Closed-loop approaches for innovative neuroprostheses

The goal of this thesis is to study new ways to interact with the nervous system in case of damage or pathology. In particular, I focused my effort towards the development of innovative, closed-loop stimulation protocols in various scenarios: in vitro, ex vivo, in vivo

Bidirectional Neural Interface Circuits with On-Chip Stimulation Artifact Reduction Schemes

Bidirectional neural interfaces are tools designed to “communicate” with the brain via recording and modulation of neuronal activity. The bidirectional interface systems have been adopted for many applications. Neuroscientists employ them to map neuronal circuits through precise stimulation and recording. Medical doctors deploy them as adaptable medical devices which control therapeutic stimulation parameters based on monitoring real-time neural activity. Brain-machine-interface (BMI) researchers use neural interfaces to bypass the nervous system and directly control neuroprosthetics or brain-computer-interface (BCI) spellers. In bidirectional interfaces, the implantable transducers as well as the corresponding electronic circuits and systems face several challenges. A high channel count, low power consumption, and reduced system size are desirable for potential chronic deployment and wider applicability. Moreover, a neural interface designed for robust closed-loop operation requires the mitigation of stimulation artifacts which corrupt the recorded signals. This dissertation introduces several techniques targeting low power consumption, small size, and reduction of stimulation artifacts. These techniques are implemented for extracellular electrophysiological recording and two stimulation modalities: direct current stimulation for closed-loop control of seizure detection/quench and optical stimulation for optogenetic studies. While the two modalities differ in their mechanisms, hardware implementation, and applications, they share many crucial system-level challenges. The first method aims at solving the critical issue of stimulation artifacts saturating the preamplifier in the recording front-end. To prevent saturation, a novel mixed-signal stimulation artifact cancellation circuit is devised to subtract the artifact before amplification and maintain the standard input range of a power-hungry preamplifier. Additional novel techniques have been also implemented to lower the noise and power consumption. A common average referencing (CAR) front-end circuit eliminates the cross-channel common mode noise by averaging and subtracting it in analog domain. A range-adapting SAR ADC saves additional power by eliminating unnecessary conversion cycles when the input signal is small. Measurements of an integrated circuit (IC) prototype demonstrate the attenuation of stimulation artifacts by up to 42 dB and cross-channel noise suppression by up to 39.8 dB. The power consumption per channel is maintained at 330 nW, while the area per channel is only 0.17 mm2. The second system implements a compact headstage for closed-loop optogenetic stimulation and electrophysiological recording. This design targets a miniaturized form factor, high channel count, and high-precision stimulation control suitable for rodent in-vivo optogenetic studies. Monolithically integrated optoelectrodes (which include 12 µLEDs for optical stimulation and 12 electrical recording sites) are combined with an off-the-shelf recording IC and a custom-designed high-precision LED driver. 32 recording and 12 stimulation channels can be individually accessed and controlled on a small headstage with dimensions of 2.16 x 2.38 x 0.35 cm and mass of 1.9 g. A third system prototype improves the optogenetic headstage prototype by furthering system integration and improving power efficiency facilitating wireless operation. The custom application-specific integrated circuit (ASIC) combines recording and stimulation channels with a power management unit, allowing the system to be powered by an ultra-light Li-ion battery. Additionally, the µLED drivers include a high-resolution arbitrary waveform generation mode for shaping of µLED current pulses to preemptively reduce artifacts. A prototype IC occupies 7.66 mm2, consumes 3.04 mW under typical operating conditions, and the optical pulse shaping scheme can attenuate stimulation artifacts by up to 3x with a Gaussian-rise pulse rise time under 1 ms.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147674/1/mendrela_1.pd

Development and modelling of a versatile active micro-electrode array for high density in-vivo and in-vitro neural signal investigation

