A 1024-Channel 268 nW/pixel 36×36 µm2/channel Data-Compressive Neural Recording IC for High-Bandwidth Brain-Computer Interfaces.

This paper presents a data-compressive neural recording IC for single-cell resolution high-bandwidth brain-computer interfaces. The IC features wired-OR lossy compression during digitization, thus preventing data deluge and massive data movement. By discarding unwanted baseline samples of the neural signals, the output data rate is reduced by 146× on average while allowing the reconstruction of spike samples. The recording array consists of pulse position modulation-based active digital pixels with a global single-slope analog-to-digital conversion scheme, which enables a low-power and compact pixel design with significantly simple routing and low array readout energy. Fabricated in a 28-nm CMOS process, the neural recording IC features 1024 channels (i.e., 32 × 32 array) with a pixel pitch of 36 µm that can be directly matched to a high-density microelectrode array. The pixel achieves 7.4 µVrms input-referred noise with a -3 dB bandwidth of 300-Hz to 5-kHz while consuming only 268 nW from a single 1-V supply. The IC achieves the smallest area per channel (36 × 36 µm2) and the highest energy efficiency among the state-of-the-art neural recording ICs published to date.

A Reconfigurable, Nonlinear, Low-Power, VCO-Based ADC for Neural Recording Applications.

Neural recording systems play a crucial role in comprehending the intricacies of the brain and advancing treatments for neurological disorders. Within these systems, the analog-to-digital converter (ADC) serves as a fundamental component, converting the electrical signals from the brain into digital data that can be further processed and analyzed by computing units. This research introduces a novel nonlinear ADC designed specifically for spike sorting in biomedical applications. Employing MOSFET varactors and voltage-controlled oscillators (VCOs), this ADC exploits the nonlinear capacitance properties of MOSFET varactors, achieving a parabolic quantization function that digitizes the noise with low resolution and the spikes with high resolution, effectively suppressing the background noise present in biomedical signals. This research aims to develop a reconfigurable, nonlinear voltage-controlled oscillator (VCO)-based ADC, specifically designed for implantable neural recording systems used in neuroprosthetics and brain-machine interfaces. The proposed design enhances the signal-to-noise ratio and reduces power consumption, making it more efficient for real-time neural data processing. By improving the performance and energy efficiency of these devices, the research contributes to the development of more reliable medical technologies for monitoring and treating neurological disorders. The quantization step of the ADC spans from 44.8 mV in the low-amplitude range to 1.4 mV in the high-amplitude range. The circuit was designed and simulated utilizing a 180 nm CMOS process; however, no physical prototype has been fabricated at this stage. Post-layout simulations confirm the expected performance. Occupying a silicon area is 0.09 mm2. Operating at a sampling frequency of 16 kS/s and a supply voltage of 1 volt, this ADC consumes 62.4 µW.

Long-term mesoscale imaging of 3D intercellular dynamics across a mammalian organ.

A comprehensive understanding of physio-pathological processes necessitates non-invasive intravital three-dimensional (3D) imaging over varying spatial and temporal scales. However, huge data throughput, optical heterogeneity, surface irregularity, and phototoxicity pose great challenges, leading to an inevitable trade-off between volume size, resolution, speed, sample health, and system complexity. Here, we introduce a compact real-time, ultra-large-scale, high-resolution 3D mesoscope (RUSH3D), achieving uniform resolutions of 2.6 × 2.6 × 6 µm3 across a volume of 8,000 × 6,000 × 400 µm3 at 20 Hz with low phototoxicity. Through the integration of multiple computational imaging techniques, RUSH3D facilitates a 13-fold improvement in data throughput and an orders-of-magnitude reduction in system size and cost. With these advantages, we observed premovement neural activity and cross-day visual representational drift across the mouse cortex, the formation and progression of multiple germinal centers in mouse inguinal lymph nodes, and heterogeneous immune responses following traumatic brain injury-all at single-cell resolution, opening up a horizon for intravital mesoscale study of large-scale intercellular interactions at the organ level.

Glial scarring around intra-cortical MEA implants with flexible and free microwires inserted using biodegradable PLGA needles.

