Neuroprosthetic Research Articles

Activity-dependent recruitment of inhibition and excitation in the awake mammalian cortex during electrical stimulation

Electrical stimulation is an effective tool for mapping and altering brain connectivity, with applications ranging from treating pharmacology-resistant neurological disorders to providing sensory feedback for neural prostheses. Paramount to the success of these applications is the ability to manipulate electrical currents to precisely control evoked neural activity patterns. However, little is known about stimulation-evoked responses in inhibitory neurons nor how stimulation-evoked activity patterns depend on ongoing neural activity. In this study, we used 2-photon imaging and cell-type specific labeling to measure single-cell responses of excitatory and inhibitory neurons to electrical stimuli in the visual cortex of awake mice. Our data revealed strong interactions between electrical stimulation and pre-stimulus activity of single neurons in awake animals and distinct recruitment and response patterns for excitatory and inhibitory neurons. This work demonstrates the importance of cell-type-specific labeling of neurons in future studies.

High-resolution neural recordings improve the accuracy of speech decoding

Patients suffering from debilitating neurodegenerative diseases often lose the ability to communicate, detrimentally affecting their quality of life. One solution to restore communication is to decode signals directly from the brain to enable neural speech prostheses. However, decoding has been limited by coarse neural recordings which inadequately capture the rich spatio-temporal structure of human brain signals. To resolve this limitation, we performed high-resolution, micro-electrocorticographic (µECoG) neural recordings during intra-operative speech production. We obtained neural signals with 57× higher spatial resolution and 48% higher signal-to-noise ratio compared to macro-ECoG and SEEG. This increased signal quality improved decoding by 35% compared to standard intracranial signals. Accurate decoding was dependent on the high-spatial resolution of the neural interface. Non-linear decoding models designed to utilize enhanced spatio-temporal neural information produced better results than linear techniques. We show that high-density µECoG can enable high-quality speech decoding for future neural speech prostheses.

Volitional activation of remote place representations with a hippocampal brain-machine interface.

The hippocampus is critical for recollecting and imagining experiences. This is believed to involve voluntarily drawing from hippocampal memory representations of people, events, and places, including maplike representations of familiar environments. However, whether representations in such "cognitive maps" can be volitionally accessed is unknown. We developed a brain-machine interface to test whether rats can do so by controlling their hippocampal activity in a flexible, goal-directed, and model-based manner. We found that rats can efficiently navigate or direct objects to arbitrary goal locations within a virtual reality arena solely by activating and sustaining appropriate hippocampal representations of remote places. This provides insight into the mechanisms underlying episodic memory recall, mental simulation and planning, and imagination and opens up possibilities for high-level neural prosthetics that use hippocampal representations.

Neural prosthesis control restores near-normative neuromechanics in standing postural control.

Current lower-limb prostheses do not provide active assistance in postural control tasks to maintain the user's balance, particularly in situations of perturbation. In this study, we aimed to address this missing function by enabling neural control of robotic lower-limb prostheses. Specifically, electromyographic (EMG) signals (amplified neural control signals) recorded from antagonistic residual ankle muscles were used to drive a robotic prosthetic ankle directly and continuously. Participants with transtibial amputation were recruited and trained in using the EMG-driven robotic ankle. We studied how using the EMG-controlled ankle affected the participants' anticipatory and compensatory postural control strategies and stability under expected perturbations compared with using their daily passive devices. We investigated the similarity of neuromuscular coordination (by analyzing motor modules) of the participants, using either device in a postural sway task, to that of able-bodied controls. Results showed that, compared with their passive prosthesis, the EMG-controlled prosthesis enabled participants to use near-normative postural control strategies, as evidenced by improved between-limb symmetry in intact-prosthetic center-of-pressure and joint angle excursions. Participants substantially improved postural stability, as evidenced by a reduction in steps or falls using the EMG-controlled prosthetic ankle. Furthermore, after relearning to use residual ankle muscles to drive the robotic ankle in postural control, nearly all participants' motor module structure shifted toward that observed in individuals without limb amputations. Here, we have demonstrated the potential benefit of direct EMG control of robotic lower limb prostheses to restore normative postural control strategies (both neural and biomechanical) toward enhancing standing postural stability in amputee users.

