Improving the mechanistic study of neuromuscular diseases through the development of a fully wireless and implantable recording device

Neuromuscular diseases manifest by a handful of known phenotypes affecting the peripheral nerves, skeletal muscle fibers, and neuromuscular junction. Common signs of these diseases include demyelination, myasthenia, atrophy, and aberrant muscle activity—all of which may be tracked over time using one or more electrophysiological markers. Mice, which are the predominant mammalian model for most human diseases, have been used to study congenital neuromuscular diseases for decades. However, our understanding of the mechanisms underlying these pathologies is still incomplete. This is in part due to the lack of instrumentation available to easily collect longitudinal, in vivo electrophysiological activity from mice. There remains a need for a fully wireless, batteryless, and implantable recording system that can be adapted for a variety of electrophysiological measurements and also enable long-term, continuous data collection in very small animals. To meet this need a miniature, chronically implantable device has been developed that is capable of wirelessly coupling energy from electromagnetic fields while implanted within a body. This device can both record and trigger bioelectric events and may be chronically implanted in rodents as small as mice. This grants investigators the ability to continuously observe electrophysiological changes corresponding to disease progression in a single, freely behaving, untethered animal. The fully wireless closed-loop system is an adaptable solution for a range of long-term mechanistic and diagnostic studies in rodent disease models. Its high level of functionality, adjustable parameters, accessible building blocks, reprogrammable firmware, and modular electrode interface offer flexibility that is distinctive among fully implantable recording or stimulating devices. The key significance of this work is that it has generated novel instrumentation in the form of a fully implantable bioelectric recording device having a much higher level of functionality than any other fully wireless system available for mouse work. This has incidentally led to contributions in the areas of wireless power transfer and neural interfaces for upper-limb prosthesis control. Herein the solution space for wireless power transfer is examined including a close inspection of far-field power transfer to implanted bioelectric sensors. Methods of design and characterization for the iterative development of the device are detailed. Furthermore, its performance and utility in remote bioelectric sensing applications is demonstrated with humans, rats, healthy mice, and mouse models for degenerative neuromuscular and motoneuron diseases

Rhythmic Action Synchronizes Memory Replay During Reinforcement Learning

Our cognitive abilities - learning from the past, sensing the current environment, planning into the future, executing an action, and infusing value into an experience - all rely on precisely timed and widespread electrical communications across neural networks. The brain’s hippocampal formation receives multimodal input, forges episodic associations, and predicts future state. Oscillating electrical bursts originating from the hippocampus, termed ‘sharp-wave ripples’ (SWR), often contain patterns of previously expressed neural spike sequences, and are necessary for certain forms of learning and memory. The discharge of SWR-replay resonates in remote parts of the brain and displays specific characteristics depending on a subject’s state of awareness and sensory context. In the sleep state, when motoric repertoire is limited, waves of breathing synchronize neural activity in several regions of the brain, including SWRs of the hippocampus. During active sensation of the awake state, cyclic licking dynamically entrains taste-reward networks in subcortical and cortical areas throughout learning. However, the neural correlates linking oromotor movements in the active learning state to the memory system of the hippocampal formation have not yet been established. Given the recurrence of SWR-replay during rhythmic ingestion of reinforcement learning and the hierarchical coupling of orofacial behaviors, we hypothesized that repeated licking could provide the oscillatory framework to synchronize memory reactivation during active learning. We approach this question with new technology development to track licking events at a reward port (P-event) during behavior on a spatial alternation task. Additionally, we developed a modular brain implant to simultaneously record from hippocampal area CA1 and medial entorhinal cortex (MEC) - interconnected brain regions that are crucial to episodic memory processing. Along with the co-modulation of individual neurons by licking and SWRs, we provide the first evidence that SWRs detected in dorsal CA1 synchronize with the phase of P-event cycle during learning. Furthermore, we confirmed that SWRs occurring during licking bouts contain neural reactivation of active navigation and trigger enhanced ripple-frequency power in downstream MEC. These results connect movement with memory and may assist in addressing abnormal ingestion behaviors that negatively affect mental or physical healt

Wireless power and data transmission to high-performance implantable medical devices