Introduction: Many invasive and noninvasive neurotechnologies are being developed to help treat neurological pathologies and disorders. Making a brain implant safe, stable, and efficient in the long run is one of the requirements to conform with neuroethics and overcome limitations for numerous promising neural treatments. A main limitation is low biocompatibility, characterized by the damage implants create in brain tissue and their low adhesion to it. This damage is partly linked to friction over time due to the mechanical mismatch between the soft brain tissue and the more rigid wires. Methods: Here, we performed a short biocompatibility assessment of bio-inspired intra-cortical implants named "Neurosnooper" made of a microelectrode array consisting of a thin, flexible polymer-metal-polymer stack with microwires that mimic axons. Implants were assembled into poly-lactic-glycolic acid (PLGA) biodegradable needles for their intra-cortical implantation. Results and Discussion: The study of glial scars around implants, at 7 days and 2 months post-implantation, revealed a good adhesion between the brain tissue and implant wires and a low glial scar thickness. The lowest corresponds to electrode wires with a section size of 8 µm × 10 µm, compared to implants with the 8 µm × 50 µm electrode wire section size, and a straight shape appears to be better than a zigzag. Therefore, in addition to flexibility, size and shape parameters are important when designing electrode wires for the next generation of clinical intra-cortical implants.

Modeling and Analysis of Environmental Electromagnetic Interference in Multiple-Channel Neural Recording Systems for High Common-Mode Interference Rejection Performance.

Environmental electromagnetic interference (EMI) has always been a major interference source for multiple-channel neural recording systems, and little theoretical work has been attempted to address it. In this paper, equivalent circuit models are proposed to model both electromagnetic interference sources and neural signals in such systems, and analysis has been performed to generate the design guidelines for neural probes and the subsequent recording circuit towards higher common-mode interference (CMI) rejection performance while maintaining the recorded neural action potential (AP) signal quality. In vivo animal experiments with a configurable 32-channel neural recording system are carried out to validate the proposed models and design guidelines. The results show the power spectral density (PSD) of environmental 50 Hz EMI interference is reduced by three orders from 4.43 × 10-3 V2/Hz to 4.04 × 10-6 V2/Hz without affecting the recorded AP signal quality in an unshielded experiment environment.

Increasing Robustness of Intracortical Brain-Computer Interfaces for Recording Condition Changes via Data Augmentation.

BACKGROUND AND OBJECTIVE: Intracortical brain-computer interfaces (iBCIs) aim to help paralyzed individuals restore their motor functions by decoding neural activity into intended movement. However, changes in neural recording conditions hinder the decoding performance of iBCIs, mainly because the neural-to-kinematic mappings shift. Conventional approaches involve either training the neural decoders using large datasets before deploying the iBCI or conducting frequent calibrations during its operation. However, collecting data for extended periods can cause user fatigue, negatively impacting the quality and consistency of neural signals. Furthermore, frequent calibration imposes a substantial computational load. METHODS: This study proposes a novel approach to increase iBCIs' robustness against changing recording conditions. The approach uses three neural augmentation operators to generate augmented neural activity that mimics common recording conditions. Then, contrastive learning is used to learn latent factors by maximizing the similarity between the augmented neural activities. The learned factors are expected to remain stable despite varying recording conditions and maintain a consistent correlation with the intended movement. RESULTS: Experimental results demonstrate that the proposed iBCI outperformed the state-of-the-art iBCIs and was robust to changing recording conditions across days for long-term use on one publicly available nonhuman primate dataset. It achieved satisfactory offline decoding performance, even when a large training dataset was unavailable. CONCLUSIONS: This study paves the way for reducing the need for frequent calibration of iBCIs and collecting a large amount of annotated training data. Potential future works aim to improve offline decoding performance with an ultra-small training dataset and improve the iBCIs' robustness to severely disabled electrodes.

Injectable 2D Material-Based Sensor Array for Minimally Invasive Neural Implants.

