Benjamin I. Rapoport

A Glucose Fuel Cell for Implantable Brain–Machine Interfaces

We have developed an implantable fuel cell that generates power through glucose oxidation, producing steady-state power and up to peak power. The fuel cell is manufactured using a novel approach, employing semiconductor fabrication techniques, and is therefore well suited for manufacture together with integrated circuits on a single silicon wafer. Thus, it can help enable implantable microelectronic systems with long-lifetime power sources that harvest energy from their surrounds. The fuel reactions are mediated by robust, solid state catalysts. Glucose is oxidized at the nanostructured surface of an activated platinum anode. Oxygen is reduced to water at the surface of a self-assembled network of single-walled carbon nanotubes, embedded in a Nafion film that forms the cathode and is exposed to the biological environment. The catalytic electrodes are separated by a Nafion membrane. The availability of fuel cell reactants, oxygen and glucose, only as a mixture in the physiologic environment, has traditionally posed a design challenge: Net current production requires oxidation and reduction to occur separately and selectively at the anode and cathode, respectively, to prevent electrochemical short circuits. Our fuel cell is configured in a half-open geometry that shields the anode while exposing the cathode, resulting in an oxygen gradient that strongly favors oxygen reduction at the cathode. Glucose reaches the shielded anode by diffusing through the nanotube mesh, which does not catalyze glucose oxidation, and the Nafion layers, which are permeable to small neutral and cationic species. We demonstrate computationally that the natural recirculation of cerebrospinal fluid around the human brain theoretically permits glucose energy harvesting at a rate on the order of at least 1 mW with no adverse physiologic effects. Low-power brain–machine interfaces can thus potentially benefit from having their implanted units powered or recharged by glucose fuel cells.

Low-Power Circuits for Brain–Machine Interfaces

This paper presents work on ultra-low-power circuits for brain–machine interfaces with applications for paralysis prosthetics, stroke, Parkinsons disease, epilepsy, prosthetics for the blind, and experimental neuroscience systems. The circuits include a micropower neural amplifier with adaptive power biasing for use in multi-electrode arrays; an analog linear decoding and learning architecture for data compression; low-power radio-frequency (RF) impedance-modulation circuits for data telemetry that minimize power consumption of implanted systems in the body; a wireless link for efficient power transfer; mixed-signal system integration for efficiency, robustness, and programmability; and circuits for wireless stimulation of neurons with power-conserving sleep modes and awake modes. Experimental results from chips that have stimulated and recorded from neurons in the zebra finch brain and results from RF power-link, RF data-link, electrode-recording and electrode-stimulating systems are presented. Simulations of analog learning circuits that have successfully decoded prerecorded neural signals from a monkey brain are also presented.

Metabolic factors limiting performance in marathon runners.

Each year in the past three decades has seen hundreds of thousands of runners register to run a major marathon. Of those who attempt to race over the marathon distance of 26 miles and 385 yards (42.195 kilometers), more than two-fifths experience severe and performance-limiting depletion of physiologic carbohydrate reserves (a phenomenon known as ‘hitting the wall’), and thousands drop out before reaching the finish lines (approximately 1–2% of those who start). Analyses of endurance physiology have often either used coarse approximations to suggest that human glycogen reserves are insufficient to fuel a marathon (making ‘hitting the wall’ seem inevitable), or implied that maximal glycogen loading is required in order to complete a marathon without ‘hitting the wall.’ The present computational study demonstrates that the energetic constraints on endurance runners are more subtle, and depend on several physiologic variables including the muscle mass distribution, liver and muscle glycogen densities, and running speed (exercise intensity as a fraction of aerobic capacity) of individual runners, in personalized but nevertheless quantifiable and predictable ways. The analytic approach presented here is used to estimate the distance at which runners will exhaust their glycogen stores as a function of running intensity. In so doing it also provides a basis for guidelines ensuring the safety and optimizing the performance of endurance runners, both by setting personally appropriate paces and by prescribing midrace fueling requirements for avoiding ‘the wall.’ The present analysis also sheds physiologically principled light on important standards in marathon running that until now have remained empirically defined: The qualifying times for the Boston Marathon.

Low-Power Circuits for Brain-Machine Interfaces

This paper presents work on ultra-low-power circuits for brain-machine interfaces with applications for paralysis prosthetics, prosthetics for the blind, and experimental neuroscience systems. The circuits include a micropower neural amplifier with adaptive power biasing for use in multi-electrode arrays; an analog linear decoding and learning architecture for data compression; radio-frequency (RF) impedance modulation for low-power data telemetry; a wireless link for efficient power transfer; mixed-signal system integration for efficiency, robustness, and programmability; and circuits for wireless stimulation of neurons. Experimental results from chips that have recorded from and stimulated neurons in the zebra-finch brain and from RF power-link systems are presented. Circuit simulations that have successfully processed prerecorded data from a monkey brain and from an RF data telemetry system are also presented.