Novel techniques for high-performance wireless power transmission and data interfacing with implantable medical devices (IMDs) were proposed. Several system- and circuit-level techniques were developed towards the design of a novel wireless data and power transmission link for a multi-channel inductively-powered wireless implantable neural-recording and stimulation system. Such wireless data and power transmission techniques have promising prospects for use in IMDs such as biosensors and neural recording/stimulation devices, neural interfacing experiments in enriched environments, radio-frequency identification (RFID), smartcards, near-field communication (NFC), wireless sensors, and charging mobile devices and electric vehicles. The contributions in wireless power transfer are the development of an RFID-based closed-loop power transmission system, a high-performance 3-coil link with optimal design procedure, circuit-based theoretical foundation for magnetic-resonance-based power transmission using multiple coils, a figure-of-merit for designing high-performance inductive links, a low-power and adaptive power management and data transceiver ASIC to be used as a general-purpose power module for wireless electrophysiology experiments, and a Q-modulated inductive link for automatic load matching. In wireless data transfer, the contributions are the development of a new modulation technique called pulse-delay modulation for low-power and wideband near-field data communication and a pulse-width-modulation impulse-radio ultra-wideband transceiver for low-power and wideband far-field data transmission.Ph.D

VLSI Circuits for Bidirectional Neural Interfaces

Medical devices that deliver electrical stimulation to neural tissue are important clinical tools that can augment or replace pharmacological therapies. The success of such devices has led to an explosion of interest in the field, termed neuromodulation, with a diverse set of disorders being targeted for device-based treatment. Nevertheless, a large degree of uncertainty surrounds how and why these devices are effective. This uncertainty limits the ability to optimize therapy and gives rise to deleterious side effects. An emerging approach to improve neuromodulation efficacy and to better understand its mechanisms is to record bioelectric activity during stimulation. Understanding how stimulation affects electrophysiology can provide insights into disease, and also provides a feedback signal to autonomously tune stimulation parameters to improve efficacy or decrease side-effects. The aims of this work were taken up to advance the state-of-the-art in neuro-interface technology to enable closed-loop neuromodulation therapies. Long term monitoring of neuronal activity in awake and behaving subjects can provide critical insights into brain dynamics that can inform system-level design of closed-loop neuromodulation systems. Thus, first we designed a system that wirelessly telemetered electrocorticography signals from awake-behaving rats. We hypothesized that such a system could be useful for detecting sporadic but clinically relevant electrophysiological events. In an 18-hour, overnight recording, seizure activity was detected in a pre-clinical rodent model of global ischemic brain injury. We subsequently turned to the design of neurostimulation circuits. Three critical features of neurostimulation devices are safety, programmability, and specificity. We conceived and implemented a neurostimulator architecture that utilizes a compact on-chip circuit for charge balancing (safety), digital-to-analog converter calibration (programmability) and current steering (specificity). Charge balancing accuracy was measured at better than 0.3%, the digital-to-analog converters achieved 8-bit resolution, and physiological effects of current steering stimulation were demonstrated in an anesthetized rat. Lastly, to implement a bidirectional neural interface, both the recording and stimulation circuits were fabricated on a single chip. In doing so, we implemented a low noise, ultra-low power recording front end with a high dynamic range. The recording circuits achieved a signal-to-noise ratio of 58 dB and a spurious-free dynamic range of better than 70 dB, while consuming 5.5 μW per channel. We demonstrated bidirectional operation of the chip by recording cardiac modulation induced through vagus nerve stimulation, and demonstrated closed-loop control of cardiac rhythm

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

A Closed-Loop Bidirectional Brain-Machine Interface System For Freely Behaving Animals