Intracranial implants for diagnosis and treatment of brain diseases have been developed over the past few decades. However, the platform of conventional implantable devices still relies on invasive probes and bulky sensors in conjunction with large-area craniotomy and provides only limited biometric information. Here, an implantable multi-modal sensor array that can be injected through a small hole in the skull and inherently spread out for conformal contact with the cortical surface is reported. The injectable sensor array, composed of graphene multi-channel electrodes for neural recording and electrical stimulation and MoS2-based sensors for monitoring intracranial temperature and pressure, is designed based on a mesh structure whose elastic restoring force enables the contracted device to spread out. It is demonstrated that the sensor array injected into a rabbit's head can detect epileptic discharges on the surface of the cortex and mitigate it by electrical stimulation while monitoring both intracranial temperature and pressure. This method provides good potential for implanting a variety of functional devices via minimally invasive surgery.

Carbon Nanotube-Based Printed All-Organic Microelectrode Arrays for Neural Stimulation and Recording.

In this paper, we report a low-cost printing process of carbon nanotube (CNT)-based, all-organic microelectrode arrays (MEAs) suitable for in vitro neural stimulation and recording. Conventional MEAs have been mainly composed of expensive metals and manufactured through high-cost and complex lithographic processes, which have limited their accessibility for neuroscience experiments and their application in various studies. Here, we demonstrate a printing-based fabrication method for microelectrodes using organic CNT/paraffin ink, coupled with the deposition of an insulating layer featuring single-cell-sized sensing apertures. The simple microfabrication processes utilizing the economic and readily available ink offer potential for cost reduction and improved accessibility of MEAs. Biocompatibility of the fabricated microelectrode was suggested through a live/dead assay of cultured neural cells, and its large electric double layer capacitance was revealed by cyclic voltammetry that was crucial for preventing cytotoxic electrolysis during electric neural stimulation. Furthermore, the electrode exhibited sufficiently low electric impedance of 2.49 Ω·cm2 for high signal-to-noise ratio neural recording, and successfully captured model electric waves in physiological saline solution. These results suggest the easily producible and low-cost printed all-organic microelectrodes are available for neural stimulation and recording, and we believe that they can expand the application of MEA in various neuroscience research.

Anti-artifacts techniques for neural recording front-ends in closed-loop brain-machine interface ICs.

In recent years, thanks to the development of integrated circuits, clinical medicine has witnessed significant advancements, enabling more efficient and intelligent treatment approaches. Particularly in the field of neuromedical, the utilization of brain-machine interfaces (BMI) has revolutionized the treatment of neurological diseases such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. The BMI acquires neural signals via recording circuits and analyze them to regulate neural stimulator circuits for effective neurological treatment. However, traditional BMI designs, which are often isolated, have given way to closed-loop brain-machine interfaces (CL-BMI) as a contemporary development trend. CL-BMI offers increased integration and accelerated response speed, marking a significant leap forward in neuromedicine. Nonetheless, this advancement comes with its challenges, notably the stimulation artifacts (SA) problem inherent to the structural characteristics of CL-BMI, which poses significant challenges on the neural recording front-ends (NRFE) site. This paper aims to provide a comprehensive overview of technologies addressing artifacts in the NRFE site within CL-BMI. Topics covered will include: (1) understanding and assessing artifacts; (2) exploring the impact of artifacts on traditional neural recording front-ends; (3) reviewing recent technological advancements aimed at addressing artifact-related issues; (4) summarizing and classifying the aforementioned technologies, along with an analysis of future trends.

Sensorimotor content of multi-unit activity recorded in the paramedian lobule of the cerebellum using carbon fiber microelectrode arrays.

