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Auditory brainstem implants (ABI) can enable hearing sensation through electrical stimulation of the cochlear nucleus. The basic stimulation and signal coding strategies of the ABI are based on those of the cochlear implant. This may not always be optimal, and ABI-specific strategies may be preferred. In a cohort of ten ABI users, we examined the feasibility of measuring local evoked potentials (LEP) via fine-grained stimulation with a forward masking paradigm. We introduce a new baseline-dependent definition of LEP amplitude for analyzing the LEP amplitude growth function to obtain threshold stimulation levels and slope values. The processing of biphasic pulses by the cochlear nucleus and the influence of the leading phase polarity were examined. There were no statistically significant differences in LEP thresholds or slopes between cathodic and anodic leading pulses. LEP thresholds measured with cathodic leading pulses (r = 0.77, t31 = 6.81, p < 0.0001) and anodic leading pulses (r = 0.70, t27 = 45.14, p < 0.0001) correlated significantly with perceptual hearing thresholds. The correlation analysis was impacted by outlier values, especially in the case of LEP thresholds measured with anodic leading pulses. Cathodic leading pulses had significantly shorter LEP peak latencies (t104.8 = 2.63, p < 0.01). These results show that the cathodic leading pulses are superior for eliciting LEPs. We suggest that cathodic leading pulses should be the basis for ABI-specific coding strategies. [ABSTRACT FROM AUTHOR]

Abstract Various spike patterns from sensory/motor neurons provide information about the dynamic sensory stimuli. Based on the information theory, neuroscientists have revealed the influence of spike variables on information transmission. Among diverse spike variables, inter-trial heterogeneity, known as jitter, has been observed in physiological neuron activity and responses to artificial stimuli, and it is recognized to contribute to information transmission. However, the relationship between inter-trial heterogeneity and information remains unexplored. Therefore, understanding how jitter impacts the heterogeneity of spiking activities and information encoding is crucial, as it offers insights into stimulus conditions and the efficiency of neural systems. Here, we systematically explored how neural information is altered by number of neurons as well as by each of three fundamental spiking characteristics: mean firing rate (MFR), duration, and cross-correlation (spike time tiling coefficient; STTC). First, we generated groups of spike trains to have specific average values for those characteristics. Second, we quantified the transmitted information rate as a function of each parameter. As population size, MFR, and duration increased, the information rate was enhanced but gradually saturated with further increments in number of cells and MFR. Regarding the cross-correlation level, homogeneous and heterogeneous spike trains (STTCAVG = 0.9 and 0.1) showed the lowest and highest information transmission, respectively. Interestingly however, when jitters were added to mimic physiological noisy environment, the information was reduced by ~ 46% for the spike trains with STTCAVG = 0.1 but rather substantially increased by ~ 63% for the spike trains with STTCAVG = 0.9. Our study suggests that optimizing various spiking characteristics may enhance the robustness and amount of neural information transmitted.

This paper demonstrates, for the first time, the stability of synthetic diamond as a passive layer within neural implants. Leveraging the exceptional biocompatibility of intrinsic nanocrystalline diamond, a comprehensive review of material aging analysis in the context of in-vivo implants is provided. This work is based on electric impedance monitoring through the formulation of an analytical model that scrutinizes essential parameters such as the deposited metal resistivity, insulation between conductors, changes in electrode geometry, and leakage currents. The evolution of these parameters takes place over an equivalent period of approximately 10 years. The analytical model, focusing on a fractional capacitor, provides nuanced insights into the surface conductivity variation. A comparative study is performed between a classical polymer material (SU8) and synthetic diamond. Samples subjected to dynamic impedance analysis reveal distinctive patterns over time, characterized by their physical degradation. The results highlight the very high stability of diamond, suggesting promise for the electrode's enduring viability. To support this analysis, microscopic and optical measurements conclude the paper and confirm the high stability of diamond and its strong potential as a material for neural implants with long-life use. [ABSTRACT FROM AUTHOR]

Bionic vision systems are currently limited by indiscriminate activation of all retinal ganglion cells (RGCs)– despite the dozens of known RGC types which each encode a different visual message. Here, we use spike-triggered averaging to explore how electrical responsiveness varies across RGC types toward the goal of using this variation to create type-selective electrical stimuli. A battery of visual stimuli and a randomly distributed sequence of electrical pulses were delivered to healthy and degenerating (4-week-old rd10) mouse retinas. Ganglion cell spike trains were recorded during stimulation using a 60-channel microelectrode array. Hierarchical clustering divided the recorded RGC populations according to their visual and electrical response patterns. Novel electrical stimuli were presented to assess type-specific selectivity. In healthy retinas, responses fell into 35 visual patterns and 14 electrical patterns. In degenerating retinas, responses fell into 12 visual and 23 electrical patterns. Few correspondences between electrical and visual response patterns were found except for the known correspondence of ON visual type with upward deflecting electrical type and OFF cells with downward electrical profiles. Further refinement of the approach presented here may yet yield the elusive nuances necessary for type-selective stimulation. This study greatly deepens our understanding of electrical input filters in the context of detailed visual response characterization and includes the most complete examination yet of degenerating electrical input filters.

