Neurochemostat: A Neural Interface SoC With Integrated Chemometrics for Closed-Loop Regulation of Brain Dopamine

Multimodal Technologies for Closed-Loop Neural Modulation and Sensing

Existing methods for studying neural circuits and treating neurological disorders are typically based on physical and chemical cues to manipulate and record neural activities. These approaches often involve predefined, rigid, and unchangeable signal patterns, which cannot be adjusted in real time according to the patient's condition or neural activities. With the continuous development of neural interfaces, conducting in vivo research on adaptive and modifiable treatments for neurological diseases and neural circuits is now possible. In this review, current and potential integration of various modalities to achieve precise, closed-loop modulation, and sensing in neural systems are summarized. Advanced materials, devices, or systems that generate or detect electrical, magnetic, optical, acoustic, or chemical signals are highlighted and utilized to interact with neural cells, tissues, and networks for closed-loop interrogation. Further, the significance of developing closed-loop techniques for diagnostics and treatment of neurological disorders such as epilepsy, depression, rehabilitation of spinal cord injury patients, and exploration of brain neural circuit functionality is elaborated.

SmartFSCV: An Artificial Intelligence Enabled Miniaturised FSCV Device Targeting Serotonin

Goal: Dynamically monitoring serotonin in real-time within target brain regions would significantly improve the diagnostic and therapeutic approaches to a variety of neurological and psychiatric disorders. Current systems for measuring serotonin lack immediacy and portability and are bulky and expensive. Methods: We present a new miniaturised device, named SmartFSCV, designed to monitor dynamic changes of serotonin using fast-scan cyclic voltammetry (FSCV). This device outputs a precision voltage potential between -3 to +3 V, and measures current between -1.5 to +1.5 μA with nano-ampere accuracy. The device can output modifiable arbitrary waveforms for various measurements and uses an N-shaped waveform at a scan-rate of 1000 V/s for sensing serotonin. Results: Four experiments were conducted to validate SmartFSCV: static bench test, dynamic serotonin test and two artificial intelligence (AI) algorithm tests. Conclusions: These tests confirmed the ability of SmartFSCV to accurately sense and make informed decisions about the presence of serotonin using AI.

Development of a Smart Wireless Multisensor Platform for an Optogenetic Brain Implant

Implantable cell replacement therapies promise to completely restore the function of neural structures, possibly changing how we currently perceive the onset of neurodegenerative diseases. One of the major clinical hurdles for the routine implementation of stem cell therapies is poor cell retention and survival, demanding the need to better understand these mechanisms while providing precise and scalable approaches to monitor these cell-based therapies in both pre-clinical and clinical scenarios. This poses significant multidisciplinary challenges regarding planning, defining the methodology and requirements, prototyping and different stages of testing. Aiming toward an optogenetic neural stem cell implant controlled by a smart wireless electronic frontend, we show how an iterative development methodology coupled with a modular design philosophy can mitigate some of these challenges. In this study, we present a miniaturized, wireless-controlled, modular multisensor platform with fully interfaced electronics featuring three different modules: an impedance analyzer, a potentiostat and an optical stimulator. We show the application of the platform for electrical impedance spectroscopy-based cell monitoring, optical stimulation to induce dopamine release from optogenetically modified neurons and a potentiostat for cyclic voltammetry and amperometric detection of dopamine release. The multisensor platform is designed to be used as an opto-electric headstage for future in vivo animal experiments.

A Multimodality Electrochemical and Impedance Spectroscopy System-on-a-Chip With Temperature Sensing and Impedance-Boosting Techniques

This article presents a multimodal electrochemical sensing system-on-chip (SoC), including the functions of cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and temperature sensing. CV readout circuitry achieves an adaptive readout current range of 145.5 dB through an automatic range adjustment and resolution scaling technique. EIS has an impedance resolution of 9.2 m Ω/√ Hz at a sweep frequency of 10 kHz and an output current of up to 120 μA. With an impedance boost mechanism, the maximum detectable load impedance is extended to 22.95 k Ω, while the total harmonic distortion is less than 1%. A resistor-based temperature sensor using a swing-boosted relaxation oscillator can achieve a resolution of 31 mK in 0-85 °C. The design is implemented in a 0.18 μm CMOS process. The total power consumption is 1 mW.