Distributed feedforward and feedback cortical processing supports human speech production.

Speech production is a complex human function requiring continuous feedforward commands together with reafferent feedback processing. These processes are carried out by distinct frontal and temporal cortical networks, but the degree and timing of their recruitment and dynamics remain poorly understood. We present a deep learning architecture that translates neural signals recorded directly from the cortex to an interpretable representational space that can reconstruct speech. We leverage learned decoding networks to disentangle feedforward vs. feedback processing. Unlike prevailing models, we find a mixed cortical architecture in which frontal and temporal networks each process both feedforward and feedback information in tandem. We elucidate the timing of feedforward and feedback-related processing by quantifying the derived receptive fields. Our approach provides evidence for a surprisingly mixed cortical architecture of speech circuitry together with decoding advances that have important implications for neural prosthetics.

Dense, 11 V-Tolerant, Balanced Stimulator IC with Digital Time-Domain Calibration for 100 nA Error.

This article presents a multichannel neurostimulator implementing a novel charge balancing technique to achieve maximal integration. Safe neurostimulation demands accurate charge balancing of the stimulation waveforms to prevent charge build-up on the electrode-tissue interface. We propose digital time-domain calibration (DTDC), which adjusts the second phase of the biphasic stimulation pulses digitally, based on a one-time characterization of all stimulator channels with an on-chip ADC. Accurate control of the stimulation current amplitude is loosened in exchange for time-domain corrections, relieving circuit matching constraints and consequentially saving channel area. A theoretical analysis of DTDC is presented, establishing expressions for the required time resolution and the new, relaxed circuit matching constraints. To validate the DTDC principle, a 16-channel stimulator was implemented in 65 nm CMOS, requiring only 0.0141 mm 2 area/channel. Despite being implemented in a standard CMOS technology, 10.4 V compliance is achieved for compatibility with high-impedance microelectrode arrays typical for high-resolution neural prostheses. To the authors' knowledge, this is the first stimulator in a 65 nm low-voltage process achieving over 10 V output swing. Measurements after calibration show the DC error is successfully reduced below 96 nA on all channels. Static power consumption is 20.3 µW/channel.

Biodegradable Oxide Neuromorphic Transistors for Neuromorphic Computing and Anxiety Disorder Emulation.

Brain-inspired neuromorphic computing and portable intelligent electronic products have received increasing attention. In the present work, nanocellulose-gated indium tin oxide neuromorphic transistors are fabricated. The device exhibits good electrical performance. Short-term synaptic plasticities were mimicked, including excitatory postsynaptic current, paired-pulse facilitation, and dynamic high-pass synaptic filtering. Interestingly, an effective linear synaptic weight updating strategy was adopted, resulting in an excellent recognition accuracy of ∼92.93% for the Modified National Institute of Standard and Technology database adopting a two-layer multilayer perceptron neural network. Moreover, with unique interfacial protonic coupling, anxiety disorder behavior was conceptually emulated, exhibiting "neurosensitization", "primary and secondary fear", and "fear-adrenaline secretion-exacerbated fear". Finally, the neuromorphic transistors could be dissolved in water, demonstrating potential in "green" electronics. These findings indicate that the proposed oxide neuromorphic transistors would have potential as implantable chips for nerve health diagnosis, neural prostheses, and brain-machine interfaces.

Towards hippocampal navigation for brain–computer interfaces

Automatic wheelchairs directly controlled by brain activity could provide autonomy to severely paralyzed individuals. Current approaches mostly rely on non-invasive measures of brain activity and translate individual commands into wheelchair movements. For example, an imagined movement of the right hand would steer the wheelchair to the right. No research has investigated decoding higher-order cognitive processes to accomplish wheelchair control. We envision an invasive neural prosthetic that could provide input for wheelchair control by decoding navigational intent from hippocampal signals. Navigation has been extensively investigated in hippocampal recordings, but not for the development of neural prostheses. Here we show that it is possible to train a decoder to classify virtual-movement speeds from hippocampal signals recorded during a virtual-navigation task. These results represent the first step toward exploring the feasibility of an invasive hippocampal BCI for wheelchair control.