Neuralink is a neurotechnology company founded by Elon Musk in 2016, which has been quietly developing revolutionary technology allowing for ultra-high precision bidirectional communication between external devices and the brain. In this paper, we explore the multifaceted ethical considerations surrounding neural interfaces, analyzing potential societal impacts, risks, and call for a need for responsible innovation. Despite the technological, medical, medicolegal, and ethical challenges ahead, neural interface technology remains extremely promising and has the potential to create a new era of medicine. [ABSTRACT FROM AUTHOR]

Introduction: Electrophysiological investigations of intact neural circuits are challenged by the gentle and complex nature of neural tissues. Bi-directional electrophysiological interfacing with the retina, in its intact form, is particularly demanding and currently there is no feasible approach to achieve such investigations. Here we present a feasibility study of a novel soft multi-electrode array suitable for bi-directional electrophysiological study of the intact retina. Methods: Screen-printed soft electrode arrays were developed and tested. The soft probes were designed to accommodate the curvature of the retina in the eye and o [ABSTRACT FROM AUTHOR]

Bionic vision systems are currently limited by indiscriminate activation of all retinal ganglion cells (RGCs)– despite the dozens of known RGC types which each encode a different visual message. Here, we use spike-triggered averaging to explore how electrical responsiveness varies across RGC types toward the goal of using this variation to create type-selective electrical stimuli. A battery of visual stimuli and a randomly distributed sequence of electrical pulses were delivered to healthy and degenerating (4-week-old rd10) mouse retinas. Ganglion cell spike trains were recorded during stimulation using a 60-channel microelectrode array. Hierarchical clustering divided the recorded RGC populations according to their visual and electrical response patterns. Novel electrical stimuli were presented to assess type-specific selectivity. In healthy retinas, responses fell into 35 visual patterns and 14 electrical patterns. In degenerating retinas, responses fell into 12 visual and 23 electrical patterns. Few correspondences between electrical and visual response patterns were found except for the known correspondence of ON visual type with upward deflecting electrical type and OFF cells with downward electrical profiles. Further refinement of the approach presented here may yet yield the elusive nuances necessary for type-selective stimulation. This study greatly deepens our understanding of electrical input filters in the context of detailed visual response characterization and includes the most complete examination yet of degenerating electrical input filters. [ABSTRACT FROM AUTHOR]

IntroductionElectrophysiological investigations of intact neural circuits are challenged by the gentle and complex nature of neural tissues. Bi-directional electrophysiological interfacing with the retina, in its intact form, is particularly demanding and currently there is no feasible approach to achieve such investigations. Here we present a feasibility study of a novel soft multi-electrode array suitable for bi-directional electrophysiological study of the intact retina.MethodsScreen-printed soft electrode arrays were developed and tested. The soft probes were designed to accommodate the curvature of the retina in the eye and offer an opportunity to study the retina in its intact form.ResultsFor the first time, we show both electrical recording and stimulation capabilities from the intact retina. In particular, we demonstrate the ability to characterize retina responses to electrical stimulation and reveal stable, direct, and indirect responses compared with ex-vivo conditions.DiscussionThese results demonstrate the unique performances of the new probe while also suggesting that intact retinas retain better stability and robustness than ex-vivo retinas making them more suitable for characterizing retina responses to electrical stimulation.

Devices capable of recording or stimulating neuronal signals have created new opportunities to understand normal physiology and treat sources of pathology in the brain. However, it is possible that the tissue response to implanted electrodes may influence the nature of the signals detected or stimulated. In this study, we characterized structural and functional changes in deep layer pyramidal neurons surrounding silicon or polyimide-based electrodes implanted in the motor cortex of rats. Devices were captured in 300 µm-thick tissue slices collected at the 1 or 6 week time point post-implantation, and individual neurons were assessed using a combination of whole-cell electrophysiology and 2-photon imaging. We observed disrupted dendritic arbors and a significant reduction in spine densities in neurons surrounding devices. These effects were accompanied by a decrease in the frequency of spontaneous excitatory post-synaptic currents, a reduction in sag amplitude, an increase in spike frequency adaptation, and an increase in filopodia density. We hypothesize that the effects observed in this study may contribute to the signal loss and instability that often accompany chronically implanted electrodes. Implanted electrodes in the brain can be used to treat sources of pathology and understand normal physiology by recording or stimulating electrical signals generated by local neurons. However, a foreign body response following implantation undermines the performance of these devices. While several studies have investigated the biological mechanisms of device-tissue interactions through histology, transcriptomics, and imaging, our study is the first to directly interrogate effects on the function of neurons surrounding electrodes using single-cell electrophysiology. Additionally, we provide new, detailed assessments of the impacts of electrodes on the dendritic structure and spine morphology of neurons, and we assess effects for both traditional (silicon) and newer polymer electrode materials. These results reveal new potential mechanisms of electrode-tissue interactions. [Display omitted] [ABSTRACT FROM AUTHOR]

Introduction: Brain-machine interfaces have reached an unprecedented capacity to measure and drive activity in the brain, allowing restoration of impaired sensory, cognitive or motor function. Classical control theory is pushed to its limit when aiming to design control laws that are suitable for large-scale, complex neural systems. This work proposes a scalable, data-driven, unified approach to study brain-machine-environment interaction using established tools from dynamical systems, optimal control theory, and deep learning. Methods: To unify the methodology, we define the environment, neural system, and prosthesis in terms of differential equations with learnable parameters, which effectively reduce to recurrent neural networks in the discrete-time case. Drawing on tools from optimal control, we describe three ways to train the system: Direct optimization of an objective function, oracle-based learning, and reinforcement learning. These approaches are adapted to different assumptions about knowledge of system equations, linearity, differentiability, and observability. Results: We apply the proposed framework to train an in-silico neural system to perform tasks in a linear and a nonlinear environment, namely particle stabilization and pole balancing. After training, this model is perturbed to simulate impairment of sensor and motor function. We show how a prosthetic controller can be trained to restore the behavior of the neural system under increasing levels of perturbation. Discussion: We expect that the proposed framework will enable rapid and flexible synthesis of control algorithms for neural prostheses that reduce the need for in-vivo testing. We further highlight implications for sparse placement of prosthetic sensor and actuator components. [ABSTRACT FROM AUTHOR]

IntroductionBrain-machine interfaces have reached an unprecedented capacity to measure and drive activity in the brain, allowing restoration of impaired sensory, cognitive or motor function. Classical control theory is pushed to its limit when aiming to design control laws that are suitable for large-scale, complex neural systems. This work proposes a scalable, data-driven, unified approach to study brain-machine-environment interaction using established tools from dynamical systems, optimal control theory, and deep learning.MethodsTo unify the methodology, we define the environment, neural system, and prosthesis in terms of differential equations with learnable parameters, which effectively reduce to recurrent neural networks in the discrete-time case. Drawing on tools from optimal control, we describe three ways to train the system: Direct optimization of an objective function, oracle-based learning, and reinforcement learning. These approaches are adapted to different assumptions about knowledge of system equations, linearity, differentiability, and observability.ResultsWe apply the proposed framework to train an in-silico neural system to perform tasks in a linear and a nonlinear environment, namely particle stabilization and pole balancing. After training, this model is perturbed to simulate impairment of sensor and motor function. We show how a prosthetic controller can be trained to restore the behavior of the neural system under increasing levels of perturbation.DiscussionWe expect that the proposed framework will enable rapid and flexible synthesis of control algorithms for neural prostheses that reduce the need for in-vivo testing. We further highlight implications for sparse placement of prosthetic sensor and actuator components.