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.

Real-Time Brain Oscillation Detection and Phase-Locked Stimulation Using Autoregressive Spectral Estimation and Time-Series Forward Prediction

Neural oscillations are important features in a working central nervous system, facilitating efficient communication across large networks of neurons. They are implicated in a diverse range of processes such as synchronization and synaptic plasticity, and can be seen in a variety of cognitive processes. For example, hippocampal theta oscillations are thought to be a crucial component of memory encoding and retrieval. To better study the role of these oscillations in various cognitive processes, and to be able to build clinical applications around them, accurate and precise estimations of the instantaneous frequency and phase are required. Here, we present methodology based on autoregressive modeling to accomplish this in real time. This allows the targeting of stimulation to a specific phase of a detected oscillation. We first assess performance of the algorithm on two signals where the exact phase and frequency are known. Then, using intracranial EEG recorded from two patients performing a Sternberg memory task, we characterize our algorithms phase-locking performance on physiologic theta oscillations: optimizing algorithm parameters on the first patient using a genetic algorithm, we carried out cross-validation procedures on subsequent trials and electrodes within the same patient, as well as on data recorded from the second patient.

A biomimetic adaptive algorithm and low-power architecture for implantable neural decoders

Algorithmically and energetically efficient computational architectures that operate in real time are essential for clinically useful neural prosthetic devices. Such devices decode raw neural data to obtain direct control signals for external devices. They can also perform data compression and vastly reduce the bandwidth and consequently power expended in wireless transmission of raw data from implantable brain-machine interfaces. We describe a biomimetic algorithm and micropower analog circuit architecture for decoding neural cell ensemble signals. The decoding algorithm implements a continuous-time artificial neural network, using a bank of adaptive linear filters with kernels that emulate synaptic dynamics. The filters transform neural signal inputs into control-parameter outputs, and can be tuned automatically in an on-line learning process. We provide experimental validation of our system using neural data from thalamic head-direction cells in an awake behaving rat.

Third-ventricular neurocysticercosis: hydraulic maneuvers facilitating endoscopic resection

BackgroundNeurocysticercosis, an infection of the central nervous system with the larval cysts of the pork tapeworm, Taenia solium, is the most common parasitic disease of the central nervous system. The disease is a major global cause of acquired epilepsy and may also manifest as intracranial hypertension due to mass effect from large cysts or to cerebrospinal fluid flow obstruction by intraventricular cysts or inflammation of the subarachnoid space. While the condition is endemic in several regions of the world and has been appreciated as a public health problem in such regions for several decades, its emergence in the USA in areas far from the Mexican border is a more recent phenomenon.MethodsWe present a case of surgically corrected acute hydrocephalus in a recent Haitian emigrant child due to a third ventricular neurocysticercal cyst complex.ResultsWe describe the endoscope-assisted en bloc removal of the complex, together with hydraulic maneuvers facilitating the removal of the intact cyst.ConclusionsSimple hydraulic maneuvers can facilitate the endoscopic en bloc removal of third ventricular neurocysticercal cysts.

Hamiltonian mapping of magnetic reconnection during the crash stage of the sawtooth instability

Since the determination of the magnetic field topology and of the safety factor profile during the crash stage of the sawtooth instability is a difficult problem both theoretically and experimentally, a complementary approach based on a mapping technique usually referred to as the Tokamap [Balescu et al., Phys. Rev. E 58, 951 (1998)] is proposed to reconstruct the stochastic magnetic field evolution. It is shown that this method, when combined with the constraints on the magnetic fluxes provided by the theories of the sawtooth instability, is able to generate poloidal cross sections of the magnetic field topology during the crash phase of the instability that have behavior similar to the experimental ones. The method is applied to both the complete and the incomplete reconnection of the magnetic field.

A method for real-time cortical oscillation detection and phase-locked stimulation

Neural oscillations are important features in a working central nervous system, facilitating efficient communication across large networks of neurons. To better study the role of these oscillations in various cognitive processes, and to be able to build clinical applications around them, accurate and precise estimations of the instantaneous frequency and phase are required. Here, we present methodology based on autoregressive modeling to accomplish this in real time. This allows the targeting of stimulation to a specific phase of a detected oscillation. Using intracranial EEG recorded from two patients performing a Sternberg memory task, we characterize our algorithms phase-locking performance on physiologic theta oscillations.