The cerebellum takes in a great deal of sensory information from the periphery and descending signals from the cerebral cortices. It has been debated whether the paramedian lobule (PML) in the rat and its paravermal regions that project to the interpositus nucleus (IPN) are primarily involved in motor execution or motor planning. Studies that have relied on single spike recordings in behaving animals have led to conflicting conclusions regarding this issue. In this study, we tried a different approach and investigated the correlation of field potentials and multi-unit signals recorded with multi-electrode arrays from the PML cortex along with the forelimb electromyography (EMG) signals in rats during behavior. Linear regression was performed to predict the EMG signal envelopes using the PML activity for various time shifts (±25, ±50, ±100, and ± 400 ms) between the two signals to determine a causal relation. The highest correlations (~0.5 on average) between the neural and EMG envelopes were observed for zero and small (±25 ms) time shifts and decreased with larger time shifts in both directions, suggesting that paravermal PML is involved both in processing of sensory signals and motor execution in the context of forelimb reaching behavior. EMG envelopes were predicted with higher success rates when neural signals from multiple phases of the behavior were utilized for regression. The forelimb extension phase was the most difficult to predict while the releasing of the bar phase prediction was the most successful. The high frequency (>300 Hz) components of the neural signal, reflecting multi-unit activity, had a higher contribution to the EMG prediction than did the lower frequency components, corresponding to local field potentials. The results of this study suggest that the paravermal PML in the rat cerebellum is primarily involved in the execution of forelimb movements rather than the planning aspect and that the PML is more active at the initiation and termination of the behavior, rather than the progression.

A Nanozyme-Based Electrode for High-Performance Neural Recording.

Implanted neural electrodes have been widely used to treat brain diseases that require high sensitivity and biocompatibility at the tissue-electrode interface. However, currently used clinical electrodes cannot meet both these requirements simultaneously, which hinders the effective recording of electronic signals. Herein, nanozyme-based neural electrodes incorporating bioinspired atomically precise clusters are developed as a general strategy with a heterogeneous design for multiscale and ultrasensitive neural recording via quantum transport and biocatalytic processes. Owing to the dual high-speed electronic and ionic currents at the electrode-tissue interface, the impedance of nanozyme electrodes is 26 times lower than that of state-of-the-art metal electrodes, and the acquisition sensitivity for the local field potential is ≈10 times higher than that of clinical PtIr electrodes, enabling a signal-to-noise ratio (SNR) of up to 14.7 dB for single-neuron recordings in rats. The electrodes provide more than 100-fold higher antioxidant and multi-enzyme-like activities, which effectively decrease 67% of the neuronal injury area by inhibiting glial proliferation and allowing sensitive and stable neural recording. Moreover, nanozyme electrodes can considerably improve the SNR of seizures in acute epileptic rats and are expected to achieve precise localization of seizure foci in clinical settings.

Free-Standing Carbon Nanotube Embroidered Graphene Film Electrode Array for Stable Neural Interfacing.

Implantable neural probes that are mechanically flexible yet robust are attractive candidates for achieving stable neural interfacing in the brain. Current flexible neural probes consist mainly of metal thin-film electrodes integrated on micrometer-thick polymer substrates, making it challenging to achieve electrode-tissue interfacing on the cellular scale. Here, we describe implantable neural probes that consist of robust carbon nanotube network embroidered graphene (CeG) films as free-standing recording microelectrodes. Our CeG film microelectrode arrays (CeG_MEAs) are ultraflexible yet mechanically robust, thus enabling cellular-scale electrode-tissue interfacing. Chronically implanted CeG_MEAs can stably track the activities of the same population of neurons over two months. Our results highlight the potential of ultraflexible and free-standing carbon nanofilms for stable neural interfacing in the brain.

Recent Advancements in Graphene-Based Implantable Electrodes for Neural Recording/Stimulation.

Implantable electrodes represent a groundbreaking advancement in nervous system research, providing a pivotal tool for recording and stimulating human neural activity. This capability is integral for unraveling the intricacies of the nervous system's functionality and for devising innovative treatments for various neurological disorders. Implantable electrodes offer distinct advantages compared to conventional recording and stimulating neural activity methods. They deliver heightened precision, fewer associated side effects, and the ability to gather data from diverse neural sources. Crucially, the development of implantable electrodes necessitates key attributes: flexibility, stability, and high resolution. Graphene emerges as a highly promising material for fabricating such electrodes due to its exceptional properties. It boasts remarkable flexibility, ensuring seamless integration with the complex and contoured surfaces of neural tissues. Additionally, graphene exhibits low electrical resistance, enabling efficient transmission of neural signals. Its transparency further extends its utility, facilitating compatibility with various imaging techniques and optogenetics. This paper showcases noteworthy endeavors in utilizing graphene in its pure form and as composites to create and deploy implantable devices tailored for neural recordings and stimulations. It underscores the potential for significant advancements in this field. Furthermore, this paper delves into prospective avenues for refining existing graphene-based electrodes, enhancing their suitability for neural recording applications in in vitro and in vivo settings. These future steps promise to revolutionize further our capacity to understand and interact with the neural research landscape.

