Jonathan Viventi, Ph.D. - US grants

The investigators have developed flexible, active, multiplexed recording devices to enable interface with thousands of electrodes implanted on the surface of the brain. While this technology has enabled a much finer view of the electrical activity of the brain, the analytical methods to process, categorize and respond to the huge volumes of data produced by these devices are presently lacking. Many existing neurological data analyses rely on manual inspection. With new neural interfaces with thousands of channels, the data volume is infeasible for manual review. Further, manual inspection can miss subtle features that automated machine learning techniques can detect. In this research, the investigators develop efficient and sensitive automated methods to analyze micro-electrocorticographic (µECoG) data from patients with epilepsy. These methods are used to segment, categorize and predict spatiotemporal epileptiform discharge (or spike) patterns. Understanding the ordering and relationships between these patterns is a key to developing better seizure detection and prediction techniques and ultimately better therapies for patients with epilepsy.

This research comprises four interconnected components. The first component develops techniques for detecting and isolating spike segments, and for extracting features that capture the spatio-temporal pattern of each spike. The second component develops unsupervised clustering algorithms that can identify distinct clusters of spike motion patterns based on carefully chosen features. The thir-d component develops classifiers that can categorize each spike into a few classes (inter-ical, pre-ictal, ictal and post-ictal) based on not only its spatio-temporal pattern, but also the patterns of past spikes. The final component develops methods to predict spike wavefront locations. The combination of these methods will enable seizure prediction and real-time responsive brain stimulation to suppress seizures.

Fundamental to understanding information processing in the brain are methods that can systematically characterize the structure and dynamics of neural circuits that underlie perception and cognition. Micro- electrocorticography (µECoG) is the practice of using microelectrodes placed directly on the exposed surface of the brain to record electrical activity from the cerebral cortex. Recent advances in µECoG provide unique opportunities to observe large regions of the neural cortex at unprecedented spatial and temporal resolution. However, uncovering the structure of complex neural circuits is challenging. This interdisciplinary project develops methods for learning high-dimensional nonlinear systems with a particular focus on these systems as they arise in cortical networks and validates these techniques on state-of-the-art µECoG systems.

Three thrusts are considered: The first considers the general problem of state estimation in high-dimensional dynamical systems using decomposition methods including distributed Kalman and particle filtering and graphical models. The main goal is to provide computationally scalable and flexible approaches with provable guarantees. The second combines these state estimation methods with Bayesian parameter estimation and compressed sensing techniques to identify connectivity and nonlinear dynamics in the networks. The third validates these methods on identification of neural models from µECoG arrays. Applications to neural mapping, auditory and visual stimuli decoding are explored. In particular, the project seeks to demonstrate the method on using recordings from rat primary auditory cortex and cat visual cortex using a novel, flexible, high-resolution electrode array.

Abstract In this proposal, we will develop next-generation flexible micro-electrocortigraphic (µECoG) and penetrating electrode arrays using active electronics in complementary metal-oxide-semiconductor (CMOS) technology. Active electronics enable amplification and multiplexing directly at each electrode, eliminating the need for implanted electrodes to be individually wired to remote electronics and greatly increasing the number and density of electrodes that can be recorded and stimulated. The flexibility of our arrays allows them to conform to the irregular geometry of the brain, yielding higher fidelity signals and reduces damage to the brain when used in penetrating configurations. Integrated wireless data and power enables completely tether-free implants. Together, these innovations enable us to take high resolution measurements over large areas of the brain while being less invasive, a substantial improvement over the current state-of-the-art. In surface recording structures, we will demonstrate electrode arrays of up to 65,536 electrodes and amplifiers, spaced just 25.4µm apart, where each electrode can be simultaneously sampled at 20 ksps, enabling a cellular-resolution brain interface across a 64 mm² brain area. Each electrode can also be independently stimulated, or stimulated with patterns of activation, mimicking more natural excitation patterns. In penetrating arrays, we will demonstrate fully integrated, flexible penetrating neural probes with up to 512 electrodes per shank. The probe ?head? containing active electronics will fold over the outer surface of the cortex, at the point of the probe?s insertion, positioning its inductor for a near-field link through the skull. This link will be powered wirelessly with near-field radio-frequency data telemetry, eliminating the need to run wired interconnections through the skull. Integration with wireless interfaces will permit sealing chronically- implantable probes subcutaneously and in a manner in which the entire probe floats on the brain. The developed technologies will be rigorously tested in vitro and in vivo. This project will make high density electrode arrays based on manufacturable flexible CMOS technology available for the broader neuroscience community, enabling studies of large-scale recording and modulation in the nervous system. The innovations generated through this work have the potential to revolutionize our ability to understand the brain, and will improve epilepsy surgery outcomes as well as advance the performance of motor and auditory prosthetics. This project leverages a successful, long-term collaboration between clinicians, engineers, material scientists and neuroscientists at Duke University, Columbia University, New York University and the University of Illinois at Urbana-Champaign, to translate active, flexible electronics technology into next generation implantable neurological devices.

Project Summary / Abstract The purpose of this project is to optimize circuit and system architectures of active electrode arrays which will provide low-noise, multiplexed acquisition of neural signals from thousands of electrodes. We will reduce noise by exploiting a novel current-sensing circuit approach and new multiplexing strategies, such as Code-Division Multiple Access (CDMA). We will also apply novel system level de-noising approaches using kriging. Finally, we will demonstrate our low noise, active arrays using a unique, ultra-flexible 3D neural interface paradigm: Neuro-CROWN: CMOS-based, ROlling-enabled, loW-noise Neuroelectronics. These electrode arrays include thousands of electrodes that can be used for both recording and stimulation, enabling studies that require recordings from multiple, large cortical regions in rodents and non-human primates (NHP) at cellular scale. The electrode arrays are extremely thin (<25 µm) and flexible, and are made in both non-penetrating and 3D penetrating configurations, of which the latter will be formed from a simple and unique rolling of 2D soft electrode array (ROSE) method. Amplifiers and multiplexers integrated directly into the electrode array, using commercially fabricated silicon transistors, intelligently combine signals inside the array so that recording from up to 4,096 electrodes is possible with fewer than 20 multiplexed external wire connections. The small number of interface wires facilitates long-term experiments in chronically-implanted, freely-behaving animals and eases future wireless integration. The electrode arrays will be manufactured in large quantities (3800 devices / run) using full wafers at X-fab. By leveraging a cost-effective manufacturing process, the raw materials cost of each electrode array will be ~$10, excluding post processing labor which will be supplied by this program. Our dissemination program will make device broadly available to a large cohort of end users. We have previously disseminated early-stage technology to ~10 labs are now scaling up to disseminate that technology to ~100 labs. Based on feedback we received during this dissemination effort, neural interfaces with high SNR and 3-dimensional measurement are critically important to neuroscience research, motivating this project. We will disseminate this new technology to at least 10 labs in this effort, solicited from the neuroscience community at large. We will solicit end user feedback through a workshop at the Society for Neuroscience (SfN) meeting and use this feedback to shape our device designs. This project seeks to enable BRAIN Initiative investigators and the broader neuroscience community to perform very large-scale recordings in animal models. Further, the research enabled by this technology will be able to be rapidly translated to humans in the future, through parallel, separately-funded efforts by our team to bring actively-multiplexed electrode arrays to human use.