Recent Advances in Neural Recording Microsystems

The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field

A 256-input micro-electrode array with integrated cmos amplifiers for neural signal recording

Thesis (Ph.D.)--Boston UniversityThe nervous system communicates and processes information through its basic structural units -- individual neurons (nerve cells). Neurons convey neural information via electrical and chemical signals, which makes electrophysiological recording techniques very important in the study of neurophysiology. Specifically, active microelectrode arrays (MEAs) with amplifiers integrated on the same substrate are used because they provide a very powerful neural electrical recording technique that can be directly interfaced to acute slices and cell cultures. 2D planer electrodes are typically used for recording from neural cultures in vitro, while in vivo recording in live animals invariably requires the use of 3D electrodes. I have designed an active MEA with neural amplifiers and 3D electrodes, all integrated on a single chip. The electrodes are commercially available 3D C4 (Controlled Collapse Chip Connect) flip-chip bonding solder balls that have a diameter of 100 µm and a pitch of 200 µm. An active MEA neural recording chip -- the Multiple-Input Neural Sensor (MINS) chip -- was designed and fabricated using the IBM BiCMOS 8HP 0.13 µm technology. The MINS IC has 256 input channels that are time-division multiplexed into two output pads. Each channel was designed to work at a 20 kHz frame rate with a total voltage gain of 60 dB per channel with an input-referred noise voltage of 5.3 µVrms over 10 Hz to 10 kHz. The entire MINS chip has an area of 4 x 4 mm^2 with 256 input C4s plus 20 wire-bond pads on two adjacent edges of the chip for power, control, and outputs. The fabricated MINS chips are wire-bonded to standard pin grid array (PGA), open-top PGA, and custom-designed printed circuit board (PCB) packages for electrical, in vitro, and in vivo testing, respectively. After process variation correction, the voltage gain of the 256 neural amplifiers, measured in vitro across several chips, has a mean value of 58.7 dB and a standard deviation of 0.37 dB. Measurements done with the electrical testing package demonstrate that the MINS IC has a flat frequency response from 0.05 Hz to 1.4 MHz, an input-referred noise voltage of 4.6 µVrms over 10 Hz to 10 kHz, an output voltage swing as large as 1.5 V peak-to-peak, and a total power consumption of 11.25 mW, or 43.9 µW per input channel

Advances in Microelectronics for Implantable Medical Devices

Implantable medical devices provide therapy to treat numerous health conditions as well as monitoring and diagnosis. Over the years, the development of these devices has seen remarkable progress thanks to tremendous advances in microelectronics, electrode technology, packaging and signal processing techniques. Many of today’s implantable devices use wireless technology to supply power and provide communication. There are many challenges when creating an implantable device. Issues such as reliable and fast bidirectional data communication, efficient power delivery to the implantable circuits, low noise and low power for the recording part of the system, and delivery of safe stimulation to avoid tissue and electrode damage are some of the challenges faced by the microelectronics circuit designer. This paper provides a review of advances in microelectronics over the last decade or so for implantable medical devices and systems. The focus is on neural recording and stimulation circuits suitable for fabrication in modern silicon process technologies and biotelemetry methods for power and data transfer, with particular emphasis on methods employing radio frequency inductive coupling. The paper concludes by highlighting some of the issues that will drive future research in the field

A novel Three-Dimensional Micro-Electrode Array for in-vitro electrophysiological applications