Computational analysis of efficient organic solar cell-based retinal prosthesis using plasmonic gold nanoparticles.

Photovoltaic restoration of vision, especially in conjunction with the use of silicon photodiodes, has gained attention for use in patients affected by blindness due to retinal layer disease. Although the use of silicon photodiodes offers miniaturization of the implant unit and increase in the stimulation channel, the implant unit may suffer from the fracture of these brittle photodiodes when mechanical pressure exerted. We present an organic solar cell (OSC)-based retinal prosthesis in which spherical gold nanoparticles (AuNPs) are embedded into the active layer to increase the efficiency of the bioelectric interface. We demonstrate computationally that a modeled OSC incorporating spherical AuNPs has three times higher efficiency than that of a bare OSC presented before for retinal prostheses. Our AuNP based OSC was able to activate the neuron at the minimum light intensity of 0.26 mW/mm2, which is lower than that of the bare OSC. The use of AuNPs in OSC allows device miniaturization or lowering of the light exposure required for neural activation using a photovoltaic retinal prosthesis, which can generally be applied in a broad range of neural prostheses.

Non-pharmacological and drug treatment of autonomic dysfunction in multiple system atrophy: current status and future directions.

Multiple system atrophy (MSA) is a sporadic, fatal, and rapidly progressive neurodegenerative disease of unknown etiology that is clinically characterized by autonomic failure, parkinsonism, cerebellar ataxia, and pyramidal signs in any combination. Early onset and extensive autonomic dysfunction, including cardiovascular dysfunction characterized by orthostatic hypotension (OH) and supine hypertension, urinary dysfunction characterized by overactive bladder and incomplete bladder emptying, sexual dysfunction characterized by sexual desire deficiency and erectile dysfunction, and gastrointestinal dysfunction characterized by delayed gastric emptying and constipation, are the main features of MSA. Autonomic dysfunction greatly reduces quality of life and increases mortality. Therefore, early diagnosis and intervention are urgently needed to benefit MSA patients. In this review, we aim to discuss the systematic treatment of autonomic dysfunction in MSA, and focus on the current methods, starting from non-pharmacological methods, such as patient education, psychotherapy, diet change, surgery, and neuromodulation, to various drug treatments targeting autonomic nerve and its projection fibers. In addition, we also draw attention to the interactions among various treatments, and introduce novel methods proposed in recent years, such as gene therapy, stem cell therapy, and neural prosthesis implantation. Furthermore, we elaborate on the specific targets and mechanisms of action of various drugs. We would like to call for large-scale research to determine the efficacy of these methods in the future. Finally, we point out that studies on the pathogenesis of MSA and pathophysiological mechanisms of various autonomic dysfunction would also contribute to the development of new promising treatments and concepts.

Extracting Semantic-Dynamic Features for Long-Term Stable Brain Computer Interface

Brain-computer Interface (BCI) builds a neural signal to the motor command pathway, which is a prerequisite for the realization of neural prosthetics. However, a long-term stable BCI suffers from the neural data drift across days while retraining the BCI decoder is expensive and restricts its application scenarios. Recent solutions of neural signal recalibration treat the continuous neural signals as discrete, which is less effective in temporal feature extraction. Inspired by the observation from biologists that low-dimensional dynamics could describe high-dimensional neural signals, we model the underlying neural dynamics and propose a semantic-dynamic feature that represents the semantics and dynamics in a shared feature space facilitating the BCI recalibration. Besides, we present the joint distribution alignment instead of the common used marginal alignment strategy, dealing with the various complex changes in neural data distribution. Our recalibration approach achieves state-of-the-art performance on the real neural data of two monkeys in both classification and regression tasks. Our approach is also evaluated on a simulated dataset, which indicates its robustness in dealing with various common causes of neural signal instability.