On-demand electrically controlled melatonin release from PEDOT/SNP composite improves quality of chronic neural recording.

Long-time and high-quality signal acquisition performance from implantable electrodes is the key to establish stable and efficient brain-computer interface (BCI) connections. The chronic performance of implantable electrodes is hindered by the inflammatory response of brain tissue. In order to solve the material limitation of biological interface electrodes, we designed sulfonated silica nanoparticles (SNPs) as the dopant of Poly (3,4-ethylenedioxythiophene) (PEDOT) to modify the implantable electrodes. In this work, melatonin (MT) loaded SNPs were incorporated in PEDOT via electrochemical deposition on nickel-chromium (Ni-Cr) alloy electrode and carbon nanotube (CNT) fiber electrodes, without affecting the acute neural signal recording capacity. After coating with PEDOT/SNP-MT, the charge storage capacity of both electrodes was significantly increased, and the electrochemical impedance at 1 kHz of the Ni-Cr alloy electrodes was significantly reduced, while that of the CNT electrodes was significantly increased. In addition, this study inspected the effect of electrically triggered MT release every other day on the quality and longevity of neural recording from implanted neural electrodes in rat hippocampus for 1 month. Both MT modified Ni-Cr alloy electrodes and CNT electrodes showed significantly higher spike amplitude after 26-day recording. Significantly, the histological studies showed that the number of astrocytes around the implanted Ni-Cr alloy electrodes was significantly reduced after MT release. These results demonstrate the potent outcome of PEDOT/SNP-MT treatment in improving the chronic neural recording quality possibly through its anti-inflammatory property.

An AC-Coupled 1st-order Δ-ΔΣ Readout IC for Area-Efficient Neural Signal Acquisition.

The current demand for high-channel-count neural-recording interfaces calls for more area- and power-efficient readout architectures that do not compromise other electrical performances. In this paper, we present a miniature 128-channel neural recording integrated circuit (NRIC) for the simultaneous acquisition of local field potentials (LFPs) and action potentials (APs), which can achieve a very good compromise between area, power, noise, input range and electrode DC offset cancellation. An AC-coupled 1st-order digitally-intensive Δ-ΔΣ architecture is proposed to achieve this compromise and to leverage the advantages of a highly-scaled technology node. A prototype NRIC, including 128 channels, a newly-proposed area-efficient bulk-regulated voltage reference, biasing circuits and a digital control, has been fabricated in 22-nm FDSOI CMOS and fully characterized. Our proposed architecture achieves a total area per channel of 0.005 mm2, a total power per channel of 12.57 µW, and an input-referred noise of 7.7 ± 0.4 µVrms in the AP band and 11.9 ± 1.1 µVrms in the LFP band. A very good channel-to-channel uniformity is demonstrated by our measurements. The chip has been validated in vivo, demonstrating its capability to successfully record full-band neural signals.

Circuit-Level Modeling and Simulation of Wireless Sensing and Energy Harvesting With Hybrid Magnetoelectric Antennas for Implantable Neural Devices.

A magnetoelectric antenna (ME) can exhibit the dual capabilities of wireless energy harvesting and sensing at different frequencies. In this article, a behavioral circuit model for hybrid ME antennas is described to emulate the radio frequency (RF) energy harvesting and sensing operations during circuit simulations. The ME antenna of this work is interfaced with a CMOS energy harvester chip towards the goal of developing a wireless communication link for fully integrated implantable devices. One role of the integrated system is to receive pulse-modulated power from a nearby transmitter, and another role is to sense and transmit low-magnitude neural signals. The measurements reported in this paper are the first results that demonstrate simultaneous low-frequency wireless magnetic sensing and high-frequency wireless energy harvesting at two different frequencies with one dual-mode ME antenna. The proposed behavioral ME antenna model can be utilized during design optimizations of energy harvesting circuits. Measurements were performed to validate the wireless power transfer link with an ME antenna having a 2.57 GHz resonance frequency connected to an energy harvester chip designed in 65nm CMOS technology. Furthermore, this dual-mode ME antenna enables concurrent sensing using a carrier signal with a frequency that matches the second 63.63 MHz resonance mode. A wireless test platform has been developed for evaluation of ME antennas as a tool for neural implant design, and this prototype system was utilized to provide first experimental results with the transmission of magnetically modulated action potential waveforms.