Hybrid Neural Autoencoders for Stimulus Encoding in Visual and Other Sensory Neuroprostheses

Sensory neuroprostheses are emerging as a promising technology to restore lost sensory function or augment human capabilities. However, sensations elicited by current devices often appear artificial and distorted. Although current models can predict the neural or perceptual response to an electrical stimulus, an optimal stimulation strategy solves the inverse problem: what is the required stimulus to produce a desired response? Here, we frame this as an end-to-end optimization problem, where a deep neural network stimulus encoder is trained to invert a known and fixed forward model that approximates the underlying biological system. As a proof of concept, we demonstrate the effectiveness of this hybrid neural autoencoder (HNA) in visual neuroprostheses. We find that HNA produces high-fidelity patient-specific stimuli representing handwritten digits and segmented images of everyday objects, and significantly outperforms conventional encoding strategies across all simulated patients. Overall this is an important step towards the long-standing challenge of restoring high-quality vision to people living with incurable blindness and may prove a promising solution for a variety of neuroprosthetic technologies.

Evolution of Biotelemetry in Medical Devices: From Radio Pills to mm-Scale Implants

The advent of semiconductor technology in the mid-20th century created unprecedented opportunities to develop a new generation of small-scale wireless medical sensing devices that can support remote monitoring of patients' vital signs. The first radio pills were developed as early as the 1950's using only a few transistors. These swallowable capsules could sense and wirelessly transmit vital parameters from inside the human body. Since then we have witnessed the rapid progress of medical devices driven by the evolution of semiconductor technology, from single-transistor oscillators to complex mixed-signal multi-channel and multi-modal systems. This paper retraces the evolution of biotelemetry devices from their very early inception to the smart miniaturized systems of modern days, focusing on semiconductor-enabled sensing methods and circuits developed over the last six decades. The paper also includes the author's perspective on current and future trends in the development of CMOS-based biotelemeters, focusing on concepts of implant modularity, miniaturization and hybrid energy harvesting solutions.

A 26.5 pArms Neurotransmitter Front-End With Class-AB Background Subtraction

This paper presents an analog front-end (AFE) for fast-scan cyclic voltammetry (FSCV) with analog background subtraction using a pseudo-differential sensing scheme to cancel the large non-faradaic current before seeing the front-end. As a result, the AFE can be compact and low-power compared to conventional FSCV AFEs with dedicated digital back-ends to digitize and subtract the background from subsequent recordings. The reported AFE, fabricated in a 0.18- μ m CMOS process, consists of a class-AB common-mode rejection circuit, a low-input-impedance current conveyor, and a 1^st-order current-mode delta-sigma (ΔΣ) modulator with an infinite impulse response quantizer. This AFE achieves an effective dynamic range of 83 dB with a state-of-the-art 39.2 pA_rms input-referred noise when loaded with a 1 nF input capacitance (26.5 pA_rms open-circuit) across a 5 kHz bandwidth while consuming an average power of 3.7 μW. This design was tested with carbon-fiber microelectrodes scanned at 300 V/s using flow-injection of dopamine, a key neurotransmitter.

Wireless Multimodality Sensing System-on-a-Chip With Time-Based Resolution Scaling Technique and Analog Waveform Generator in 0.18 μm CMOS for Chronic Wound Care

Multimodal sensing can provide a comprehensive and accurate diagnosis of biological information. This paper presents a fully integrated wireless multimodal sensing chip with voltammetric electrochemical sensing at a scanning rate range of 0.08-400 V/s, temperature monitoring, and bi-phasic electrical stimulation for wound healing progress monitoring. The time-based readout circuitry can achieve a 1-20X scalable resolution through dynamic threshold voltage adjustment. A low-noise analog waveform generator is designed using current reducer techniques to eliminate the large passive components. The chip is fabricated via a 0.18 μm CMOS process. The design achieves R² linearity of 0.995 over a wide current detection range (2 pA-12 μA) while consuming 49 μW at 1.2 V supply. The temperature sensing circuit achieves a 43 mK resolution from 20 to 80 degrees. The current stimulator provides an output current ranging from 8 μA to 1 mA in an impedance range of up to 3 kΩ. A wakeup receiver with data correlators is used to control the operation modes. The sensing data are wirelessly transmitted to the external readers. The proposed sensing IC is verified for measuring critical biomarkers, including C-reactive protein, uric acid, and temperature.