Advanced electrochemical potential monitoring for improved understanding of electrical neurostimulation protocols

Objective. Current-controlled neurostimulation is increasingly used in the clinical treatment of neurological disorders and widely applied in neural prostheses such as cochlear implants. Despite its importance, time-dependent potential traces of electrodes during microsecond-scale current pulses, especially with respect to a reference electrode (RE), are not precisely understood. However, this knowledge is critical to predict contributions of chemical reactions at the electrodes, and ultimately electrode stability, biocompatibility, and stimulation safety and efficacy. Approach. We assessed the electrochemistry of neurostimulation protocols in vitro with Pt microelectrodes from millisecond (classical electroanalysis) to microsecond (neurostimulation) timescales. We developed a dual-channel instrumentation amplifier to include a RE in neurostimulation setups. Uniquely, we combined potential measurements with potentiostatic prepolarization to control and investigate the surface status, which is not possible in typical stimulation setups. Main results. We thoroughly validated the instrumentation and highlighted the importance of monitoring individual electrochemical electrode potentials in different configurations of neurostimulation. We investigated electrode processes such as oxide formation and oxygen reduction by chronopotentiometry, bridging the gap between milli- and microsecond timescales. Our results demonstrate how much impact on potential traces the electrode’s initial surface state and electrochemical surface processes have, even on a microsecond scale. Significance. Our unique use of preconditioning in combination with stimulation reveals that interpreting potential traces with respect to electrode processes is misleading without rigorous control of the electrode’s surface state. Especially in vivo, where the microenvironment is unknown, simply measuring the voltage between two electrodes cannot accurately reflect the electrode’s state and processes. Potential boundaries determine charge transfer, corrosion, and alterations of the electrode/tissue interface such as pH and oxygenation, particularly in long-term in vivo use. Our findings are relevant for all use-cases of constant-current stimulation, strongly advocating for electrochemical in situ investigations in many applications like the development of new electrode materials and stimulation methods.

Cortical layering disrupts multi-electrode current steering

Objective. Blindness affects approximately 40 million people worldwide and has inspired the development of cortical visual prostheses for restoring sight. Cortical visual prostheses electrically stimulate neurons of the visual cortex to artificially evoke visual percepts. Of the 6 layers of the visual cortex, layer 4 contains neurons that are likely to evoke a visual percept. Intracortical prostheses therefore aim to target layer 4; however, this can be difficult due to cortical curvature, inter-subject cortical variability, blindness-induced anatomical changes in cortex, and electrode placement variations. We investigated the feasibility of using current steering to stimulate specific cortical layers between electrodes in the laminar column. Approach. We explored whether the multiunit neural activity peak can be manipulated between two simultaneously stimulating electrodes in different layers of the cortical column. A 64-channel, 4-shank electrode array was implanted into the visual cortex of Sprague–Dawley rats (n = 7) orthogonal to the cortical surface. A remote return electrode was positioned over the frontal cortex in the same hemisphere. Charge was supplied to two stimulating electrodes along a single shank. Differing ratios of charge (100:0, 75:25, 50:50) and separation distances (300–500 μm) were tested. Results. Current steering across the cortical layers did not result in a consistent shift of the neural activity peak. Both single-electrode and dual-electrode stimulation induced activity throughout the cortical column. This contrasts observations that current steering evoked a controllable peak of neural activity between electrodes implanted at similar cortical depths. However, dual-electrode stimulation across the layers did reduce the stimulation threshold at each site compared to single-electrode stimulation. Significance. Multi-electrode stimulation is not suitable for targeted activation of layers using current steering. However, it can be used to reduce activation thresholds at adjacent electrodes within a given cortical layer. This may be applied to reduce the stimulation side effects of neural prostheses, such as seizures.

Light at the end of the tunnel of Corti

Light at the end of the tunnel of Corti

Near-Infrared Light-Responsive Hydrogels for Highly Flexible Bionic Photosensors.