OBJECTIVE:Direct electrical stimulation (DES) is a clinical gold standard for human brain mapping and readily evokes conscious percepts, yet the neurophysiological changes underlying these percepts are not well understood. APPROACH:To determine the neural correlates of DES, we stimulated the somatosensory cortex of ten human participants at frequency-amplitude combinations that both elicited and failed to elicit conscious percepts, meanwhile recording neural activity directly surrounding the stimulation site. We then compared the neural activity of perceived trials to that of non-perceived trials. MAIN RESULTS:We found that stimulation evokes distributed high gamma activity, which correlates with conscious perception better than stimulation parameters themselves. SIGNIFICANCE:Our findings suggest that high gamma activity is a reliable biomarker for perception evoked by both natural and electrical stimuli.

Introduction: Different eye movement analysis algorithms are used in vestibular implant research to quantify the electrically evoked vestibulo-ocular reflex (eVOR). Often, standard techniques are used as applied for quantification of the natural VOR in healthy subjects and patients with vestibular loss. However, in previous research, it was observed that the morphology of the VOR and eVOR may differ substantially. In this study, it was investigated if the analysis techniques for eVOR need to be adapted to optimize a truthful quantification of the eVOR (VOR gain, orientation of the VOR axis, asymmetry, and phase shift). Methods: "Natural" VOR responses were obtained in six age-matched healthy subjects, and eVOR responses were obtained in eight bilateral-vestibulopathy patients fitted with a vestibular implant. Three conditions were tested: "nVOR" 1-Hz sinusoidal whole-body rotations of healthy subjects in a rotatory chair, "eVOR" 1-Hz sinusoidal electrical vestibular implant stimulation without whole-body rotations in bilateral-vestibulopathy patients, and "dVOR" 1-Hz sinusoidal whole-body rotations in bilateral-vestibulopathy patients using the chair-mounted gyroscope output to drive the electrical vestibular implant stimulation (therefore also in sync 1 Hz sinusoidal). VOR outcomes were determined from the obtained VOR responses, using three different eye movement analysis paradigms: (1) peak eye velocity detection using the raw eye traces; (2) peak eye velocity detection using full-cycle sine fitting of eye traces; (3) peak eye velocity detection using half-cycle sine fitting of eye traces. Results: The type of eye movement analysis algorithm significantly influenced VOR outcomes, especially regarding the VOR gain and asymmetry of the eVOR in bilateral-vestibulopathy patients fitted with a vestibular implant. Full-cycle fitting lowered VOR gain in the eVOR condition (mean difference: 0.14 ± 0.06 95% CI, p = 0.018). Half-cycle fitting lowered VOR gain in the dVOR condition (mean difference: 0.08 ± 0.04 95% CI, p = 0.009). In the eVOR condition, half-cycle fitting was able to demonstrate the asymmetry between the excitatory and inhibitory phases of stimulation in comparison with the full-cycle fitting (mean difference: 0.19 ± 0.12 95% CI, p = 0.024). The VOR axis and phase shift did not differ significantly between eye movement analysis algorithms. In healthy subjects, no clinically significant effect of eye movement analysis algorithms on VOR outcomes was observed. Conclusion: For the analysis of the eVOR, the excitatory and inhibitory phases of stimulation should be analysed separately due to the inherent asymmetry of the eVOR. A half-cycle fitting method can be used as a more accurate alternative for the analysis of the full-cycle traces. [ABSTRACT FROM AUTHOR]

Intracortical microstimulation to the visual cortex is thought to be a feasible technique for inducing localized phosphenes in patients with acquired blindness, and thereby for visual prosthesis. In order to design effective stimuli for the prosthesis, it is important to elucidate relationships between the spatio-temporal patterns of stimuli and the resulting neural responses and phosphenes through pre-clinical animal studies. However, the physiological basis of effective spatial patterns of the stimuli for the prosthesis has been little investigated in the literature, at least partly because that the previously developed multi-channel stimulation systems were designed specifically for the clinical use. In the present, a 64-channel stimulation module was developed as a scalable tool for animal experiments. The operations of the module were verified by not only dry-bench tests but also physiological animal experiments in vivo. The results demonstrated its usefulness for examining the stimulus-response relationships in a quantitative manner, and for inducing the multi-site neural excitations with a multi-electrode array. In addition, this stimulation module could be used to generate spatially patterned stimuli with up to 4,096 channels in a dynamic way, in which the stimulus patterns can be updated at a certain frame rate in accordance with the incoming visual scene. The present study demonstrated that our stimulation module is applicable to the physiological and other future studies in animals on the cortical prostheses.

