Browsing by Author "Viventi, Jonathan V"

High density electrode arrays that can record spatially and temporally detailed neural information provide a new horizon for the scientific exploration of the brain. Chief amongst these new tools is micro-electrocorticography (µECoG), which has grown in usage over the past two decades. As µECoG arrays increase in contact number, density, and complexity, the form factors of arrays will also have to change in tandem – particularly the size and spacing (pitch) of electrode contacts on the array. The continued growth of the field of µECoG research and innovation is hampered by a lack of understanding of how fundamental design aspects of the arrays may impact the information obtained from µECoG in different recording bands of interest and animal models. Utilizing thin-film fabrication to create novel experimental arrays and novel analysis techniques, the work in this dissertation provides an understanding of how differences in electrode contact size and spacing can impact neural metric acquisition in four experimentally and clinically relevant frequency bands of local field potential (LFP), high gamma (HGB), spike band power (SBP), and high frequency broadband (HFB). This dissertation provides innovative arrays that allow for experimental variation within a recording session, unlike much of the work previously published comparing contact size and pitch.

This dissertation shows my work of designing, testing, and implementing novel designs of μECoG arrays to explore the questions of how contact size and pitch may impact neural metrics in rodents and non-human primates (NHPs). In Chapter 2, I used a novel 60-channel array with four different contact size diameters in rodents to explore how contact size, impedance, and noise may impact neural metrics we collect in auditory experiments. We determined that contact size may selectively play a role in neural metric information content acquisition, and that the factors of impedance and noise can impact them significantly in higher frequency bands. This work also showed the ability to resolve multi-unit spiking activity from the surface of the brain. In Chapter 3, I show results obtained using a 61-channel array with different contact pitch in rodents, giving clarity to how the spatial sampling of the neural field may be impacted by the pitch of the electrode contacts used. These results suggest the neural field in higher frequency bands show greater changes at shorter field lengths than lower frequency bands. In Chapter 4, I utilized a larger 244-channel array in a NHP with varied contact sizes to explore how contact size may impact information content obtained from NHPs in the motor-related areas of the brain. Chapter 5 concludes the investigation of how design characteristics may impact neural information content by using an array with a local reference electrode contact to explore how local re-referencing can improve the neural metrics obtained.

The results from this dissertation provide a comprehensive understanding to how the information in the neural field may be impacted by the electrode designs chosen. The utilization of novel in-house fabricated arrays provides a method to explore these neuroscience questions rapidly and at low-cost.

The human brain has roughly 86 billion neurons. Deciphering how the brain functions remains difficult because of our limited tools to interface with this massive number of neurons. Many important insights have been discovered by researchers using electrodes that are capable of interfacing with only a few dozen neurons at a time. Clinical diagnoses and surgical treatment plans are currently made with only a few dozen electrode contacts on the brain surface. However, recent research with limited high-resolution sampling has revealed hidden rich neural dynamics that were missed by the low-resolution clinical electrodes. All current neural interface technologies are restricted to record from either a small population of neurons in a small area, or they record greatly averaged and coarsely sampled signals from a large area. To be able to record at high resolution from large areas of the brain requires more electrodes and more wires. Routing more wires out of the head is challenging, due to the space constraints inside the skull. One way to solve this challenge in scalability is to integrate powered electronics directly at each electrode site. However, this incorporating actively-powered electronics in a thin and flexible form factor that must survive for many years implanted in the brain presents a challenge that has never before been solved. To effectively capture micro-scale neural activity simultaneously across large brain regions presents three major technological challenges: accessibility, reliability, and scalability. This dissertation describes my efforts to solve these three challenges. Chapters 2 and 4, describe utilizing commercial flexible printed circuit (FPC) technology to achieve large scale manufacturing of high-resolution neural interfaces. Rather than using conventional polyimide for these neural interfaces, I investigated using liquid crystal polymer (LCP) as the substrate to improve the reliability of the devices. The LCP electrodes were tested in rats for over a year (Chapter 2). I also scaled up the designs and performed pilot intra-operative research studies in humans (Chapter 4). To increase the coverage of these LCP electrodes, I developed a silicone molding technique, which was tested in non-human primates (NHP) for over a year (Chapter 3). This work further demonstrated the utility of high-resolution electrodes in decoding studies. We were able to demonstrate improved decoding using optimal electrode selection. This result will enable future studies to improve decoding accuracy without significantly increasing power consumption by requiring all the channels to be wirelessly transmitted. Next, I solved the challenge of reliably integrating actively-powered electronics into a flexible neural interface, leading to a next-generation neural interface, the Neural Matrix. This technology demonstrated stable recording performance over a year of chronic implantation in rats (Chapter 5). The use of thermally-grown silicon dioxide (SiO2) and capacitive sensing provided key innovations to extend the reliability of the encapsulation for the active array. Finally, the incorporation of hermetic vertical interconnect access (VIA) structures made from highly doped silicon enabled direct faradic sensing and the possibility for electrical stimulation, without compromising device longevity (Chapter 6). These set of results from in vitro, acute, intra-operative, and chronic settings provide a robust demonstration of the novel neural interface technologies I have developed. The methodology and technology developed in this dissertation will enable broad improvements in clinical care and neuroscience research.