Soft biological tissues perform various functions. Sensory nerves bring sensations of light, voice, touch, pain, or temperature variation to the central nervous system. Animal senses have inspired tremendous sensors for biomedical applications. Following the same principle as photosensitive nerves, we design flexible ionic hydrogels to achieve a biologic photosensor. The photosensor allows responding to near-infrared light, which is converted into a sensory electric signal that can communicate with nerve cells. Furthermore, with adjustable thermal and/or electrical signal outputs, it provides abundant tools for biological regulation. The tunable photosensitive performances, high flexibility, and low cost endow the photosensor with widespread applications ranging from neural prosthetics to human-machine interfacing systems.

A POLY(SBMA) ZWITTERIONIC HYDROGEL COATING OF POLYIMIDE SURFACES TO REDUCE THE FOREIGN BODY REACTION TO INVASIVE NEURAL INTERFACES

Intraneural electrodes can be harnessed to control neural prosthetic devices in human amputees. However, in chronic implants we witness a gradual loss of device functionality and electrode isolation due to a nonspecific inflammatory response to the implanted material, called foreign body reaction (FBR). FBR may eventually lead to a fibrous encapsulation of the electrode surface. Poly(ethylene glycol) (PEG) is one of the most common low-fouling materials used to coat and protect electrode surfaces. Yet, PEG can easily undergo encapsulation and oxidative damage in long-term in vivo applications. Poly(sulfobetaine methacrylate) - poly(SBMA) - zwitterionic hydrogels may represent more promising alternatives to minimize the FBR due to their ultra-low fouling features. Here, we tested and compared the poly(SBMA) zwitterionic hydrogel coating with the PEG coating in reducing adhesion and activation of pro-inflammatory and pro-fibrotic cells to polyimide surfaces, which are early hallmarks of FBR. We aimed to coat polyimide surfaces with a hydrogel thin film and analysed the release of a model drug from the hydrogel.We performed hydrogel synthesis, mechanical characterization and biocompatibility analysis. Cell adhesion, viability and morphology of human myofibroblasts cultured on PEG- and hydrogel-coated surfaces were evaluated through confocal microscopy-based high-content analysis (HCA). Reduced activation of pro-inflammatory human macrophages cultured on hydrogels was assessed as well as the hydrogel drug release profile.Because of its high hydration, biocompatibility, low stiffness and ultra-low fouling characteristics the hydrogel enabled lower adhesion and activation of pro-inflammatory and pro-fibrotic cells vs. polystyrene controls, and showed a long-term release of the anti-fibrotic drug Everolimus. Furthermore, a polyimide surface was successfully coated with a hydrogel thin film.Our soft zwitterionic hydrogel could outperform PEG as more suitable coating material of neural electrodes for mitigating the FBR. Such poly(SBMA)-based biomaterial could also be envisioned as long-term delivery system for a sustained release of anti-inflammatory and anti-fibrotic drugs in vivo.

Evaluation of a clinical method for selective electrode deactivation in cochlear implant programming.

Cochlear implants are a neural prosthesis used to restore the perception of hearing in individuals with severe-to-profound hearing loss by stimulating the auditory nerve with electrical current through a surgically implanted electrode array. The integrity of the interface between the implanted electrode array and the auditory nerve contributes to the variability in outcomes experienced by cochlear implant users. Strategies to identify and eliminate poorly encoding electrodes have been found to be effective in improving outcomes with the device, but application is limited in a clinical setting. The purpose of this study was to evaluate a clinical method used to identify and selectively deactivate cochlear implants (CI) electrodes related to poor electrode-neural interface. Thirteen adult CI users participated in a pitch ranking task to identify indiscriminate electrode pairs. Electrodes associated with indiscriminate pairs were selectively deactivated, creating an individualized experimental program. Speech perception was evaluated in the baseline condition and with the experimental program before and after an acclimation period. Participant preference responses were recorded at each visit. Statistically significant improvements using the experimental program were found in at least one measure of speech perception at the individual level in four out of 13 participants when tested before acclimation. Following an acclimation period, ten out of 13 participants demonstrated statistically significant improvements in at least one measure of speech perception. Statistically significant improvements were found with the experimental program at the group level for both monosyllabic words (p = 0.006) and sentences in noise (p = 0.020). Additionally, ten participants preferred the experimental program prior to the acclimation period and eleven preferred the experimental program following the acclimation period. Results from this study suggest that electrode deactivation may yield improvement in speech perception following an acclimation period. A majority of CI users in our study reported a preference for the experimental program. This method proved to be a suitable clinical strategy for identifying and deactivating poorly encoding electrodes in adult CI users.