The electrophysiological observation of neurological cells has allowed much knowledge to be gathered regarding how living organisms are believed to acquire and process sensation. Although much has been learned about neurons in isolation, there is much more to be discovered in how these neurons communicate within large networks. The challenges of measuring neurological networks at the scale, density and chronic level of non invasiveness required to observe neurological processing and decision making are manifold, however methods have been suggested that have allowed small scale networks to be observed using arrays of micro-fabricated electrodes. These arrays transduce ionic perturbations local to the cell membrane in the extracellular fluid into small electrical signals within the metal that may be measured. A device was designed for optimal electrical matching to the electrode interface and maximal signal preservation of the received extracellular neural signals. Design parameters were developed from electrophysiological computer simulations and experimentally obtained empirical models of the electrode-electrolyte interface. From this information, a novel interface based signal filtering method was developed that enabled high density amplifier interface circuitry to be realised. A novel prototype monolithic active electrode was developed using CMOS microfabrication technology. The device uses the top metallization of a selected process to form the electrode substrate and compact amplification circuitry fabricated directly beneath the electrode to amplify and separate the neural signal from the baseline offsets and noise of the electrode interface. The signal is then buffered for high speed sampling and switched signal routing. Prototype 16 and 256 active electrode array with custom support circuitry is presented at the layout stage for a 20 μm diameter 100 μm pitch electrode array. Each device consumes 26.4 μW of power and contributes 4.509 μV (rms) of noise to the received signal over a controlled bandwidth of 10 Hz - 5 kHz. The research has provided a fundamental insight into the challenges of high density neural network observation, both in the passive and the active manner. The thesis concludes that power consumption is the fundamental limiting factor of high density integrated MEA circuitry; low power dissipation being crucial for the existence of the surface adhered cells under measurement. With transistor sizing, noise and signal slewing each being inversely proportional to the dc supply current and the large power requirements of desirable ancillary circuitry such as analogue-to-digital converters, a situation of compromise is approached that must be carefully considered for specific application design

Wireless tools for neuromodulation

Epilepsy is a spectrum of diseases characterized by recurrent seizures. It is estimated that 50 million individuals worldwide are affected and 30% of cases are medically refractory or drug resistant. Vagus nerve stimulation (VNS) and deep brain stimulation (DBS) are the only FDA approved device based therapies. Neither therapy offers complete seizure freedom in a majority of users. Novel methodologies are needed to better understand mechanisms and chronic nature of epilepsy. Most tools for neuromodulation in rodents are tethered. The few wireless devices use batteries or are inductively powered. The tether restricts movement, limits behavioral tests, and increases the risk of infection. Batteries are large and heavy with a limited lifetime. Inductive powering suffers from rapid efficiency drops due to alignment mismatches and increased distances. Miniature wireless tools that offer behavioral freedom, data acquisition, and stimulation are needed. This dissertation presents a platform of electrical, optical and radiofrequency (RF) technologies for device based neuromodulation. The platform can be configured with features including: two channels differential recording, one channel electrical stimulation, and one channel optical stimulation. Typical device operation consumes less than 4 mW. The analog front end has a bandwidth of 0.7 Hz - 1 kHz and a gain of 60 dB, and the constant current driver provides biphasic electrical stimulation. For use with optogenetics, the deep brain optical stimulation module provides 27 mW/mm2 of blue light (473 nm) with 21.01 mA. Pairing of stimulating and recording technologies allows closed-loop operation. A wireless powering cage is designed using the resonantly coupled filter energy transfer (RCFET) methodology. RF energy is coupled through magnetic resonance. The cage has a PTE ranging from 1.8-6.28% for a volume of 11 x 11 x 11 in3. This is sufficient to chronically house subjects. The technologies are validated through various in vivo preparations. The tools are designed to study epilepsy, SUDEP, and urinary incontinence but can be configured for other studies. The broad application of these technologies can enable the scientific community to better study chronic diseases and closed-loop therapies

Multi-electrode array recording and data analysis methods for molluscan central nervous systems