A brain-machine interface (BMI) creates an artificial pathway between the brain and the external world. The research and applications of BMI have received enormous attention among the scientific community as well as the public in the past decade. However, most research of BMI relies on experiments with tethered or sedated animals, using rack-mount equipment, which significantly restricts the experimental methods and paradigms. Moreover, most research to date has focused on neural signal recording or decoding in an open-loop method. Although the use of a closed-loop, wireless BMI is critical to the success of an extensive range of neuroscience research, it is an approach yet to be widely used, with the electronics design being one of the major bottlenecks. The key goal of this research is to address the design challenges of a closed-loop, bidirectional BMI by providing innovative solutions from the neuron-electronics interface up to the system level. Circuit design innovations have been proposed in the neural recording front-end, the neural feature extraction module, and the neural stimulator. Practical design issues of the bidirectional neural interface, the closed-loop controller and the overall system integration have been carefully studied and discussed.To the best of our knowledge, this work presents the first reported portable system to provide all required hardware for a closed-loop sensorimotor neural interface, the first wireless sensory encoding experiment conducted in freely swimming animals, and the first bidirectional study of the hippocampal field potentials in freely behaving animals from sedation to sleep. This thesis gives a comprehensive survey of bidirectional BMI designs, reviews the key design trade-offs in neural recorders and stimulators, and summarizes neural features and mechanisms for a successful closed-loop operation. The circuit and system design details are presented with bench testing and animal experimental results. The methods, circuit techniques, system topology, and experimental paradigms proposed in this work can be used in a wide range of relevant neurophysiology research and neuroprosthetic development, especially in experiments using freely behaving animals

Flexible, stretchable, and transient electronics for integration with the human body

Technologies capable of establishing intimate, long-lived interfaces to the human body have broad utility in continuous measurement of physiological status, with the potential to significantly lower tissue injury and irritation after implants. The development of such soft, biocompatible platforms and integrating them into a biotissue-interfaced system requires suitable choice of materials and engineered structures. Specific directions include overall miniaturization (e.g., Si nanomembrane) or composite material structure (e.g., carbon black doped elastomer) that provide effective mechanics to match those of biological tissues. This dissertation presents combined experimental and theoretical investigations of such functional systems that offer flexibility and stretchability, while maintaining operational performance and mechanical robustness. The dissertation begins with a fundamental study of responsive monocrystalline silicon nanomembrane as a flexible electromechanical sensor element. Subsequent chapters highlight integration with active components for wireless addressing, multiplexing, and local amplification, with multimodal operation in a thin, soft, skin-like platform. The resulting biointegrated system enables (1) sensitive health monitoring system, (2) multifunctional tactile sensor, (3) high-density neural interfaces, and (4) physically transient, implantable electronics, all with the capability of stable operation for long timeframes

Closing the Loop: Exploring the Use of Sacral Dorsal Root Ganglia Signals for Adaptive Neuromodulation of Bladder Function

Overactive bladder (OAB) is a highly prevalent condition which negatively affects the physical and mental health of millions of people worldwide. Sacral neuromodulation (SNM) is a third-line therapy that provides improved efficacy and less adherence issues as compared to conventional treatments. There have been ~300,000 SNM implants since the therapy was first introduced over 20 years ago. While SNM is delivered in an open-loop fashion, the therapy could have improved clinical efficacy by adopting a closed-loop stimulation paradigm that uses objective physiological feedback. One promising approach to obtain such feedback is by tapping into the nervous system that innervates the bladder. This dissertation focuses on using sacral level dorsal root ganglia (DRG) neural signals to provide sensory feedback for adaptive SNM a feline model. This work began with exploring machine learning algorithms and feature selection methods for bladder pressure decoding in an offline analysis of DRG signals. A Kalman filter delivered the highest performance based on correlation coefficient between the pressure measurements and algorithm estimation. Additionally, firing rate normalization significantly contributed to lowering the normalized error, and a correlation coefficient-based channel selection method provided the lowest error compared to other channel selection methods. Following algorithm optimization, this work implemented the optimized algorithm and feature selection method in real-time in anesthetized healthy bladder and simulated OAB feline models. A 0.88 ± 0.16 decoding correlation coefficient fit was achieved by the algorithm across 35 normal and simulated OAB bladder fill sequences in five experiments. Additionally, closed-loop neuromodulation was demonstrated using the estimated pressure to trigger pudendal nerve stimulation, which increased bladder capacity by 40% in two trials. Finally, closed-loop SNM with the DRG sensory feedback algorithm was performed in anesthetized experiments. Our approach increased bladder capacity by 13.8% over no stimulation (p < 0.001). While there was no statistical difference in bladder capacity between closed-loop and continuous stimulation (p = 0.80), closed-loop stimulation reduced stimulation time by 57.7%. Interestingly, clearly-identified bladder single units had a reduced sensitivity during stimulation, suggesting a potential mechanism of SNM. This dissertation also developed a method for chronic behavioral monitoring and neuromodulation of bladder function in a feline model. We tracked urodynamic parameters across multiple week testing intervals. We observed that animals could tolerate pudendal nerve stimulation above motor threshold. Interestingly, stimulation at 5 and 33 Hz appeared to have a modulatory effect on voiding interval and efficiency in line with prior work under anesthesia. Overall, this work demonstrated that sacral level DRG are a viable sensory feedback target for adaptive SNM. This dissertation also investigated a behavioral paradigm that will be useful for system validation in awake and chronic experiments. Behavioral experiments such as these, as well as development of low-power systems for adaptive monitoring and feedback, are a crucial step prior to clinical translation of this method. Ultimately, implementation of closed-loop adaptive SNM will lead to an improved therapy and greater potential benefit for the millions of individuals with OAB.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/166129/1/aileenou_1.pd