Intracortical brain-machine interfaces have shown promise for restoring function to people with paralysis, but their translation to portable and implantable devices is hindered by their high power consumption. Recent devices have drastically reduced power consumption compared to standard experimental brain-machine interfaces, but still require wired or wireless connections to computing hardware for feature extraction and inference. Here, we introduce a Neural Recording And Decoding (NeuRAD) application specific integrated circuit (ASIC) in 180 nm CMOS that can extract neural spiking features and predict two-dimensional behaviors in real-time. To reduce amplifier and feature extraction power consumption, the NeuRAD has a hardware accelerator for extracting spiking band power (SBP) from intracortical spiking signals and includes an M0 processor with a fixed-point Matrix Acceleration Unit (MAU) for efficient and flexible decoding. We validated device functionality by recording SBP from a nonhuman primate implanted with a Utah microelectrode array and predicting the one- and two-dimensional finger movements the monkey was attempting to execute in closed-loop using a steady-state Kalman filter (SSKF). Using the NeuRAD’s real-time predictions, the monkey achieved 100% success rate and 0.82 s mean target acquisition time to control one-dimensional finger movements using just 581 $\mu$ W. To predict two-dimensional finger movements, the NeuRAD consumed 588 $\mu$ W to enable the monkey to achieve a 96% success rate and 2.4 s mean acquisition time. By employing SBP, ASIC brain-machine interfaces can close the gap to enable fully implantable therapies for people with paralysis. [ABSTRACT FROM AUTHOR]

Patients fit with cochlear implants (CIs) commonly indicate at the time of device fitting and for some time after, that the speech signal sounds abnormal. A high pitch or timbre is one component of the abnormal percept. In this project, our aim was to determine whether a number of years of CI use reduced perceived upshifts in frequency spectrum and/or voice fundamental frequency. The participants were five individuals who were deaf in one ear and who had normal hearing in the other ear. The deafened ears had been implanted with a 18.5 mm electrode array which resulted in signal input frequencies being directed to locations in the spiral ganglion (SG) that were between one and two octaves higher than the input frequencies. The patients judged the similarity of a clean signal (a male-voice sentence) presented to their implanted ear and candidate, implant-like, signals presented to their normal-hearing (NH) ear. Matches to implant sound quality were obtained, on average, at 8 months after device activation (see section "Time 1") and at 35 months after activation (see section "Time 2"). At Time 1, the matches to CI sound quality were characterized, most generally, by upshifts in the frequency spectrum and in voice pitch. At Time 2, for four of the five patients, frequency spectrum values remained elevated. For all five patients F0 values remained elevated. Overall, the data offer little support for the proposition that, for patients fit with shorter electrode arrays, cortical plasticity nudges the cortical representation of the CI voice toward more normal, or less upshifted, frequency values between 8 and 35 months after device activation. Cortical plasticity may be limited when there are large differences between frequencies in the input signal and the locations in the SG stimulated by those frequencies. [ABSTRACT FROM AUTHOR]

Patients fit with cochlear implants (CIs) commonly indicate at the time of device fitting and for some time after, that the speech signal sounds abnormal. A high pitch or timbre is one component of the abnormal percept. In this project, our aim was to determine whether a number of years of CI use reduced perceived upshifts in frequency spectrum and/or voice fundamental frequency. The participants were five individuals who were deaf in one ear and who had normal hearing in the other ear. The deafened ears had been implanted with a 18.5 mm electrode array which resulted in signal input frequencies being directed to locations in the spiral ganglion (SG) that were between one and two octaves higher than the input frequencies. The patients judged the similarity of a clean signal (a male-voice sentence) presented to their implanted ear and candidate, implant-like, signals presented to their normal-hearing (NH) ear. Matches to implant sound quality were obtained, on average, at 8 months after device activation (see section “Time 1”) and at 35 months after activation (see section “Time 2”). At Time 1, the matches to CI sound quality were characterized, most generally, by upshifts in the frequency spectrum and in voice pitch. At Time 2, for four of the five patients, frequency spectrum values remained elevated. For all five patients F0 values remained elevated. Overall, the data offer little support for the proposition that, for patients fit with shorter electrode arrays, cortical plasticity nudges the cortical representation of the CI voice toward more normal, or less upshifted, frequency values between 8 and 35 months after device activation. Cortical plasticity may be limited when there are large differences between frequencies in the input signal and the locations in the SG stimulated by those frequencies.

Retinal prosthesis is one of the key strategies for tackling retinitis pigmentosa – a group of genetic disorders that can cause blindness. However, further advancement of retinal implant is hindered by some technical limitations. Current state-of-the-art retinal implants failed within two-three years after implant and showed a significant decrement in their visual acuity. The purpose of this investigation is to search for a material that is better suited for the packaging of retinal implants. Specifically, the study was conducted under the assumption that image acquisition is done internally, i.e. without the use of an external camera. Polydimethylsiloxane (PDMS) and polyimide (PI) are some of the most commonly used materials in implantable devices. In addition, colorless transparent polyimide (CPI) was studied to overcome the optical limitations of PI. Finally, liquid crystal polymer (LCP) and cyclic olefin copolymer (COC) were added in the comparison due to their promising results regarding their expected longevity. In this experiment, PDMS, CPI, and COC outperformed the other materials in optical transparency. Considering the excellent encapsulation properties due to thermoplasticity while being able to be fabricated into a micro-lens array, we believe COC is worth further in-depth investigation for further advancement in the field of a retinal prosthesis. [ABSTRACT FROM AUTHOR]

This paper presents a frequency-splitting-based wireless power and data transfer IC that simultaneously delivers power and forward data over a single inductive link. For data transmission, frequency-shift keying (FSK) is utilized because the FSK modulation scheme supports continuous wireless power transmission without disruption of the carrier amplitude. Moreover, the link that manifests the frequency-splitting characteristic due to a close distance between coupled coils provides wide bandwidth for data delivery without degrading the quality factors of the coils. It results in large power delivery, high data rate, and high power transfer efficiency. The presented IC fabricated in a 180-nm BCD process simultaneously achieves up-to-115-mW wireless power delivery to the load and 2.5-Mb/s downlink data rate over the single inductive link. The measured overall power efficiency from the DC power supply at the transmitter module to the load at the receiver module reaches 56.7 $\%$ at its maximum, and the bit error rate is lower than 10 $^{-6}$ at 2.5 Mb/s. As a result, the figure of merit (FoM) for data transmission is enhanced by 2 times, and the FoM for power delivery is improved by 38.7 times compared to prior state-of-the-arts using a single inductive link. [ABSTRACT FROM AUTHOR]

