset of an extravasation with high sensitivity and specificity. There were 66 subcutaneous injections performed at the antecubital fossa in 35 volunteers to evaluate the new method. Sensitivity was found to be 98.8% (95% confidence interval, 97.6%-100%), whereas specificity was found to be 99.97% (95% confidence interval, 99.90%-100%).

Millions of people worldwide suffer from diseases that lead to paralysis through disruption of signal pathways between the brain and the muscles. Neuroprosthetic devices are designed to restore lost function and could be used to form an electronic 'neural bypass' to circumvent disconnected pathways in the nervous system. It has previously been shown that intracortically recorded signals can be decoded to extract information related to motion, allowing non-human primates and paralysed humans to control computers and robotic arms through imagined movements. In non-human primates, these types of signal have also been used to drive activation of chemically paralysed arm muscles. Here we show that intracortically recorded signals can be linked in real-time to muscle activation to restore movement in a paralysed human. We used a chronically implanted intracortical microelectrode array to record multiunit activity from the motor cortex in a study participant with quadriplegia from cervical spinal cord injury. We applied machine-learning algorithms to decode the neuronal activity and control activation of the participant's forearm muscles through a custom-built high-resolution neuromuscular electrical stimulation system. The system provided isolated finger movements and the participant achieved continuous cortical control of six different wrist and hand motions. Furthermore, he was able to use the system to complete functional tasks relevant to daily living. Clinical assessment showed that, when using the system, his motor impairment improved from the fifth to the sixth cervical (C5-C6) to the seventh cervical to first thoracic (C7-T1) level unilaterally, conferring on him the critical abilities to grasp, manipulate, and release objects. This is the first demonstration to our knowledge of successful control of muscle activation using intracortically recorded signals in a paralysed human. These results have significant implications in advancing neuroprosthetic technology for people worldwide living with the effects of paralysis.