Neuroprosthetic Research Articles

Cochlear implant material effects on inflammatory cell function and foreign body response

ObjectivesThe objectives of this study were to assess the effects of cochlear implant (CI) biomaterials on the function of macrophages and fibroblasts, two key mediators of the foreign body response (FBR) and to determine how these materials influence fibrous tissue growth and new bone formation within the cochlea. MethodsMacrophages and fibroblasts were cultured on polydimethylsiloxane (PDMS) and platinum substrates and human CI electrodes in vitro. Cell count, cell proliferation, cytokine production, and cell adhesion were measured. CI electrodes were implanted into murine cochleae for three weeks without electrical stimulation. Implanted cochleae were harvested for 3D X-ray microscopy with the CI left in-situ. The location of new bone growth within the scala tympani (ST) with reference to different portions of the implant (PDMS vs platinum) was quantified. ResultsCell counts of macrophages and fibroblasts were significantly higher on platinum substrates and platinum contacts of CI electrodes. Fibroblast proliferation was greater on platinum relative to PDMS, and cells grown on platinum formed more/larger focal adhesions. 3D X-ray microscopy showed neo-ossification in the peri‑implant areas of the ST. Volumetric quantification of neo-ossification showed a trend toward greater bone formation adjacent to the platinum electrodes compared to areas opposite or away from the platinum electrode bearing surfaces. ConclusionsFibrotic reactions are biomaterial specific, as demonstrated by the differences in cell adhesion, proliferation, and fibrosis on platinum and PDMS. The inflammatory reaction to platinum contacts on CI electrodes likely contributes to fibrosis to a greater degree than PDMS, and platinum contacts may influence the deposition of new bone, as demonstrated in the in vivo data. This information can potentially be used to influence the design of future generations of neural prostheses.

Investigation of neural electrode fabrication process on Polycarbonate substrate.

Polycarbonate is a polymer that has been widely used including medical application due to its useful properties. It has high temperature resistance, biocompatibility, transparency and low water absorption rate, which are needful characteristics for packaging material of implantable neural prosthetic devices. In this study, we investigated fabrication of neural electrode with polycarbonate film using standard photolithography process and heated hydraulic press for thermal lamination. First, oxygen plasma surface treatment was performed to increase the adhesion between metal and polycarbonate film. Then thin layer of titanium and gold layer were deposited. Metal layer is patterned through standard photolithography techniques. After completing the metal patterning, thermal lamination was performed with site opened polycarbonate film.

A Comparison of Delay-and-Add and Maximum Likelihood Estimation for Velocity-Selective Recording Using Multi-Electrode Cuffs.

Extracting information from the peripheral nervous system with implantable devices remains a significant challenge that limits the advancement of closed-loop neural prostheses. Linear electrode arrays can record neural signals with both temporal and spatial selectivity, and velocity selective recording using the delay-and-add algorithm can enable classification based on fibre type. The maximum likelihood estimation method also measures velocity and is frequently used in electromyography but has never been applied to electroneurography. Therefore, this study compares the two algorithms using in-vivo recordings of electrically evoked compound action potentials from the ulnar nerve of a pig. The performance of these algorithms was assessed using the velocity quality factor (Q-factor), computational time and the influence of the number of channels. The results show that the performance of both algorithms is significantly influenced by the number of channels in the recording array, with accuracies ranging from 77% with only two channels to 98% for 11 channels. Both algorithms were comparable in accuracy and Q-factor for all channels, with the delay-and-add having a slight advantage in the Q-factor.

Temporal Pattern Coding in Ionic Memristor‐Based Spiking Neurons for Adaptive Tactile Perception

AbstractBiological neurons encode signals through firing voltage spike trains having unique temporal patterns, enabling efficient information representation and processing. Realization of these rich neuronal firing characteristics in a single electronic device, without circuitry and software assistance, promise compact and functional neuromorphic hardware for advanced artificial intelligence applications. Here, a Pt/Co3O4‐x/ITO‐based ionic memristor is reported that can faithfully produce voltage spike trains exhibiting diverse temporal patterns of biological neurons, under electric current stimulation. The spiking behaviors stem from the redistribution of ions in the device, governed by the current induced electric field and Joule heating effects. Tonic, phasic, burst, and adaptive firing patterns of neurons are demonstrated. Particularly, the adaptive firing characteristics allow the memristor to reduce the response to invariant current stimulation and to respond to current changes with enhanced sensitivity, implementing neuronal adaptive coding function. Integrating such memristors with pressure sensors yields an artificial tactile sensory system that can adaptively perceive small pressure variations in the presence of strong static pressure backgrounds, enabling accurate identification of touched objects in ever‐changing environments. This work opens up an avenue toward advanced neuromorphic hardware for smart neural prosthetics and bionic robotics applications.