Implantable electrical devices offer a variety of potential diagnostic options and treatments in different medical fields. Especially in the absence of specific drugs, the implantable nerve electrodes (INEs) are one of the main treatments for neurological diseases such as epilepsy, Parkinson’s disease, Alzheimer’s disease, etc. INEs are helpful in studying and regulating nervous system via recording nerve electrical signals or stimulating nerve tissue, but the mechanical mismatch between rigid electrode and soft biological tissue is a critical challenge for long-term implantation. The advances in micromachining technologies and materials have greatly promoted the development of INEs, such as enhanced biocompatibility, reduced foreign body reactions, and structural innovation. In particular, the mechanical performances of flexible implantable electrode and soft biological tissue matched better with less tissue damage. In this review, the implantable flexible nerve electrodes (IFNEs) based on functionalized substrates, intelligent electrodes, and innovative structures are elaborated and discussed. We summarized their various applications in neural prosthesis and neural signal recording. We also discussed the questions and possible methods for developing future IFNEs in the perspective.

Paralysis of the trunk results in seated instability leading to difficulties performing activities of daily living. Functional neuromuscular stimulation (FNS) combined with control systems have the potential to restore some dynamic functions of the trunk. However, design of multi-joint, multi-muscle control systems requires characterization of the stimulation-driven muscles responsible for movement. Objective: This study characterizes the input-output properties of paralyzed trunk muscles activated by FNS, and explores co-activation of muscles. Methods: Four participants with various spinal cord injuries (C7 AIS-B, T4 AIS-B, T5 AIS-A, C5 AIS-C) were constrained so lumbar forces were transmitted to a load cell while an implanted neuroprosthesis activated otherwise paralyzed hip and paraspinal muscles. Isometric force recruitment curves in the nominal seated position were generated by inputting the level of stimulation (pulse width modulation) while measuring the resulting muscle force. Two participants returned for a second experiment where muscles were co-activated to determine if their actions combined linearly. Results: Recruitment curves of most trunk and hip muscles fit sigmoid shaped curves with a regression coefficient above 0.75, and co-activation of the muscles combined linearly across the hip and lumbar joint. Subject specific perturbation plots showed one subject is capable of resisting up to a 300N perturbation anteriorly and 125N laterally; with some subjects falling considerably below these values. Conclusion: Development of a trunk stability control system can use sigmoid recruitment dynamics and assume muscle forces combine linearly. Significance: This study informs future designs of multi-muscle, and multi-dimensional FNS systems to maintain seated posture and stability. [ABSTRACT FROM AUTHOR]

Objective: Cortical neural prostheses that aim to restore useful vision, hearing, and tactile sensations require the ability to selectively target different cortical regions simultaneously. Electrical stimulation via intracortical electrodes has been used to create spatial patterns of cortical activation. However, their efficacy remains limited due to the inability of conventional electrodes to confine activation to specific cortical regions around each electrode. Magnetic stimulation from single bent wires can selectively activate pyramidal neurons while avoiding passing axons, thereby confining activation to small cortical regions. This paper presents a novel bent flat microwire array and demonstrates its effectiveness for selective activation of cortical columns in mouse brain slices. Methods: A computational model was developed to compare the spatial resolution of magnetic stimulation from bent wire arrays with 280 and 530 μm tip spacings. The same array designs were fabricated for use in electrophysiological experiments, i.e., calcium imaging (GCaMP6s) of mouse brain slices. Results: All fabricated array designs reliably produced spatially discrete cortical activations at low stimulus amplitudes, but the 280-μm-spacing produced strong interference (constructive or destructive) at high stimulus amplitudes, thereby resulting in single strong activations or two asymmetric activations. 4-channel bent wire arrays with spacing of 340 μm avoided the interference and produced clearer spatial patterns of activation than electrodes. Conclusion: Bent wire array designs can influence the strength or the spatial resolution of multichannel magnetic stimulation. Significance: These results suggest that bent microwire arrays can enhance the selectivity of multichannel stimulation of brain and therefore may help to develop reliable and effective cortical neural prostheses. [ABSTRACT FROM AUTHOR]

Transvenous treatment of paralysis is a concept less than a decade old. The Stentrode (Synchron, Inc, New York, USA) is a novel electrode on stent device intended to be implanted in the superior sagittal sinus adjacent to the motor cortex. Initial animal studies in sheep demonstrated the safety of the implant as well as its accuracy in detecting neural signals at both short and long term. Early human trials have shown the safety of the device and demonstrated the use of the Stentrode system in facilitating patients with paralysis to carry out daily activities such as texting, email, and personal finance. This is an emerging technology with promise, although certainly more research is required to better understand the capabilities and limitations of the device., Competing Interests: Disclosure K. Yaeger has no relevant disclosures. J. Mocco is Chief Medical Officer for Synchron a company developing a transvenous BCI for paralysis therapy and therefore has stock and a consulting agreement., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Neural prostheses enable users to effect movement through a variety of actuators by translating brain signals into movement control signals. However, to achieve more natural limb movements from these devices, the restoration of somatosensory feedback is required. We used feature-learnability, a machine-learning approach, to assess signal features for their capacity to enhance decoding performance of neural signals evoked by natural tactile and proprioceptive somatosensory stimuli, recorded from the surface of the dorsal column nuclei (DCN) in urethane-anesthetized rats. The highest performing individual feature, spike amplitude, classified somatosensory DCN signals with 70% accuracy. The highest accuracy achieved was 87% using 13 features that were extracted from both high and low-frequency (LF) bands of DCN signals. In general, high-frequency (HF) features contained the most information about peripheral somatosensory events, but when features were acquired from short time-windows, classification accuracy was significantly improved by adding LF features to the feature set. We found that proprioception-dominated stimuli generalize across animals better than tactile-dominated stimuli, and we demonstrate how information that signal features contribute to neural decoding changes over the time-course of dynamic somatosensory events. These findings may inform the biomimetic design of artificial stimuli that can activate the DCN to substitute somatosensory feedback. Although, we investigated somatosensory structures, the feature set we investigated may also prove useful for decoding other (e.g., motor) neural signals.

