Self-organization of "fibro-axonal" composite tissue around unmodified metallic micro-electrodes can form a functioning interface with a peripheral nerve: A new direction for creating long-term neural interfaces.

INTRODUCTION: A long-term peripheral neural interface is an area of intense research. The use of electrode interfaces is limited by the biological response to the electrode material. METHODS: We created an electrode construct to harbor the rat sciatic nerve with interposition of autogenous adipose tissue between the nerve and the electrode. The construct was implanted for 10 weeks. RESULTS: Immunohistochemistry showed a unique laminar pattern of axonal growth layered between fibro-collagenous tissue, forming a physical interface with the tungsten micro-electrode. Action potentials transmitted across the intrerface showed mean conduction velocities varying between 6.99 ± 2.46 and 20.14 ± 4 m/s. CONCLUSIONS: We have demonstrated the feasibility of a novel peripheral nerve interface through modulation of normal biologic phenomena. It has potential applications as a chronic implantable neural interface.

Group-Chopping: An 8-Channel, 0.04% Gain Mismatch, 2.1 µW 0.017 mm2 Instrumentation Amplifier for Bio-Potential Recording.

An 8-channel AFE with a group-chopping instrumentation amplifier (GCIA) is proposed for bio-potential recording applications. The group-chopping technique cascades chopper switches to progressively swap channels and dynamically removes gain mismatch among all channels. An 8-phase non-overlapping clocking scheme is developed and achieves excellent between-channel gain mismatch characteristics. The dynamic offsets among all channels are mitigated by the GCIA as well. The GCIA is the first work that minimizes the gain mismatch across more than two channels. With the help of the group-chopping, combined with an area-efficient open-loop structure, the GCIA shows <0.04% between-channel gain mismatch, the lowest mismatch reported to date. The chip is fabricated in 0.18µm 1P6M CMOS, occupies only 0.017 mm2/Ch., consumes 2.1 µW/Ch. under 0.5 V supply and achieves an NEF of 2.1.

STEER: 3D Printed Guide for Nerve Regrowth Control and Neural Interface in Non-Human Primate Model.

OBJECTIVE: Peripheral neural interface (PNI) with a stable integration of synthetic elements with neural tissue is key for successfulneuro-prosthetic applications. An inevitable phenomenon of reactive fibrosis is a primary hurdle for long term functionality of PNIs. This proof-of-concept study aimed to fabricate and test a novel, stable PNI that harnesses fibro-axonal outgrowth at the nerve end and includes fibrosis in the design. METHODS: Two non-human primates were implanted with Substrate-guided, Tissue-Electrode Encapsulation and Integration (STEER) PNIs. The implant included a 3D printed guide that strove to steer the regrowing nerve towards encapsulation of the electrodes into a fibro-axonal tissue. After four months from implantation, we performed electrophysiological measurements to test STEER's functionality and examined the macro and micro- morphology of the outgrowth tissue. RESULTS: We observed a highly structured fibro-axonal composite within the STEER PNI. A conduction of intracranially generated action potentials was successfully recorded across the neural interface. Immunohistology demonstrated uniquely configured laminae of myelinated axons encasing the implant. CONCLUSION: STEER PNI reconfigured the structure of the fibro-axonal tissue and facilitated long-term functionality and stability of the neural interface. SIGNIFICANCE: The results point to the feasibility of our concept for creating a stable PNI with long-term electrophysiologic functionality by using simple design and materials.

Unveiling Stimulation Secrets of Electrical Excitation of Neural Tissue Using a Circuit Probability Theory.

Electrical excitation of neural tissue has wide applications, but how electrical stimulation interacts with neural tissue remains to be elucidated. Here, we propose a new theory, named the Circuit-Probability theory, to reveal how this physical interaction happen. The relation between the electrical stimulation input and the neural response can be theoretically calculated. We show that many empirical models, including strength-duration relationship and linear-non-linear-Poisson model, can be theoretically explained, derived, and amended using our theory. Furthermore, this theory can explain the complex non-linear and resonant phenomena and fit in vivo experiment data. In this letter, we validated an entirely new framework to study electrical stimulation on neural tissue, which is to simulate voltage waveforms using a parallel RLC circuit first, and then calculate the excitation probability stochastically.

A 3-Mbps, 802.11g-Based EMG Recording System With Fully Implantable 5-Electrode EMG Acquisition Device.

We have developed a 5-electrode recording system that combines an implantable electromyography (EMG) device package with transcutaneous inductive power transmission, near-infrared (NIR) transcutaneous data telemetry and 3 Mbps Wi-Fi data acquisition for chronic EMG recording in vivo. This system comprises a hermetically-sealed single-chip, 5-electrode Implantable EMG Acquisition Device (IEAD), a custom external powering and Implant Telemetry Module (ITM), and a custom Wi-Fi-based Raspberry Pi-based Data Acquisition (RaspDAQ) and relay device. The external unit (ITM and RaspDAQ) is powered entirely by a single battery to achieve the objective of untethered EMG recording, for the convenience of clinicians and animal researchers. The IEAD acquires intramuscular EMG signals at 17.85 ksps/electrode while being powered transcutaneously by the ITM using 22 MHz near-field inductive coupling. The acquired EMG data is transmitted transcutaneously via NIR telemetry to the ITM, which in turn, transfers the data to the RaspDAQ for relaying to a laptop computer for display and storage. We have also validated the complete system by acquiring EMG signals from rodents for up to two months. Following the explantation of the devices, we have also conducted failure and histological analysis on the devices and the surrounding tissue, respectively.