Microelectrode arrays (MEAs) represent a powerful and popular tool to study in vitro neuronal networks and acute brain slices. The research standard for MEAs is planar or 2D-MEAs, which have been in existence for over 30 years and used for extracellular recording and stimulation from cultured neuronal cells and tissue slices. However, planar MEAs suffer from rapid data attenuation in the z-direction when stimulating/recording from 3D in-vitro neuronal cultures or brain slices. The existing proposed 3D in-vitro neuronal models allow to record the electrophysiological activity of the 3D network only from the bottom layer (i.e. the one directly coupled to the planar MEAs). Thus, to further develop and optimize such 3D neuronal network systems and to study and understand how the 3D neuronal network dynamics changes in different layers of the 3D structure, new three-dimensional microelectrodes arrays (3D-MEAs) are required. Early attempts in this field resulted in interesting integrated approaches toward protruding or spiked 3D-MEAs. Although these first prototypes could be successfully employed with brain slices, the limited heights of the electrodes (up to max 70 \u3bcm) and the peculiar shape of the recording areas made them not an ideal solution for 3D neuronal cultures. Moreover, a convenient and versatile method for the fabrication of multilevel 3D microelectrode arrays has yet to be obtained, due to the usually complicated and expensive designs and a lack of a full compatibility with standard MEAs both in terms of materials and recording area dimensions. To overcome the afore-mentioned challenges, in this work, I present the design, microfabrication, and characterization of a new 3D-MEA composed of pillar-shaped gold 3D structures with heights of more than 100 \u3bcm that can be used, in principle, on every kind of MEA, both custom-made and commercial. I successfully demonstrate the capability and ability of such 3D-MEA to record electrophysiological spontaneous activity from 3D engineered in-vitro neuronal networks and both 4-AP-induced epileptiform-like and electrically-evoked activity from mouse acute brain slices. I also demonstrate how the developed 3D-MEA allows better recording and stimulating conditions while interfacing with acute brain slices as compared to planar electrode arrays and previously reported 3D MEA technologies

On the way to large-scale and high-resolution brain-chip interfacing

Brain-chip-interfaces (BCHIs) are hybrid entities where chips and nerve cells establish a close physical interaction allowing the transfer of information in one or both directions. Typical examples are represented by multi-site-recording chips interfaced to cultured neurons, cultured/acute brain slices, or implanted “in vivo”. This paper provides an overview on recent achievements in our laboratory in the field of BCHIs leading to enhancement of signals transmission from nerve cells to chip or from chip to nerve cells with an emphasis on in vivo interfacing, either in terms of signal-to-noise ratio or of spatiotemporal resolution. Oxide-insulated chips featuring large-scale and high-resolution arrays of stimulation and recording elements are presented as a promising technology for high spatiotemporal resolution interfacing, as recently demonstrated by recordings obtained from hippocampal slices and brain cortex in implanted animals. Finally, we report on an automated tool for processing and analysis of acquired signals by BCHIs

Electrical modeling of the cell-electrode interface for recording neural activity from high-density microelectrode arrays

Accurate electrical models are needed to support the design of modern microelectrode arrays. The point-contact model is presented thoroughly, and an area-contact model is analytically derived in order to model the electrical characteristics of the cell-electrode interface at subcellular resolution. An optimum electrode diameter for recording the electrical activity of neurons is analytically determined at 8 um, with a cell diameter of 10 um and a typical load capacitance of 10 pF. Finally, three- dimensional tip electrodes are characterized using the area- contact model. An improvement of the electrical coupling up to 20 dB is observed for small electrodes, in simulation

SpikeInterface, a unified framework for spike sorting

Much development has been directed toward improving the performance and automation of spike sorting. This continuous development, while essential, has contributed to an over-saturation of new, incompatible tools that hinders rigorous benchmarking and complicates reproducible analysis. To address these limitations, we developed SpikeInterface, a Python framework designed to unify preexisting spike sorting technologies into a single codebase and to facilitate straightforward comparison and adoption of different approaches. With a few lines of code, researchers can reproducibly run, compare, and benchmark most modern spike sorting algorithms; pre-process, post-process, and visualize extracellular datasets; validate, curate, and export sorting outputs; and more. In this paper, we provide an overview of SpikeInterface and, with applications to real and simulated datasets, demonstrate how it can be utilized to reduce the burden of manual curation and to more comprehensively benchmark automated spike sorters.ISSN:2050-084