Ultraflexible Neural Probes for Multidirectional Neuronal Activity Recordings over Large Spatial and Temporal Scales.

The widespread dissemination of ultraflexible neural probes depends on the development of advanced materials and implementation strategies that can allow reliable implantation of ultraflexible neural probes into targeted brain regions, especially deep and difficult-to-access brain regions. Here, we report ultraflexible and multidirectional probes that are encapsulated in a biocompatible polymer alloy with controllable dissolution kinetics. Our probes can be reliably implanted into targeted brain regions over large spatial scales, including deep hindbrain regions that are anatomically difficult-to-access in vivo. Chronically implanted probes can enable long-term, multidirectional recordings from several hundreds of neurons across distributed brain regions. In particular, our results show that 87.0% of chronically recorded neurons in the hindbrain are interneurons, whereas only 41.9% of chronically recorded neurons in the cortex are interneurons. These results demonstrate that our ultraflexible neural probes are a promising tool for large-scale, long-term neural circuit dissection in the brain.

Low-invasive neural recording in mouse models with diabetes via an ultrasmall needle-electrode.

Diabetes is known to cause a variety of complications, having a high correlation with Alzheimer's disease. Electrophysiological recording using a microscale needle electrode is a promising technology for the study, however, diabetic brain tissue is more difficult to record neuronal activities than normal tissue due to these complications including the development of cerebrovascular disease. Here we show an electrophysiological methodology for diabetic db/db mice (+Leprdb/+Leprdb) using a 4-µm-tip diameter needle-electrode device. The needle electrode minimized the tissue injury when compared to a typical larger metal electrode, as confirmed by bleeding during penetration. The proposed electrode device showed both acute and chronic in vivo recording capabilities for diabetic mice while reducing the glial cells' responses. Because of these device characteristics, the 4-µm-tip diameter needle-electrode will allow electrophysiological studies on diabetes models of not only mice, as proven in this study, but also other animals.

Conducting Polymer-Hydrogel Interpenetrating Networks for Improving the Electrode-Neural Interface.

Implantable neural microelectrodes are recognized as a bridge for information exchange between inner organisms and outer devices. Combined with novel modulation technologies such as optogenetics, it offers a highly precise methodology for the dissection of brain functions. However, achieving chronically effective and stable microelectrodes to explore the electrophysiological characteristics of specific neurons in free-behaving animals continually poses great challenges. To resolve this, poly(3,4-ethylenedioxythiophene)/poly(styrenesulfonate)/poly(vinyl alcohol) (PEDOT/PSS/PVA) interpenetrating conducting polymer networks (ICPN) are fabricated via a hydrogel scaffold precoating and electrochemical polymerization process to improve the performance of neural microelectrodes. The ICPN films exhibit robust interfacial adhesion, a significantly lower electrochemical impedance, superior mechanical properties, and improved electrochemical stability compared to the pure poly(3,4-ethylenedioxythiophene)/poly(styrenesulfonate)(PEDOT/PSS) films, which may be attributed to the three-dimensional (3D) porous microstructure of the ICPN. Hippocampal neurons and rat pheochromocytoma cells (PC12 cells) adhesion on ICPN and neurite outgrowth are observed, indicating enhanced biocompatibility. Furthermore, alleviated tissue response at the electrode-neural tissue interface and improved recording signal quality are confirmed by histological and electrophysiological studies, respectively. Owing to these merits, optogenetic modulations and electrophysiological recordings are performed in vivo, and an anxiolytic effect of hippocampal glutamatergic neurons on behavior is shown. This study demonstrates the effectiveness and advantages of ICPN-modified neural implants for in vivo applications.