Celebrating the one millionth cochlear implant

Cochlear implants have been the most successful neural prosthesis, with one million users globally. Researchers used the source-filter model and speech vocoder to design the modern multi-channel implants, allowing implantees to achieve 70%-80% correct sentence recognition in quiet, on average. Researchers also used the cochlear implant to help understand basic mechanisms underlying loudness, pitch, and cortical plasticity. While front-end processing advances improved speech recognition in noise, the unilateral implant speech recognition in quiet has plateaued since the early 1990s. This lack of progress calls for action on re-designing the cochlear stimulating interface and collaboration with the general neurotechnology community.

A flexible biohybrid reflex arc mimicking neurotransmitter transmission

The simulation of neurotransmitter transmission in the biological nervous system holds great potential for the next generation of implantable neurological devices. Two challenges are (1) the fabrication of synaptic devices with selectivity to neurotransmitters in artificial reflex arcs and (2) the direct construction of biocompatible interfaces between biological tissues and devices to form biofeedback. Herein, we report the construction of a flexible biohybrid artificial motor biofeedback inspired by neuromuscular transmission of acetylcholine (ACh). Biocompatible interfaces are built by seeding biological cells on the gate of the organic synapse. The ACh secreted from rat adrenal pheochromocytoma (PC-12) cells is converted by the organic synapse into electrical signals to modulate the excitatory postsynaptic currents (EPSCs) to drive the soft actuator. This reflex arc enables memory consolidation on exposure to ACh, exhibiting both short-term plasticity and specificity. This method may provide a way for nerve repair and development of neural prosthetics and facilitate human-machine interfaces. • The organic synaptic device exhibits ACh-mediated plasticity with high selectivity • PC-12 cells act as the presynaptic membrane in the artificial neuromuscular junction • The reflex arc converts biological signals into movement of the actuator A flexible biohybrid reflex arc system is constructed by Shao et al. to mimic biological neuromuscular information transmission. The device converts chemical signals of ACh secreted by PC-12 cells into electrical signals, and the resulting excitatory postsynaptic currents (EPSCs) of the organic synaptic transistor are amplified to drive the movement of artificial muscle.

Application of all-optical magnetometry for detection of weak magnetic fields generated by a current-carrying metallised track

A pump – probe configuration is used to register zero-field level-crossing resonances in Rb vapour contained in a cell with antirelaxation coated walls. The scheme is applied to detect the weak magnetic field generated by a current-carrying micro-wire (metallised track). Such micro-wires can be employed in neural prostheses and hybrid bionic systems as micro-electrode arrays. More specifically, the operation of such live micro-wire belonging to a neuronal – electrode interface is demonstrated, which indicates the possibility of remote testing of the operation (conducting/nonconducting) of micro-wires for in neural implants.

The Results of Cochlear Implantation in the Inner Ear Malformations.

Cochlear implants are arguably the most successful neural prosthesis today. Cochlear implantation has several difficulties in patients with internal ear anomalies. This study was performed to analyze intraoperative, postoperative findings, and auditory performance of 55 patients who had inner ear malformations and were treated with cochlear implants at Otorhinolaryngology Department of Çukurova University Hospital. Auditory performances were analyzed in 30 of 55 patients. Patients with cochlear anomalies were evaluated as group I, patients with vestibular malformation as group II, and patients with the normal bone labyrinth as group III. Listening progress profiles and meaningful auditory integration scale tests were used to determine performances. Comparison between the listening progress profiles test performance of the groups at 12th and 18th month of group I was significantly lower than other groups (P < .05) and reached at the same level after the 24th month. Comparison between the meaningful auditory integration scale test performance of groups at 24th and 36th month of group I was significantly lower than other groups (P < .05). Perilymph gusher was observed in 3 patients who had incomplete partition I malformation. Oozing was observed in 50% of incomplete partition II patients. Facial nerve traced a variant course in 4 of 55 patients and 6 patients had postoperative meningitis. Initially patients with inner ear anomalies showed that the level of language development was worse than patients with normal bone anatomy. However, it was shown that they both reached the same point as a result. Facial nerve anomaly and meningitis risk is higher in patients with inner ear malformations.