The temporal pattern of action potentials can convey rich information in a variety of sensory systems. We describe a new non-invasive technique that enables precise, reliable generation of action potential patterns in tactile peripheral afferent neurons by brief taps on the skin. Using this technique, we demonstrate sophisticated coding of temporal information in the somatosensory system, that shows that perceived vibration frequency is not encoded in peripheral afferents as was expected by either their firing rate or the underlying periodicity of the stimulus. Instead, a burst gap or silent gap between trains of action potentials conveys frequency information. This opens the possibility of new encoding strategies that could be deployed to convey sensory information using mechanical or electrical stimulation in neural prostheses and brain-machine interfaces, and may extend to senses beyond artificial encoding of aspects of touch. We argue that a focus on appropriate use of effective temporal coding offers more prospects for rapid improvement in the function of these interfaces than attempts to scale-up existing devices.

Background: Electrical stimulation of residual afferent nerve fibers can evoke sensations from a missing limb after amputation, and bionic arms endowed with artificial sensory feedback have been shown to confer functional and psychological benefits. Here we explore the extent to which artificial sensations can be discriminated based on location, quality, and intensity.Methods: We implanted Utah Slanted Electrode Arrays (USEAs) in the arm nerves of three transradial amputees and delivered electrical stimulation via different electrodes and frequencies to produce sensations on the missing hand with various locations, qualities, and intensities. Participants performed blind discrimination trials to discriminate among these artificial sensations.Results: Participants successfully discriminated cutaneous and proprioceptive sensations ranging in location, quality and intensity. Performance was significantly greater than chance for all discrimination tasks, including discrimination among up to ten different cutaneous location-intensity combinations (15/30 successes, p < 0.0001) and seven different proprioceptive location-intensity combinations (21/40 successes, p < 0.0001). Variations in the site of stimulation within the nerve, via electrode selection, enabled discrimination among up to five locations and qualities (35/35 successes, p < 0.0001). Variations in the stimulation frequency enabled discrimination among four different intensities at the same location (13/20 successes, p < 0.0005). One participant also discriminated among individual stimulation of two different USEA electrodes, simultaneous stimulation on both electrodes, and interleaved stimulation on both electrodes (20/24 successes, p < 0.0001).Conclusion: Electrode location, stimulation frequency, and stimulation pattern can be modulated to evoke functionally discriminable sensations with a range of locations, qualities, and intensities. This rich source of artificial sensory feedback may enhance functional performance and embodiment of bionic arms endowed with a sense of touch. [ABSTRACT FROM AUTHOR]

Sensory feedback is critical to the performance of neural prostheses that restore movement control after neurological injury. Recent advances in direct neural control of paralyzed arms present new requirements for miniaturized, low-power sensor systems. To address this challenge, we developed a fully-integrated wireless sensor-brain-machine interface (SBMI) system for communicating key somatosensory signals, fingertip forces and limb joint angles, to the brain. The system consists of a tactile force sensor, an electrogoniometer, and a neural interface. The tactile force sensor features a novel optical waveguide on CMOS design for sensing. The electrogoniometer integrates an ultra low-power digital signal processor (DSP) for real-time joint angle measurement. The neural interface enables bidirectional neural stimulation and recording. Innovative designs of sensors and sensing interfaces, analog-to-digital converters (ADC) and ultra wide-band (UWB) wireless transceivers have been developed. The prototypes have been fabricated in 180nm standard CMOS technology and tested on the bench and in vivo. The developed system provides a novel solution for providing somatosensory feedback to next-generation neural prostheses. [ABSTRACT FROM AUTHOR]

A 1225-Channel Neuromorphic Retinal Prosthesis (RP) SoC is presented. Existing RP SoCs directly convert light intensity to electrical stimulus, which limit the adoption of delicate stimulus patterns to increase visual acuity. Moreover, a conventional centralized image processor leads to the local hot spot that poses a risk to the nearby retinal cells. To solve these issues, the proposed SoC adopts a distributed Neuromorphic Image Processor (NMIP) located within each pixel that extracts the outline of the incoming image, which reduces current dispersion and stimulus power compared with light-intensity proportional stimulus pattern. A spike-based asynchronous digital operation results in the power consumption of 56.3 nW/Ch without local temperature hot spot. At every 5×5 pixels, the localized (49-point) temperature-regulation circuit limits the temperature increase of neighboring retinal cells to less than 1 °C, and the overall power consumption of the SoC to be less than that of the human eye. The 1225-channel SoC fabricated in 0.18 μm 1P6M CMOS occupies 15mm2 while consuming 2.7 mW, and is successfully verified with image reconstruction demonstration. [ABSTRACT FROM AUTHOR]

The efficiency and safety of neuronal stimulation with implants strongly depend on the electrode material. Microelectrodes composed of iridium oxide are becoming increasingly important as they exhibit excellent charge injection capacity (CIC) as well as charge storage capacity (CSC). We present the development of a robust process for the fabrication of sputtered iridium oxide films (SIROF). This process has been used for the "RETINA IMPLANT Alpha AMS" for several years of subretinal stimulation. In this paper, we describe the full experimental investigation of the electrode material. The electrochemical and morphological properties were investigated by cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), voltage transient measurements, and focused ion beam-scanning electron microscopy (FIB-SEM). The implementation on the CMOS chip of the retinal prosthesis is presented. The deposition process window was investigated extensively. Major changes in process parameters lead to a difference in impedance of only 10% of the mean. Accelerated aging tests revealed a long-term stability of the electrodes of at least 10 years under conditions of use. The SIROF electrodes (diameter 30 µm) show low impedance (15.9 kO), excellent CSC (50.9 mC/cm2), and high CIC (4.2 mC/cm2). In summary, the robustness of the presented deposition process and the large process window enable the integration of high-quality SIROF microelectrodes in active implants and thus long-term stability in a wide range of safe electrical stimulation. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this paper is to establish fabrication method of cyclic olefin copolymer(COC)-based neural electrode. Methods: The fabrication started with preparing COC pellets into COC films by compression molding. Metal layers were deposited on the COC film and attached to a silicon wafer. Laser ablation was used to cut the outer edges and mark alignment keys. The metal layers were patterned using standard photolithography procedures. Finally, the isolated electrodes were laminated. To ensure that the resulting electrode is safe and suitable for long-term implants, in vitro biocompatibility test, impedance evaluation, accelerated soak test, and repeated bend test were conducted. Results: Cytotoxicity test and elution test confirmed the biocompatibility in vitro. The basic performance was not hindered compared to other polymer-based electrodes, and the longevity of the electrode was validated by accelerated soak test. However, repeated bend test revealed that the material might not be suitable for applications where constant bending is required. Conclusion: The COC-based neural electrode was successfully fabricated. The material showed several merits such as biocompatibility, thermoplasticity, low water absorption rate, and high transparency, but should be limited to applications where repeated bending is not required. Significance: Electrical circuits in implantable prosthetic devices must be hermetically encapsulated for a long period of time. Material such as COC with extremely low water absorption rate could have a significant impact on the longevity of these devices. [ABSTRACT FROM AUTHOR]