Living Things Are Not (20th Century) Machines: Updating Mechanism Metaphors in Light of the Modern Science of Machine Behavior

One of the most useful metaphors for driving scientific and engineering progress has been that of the “machine.” Much controversy exists about the applicability of this concept in the life sciences. Advances in molecular biology have revealed numerous design principles that can be harnessed to understand cells from an engineering perspective, and build novel devices to rationally exploit the laws of chemistry, physics, and computation. At the same time, organicists point to the many unique features of life, especially at larger scales of organization, which have resisted decomposition analysis and artificial implementation. Here, we argue that much of this debate has focused on inessential aspects of machines – classical properties which have been surpassed by advances in modern Machine Behavior and no longer apply. This emerging multidisciplinary field, at the interface of artificial life, machine learning, and synthetic bioengineering, is highlighting the inadequacy of existing definitions. Key terms such as machine, robot, program, software, evolved, designed, etc., need to be revised in light of technological and theoretical advances that have moved past the dated philosophical conceptions that have limited our understanding of both evolved and designed systems. Moving beyond contingent aspects of historical and current machines will enable conceptual tools that embrace inevitable advances in synthetic and hybrid bioengineering and computer science, toward a framework that identifies essential distinctions between fundamental concepts of devices and living agents. Progress in both theory and practical applications requires the establishment of a novel conception of “machines as they could be,” based on the profound lessons of biology at all scales. We sketch a perspective that acknowledges the remarkable, unique aspects of life to help re-define key terms, and identify deep, essential features of concepts for a future in which sharp boundaries between evolved and designed systems will not exist.

Evaluation of electrochemical methods for tonic dopamine detection in vivo

Dysfunction in dopaminergic neuronal systems underlie a number of neurologic and psychiatric disorders such as Parkinson's disease, drug addiction, and schizophrenia. Dopamine systems communicate via two mechanisms, a fast "phasic" release (sub-second to second) that is related to salient stimuli and a slower "tonic" release (minutes to hours) that regulates receptor tone. Alterations in tonic levels are thought to be more critically important in enabling normal motor, cognitive, and motivational functions, and dysregulation in tonic dopamine levels are associated with neuropsychiatric disorders. Therefore, development of neurochemical recording techniques that enable rapid, selective, and quantitative measurements of changes in tonic extracellular levels are essential in determining the role of dopamine in both normal and disease states. Here, we review state-of-the-art advanced analytical techniques for in vivo detection of tonic levels, with special focus on electrochemical techniques for detection in humans.

An Electrochemical Biochip for Measuring Low Concentrations of Analytes With Adjustable Temporal Resolutions

Electrochemical micro-sensors made of nano-graphitic (NG) carbon materials could offer high sensitivity and support voltammetry measurements at vastly different temporal resolutions. Here, we implement a configurable CMOS biochip for measuring low concentrations of bio-analytes by leveraging these advantageous features of NG micro-sensors. In particular, the core of the biochip is a discrete-time ∆Σ modulator, which can be configured for optimal power consumption according to the temporal resolution requirements of the sensing experiments while providing a required precision of ≈ 13 effective number of bits. We achieve this new functionality by developing a design methodology using the physical models of transistors, which allows the operating region of the modulator to be switched on-demand between weak and strong inversion. We show the application of this configurable biochip through in-vitro measurements of dopamine with concentrations as low as 50 nM and 200 nM at temporal resolutions of 100 ms and 10 s, respectively.

Advances in neurochemical measurements: A review of biomarkers and devices for the development of closed-loop deep brain stimulation systems

Neurochemical recording techniques have expanded our understanding of the pathophysiology of neurological disorders, as well as the mechanisms of action of treatment modalities like deep brain stimulation (DBS). DBS is used to treat diseases such as Parkinson's disease, Tourette syndrome, and obsessive-compulsive disorder, among others. Although DBS is effective at alleviating symptoms related to these diseases and improving the quality of life of these patients, the mechanism of action of DBS is currently not fully understood. A leading hypothesis is that DBS modulates the electrical field potential by modifying neuronal firing frequencies to non-pathological rates thus providing therapeutic relief. To address this gap in knowledge, recent advances in electrochemical sensing techniques have given insight into the importance of neurotransmitters, such as dopamine, serotonin, glutamate, and adenosine, in disease pathophysiology. These studies have also highlighted their potential use in tandem with electrophysiology to serve as biomarkers in disease diagnosis and progression monitoring, as well as characterize response to treatment. Here, we provide an overview of disease-relevant neurotransmitters and their roles and implications as biomarkers, as well as innovations to the biosensors used to record these biomarkers. Furthermore, we discuss currently available neurochemical and electrophysiological recording devices, and discuss their viability to be implemented into the development of a closed-loop DBS system.