Clinical Basis for Creating an Osseointegrated Neural Interface.

As technology continues to improve within the neuroprosthetic landscape, there has been a paradigm shift in the approach to amputation and surgical implementation of haptic neural prosthesis for limb restoration. The Osseointegrated Neural Interface (ONI) is a proposed solution involving the transposition of terminal nerves into the medullary canal of long bones. This design combines concepts of neuroma formation and prevention with osseointegration to provide a stable environment for conduction of neural signals for sophisticated prosthetic control. While this concept has previously been explored in animal models, it has yet to be explored in humans. This anatomic study used three upper limb and three lower limb cadavers to assess the clinical feasibility of creating an ONI in humans. Anatomical measurement of the major peripheral nerves- circumference, length, and depth- were performed as they are critical for electrode design and rerouting of the nerves into the long bones. CT imaging was used for morphologic bone evaluation and virtual implantation of two osseointegrated implants were performed to assess the amount of residual medullary space available for housing the neural interfacing hardware. Use of a small stem osseointegrated implant was found to reduce bone removal and provide more intramedullary space than a traditional implant; however, the higher the amputation site, the less medullary space was available regardless of implant type. Thus the stability of the endoprosthesis must be maximized while still maintaining enough residual space for the interface components. The results from this study provide an anatomic basis required for establishing a clinically applicable ONI in humans. They may serve as a guide for surgical implementation of an osseointegrated endoprosthesis with intramedullary electrodes for prosthetic control.

Intracortical microstimulation pulse waveform and frequency recruits distinct spatiotemporal patterns of cortical neuron and neuropil activation

Objective. Neural prosthetics often use intracortical microstimulation (ICMS) for sensory restoration. To restore natural and functional feedback, we must first understand how stimulation parameters influence the recruitment of neural populations. ICMS waveform asymmetry modulates the spatial activation of neurons around an electrode at 10 Hz; however, it is unclear how asymmetry may differentially modulate population activity at frequencies typically employed in the clinic (e.g. 100 Hz). We hypothesized that stimulation waveform asymmetry would differentially modulate preferential activation of certain neural populations, and the differential population activity would be frequency-dependent. Approach. We quantified how asymmetric stimulation waveforms delivered at 10 or 100 Hz for 30 s modulated spatiotemporal activity of cortical layer II/III pyramidal neurons using in vivo two-photon and mesoscale calcium imaging in anesthetized mice. Asymmetry is defined in terms of the ratio of the duration of the leading phase to the duration of the return phase of charge-balanced cathodal- and anodal-first waveforms (i.e. longer leading phase relative to return has larger asymmetry). Main results. Neurons within 40–60 µm of the electrode display stable stimulation-induced activity indicative of direct activation, which was independent of waveform asymmetry. The stability of 72% of activated neurons and the preferential activation of 20%–90% of neurons depended on waveform asymmetry. Additionally, this asymmetry-dependent activation of different neural populations was associated with differential progression of population activity. Specifically, neural activity tended to increase over time during 10 Hz stimulation for some waveforms, whereas activity remained at the same level throughout stimulation for other waveforms. During 100 Hz stimulation, neural activity decreased over time for all waveforms, but decreased more for the waveforms that resulted in increasing neural activity during 10 Hz stimulation. Significance. These data demonstrate that at frequencies commonly used for sensory restoration, stimulation waveform alters the pattern of activation of different but overlapping populations of excitatory neurons. The impact of these waveform specific responses on the activation of different subtypes of neurons as well as sensory perception merits further investigation.

End-to-end optimization of prosthetic vision.