Neural prostheses enable users to effect movement through a variety of actuators by translating brain signals into movement control signals. However, to achieve more natural limb movements from these devices, the restoration of somatosensory feedback is required. We used feature-learnability, a machine-learning approach, to assess signal features for their capacity to enhance decoding performance of neural signals evoked by natural tactile and proprioceptive somatosensory stimuli, recorded from the surface of the dorsal column nuclei (DCN) in urethane-anesthetized rats. The highest performing individual feature, spike amplitude, classified somatosensory DCN signals with 70% accuracy. The highest accuracy achieved was 87% using 13 features that were extracted from both high and low-frequency (LF) bands of DCN signals. In general, high-frequency (HF) features contained the most information about peripheral somatosensory events, but when features were acquired from short time-windows, classification accuracy was significantly improved by adding LF features to the feature set. We found that proprioception-dominated stimuli generalize across animals better than tactile-dominated stimuli, and we demonstrate how information that signal features contribute to neural decoding changes over the time-course of dynamic somatosensory events. These findings may inform the biomimetic design of artificial stimuli that can activate the DCN to substitute somatosensory feedback. Although, we investigated somatosensory structures, the feature set we investigated may also prove useful for decoding other (e.g., motor) neural signals. [ABSTRACT FROM AUTHOR]

The temporal pattern of action potentials can convey rich information in a variety of sensory systems. We describe a new non-invasive technique that enables precise, reliable generation of action potential patterns in tactile peripheral afferent neurons by brief taps on the skin. Using this technique, we demonstrate sophisticated coding of temporal information in the somatosensory system, that shows that perceived vibration frequency is not encoded in peripheral afferents as was expected by either their firing rate or the underlying periodicity of the stimulus. Instead, a burst gap or silent gap between trains of action potentials conveys frequency information. This opens the possibility of new encoding strategies that could be deployed to convey sensory information using mechanical or electrical stimulation in neural prostheses and brain-machine interfaces, and may extend to senses beyond artificial encoding of aspects of touch. We argue that a focus on appropriate use of effective temporal coding offers more prospects for rapid improvement in the function of these interfaces than attempts to scale-up existing devices. [ABSTRACT FROM AUTHOR]

Upper limb prostheses are specialized tools, and skilled operation is learned by amputees over time. Recently, neural prostheses using implanted peripheral nerve interfaces have enabled advances in artificial somatosensory feedback that can improve prosthesis outcomes. However, the effect of sensory learning on artificial somatosensation has not been studied, despite its known influence on intact somatosensation and analogous neuroprostheses. Sensory learning involves changes in the perception and interpretation of sensory feedback and may further influence functional and psychosocial outcomes. In this mixed methods case study, we examined how passive learning over 115 days of home use of a neural-connected, sensory-enabled prosthetic hand influenced perception of artificial sensory feedback in a participant with transradial amputation. We examined perceptual changes both within individual days of use and across the duration of the study. At both time scales, the reported percept locations became significantly more aligned with prosthesis sensor locations, and the phantom limb became significantly more extended toward the prosthesis position. Similarly, the participant’s ratings of intensity, naturalness, and contact touch significantly increased, while his ratings of vibration and movement significantly decreased across-days for tactile channels. These sensory changes likely resulted from engagement of cortical plasticity mechanisms as the participant learned to use the artificial sensory feedback. We also assessed psychosocial and functional outcomes through surveys and interviews, and found that self-efficacy, perceived function, prosthesis embodiment, social touch, body image, and prosthesis efficiency improved significantly. These outcomes typically improved within the first month of home use, demonstrating rapid benefits of artificial sensation. Participant interviews indicated that the naturalness of the experience and engagement with the prosthesis increased throughout the study, suggesting that artificial somatosensation may decrease prosthesis abandonment. Our data showed that prosthesis embodiment was intricately related to naturalness and phantom limb perception, and that learning the artificial sensation may have modified the body schema. As another indicator of successfully learning to use artificial sensation, the participant reported the emergence of stereognosis later in the study. This study provides the first evidence that artificial somatosensation can undergo similar learning processes as intact sensation and highlights the importance of sensory restoration in prostheses.