In this work the use of the central nervous system (CNS) of the aquatic snail Lymnaea stagnalis on planar multi-electrode arrays (MEAs) was developed and analysis methods for the data generated were created. A variety of different combinations of configurations of tissue from the Lymnaea CNS were explored to determine the signal characteristics that could be recorded by sixty channel MEAs. In particular, the suitability of the semi-intact system consisting of the lips, oesophagus, CNS, and associated nerve connectives was developed for use on the planar MEA. The recording target area of the dorsal surface of the buccal ganglia was selected as being the most promising for study and recordings of its component cells during fictive feeding behaviour stimulated by sucrose were made. The data produced by this type of experimentation is very high volume and so its analysis required the development of a custom set of software tools. The goal of this tool set is to find the signal from individual neurons in the data streams of the electrodes of a planar MEA, to estimate their position, and then to predict their causal connectivity. To produce such an analysis techniques for noise filtration, neural spike detection, and group detection of bursts of spikes were created to pre-process electrode data streams. The Kohonen self-organising map (SOM) algorithm was adapted for the purpose of separating detected spikes into data streams representing the spike output of individual cells found in the target system. A significant addition to SOM algorithm was developed by the concurrent use of triangulation methods based on current source density analysis to predict the position of individual cells based on their spike output on more than one electrode. The likely functional connectivity of individual neurons identified by the SOM technique were analysed through the use of a statistical causality method known as Granger causality/causal connectivity. This technique was used to produce a map of the likely connectivity between neural sources

Analog VLSI Circuits for Biosensors, Neural Signal Processing and Prosthetics

Stroke, spinal cord injury and neurodegenerative diseases such as ALS and Parkinson's debilitate their victims by suffocating, cleaving communication between, and/or poisoning entire populations of geographically correlated neurons. Although the damage associated with such injury or disease is typically irreversible, recent advances in implantable neural prosthetic devices offer hope for the restoration of lost sensory, cognitive and motor functions by remapping those functions onto healthy cortical regions. The research presented in this thesis is directed toward developing enabling technology for totally implantable neural prosthetics that could one day restore lost sensory, cognitive and motor function to the victims of debilitating neural injury or disease. There are three principal components to this work. First, novel integrated biosensors have been designed and implemented to transduce weak extra-cellular electrical potentials and optical signals from cells cultured directly on the surface of the sensor chips, as well as to manipulate cells on the surface of these chips. Second, a method of detecting and identifying stereotyped neural signals, or action potentials, has been mapped into silicon circuits which operate at very low power levels suitable for implantation. Third, as one small step towards the development of cognitive neural implants, a learning silicon synapse has been implemented and a neural network application demonstrated. The original contributions of this dissertation include: * A contact image sensor that adapts to background light intensity and can asynchronously detect statistically significant optical events in real-time; * Programmable electrode arrays for enhanced electrophysiological recording, for directing cellular growth, for site-specific in situ bio-functionalization, and for analyte and particulate collection; * Ultra-low power, programmable floating gate template matching circuits for the detection and classification of neural action potentials; * A two transistor synapse that exhibits spike timing dependent plasticity and can implement adaptive pattern classification and silicon learning

Implantable Low-Noise Fiberless Optoelectrodes for Optogenetic Control of Distinct Neural Populations

The mammalian brain is often compared to an electrical circuit, and its dynamics and function are governed by communication across different types neurons. To treat neurological disorders like Alzheimer’s and Parkinson’s, which are characterized by inhibition or amplification of neural activity in a particular region or lack of communication between different regions of the brain, there is a need to understand troubleshoot neural networks at cellular or local circuit level. In this work, we introduce a novel implantable optoelectrode that can manipulate more than one neuron type at a single site, independently and simultaneously. By delivering multi-color light using a scalable optical waveguide mixer, we demonstrate manipulation of multiple neuron types at precise spatial locations in vivo for the first time. We report design, micro-fabrication and optoelectronic packaging of a fiber-less, multicolor optoelectrode. The compact optoelectrode design consists of a 7 μm x 30 μm dielectric optical waveguide mixer and eight electrical recording sites monolithically integrated on each shank of a 22 μm-thick four-shank silicon neural probe. The waveguide mixers are coupled to eight side-emitting injection laser diodes (ILDs) via gradient-index (GRIN) lenses assembled on the probe backend. GRIN-based optoelectrode enables efficient optical coupling with large alignment tolerance to provide wide optical power range (10 to 3000 mW/mm2 irradiance) at stimulation ports. It also keeps thermal dissipation and electromagnetic interference generated by light sources sufficiently far from the sensitive neural signals, allowing thermal and electrical noise management on a multilayer printed circuit board. We demonstrated device verification and validation in CA1 pyramidal layer of mice hippocampus in both anesthetized and awake animals. The packaged devices were used to manipulate variety of multi-opsin preparations in vivo expressing different combinations of Channelrhodopsin-2, Archaerhodopsin and ChrimsonR in pyramidal and parvalbumin interneuron cells. We show effective stimulation, inhibition and recording of neural spikes at precise spatial locations with less than 100 μV stimulation-locked transients on the recording channels, demonstrating novel use of this technology in the functional dissection of neural circuits.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137171/1/kkomal_1.pd