Neural prosthetics may provide a promising solution to restore visual perception in some forms of blindness. The restored prosthetic percept is rudimentary compared to normal vision and can be optimized with a variety of image preprocessing techniques to maximize relevant information transfer. Extracting the most useful features from a visual scene is a nontrivial task and optimal preprocessing choices strongly depend on the context. Despite rapid advancements in deep learning, research currently faces a difficult challenge in finding a general and automated preprocessing strategy that can be tailored to specific tasks or user requirements. In this paper, we present a novel deep learning approach that explicitly addresses this issue by optimizing the entire process of phosphene generation in an end-to-end fashion. The proposed model is based on a deep auto-encoder architecture and includes a highly adjustable simulation module of prosthetic vision. In computational validation experiments, we show that such an approach is able to automatically find a task-specific stimulation protocol. The results of these proof-of-principle experiments illustrate the potential of end-to-end optimization for prosthetic vision. The presented approach is highly modular and our approach could be extended to automated dynamic optimization of prosthetic vision for everyday tasks, given any specific constraints, accommodating individual requirements of the end-user.

A machine learning approach to characterize sequential movement-related states in premotor and motor cortices.

Nonhuman primate (NHP) movement kinematics have been decoded from spikes and local field potentials (LFPs) recorded during motor tasks. However, the potential of LFPs to provide network-like characterizations of neural dynamics during planning and execution of sequential movements requires further exploration. Is the aggregate nature of LFPs suitable to construct informative brain state descriptors of movement preparation and execution? To investigate this, we developed a framework to process LFPs based on machine-learning classifiers and analyzed LFP from a primate, implanted with several microelectrode arrays covering the premotor cortex in both hemispheres and the primary motor cortex on one side. The monkey performed a reach-to-grasp task, consisting of five consecutive states, starting from rest until a rewarding target (food) was attained. We use this five-state task to characterize neural activity within eight frequency bands, using spectral amplitude and pairwise correlations across electrodes as features. Our results show that we could best distinguish all five movement-related states using the highest frequency band (200-500 Hz), yielding an 87% accuracy with spectral amplitude, and 60% with pairwise electrode correlation. Further analyses characterized each movement-related state, showing differential neuronal population activity at above-γ frequencies during the various stages of movement. Furthermore, the topological distribution for the high-frequency LFPs allowed for a highly significant set of pairwise correlations, strongly suggesting a concerted distribution of movement planning and execution function is distributed across premotor and primary motor cortices in a specific fashion, and is most significant in the low ripple (100-150 Hz), high ripple (150-200 Hz), and multiunit frequency bands. In summary, our results show that the concerted use of novel machine-learning techniques with coarse grained queue broad signals such as LFPs may be successfully used to track and decode fine movement aspects involving preparation, reach, grasp, and reward retrieval across several brain regions.NEW & NOTEWORTHY Local field potentials (LFPs), despite lower spatial resolution compared to single-neuron recordings, can be used with machine learning classifiers to decode sequential movements involving motor preparation, execution, and reward retrieval. Our results revealed heterogeneity of neural activity on small spatial scales, further evidencing the utility of micro-electrode array recordings for complex movement decoding. With further advancement, high-dimensional LFPs may become the gold standard for brain-computer interfaces such as neural prostheses in the near future.

Exploring polymeric biomaterials in developing neural prostheses

Neuroprosthetics, with a range of applications such as cognitive, auditory, pain relief, recording, motor, and visual prosthetics have emerged as a promising field in recent years. However, poor electrical conductivity, a high disparity between tissue and interfaces and the onset of reactive gliosis post-implantation remains major challenges in the development of neuroprostheses. The choice of biomaterials in designing the neural interfaces’ in neuroprosthetic applications is of high importance, as the overall sustained performance of neuroprosthetic devices is based on the features of materials used for the neural interfaces. Numerous biomaterials, such as metals and carbon-based materials, have been used in neuroprosthetics thus far. Nonetheless, neuroprosthetics made from polymeric biomaterials are in high demand due to their high biocompatibility, conductivity, and biostability. Furthermore, polymeric biomaterials can be used as a hybrid design to overcome the limitations of other co-biomaterials. This article makes an attempt to review the polymeric biomaterials involved in this cutting-edge technology utilized for different purposes such as substrates, coatings, and miniaturization of electrodes, that might help in enriching our understanding on neuroprosthetics.