The brainstem dorsal column nuclei (DCN) are essential to inform the brain of tactile and proprioceptive events experienced by the body. However, little is known about how ascending somatosensory information is represented in the DCN. Our objective was to investigate the usefulness of high-frequency (HF) and low-frequency (LF) DCN signal features (SFs) in predicting the nerve from which signals were evoked. We also aimed to explore the robustness of DCN SFs and map their relative information content across the brainstem surface. DCN surface potentials were recorded from urethane-anesthetized Wistar rats during sural and peroneal nerve electrical stimulation. Five salient SFs were extracted from each recording electrode of a seven-electrode array. We used a machine learning approach to quantify and rank information content contained within DCN surface-potential signals following peripheral nerve activation. Machine-learning of SF and electrode position combinations was quantified to determine a hierarchy of information importance for resolving the peripheral origin of nerve activation. A supervised back-propagation artificial neural network (ANN) could predict the nerve from which a response was evoked with up to 96.8 ± 0.8% accuracy. Guided by feature-learnability, we maintained high prediction accuracy after reducing ANN algorithm inputs from 35 (5 SFs from 7 electrodes) to 6 (4 SFs from one electrode and 2 SFs from a second electrode). When the number of input features were reduced, the best performing input combinations included HF and LF features. Feature-learnability also revealed that signals recorded from the same midline electrode can be accurately classified when evoked from bilateral nerve pairs, suggesting DCN surface activity asymmetry. Here we demonstrate a novel method for mapping the information content of signal patterns across the DCN surface and show that DCN SFs are robust across a population. Finally, we also show that the DCN is functionally asymmetrically organized, which challenges our current understanding of somatotopic symmetry across the midline at sub-cortical levels.

Chemical neuromodulation of the retina using native neurotransmitters to biomimetically activate target retinal neurons through chemical synapses is a promising biomimetic alternative to electrical stimulation for restoring vision in blindness caused by photoreceptor degenerative diseases. Recent research has shown that subretinal chemical stimulation could be advantageous for treating photoreceptor degenerative diseases but many of the parameters for achieving efficacious chemical neuromodulation are yet to be explored. In this paper, we investigated how the depth at which neurotransmitter is injected subretinally affects the success rate, spike rate characteristics (i.e., amplitude, response latency, and time width), and spatial resolution of chemical stimulation in wild-type Long Evans and photoreceptor degenerated S334ter-3 transgenic rat retinas in vitro. We compared the responses to injections of glutamate at the subretinal surface and two subsurface depths near the outer and inner plexiform layers and found that while injections at all depths elicited robust retinal ganglion cell responses, they differed significantly in terms of the spike rate characteristics and spatial resolutions across injection depths. Shallow subsurface injections near the outer plexiform layer evoked the highest spike rate amplitudes and had the highest spatial resolution and success rates, while deep subsurface injections near the inner plexiform layer elicited the shortest latencies and narrowest time widths. Our results suggest that surface injections are suboptimal for subretinal chemical neuromodulation, while shallow subsurface and deep subsurface injections may optimize high spatial and high temporal resolution, respectively. These findings have great significance for the design and development of a potential neurotransmitter-based subretinal prosthesis. [ABSTRACT FROM AUTHOR]

Introduction: The vestibular implant could become a clinically useful device in the near future. This study investigated the feasibility of restoring the high-frequency dynamic visual acuity (DVA) with a vestibular implant, using the functional Head Impulse Test (fHIT).Methods: A 72-year-old female, with bilateral vestibulopathy and fitted with a modified cochlear implant incorporating three vestibular electrodes (MED-EL, Innsbruck, Austria), was available for this study. Electrical stimulation was delivered with the electrode close to the lateral ampullary nerve in the left ear. The high-frequency DVA in the horizontal plane was tested with the fHIT. After training, the patient underwent six trials of fHIT, each with a different setting of the vestibular implant: (1) System OFF before stimulation; (2) System ON, baseline stimulation; (3) System ON, reversed stimulation; (4) System ON, positive stimulation; (5) System OFF, without delay after stimulation offset; and (6) System OFF, 25 min delay after stimulation offset. The percentage of correct fHIT scores for right and left head impulses were compared between trials.Results: Vestibular implant stimulation improved the high-frequency DVA compared to no stimulation. This improvement was significant for "System ON, baseline stimulation" (p = 0.02) and "System ON, positive stimulation" (p < 0.001). fHIT scores changed from 19 to 44% (no stimulation) to maximum 75-94% (System ON, positive stimulation).Conclusion: The vestibular implant seems capable of improving the high-frequency DVA. This functional benefit of the vestibular implant illustrates again the feasibility of this device for clinical use in the near future. [ABSTRACT FROM AUTHOR]

Upper limb prostheses are specialized tools, and skilled operation is learned by amputees over time. Recently, neural prostheses using implanted peripheral nerve interfaces have enabled advances in artificial somatosensory feedback that can improve prosthesis outcomes. However, the effect of sensory learning on artificial somatosensation has not been studied, despite its known influence on intact somatosensation and analogous neuroprostheses. Sensory learning involves changes in the perception and interpretation of sensory feedback and may further influence functional and psychosocial outcomes. In this mixed methods case study, we examined how passive learning over 115 days of home use of a neural-connected, sensory-enabled prosthetic hand influenced perception of artificial sensory feedback in a participant with transradial amputation. We examined perceptual changes both within individual days of use and across the duration of the study. At both time scales, the reported percept locations became significantly more aligned with prosthesis sensor locations, and the phantom limb became significantly more extended toward the prosthesis position. Similarly, the participant's ratings of intensity, naturalness, and contact touch significantly increased, while his ratings of vibration and movement significantly decreased across-days for tactile channels. These sensory changes likely resulted from engagement of cortical plasticity mechanisms as the participant learned to use the artificial sensory feedback. We also assessed psychosocial and functional outcomes through surveys and interviews, and found that self-efficacy, perceived function, prosthesis embodiment, social touch, body image, and prosthesis efficiency improved significantly. These outcomes typically improved within the first month of home use, demonstrating rapid benefits of artificial sensation. Participant interviews indicated that the naturalness of the experience and engagement with the prosthesis increased throughout the study, suggesting that artificial somatosensation may decrease prosthesis abandonment. Our data showed that prosthesis embodiment was intricately related to naturalness and phantom limb perception, and that learning the artificial sensation may have modified the body schema. As another indicator of successfully learning to use artificial sensation, the participant reported the emergence of stereognosis later in the study. This study provides the first evidence that artificial somatosensation can undergo similar learning processes as intact sensation and highlights the importance of sensory restoration in prostheses. [ABSTRACT FROM AUTHOR]