Microelectrode cluster technology for precise interactions with neuronal circuits. Towards highly specific adaptive deep brain stimulation.

Neuro-electronic interfaces, which can be used for stable communication between neurons-computers over long periods of time, would be valuable for understanding and interacting with the nervous system. A major challenge has been to overcome the tissue reactions towards implanted electrodes. Flexible microelectrodes that cause less implantation injury and which can follow the micromotions of the brain have been considered as a solution to achieve stable neuronal recordings and stimulations. The aim of this thesis work was therefore to develop and evaluate biocompatible neuro-electronic interfaces, as well as introduce new implantation methods which together allow stable recordings and spatially precise stimulation of the brainTo this end, we have developed a new generation of ultrathin flexible electrode arrays based on 12.5 µm thin wires embedded in a gelatin vehicle providing structural support during implantation. The gelatin embedded electrodes were implanted in rat brains via a narrow track line and spread out as a cluster in the target area. In the first study, we evaluated the performance of the neural recordings for eight weeks with respect to impedance, signal amplitudes and noise levels. We found impedance, and signal to noise ratio of single units to be quite stable, suggesting high biocompatibility. In the second study, we developed a gelatin embedded microelectrode array consisting of 16 microelectrodes, distally equipped with silicone cushions to reduce vascular damage. This array was implanted medial to the subthalamic nucleus, in 6-hydroxydopamine lesioned rats (a classical animal model for Parkinson’s disease), and the effects of deep brain stimulation were evaluated for 6 weeks. Stimulation with subsets of 4-8 electrodes evoked specific motor behaviors in all the tested rats. Depending on the exact electrode combination, stimulation elicited either improvement of locomotion, or grooming and rearing, increased turning, dyskinesia, or no movement. These results suggest that improved stimulation specificity can be obtained by choosing the right group of electrodes from the cluster. In the third study, we hypothesized that reducing the tissue resistance during the insertion of the electrodes would minimize the implantation injury. To address this problem, we coated gelatin embedded needles with a layer of ice, which on melting, provided a super slippery surface during insertion into the brain. The addition of a layer of melting ice decreased the insertion force by approximately 50%, significantly reduced neuronal loss, as well as the astrocytic response, but did not have any obvious effect on microglial activation.In conclusion, this thesis presents a novel design for implantable and biocompatible neuro-electronic interfaces comprising highly flexible microelectrodes rendering stable recording properties and improved stimulation specificity. In addition, a novel implantation vehicle was developed to reduce the acute tissue reactions in response to the implantatio

Materials and neuroscience: validating tools for large-scale, high-density neural recording

