A modular neural interface for massively parallel recording and control : subsystem design considerations for research and clinical applications

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 78-79).The closed-loop Brain-Machine Interface (BMI) has long been a dream for clinicians and neuroscience researchers alike - that is, the ability to extract meaningful information from the brain, perform computation on this information, and selectively perturb neural dynamics in the brain for therapeutic benefit to the patient. Such systems have immediate application to treatment of paralysis, epilepsy and the amputated, and the potential for treatment of higher order cognitive dysfunction. Despite the promise of the BMI concept, the technology for bidirectional communication with the brain at sufficiently large scale to be truly therapeutically useful is lacking. Current state-of-the-art neuromodulation systems deliver open loop, 16-channel patterned electrical stimulation incapable of precisely targeting small numbers of neurons. Large-scale neural recording systems are limited to 16-128 electrodes, at the cost of several thousand dollars per channel. The ability to record from the awake behaving animal - let alone precisely modulate neural network dynamics in closed-loop fashion- presents a substantial challenge today.In this thesis, I present decoupled design solutions for three critical subcomponents of the closed-loop BMI - (i) a highly miniature, wirelessly powered and wirelessly controlled implantable optogenetic neuromodulation system capable of selective neural network control with single neural subtype- and millisecond-timescale precision, (ii) a prototype, highly parallel and scalable bio-potential recording system for simultaneous monitoring of many thousands of electrodes, and (iii) a space- and energy-efficient battery charger for biomedical applications. In aggregate, these systems overcome many of the fundamental architectural problems seen in the research and clinical environment today, potentially enabling a new class of neuromodulation system capable of treatment of higher-order cognitive dysfunction. In the research setting, these systems may be scaled to enable whole-brain recording, potentially yielding insights into large-scale neural network dynamics underlying disease and cognition.by Christian T. Wentz.M.Eng

A direct-to-drive neural data acquisition system

Driven by the increasing channel count of neural probes, there is much effort being directed to creating increasingly scalable electrophysiology data acquisition (DAQ) systems. However, all such systems still rely on personal computers for data storage, and thus are limited by the bandwidth and cost of the computers, especially as the scale of recording increases. Here we present a novel architecture in which a digital processor receives data from an analog-to-digital converter, and writes that data directly to hard drives, without the need for a personal computer to serve as an intermediary in the DAQ process. This minimalist architecture may support exceptionally high data throughput, without incurring costs to support unnecessary hardware and overhead associated with personal computers, thus facilitating scaling of electrophysiological recording in the future.National Institutes of Health (U.S.) (Grant 1DP1NS087724)National Institutes of Health (U.S.) (Grant 1R01DA029639)National Institutes of Health (U.S.) (Grant 1R01NS067199)National Institutes of Health (U.S.) (Grant 2R44NS070453)National Institutes of Health (U.S.) (Grant R43MH101943)New York Stem Cell FoundationPaul Allen FoundationMassachusetts Institute of Technology. Media LaboratoryGoogle (Firm)United States. Defense Advanced Research Projects Agency (HR0011-14-2-0004)Hertz Foundation (Myhrvold Family Fellowship

A wirelessly powered and controlled device for optical neural control of freely-behaving animals

Optogenetics, the ability to use light to activate and silence specific neuron types within neural networks in vivo and in vitro, is revolutionizing neuroscientists' capacity to understand how defined neural circuit elements contribute to normal and pathological brain functions. Typically, awake behaving experiments are conducted by inserting an optical fiber into the brain, tethered to a remote laser, or by utilizing an implanted light-emitting diode (LED), tethered to a remote power source. A fully wireless system would enable chronic or longitudinal experiments where long duration tethering is impractical, and would also support high-throughput experimentation. However, the high power requirements of light sources (LEDs, lasers), especially in the context of the extended illumination periods often desired in experiments, precludes battery-powered approaches from being widely applicable. We have developed a headborne device weighing 2 g capable of wirelessly receiving power using a resonant RF power link and storing the energy in an adaptive supercapacitor circuit, which can algorithmically control one or more headborne LEDs via a microcontroller. The device can deliver approximately 2 W of power to the LEDs in steady state, and 4.3 W in bursts. We also present an optional radio transceiver module (1 g) which, when added to the base headborne device, enables real-time updating of light delivery protocols; dozens of devices can be controlled simultaneously from one computer. We demonstrate use of the technology to wirelessly drive cortical control of movement in mice. These devices may serve as prototypes for clinical ultra-precise neural prosthetics that use light as the modality of biological control.National Institutes of Health (U.S.) (NIH Director’s New Innovator Award (DP2OD002002))National Institutes of Health (U.S.) (Grant 1R01DA029639)National Institutes of Health (U.S.) (Grant 1RC1MH088182)National Institutes of Health (U.S.) (Grant 1RC2DE020919)National Institutes of Health (U.S.) (Grant 1R01NS067199)National Institutes of Health (U.S.) (Grant 1R43NS070453)National Science Foundation (U.S.) (CAREER award)National Science Foundation (U.S.) (NSF Grant DMS 1042134)National Science Foundation (U.S.) (NSF Grant DMS 0848804)National Science Foundation (U.S.) (NSF Grant EFRI 0835878)Benesse FoundationGoogle (Firm)Dr. Gerald Burnett and Marjorie BurnettUnited States. Dept. of Defense (CDMRP PTSD Program)Massachusetts Institute of TechnologyBrain & Behavior Research FoundationAlfred P. Sloan FoundationSociety for NeuroscienceMassachusetts Institute of Technology. Media LaboratoryMcGovern Institute for Brain Research at MITWallace H. Coulter Foundatio

A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

In recent years, there has been a dramatic increase in the use of unmanned aerial vehicles (UAVs), particularly for small UAVs, due to their affordable prices, ease of availability, and ease of operability. Existing and future applications of UAVs include remote surveillance and monitoring, relief operations, package delivery, and communication backhaul infrastructure. Additionally, UAVs are envisioned as an important component of 5G wireless technology and beyond. The unique application scenarios for UAVs necessitate accurate air-to-ground (AG) propagation channel models for designing and evaluating UAV communication links for control/non-payload as well as payload data transmissions. These AG propagation models have not been investigated in detail when compared to terrestrial propagation models. In this paper, a comprehensive survey is provided on available AG channel measurement campaigns, large and small scale fading channel models, their limitations, and future research directions for UAV communication scenarios

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

An ultra-compact and efficient Li-ion battery charger circuit for biomedical applications

This paper describes an ultra-compact analog lithium-ion (Li-ion) battery charger for wirelessly powered implantable medical devices. The charger presented here takes advantage of the tanh output current profile of an operational transconductance amplifier (OTA) to smoothly transition between constant current (CC) and constant voltage (CV) charging regimes without the need for additional area- and power-consuming control circuitry. The proposed design eliminates the need for sense resistors in either the charging path or control loop by utilizing a current comparator to detect end-of-charge. The power management chip was fabricated in an AMI 0.5 μm CMOS process, consuming 0.15 mm[superscript 2] of area. This figure represents an order of magnitude reduction in area from previous designs. An initial proof-of-concept design achieved 75% power efficiency and charging voltage accuracy of 99.8% relative to the target 4.2 V.National Institutes of Health (U.S.) (Grant number NS-056140)United States. Office of Naval Research. (Grant number N00014-09-1-1015