Long term study of motivational and cognitive effects of low-intensity focused ultrasound neuromodulation in the dorsal striatum of nonhuman primates

Noninvasive brain stimulation using transcranial focused ultrasound (FUS) has many potential applications as a research and clinical tool, including incorporation into neural prosthetics for cognitive rehabilitation. To develop this technology, it is necessary to evaluate the safety and efficacy of FUS neuromodulation for specific brain targets and cognitive functions. It is also important to test whether repeated long-term application of FUS to deep brain targets improves or degrades behavioral and cognitive function. To this end, we investigated the effects of FUS in the dorsal striatum of nonhuman primates (NHP) performing a visual-motor decision-making task for small or large rewards. Over the course of 2 years, we performed 129 and 147 FUS applications, respectively, in two NHP. FUS (0.5 MHz @ 0.2–0.8 MPa) was applied to the putamen and caudate in both hemispheres to evaluate the effects on movement accuracy, motivation, decision accuracy, and response time. Sonicating the caudate or the putamen unilaterally resulted in modest but statistically significant improvements in motivation and decision accuracy, but at the cost of slower reaction times. The effects were dose (i.e., FUS pressure) and reward dependent. There was no effect on reaching accuracy, nor was there long-term behavioral impairment or neurological trauma evident on T1-weighted, T2-weighted, or susceptibility-weighted MRI scans. Sonication also resulted in significant changes in resting state functional connectivity between the caudate and multiple cortical regions. The results indicate that applying FUS to the dorsal striatum can positively impact the motivational and cognitive aspects of decision making. The capability of FUS to improve motivation and cognition in NHPs points to its therapeutic potential in treating a wide variety of human neural diseases, and warrants further development as a novel technique for non-invasive deep brain stimulation.

A kinematic and EMG dataset of online adjustment of reach-to-grasp movements to visual perturbations

Control of reach-to-grasp movements for deft and robust interactions with objects requires rapid sensorimotor updating that enables online adjustments to changing external goals (e.g., perturbations or instability of objects we interact with). Rarely do we appreciate the remarkable coordination in reach-to-grasp, until control becomes impaired by neurological injuries such as stroke, neurodegenerative diseases, or even aging. Modeling online control of human reach-to-grasp movements is a challenging problem but fundamental to several domains, including behavioral and computational neuroscience, neurorehabilitation, neural prostheses, and robotics. Currently, there are no publicly available datasets that include online adjustment of reach-to-grasp movements to object perturbations. This work aims to advance modeling efforts of reach-to-grasp movements by making publicly available a large kinematic and EMG dataset of online adjustment of reach-to-grasp movements to instantaneous perturbations of object size and distance performed in immersive haptic-free virtual environment (hf-VE). The presented dataset is composed of a large number of perturbation types (10 for both object size and distance) applied at three different latencies after the start of the movement.

A neurophysiologically interpretable deep neural network predicts complex movement components from brain activity

The effective decoding of movement from non-invasive electroencephalography (EEG) is essential for informing several therapeutic interventions, from neurorehabilitation robots to neural prosthetics. Deep neural networks are most suitable for decoding real-time data but their use in EEG is hindered by the gross classes of motor tasks in the currently available datasets, which are solvable even with network architectures that do not require specialized design considerations. Moreover, the weak association with the underlying neurophysiology limits the generalizability of modern networks for EEG inference. Here, we present a neurophysiologically interpretable 3-dimensional convolutional neural network (3D-CNN) that captured the spatiotemporal dependencies in brain areas that get co-activated during movement. The 3D-CNN received topography-preserving EEG inputs, and predicted complex components of hand movements performed on a plane using a back-drivable rehabilitation robot, namely (a) the reaction time (RT) for responding to stimulus (slow or fast), (b) the mode of movement (active or passive, depending on whether there was an assistive force provided by the apparatus), and (c) the orthogonal directions of the movement (left, right, up, or down). We validated the 3D-CNN on a new dataset that we acquired from an in-house motor experiment, where it achieved average leave-one-subject-out test accuracies of 79.81%, 81.23%, and 82.00% for RT, active vs. passive, and direction classifications, respectively. Our proposed method outperformed the modern 2D-CNN architecture by a range of 1.1% to 6.74% depending on the classification task. Further, we identified the EEG sensors and time segments crucial to the classification decisions of the network, which aligned well with the current neurophysiological knowledge on brain activity in motor planning and execution tasks. Our results demonstrate the importance of biological relevance in networks for an accurate decoding of EEG, suggesting that the real-time classification of other complex brain activities may now be within our reach.