Extracellular recording remains the only technique capable of measuring the activity of many neurons simultaneously with a sub-millisecond precision, in multiple brain areas, including deep structures. Nevertheless, many questions about the nature of the detected signal and the limitations/capabilities of this technique remain unanswered. The general goal of this work is to apply the methodology and concepts of materials science to answer some of the major questions surrounding extracellular recording, and thus take full advantage of this seminal technique. We start out by quantifying the effect of electrode impedance on the amplitude of measured extracellular spikes and background noise. Can we improve data quality by lowering electrode impedance? We demonstrate that if the proper recording system is used, then the impedance of a microelectrode, within the range typical of standard polytrodes (~ 0.1 to 2 MΩ), does not significantly affect a neural spike amplitude or the background noise, and therefore spike sorting. In addition to improving the performance of each electrode, increasing the number of electrodes in a single neural probe has also proven advantageous for simultaneously monitoring the activity of more neurons with better spatiotemporal resolution. How can we achieve large-scale, highdensity extracellular recordings without compromising brain tissue? Here we report the design and in vivo validation of a complementary metal–oxide–semiconductor (CMOS)-based scanning probe with 1356 electrodes arranged along approximately 8 mm of a thin shaft (50 μm thick and 100 μm wide). Additionally, given the ever-shrinking dimensions of CMOS technology, there is a drive to fabricate sub-cellular electrodes (< 10 μm). Therefore, to evaluate electrode configurations for future probe designs, several recordings from many different brain regions were performed with an ultra-dense probe containing 255 electrodes, each with a geometric area of 5 x 5 μm and a pitch of 6 μm. How can we validate neural probes with different electrode materials/configurations and different sorting algorithms? We describe a new procedure for precisely aligning two probes for in vivo “paired-recordings” such that the spiking activity of a single neuron is monitored with both a dense extracellular silicon polytrode and a juxtacellular micro-pipette. We gathered a dataset of paired-recordings, which is available online. The “ground truth” data, for which one knows exactly when a neuron in the vicinity of an extracellular probe generates an action potential, has been used for several groups to validate and quantify the performance of new algorithms to automatically detect/sort single-units

Resource efficient on-node spike sorting

Current implantable brain-machine interfaces are recording multi-neuron activity by utilising multi-channel, multi-electrode micro-electrodes. With the rapid increase in recording capability has come more stringent constraints on implantable system power consumption and size. This is even more so with the increasing demand for wireless systems to increase the number of channels being monitored whilst overcoming the communication bottleneck (in transmitting raw data) via transcutaneous bio-telemetries. For systems observing unit activity, real-time spike sorting within an implantable device offers a unique solution to this problem. However, achieving such data compression prior to transmission via an on-node spike sorting system has several challenges. The inherent complexity of the spike sorting problem arising from various factors (such as signal variability, local field potentials, background and multi-unit activity) have required computationally intensive algorithms (e.g. PCA, wavelet transform, superparamagnetic clustering). Hence spike sorting systems have traditionally been implemented off-line, usually run on work-stations. Owing to their complexity and not-so-well scalability, these algorithms cannot be simply transformed into a resource efficient hardware. On the contrary, although there have been several attempts in implantable hardware, an implementation to match comparable accuracy to off-line within the required power and area requirements for future BMIs have yet to be proposed. Within this context, this research aims to fill in the gaps in the design towards a resource efficient implantable real-time spike sorter which achieves performance comparable to off-line methods. The research covered in this thesis target: 1) Identifying and quantifying the trade-offs on subsequent signal processing performance and hardware resource utilisation of the parameters associated with analogue-front-end. Following the development of a behavioural model of the analogue-front-end and an optimisation tool, the sensitivity of the spike sorting accuracy to different front-end parameters are quantified. 2) Identifying and quantifying the trade-offs associated with a two-stage hybrid solution to realising real-time on-node spike sorting. Initial part of the work focuses from the perspective of template matching only, while the second part of the work considers these parameters from the point of whole system including detection, sorting, and off-line training (template building). A set of minimum requirements are established which ensure robust, accurate and resource efficient operation. 3) Developing new feature extraction and spike sorting algorithms towards highly scalable systems. Based on waveform dynamics of the observed action potentials, a derivative based feature extraction and a spike sorting algorithm are proposed. These are compared with most commonly used methods of spike sorting under varying noise levels using realistic datasets to confirm their merits. The latter is implemented and demonstrated in real-time through an MCU based platform.Open Acces