Efficient Universal Computing Architectures for Decoding Neural Activity

The ability to decode neural activity into meaningful control signals for prosthetic devices is critical to the development of clinically useful brain– machine interfaces (BMIs). Such systems require input from tens to hundreds of brain-implanted recording electrodes in order to deliver robust and accurate performance; in serving that primary function they should also minimize power dissipation in order to avoid damaging neural tissue; and they should transmit data wirelessly in order to minimize the risk of infection associated with chronic, transcutaneous implants. Electronic architectures for brain– machine interfaces must therefore minimize size and power consumption, while maximizing the ability to compress data to be transmitted over limited-bandwidth wireless channels. Here we present a system of extremely low computational complexity, designed for real-time decoding of neural signals, and suited for highly scalable implantable systems. Our programmable architecture is an explicit implementation of a universal computing machine emulating the dynamics of a network of integrate-and-fire neurons; it requires no arithmetic operations except for counting, and decodes neural signals using only computationally inexpensive logic operations. The simplicity of this architecture does not compromise its ability to compress raw neural data by factors greater than . We describe a set of decoding algorithms based on this computational architecture, one designed to operate within an implanted system, minimizing its power consumption and data transmission bandwidth; and a complementary set of algorithms for learning, programming the decoder, and postprocessing the decoded output, designed to operate in an external, nonimplanted unit. The implementation of the implantable portion is estimated to require fewer than 5000 operations per second. A proof-of-concept, 32-channel field-programmable gate array (FPGA) implementation of this portion is consequently energy efficient. We validate the performance of our overall system by decoding electrophysiologic data from a behaving rodent.United States. National Institutes of Health (Grant NS056140

Wireless Power Transfer For Biomedical Applications

In this research wireless power transfer using near-field inductive coupling is studied and investigated. The focus is on delivering power to implantable biomedical devices. The objective of this research is to optimize the size and performance of the implanted wireless biomedical sensors by: (1) proposing a hybrid multiband communication system for implantable devices that combines wireless communication link and power transfer, and (2) optimizing the wireless power delivery system. Wireless data and power links are necessary for many implanted biomedical devices such as biosensors, neural recording and stimulation devices, and drug delivery and monitoring systems. The contributions from this research work are summarized as follows: 1. Development of a combination of inductive power transfer and antenna system. 2. Design and optimization of novel microstrip antenna that may resonate at different ultra-high frequency bands including 415 MHz, 905 MHz, and 1300MHz. These antennas may be used to transfer power through radiation or send/receive data. 3. Design of high-frequency coil (13.56 MHz) to transfer power and optimization of the parameters for best efficiency. 4. Study of the performance of the hybrid antenna/coil system at various depths inside a body tissue model. 5. Minimizing the coupling effect between the coil and the antenna through addressed by optimizing their dimensions. 6. Study of the effects of lateral and angular misalignment on a hybrid compact system consisting of coil and antenna, as well as design and optimize the coilâs geometry which can provide maximum power efficiency under misalignment conditions. 7. Address the effects of receiver bending of a hybrid power transfer and communication system on the communication link budget and the transmitted power. 8. Study the wireless power transfer safety and security systems