Nanotools for Neuroscience and Brain Activity Mapping

Neuroscience is at a crossroads. Great effort is being invested into deciphering specific neural interactions and circuits. At the same time, there exist few general theories or principles that explain brain function. We attribute this disparity, in part, to limitations in current methodologies. Traditional neurophysiological approaches record the activities of one neuron or a few neurons at a time. Neurochemical approaches focus on single neurotransmitters. Yet, there is an increasing realization that neural circuits operate at emergent levels, where the interactions between hundreds or thousands of neurons, utilizing multiple chemical transmitters, generate functional states. Brains function at the nanoscale, so tools to study brains must ultimately operate at this scale, as well. Nanoscience and nanotechnology are poised to provide a rich toolkit of novel methods to explore brain function by enabling simultaneous measurement and manipulation of activity of thousands or even millions of neurons. We and others refer to this goal as the Brain Activity Mapping Project. In this Nano Focus, we discuss how recent developments in nanoscale analysis tools and in the design and synthesis of nanomaterials have generated optical, electrical, and chemical methods that can readily be adapted for use in neuroscience. These approaches represent exciting areas of technical development and research. Moreover, unique opportunities exist for nanoscientists, nanotechnologists, and other physical scientists and engineers to contribute to tackling the challenging problems involved in understanding the fundamentals of brain function

INTEGRATION OF CMOS TECHNOLOGY INTO LAB-ON-CHIP SYSTEMS APPLIED TO THE DEVELOPMENT OF A BIOELECTRONIC NOSE

This work addresses the development of a lab-on-a-chip (LOC) system for olfactory sensing. The method of sensing employed is cell-based, utilizing living cells to sense stimuli that are otherwise not easily sensed using conventional transduction techniques. Cells have evolved over millions of years to be exquisitely sensitive to their environment, with certain types of cells producing electrical signals in response to stimuli. The core device that is introduced here is comprised of living olfactory sensory neurons (OSNs) on top of a complementary metal-oxide-semiconductor (CMOS) integrated circuit (IC). This hybrid bioelectronic approach to sensing leverages the sensitivity of OSNs with the electronic signal processing capability of modern ICs. Intimately combining electronics with biology presents a number of unique challenges to integration that arise from the disparate requirements of the two separate domains. Fundamentally the obstacles arise from the facts that electronic devices are designed to work in dry environments while biology requires not only a wet environment, but also one that is precisely controlled and non-toxic. Design and modeling of such heterogeneously integrated systems is complicated by the lack of tools that can address the multiple domains and techniques required for integration, namely IC design, fluidics, packaging, and microfabrication, and cell culture. There also arises the issue of how to handle the vast amount of data that can be generated by such systems, and specifically how to efficiently identify signals of interest and communicate them off-chip. The primary contributions of this work are the development of a new packaging scheme for integration of CMOS ICs into fluidic LOC systems, a methodology for cross-coupled multi-domain iterative modeling of heterogeneously integrated systems, demonstration of a proof-of-concept bioelectronic olfactory sensor, and a novel event-based technique to minimize the bandwidth required to communicate the information contained in bio-potential signals produced by dense arrays of electrically active cells

Micromachined three-dimensional electrode arrays for in-vitro and in-vivo electrogenic cellular networks

This dissertation presents an investigation of micromachined three-dimensional microelectrode arrays (3-D MEAs) targeted toward in-vitro and in-vivo biomedical applications. Current 3-D MEAs are predominantly silicon-based, fabricated in a planar fashion, and are assembled to achieve a true 3-D form: a technique that cannot be extended to micro-manufacturing. The integrated 3-D MEAs developed in this work are polymer-based and thus offer potential for large-scale, high volume manufacturing. Two different techniques are developed for microfabrication of these MEAs - laser micromachining of a conformally deposited polymer on a non-planar surface to create 3-D molds for metal electrodeposition; and metal transfer micromolding, where functional metal layers are transferred from one polymer to another during the process of micromolding thus eliminating the need for complex and non-repeatable 3-D lithography processes. In-vitro and in-vivo 3-D MEAs are microfabricated using these techniques and are packaged utilizing Printed Circuit Boards (PCB) or other low-cost manufacturing techniques. To demonstrate in-vitro applications, growth of 3-D co-cultures of neurons/astrocytes and tissue-slice electrophysiology with brain tissue of rat pups were implemented. To demonstrate in-vivo application, measurements of nerve conduction were implemented. Microelectrode impedance models, noise models and various process models were evaluated. The results confirmed biocompatibility of the polymers involved, acceptable impedance range and noise of the microelectrodes, and potential to improve upon an archaic clinical diagnostic application utilizing these 3-D MEAs.Ph.D.Committee Chair: Mark G. Allen; Committee Member: Elliot L. Chaikof; Committee Member: Ionnis (John) Papapolymerou; Committee Member: Maysam Ghovanloo; Committee Member: Oliver Bran