A Platform for Spatiotemporal "Matrix" Stimulation in Brain Networks Reveals Novel Forms of Circuit Plasticity.

Here we demonstrate a facile method by which to deliver complex spatiotemporal stimulation to neural networks in fast patterns, to trigger interesting forms of circuit-level plasticity in cortical areas. We present a complete platform by which patterns of electricity can be arbitrarily defined and distributed across a brain circuit, either simultaneously, asynchronously, or in complex patterns that can be easily designed and orchestrated with precise timing. Interfacing with acute slices of mouse cortex, we show that our system can be used to activate neurons at many locations and drive synaptic transmission in distributed patterns, and that this elicits new forms of plasticity that may not be observable via traditional methods, including interesting measurements of associational and sequence plasticity. Finally, we introduce an automated “network assay” for imaging activation and plasticity across a circuit. Spatiotemporal stimulation opens the door for high-throughput explorations of plasticity at the circuit level, and may provide a basis for new types of adaptive neural prosthetics.

Time-Distributed Attention Network for EEG-Based Motor Imagery Decoding From the Same Limb.

A brain-computer interface (BCI) based on motor imagery (MI) from the same limb can provide an intuitive control pathway but has received limited attention. It is still a challenge to classify multiple MI tasks from the same limb. The goal of this study is to propose a novel decoding method to classify the MI tasks of four joints of the same upper limb and the resting state. EEG signals were collected from 20 participants. A time-distributed attention network (TD-Atten) was proposed to adaptively assign different weights to different classes and frequency bands of the input multiband Common Spatial Pattern (CSP) features. The long short-term memory (LSTM) and dense layers were then used to learn sequential information from the reweight features and perform the classification. Our proposed method outperformed other baseline and deep learning-based methods and obtained the accuracies of 46.8% in the 5-class scenario and 53.4% in the 4-class scenario. The visualization results of attention weights indicated that the proposed framework can adaptively pay attention to alpha-band related features in MI tasks, which was consistent with the analysis of brain activation patterns. These results demonstrated the feasibility and interpretability of the attention mechanism in MI decoding and the potential of this fine MI paradigm to be applied for the control of a robotic arm or a neural prosthesis.

Chapter 5 - Neuroplasticity following cochlear implants

The auditory cortex of people with sensorineural hearing loss can be re-afferented using a cochlear implant (CI): a neural prosthesis that bypasses the damaged cells in the cochlea to directly stimulate the auditory nerve. Although CIs are the most successful neural prosthesis to date, some CI users still do not achieve satisfactory outcomes using these devices. To explain variability in outcomes, clinicians and researchers have increasingly focused their attention on neuroscientific investigations that examined how the auditory cortices respond to the electric signals that originate from the CI. This chapter provides an overview of the literature that examined how the auditory cortex changes its functional properties in response to inputs from the CI, in animal models and in humans. We focus first on the basic responses to sounds delivered through electrical hearing and, next, we examine the integrity of two fundamental aspects of the auditory system: tonotopy and processing of binaural cues. When addressing the effects of CIs in humans, we also consider speech-evoked responses. We conclude by discussing to what extent this neuroscientific literature can contribute to clinical practices and help to overcome variability in outcomes.

Strategies for interface issues and challenges of neural electrodes.

Neural electrodes, as a bridge for bidirectional communication between the body and external devices, are crucial means for detecting and controlling nerve activity. The electrodes play a vital role in monitoring the state of neural systems or influencing it to treat disease or restore functions. To achieve high-resolution, safe and long-term stable nerve recording and stimulation, a neural electrode with excellent electrochemical performance (e.g., impedance, charge storage capacity, charge injection limit), and good biocompatibility and stability is required. Here, the charge transfer process in the tissues, the electrode-tissue interfaces and the electrode materials are discussed respectively. Subsequently, the latest research methods and strategies for improving the electrochemical performance and biocompatibility of neural electrodes are reviewed. Finally, the challenges in the development of neural electrodes are proposed. It is expected that the development of neural electrodes will offer new opportunities for the evolution of neural prosthesis, bioelectronic medicine, brain science, and so on.