On closed-loop brain stimulation systems for improving the quality of life of patients with neurological disorders.

Emerging brain technologies have significantly transformed human life in recent decades. For instance, the closed-loop brain-computer interface (BCI) is an advanced software-hardware system that interprets electrical signals from neurons, allowing communication with and control of the environment. The system then transmits these signals as controlled commands and provides feedback to the brain to execute specific tasks. This paper analyzes and presents the latest research on closed-loop BCI that utilizes electric/magnetic stimulation, optogenetic, and sonogenetic techniques. These techniques have demonstrated great potential in improving the quality of life for patients suffering from neurodegenerative or psychiatric diseases. We provide a comprehensive and systematic review of research on the modalities of closed-loop BCI in recent decades. To achieve this, the authors used a set of defined criteria to shortlist studies from well-known research databases into categories of brain stimulation techniques. These categories include deep brain stimulation, transcranial magnetic stimulation, transcranial direct-current stimulation, transcranial alternating-current stimulation, and optogenetics. These techniques have been useful in treating a wide range of disorders, such as Alzheimer's and Parkinson's disease, dementia, and depression. In total, 76 studies were shortlisted and analyzed to illustrate how closed-loop BCI can considerably improve, enhance, and restore specific brain functions. The analysis revealed that literature in the area has not adequately covered closed-loop BCI in the context of cognitive neural prosthetics and implanted neural devices. However, the authors demonstrate that the applications of closed-loop BCI are highly beneficial, and the technology is continually evolving to improve the lives of individuals with various ailments, including those with sensory-motor issues or cognitive deficiencies. By utilizing emerging techniques of stimulation, closed-loop BCI can safely improve patients' cognitive and affective skills, resulting in better healthcare outcomes.

An in-silico framework for modeling optimal control of neural systems.

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. 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. 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. 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.

A novel method for dynamically altering the surface area of intracranial EEG electrodes

Objective. Intracranial electroencephalogram (iEEG) plays a critical role in the treatment of neurological diseases, such as epilepsy and Parkinson’s disease, as well as the development of neural prostheses and brain computer interfaces. While electrode geometries vary widely across these applications, the impact of electrode size on iEEG features and morphology is not well understood. Some insight has been gained from computer simulations, as well as experiments in which signals are recorded using electrodes of different sizes concurrently in different brain regions. Here, we introduce a novel method to record from electrodes of different sizes in the exact same location by changing the size of iEEG electrodes after implantation in the brain. Approach. We first present a theoretical model and an in vitro validation of the method. We then report the results of an in vivo implementation in three human subjects with refractory epilepsy. We recorded iEEG data from three different electrode sizes and compared the amplitudes, power spectra, inter-channel correlations, and signal-to-noise ratio (SNR) of interictal epileptiform discharges, i.e. epileptic spikes. Main Results. We found that iEEG amplitude and power decreased as electrode size increased, while inter-channel correlation did not change significantly with electrode size. The SNR of epileptic spikes was generally highest in the smallest electrodes, but 39% of spikes had maximal SNR in larger electrodes. This likely depends on the precise location and spatial spread of each spike. Significance. Overall, this new method enables multi-scale measurements of electrical activity in the human brain that can facilitate our understanding of neurophysiology, treatment of neurological disease, and development of novel technologies.