A neuro-inspired artificial peripheral nervous system for scalable electronic skins.

The human sense of touch is essential for dexterous tool usage, spatial awareness, and social communication. Equipping intelligent human-like androids and prosthetics with electronic skins-a large array of sensors spatially distributed and capable of rapid somatosensory perception-will enable them to work collaboratively and naturally with humans to manipulate objects in unstructured living environments. Previously reported tactile-sensitive electronic skins largely transmit the tactile information from sensors serially, resulting in readout latency bottlenecks and complex wiring as the number of sensors increases. Here, we introduce the Asynchronously Coded Electronic Skin (ACES)-a neuromimetic architecture that enables simultaneous transmission of thermotactile information while maintaining exceptionally low readout latencies, even with array sizes beyond 10,000 sensors. We demonstrate prototype arrays of up to 240 artificial mechanoreceptors that transmitted events asynchronously at a constant latency of 1 ms while maintaining an ultra-high temporal precision of <60 ns, thus resolving fine spatiotemporal features necessary for rapid tactile perception. Our platform requires only a single electrical conductor for signal propagation, realizing sensor arrays that are dynamically reconfigurable and robust to damage. We anticipate that the ACES platform can be integrated with a wide range of skin-like sensors for artificial intelligence (AI)-enhanced autonomous robots, neuroprosthetics, and neuromorphic computing hardware for dexterous object manipulation and somatosensory perception.

Implantable neurotechnologies: a review of integrated circuit neural amplifiers.

Neural signal recording is critical in modern day neuroscience research and emerging neural prosthesis programs. Neural recording requires the use of precise, low-noise amplifier systems to acquire and condition the weak neural signals that are transduced through electrode interfaces. Neural amplifiers and amplifier-based systems are available commercially or can be designed in-house and fabricated using integrated circuit (IC) technologies, resulting in very large-scale integration or application-specific integrated circuit solutions. IC-based neural amplifiers are now used to acquire untethered/portable neural recordings, as they meet the requirements of a miniaturized form factor, light weight and low power consumption. Furthermore, such miniaturized and low-power IC neural amplifiers are now being used in emerging implantable neural prosthesis technologies. This review focuses on neural amplifier-based devices and is presented in two interrelated parts. First, neural signal recording is reviewed, and practical challenges are highlighted. Current amplifier designs with increased functionality and performance and without penalties in chip size and power are featured. Second, applications of IC-based neural amplifiers in basic science experiments (e.g., cortical studies using animal models), neural prostheses (e.g., brain/nerve machine interfaces) and treatment of neuronal diseases (e.g., DBS for treatment of epilepsy) are highlighted. The review concludes with future outlooks of this technology and important challenges with regard to neural signal amplification.

Correlation between muscular and nerve signals responsible for hand grasping in non-human primates.

Neuroprosthetic devices that interface with the nervous system to restore functional motor activity offer a viable alternative to nerve regeneration, especially in proximal nerve injuries like brachial plexus injuries where muscle atrophy may set in before nerve re-innervation occurs. Prior studies have used control signals from muscle or cortical activity. However, nerve signals are preferred in many cases since they permit more natural and precise control when compared to muscle activity, and can be accessed with much lower risk than cortical activity. Identification of nerve signals that control the appropriate muscles is essential for the development of such a `bionic link'. Here we examine the correlation between muscle and nerve signals responsible for hand grasping in the M. fascicularis. Simultaneous recordings were performed using a 4-channel thin-film longitudinal intra-fascicular electrode (tf-LIFE) and 9 bipolar endomysial muscle electrodes while the animal performed grasping movements. We were able to identify a high degree of correlation (r > 0.6) between nerve signals from the median nerve and movement-dependent muscle activity from the flexor muscles of the forearm, with a delay that corresponded to 25 m/s nerve conduction velocity. The phase of the flexion could be identified using a wavelet approximation of the ENG. This result confirms this approach for a future neuroprosthetic device for the treatment of peripheral nerve injuries.

A Bionic Neural Link for peripheral nerve repair.

Peripheral nerve injuries with large gaps and long nerve regrowth paths are difficult to repair using existing surgical techniques, due to nerve degeneration and muscle atrophy. This paper proposes a Bionic Neural Link (BNL) as an alternative way for peripheral nerve repair. The concept of the BNL is described, along with the hypothetical benefits. A prototype monolithic single channel BNL has been developed, which consists of 16 neural recording channels and one stimulation channel, and is implemented in a 0.35-µm CMOS technology. The BNL has been tested in in-vivo animal experiments. Full function of the BNL chip has been demonstrated.