Future of Neural Interfaces

The technological ability to capture electrophysiological activity of populations of cortical neurons through chronic implantable devices has led to significant advancements in the field of brain-computer interfaces. Recent progress in the field has been driven by developments in integrated microelectronics, wireless communications, materials science, and computational neuroscience. Here, we review major device development landmarks in the arena of neural interfaces from FDA-approved clinical systems to prototype head-mounted and fully implantable wireless systems for multi-channel neural recording. Additionally, we provide an outlook toward next-generation, highly miniaturized technologies for minimally invasive, vastly parallel neural interfaces for naturalistic, closed-loop neuroprostheses.

Closed-Loop Implantable Therapeutic Neuromodulation Systems Based on Neurochemical Monitoring

Closed-loop or intelligent neuromodulation allows adjustable, personalized neuromodulation which usually incorporates the recording of a biomarker, followed by implementation of an algorithm which decides the timing (when?) and strength (how much?) of stimulation. Closed-loop neuromodulation has been shown to have greater benefits compared to open-loop neuromodulation, particularly for therapeutic applications such as pharmacoresistant epilepsy, movement disorders and potentially for psychological disorders such as depression or drug addiction. However, an important aspect of the technique is selection of an appropriate, preferably neural biomarker. Neurochemical sensing can provide high resolution biomarker monitoring for various neurological disorders as well as offer deeper insight into neurological mechanisms. The chemicals of interest being measured, could be ions such as potassium (K⁺), sodium (Na⁺), calcium (Ca²⁺), chloride (Cl⁻), hydrogen (H⁺) or neurotransmitters such as dopamine, serotonin and glutamate. This review focusses on the different building blocks necessary for a neurochemical, closed-loop neuromodulation system including biomarkers, sensors and data processing algorithms. Furthermore, it also highlights the merits and drawbacks of using this biomarker modality.

A Bidirectional-Current CMOS Potentiostat for Fast-Scan Cyclic Voltammetry Detector Arrays

A potentiostat circuit for the application of bipolar electrode voltages and detection of bidirectional currents using a microelectrode array is presented. The potentiostat operates as a regulated-cascode amplifier for positive input currents, and as an active-input regulated-cascode mirror for negative input currents. This topology enables constant-potential amperometry and fast-scan cyclic voltammetry (FSCV) at microelectrode arrays for parallel recording of quantal release events, electrode impedance characterization, and high-throughput drug screening. A 64-channel FSCV detector array, fabricated in a 0.5-$\mu$m, 5-V CMOS process, is also demonstrated. Each detector occupies an area of 45  $\mu$m $\times$ 30 $\mu$m and consists of only 14 transistors and a 50-fF integrating capacitor. The system was validated using prerecorded input stimuli from actual FSCV measurements at a carbon-fiber microelectrode.

An investigation into closed-loop treatment of neurological disorders based on sensing mitochondrial dysfunction

Dynamic feedback based closed-loop medical devices offer a number of advantages for treatment of heterogeneous neurological conditions. Closed-loop devices integrate a level of neurobiological feedback, which allows for real-time adjustments to be made with the overarching aim of improving treatment efficacy and minimizing risks for adverse events. One target which has not been extensively explored as a potential feedback component in closed-loop therapies is mitochondrial function. Several neurodegenerative and psychiatric disorders including Parkinson's disease, Major Depressive disorder and Bipolar disorder have been linked to perturbations in the mitochondrial respiratory chain. This paper investigates the potential to monitor this mitochondrial function as a method of feedback for closed-loop neuromodulation treatments. A generic model of the closed-loop treatment is developed to describe the high-level functions of any system designed to control neural function based on mitochondrial response to stimulation, simplifying comparison and future meta-analysis. This model has four key functional components including: a sensor, signal manipulator, controller and effector. Each of these components are described and several potential technologies for each are investigated. While some of these candidate technologies are quite mature, there are still technological gaps remaining. The field of closed-loop medical devices is rapidly evolving, and whilst there is a lot of interest in this area, widespread adoption has not yet been achieved due to several remaining technological hurdles. However, the significant therapeutic benefits offered by this technology mean that this will be an active area for research for years to come.

C-FSCV: Compressive Fast-Scan Cyclic Voltammetry for Brain Dopamine Recording

This paper presents a novel compressive sensing framework for recording brain dopamine levels with fast-scan cyclic voltammetry (FSCV) at a carbon-fiber microelectrode. Termed compressive FSCV (C-FSCV), this approach compressively samples the measured total current in each FSCV scan and performs basic FSCV processing steps, e.g., background current averaging and subtraction, directly with compressed measurements. The resulting background-subtracted faradaic currents, which are shown to have a block-sparse representation in the discrete cosine transform domain, are next reconstructed from their compressively sampled counterparts with the block sparse Bayesian learning algorithm. Using a previously recorded dopamine dataset, consisting of electrically evoked signals recorded in the dorsal striatum of an anesthetized rat, the C-FSCV framework is shown to be efficacious in compressing and reconstructing brain dopamine dynamics and associated voltammograms with high fidelity (correlation coefficient, ), while achieving compression ratio, CR, values as high as ~ 5. Moreover, using another set of dopamine data recorded 5 minutes after administration of amphetamine (AMPH) to an ambulatory rat, C-FSCV once again compresses (CR = 5) and reconstructs the temporal pattern of dopamine release with high fidelity ( ), leading to a true-positive rate of 96.4% in detecting AMPH-induced dopamine transients.

Hybrid CMOS-Graphene Sensor Array for Subsecond Dopamine Detection

We introduce a hybrid CMOS-graphene sensor array for subsecond measurement of dopamine via fast-scan cyclic voltammetry (FSCV). The prototype chip has four independent CMOS readout channels, fabricated in a 65-nm process. Using planar multilayer graphene as biologically compatible sensing material enables integration of miniaturized sensing electrodes directly above the readout channels. Taking advantage of the chemical specificity of FSCV, we introduce a region of interest technique, which subtracts a large portion of the background current using a programmable low-noise constant current at about the redox potentials. We demonstrate the utility of this feature for enhancing the sensitivity by measuring the sensor response to a known dopamine concentration in vitro at three different scan rates. This strategy further allows us to significantly reduce the dynamic range requirements of the analog-to-digital converter (ADC) without compromising the measurement accuracy. We show that an integrating dual-slope ADC is adequate for digitizing the background-subtracted current. The ADC operates at a sampling frequency of 5-10 kHz and has an effective resolution of about 60 pA, which corresponds to a theoretical dopamine detection limit of about 6 nM. Our hybrid sensing platform offers an effective solution for implementing next-generation FSCV devices that can enable precise recording of dopamine signaling in vivo on a large scale.

Miniaturized FDDA and CMOS Based Potentiostat for Bio-Applications

A novel fully differential difference CMOS potentiostat suitable for neurotransmitter sensing is presented. The described architecture relies on a fully differential difference amplifier (FDDA) circuit to detect a wide range of reduction-oxidation currents, while exhibiting low-power consumption and low-noise operation. This is made possible thanks to the fully differential feature of the FDDA, which allows to increase the source voltage swing without the need for additional dedicated circuitry. The FDDA also reduces the number of amplifiers and passive elements in the potentiostat design, which lowers the overall power consumption and noise. The proposed potentiostat was fabricated in 0.18 µm CMOS, with 1.8 V supply voltage. The device achieved 5 µA sensitivity and 0.99 linearity. The input-referred noise was 6.9 µV rms and the flicker noise was negligible. The total power consumption was under 55 µW. The complete system was assembled on a 20 mm × 20 mm platform that includes the potentiostat chip, the electrode terminals and an instrumentation amplifier for redox current buffering, once converted to a voltage by a series resistor. the chip dimensions were 1 mm × 0.5 mm and the other PCB components were off-chip resistors, capacitors and amplifiers for data acquisition. The system was successfully tested with ferricyanide, a stable electroactive compound, and validated with dopamine, a popular neurotransmitter.

Generation of Stimulus Triggering from Intracortical Spike Activity for Brain-Machine-Body Interfaces (BMBIs)

Brain-machine-body interfaces (BMBIs) aim to create an artificial connection in the nervous system by converting neural activity recorded from one cortical region to electrical stimuli delivered to another cortical region, spinal cord, or muscles in real-time. In particular, conditioning-mode BMBIs utilize such activity-dependent stimulation strategies to induce functional re-organization in the nervous system and promote functional recovery after injury by exploiting mechanisms underlying neuroplasticity. This paper reports on reconfigurable, field-programmable gate array (FPGA)-based implementation of a translation algorithm to extract multichannel stimulus trigger signals from intracortical neural spike activity. The approach features digital spike discrimination based on user-set thresholding and time-amplitude windowing, decision making to support different triggering patterns for various stimulation scenarios, as well as trigger-pattern-dependent blanking schemes for robust operation in the presence of stimulus artifacts. Readily lending itself to low-power, low-area implementation for future integration, the algorithm has been synthesized on a Cyclone II FPGA using Altera's Quartus II design software and validated experimentally with prerecorded intracortical neural spike activity from an anesthetized laboratory rat.