Chronically reliable neural implants are needed to provide long lasting, high fidelity interfaces for clinical use and for basic science. Minimally invasive implants, such as micro-electrocorticographic arrays (µECoG), have been proposed in this field to minimize immune response and glial scarring reactions and promote long-term recording stability. µECoG electrodes record field potential from the surface of the pia or dura. Because of their low sensitivity to direct neuronal discharges, the neural "output" of µECoG electrodes is ambiguous. In addition, the correct sampling resolution for µECoG arrays is not precisely known, due to incomplete knowledge of the spatial correlation of surface potential. This dissertation proposes and validates µECoG characterization metrics to assist in comparing the quality of recording output between novel and reference devices, and to enable longitudinal quality tracking in chronic implants. Among this methodology is a model of spatial field variation that sheds new light on spatial bandwidth, and the prediction and denoising capabilities from correlated sampling.

In the first section of this dissertation, the rat auditory cortex model is introduced as the in vivo validation target for subsequent work. A comprehensive set of metrics, sensitive to spatial and temporal variation, are developed to summarize the background and evoked response µECoG signal. µECoG is validated as accurately reflecting auditory physiology in the acute setting, and is shown to record consistent signal in implants for 30-60 days. The second section applies these signal validation techniques to demonstrate the compatibility of neural recordings from two fundamentally different amplification technologies: a high impedance voltage amplifier and a transimpedance amplifier. The third section is a thorough investigation of recordings from µECoG arrays implanted chronically for over one year. The quantification methodologies are adapted to longitudinal quality tracking and are compared to the common status indicator of electrode impedance. The fourth section explores the problem of spatial sampling in µECoG by directly quantifying the efficiency of predicting unsampled field potential under various spatial bandwidth and noise conditions.

This set of results in acute and chronic settings provides a robust confirmation of some simple but reliable metrics of µECoG recordings. The use of these statistics in ongoing device validation work should provide nuanced and multivariate accounting of the strengths and weaknesses of neural interfaces. However, future work is required to improve sensitivity to finer scale spatio-temporal features of surface potential fields.

Speech production is a unique human function that allows us to rapidly communicate on a regular basis. Characterizing the brain mechanisms of speech not only advances the scientific understanding of human cognition, but also provides the potential for therapeutic interventions to restore communication in patients with motor speech impairments. Deciphering this speech neural mechanisms requires measuring the functional interactions between neuro-anatomical regions at precise spatial and temporal scales, a feat that has remained difficult with standard non-invasive techniques for measuring brain activity. Intracranial electrode recordings provide measurements of neural activity directly from the surface of the brain and enable more accurate representations of speech neural activations with precise timings. Further, recent technological developments in increasing the number of electrodes have improved the spatial resolution, thereby revealing hidden neural features that would have been absent with low-resolution clinical electrodes. Therefore, developing analytical frameworks to effectively quantify these spatial and temporal characteristics of speech neural activations, can not only improve the ability to accurately decode speech from the brain, but also delineate the neural mechanism that enable speech production. This dissertation proposes signal analysis and neural decoding methods to quantify the spatiotemporal dynamics of speech neural features obtained from intracranial recordings. These computational approaches validate the use of high-resolution neural recordings to achieve successful speech decoding for neuroprosthetics and to elucidate the neural mechanisms of speech production in time and space.In the first section of this dissertation (chapter 2), high-resolution micro-electrocorticographic (µECoG) arrays are introduced as a novel recording tool to improve the accuracy of neural speech decoding. Using a comprehensive set of decoding algorithms and analytic metrics, µECoG is validated against standard intracranial recordings by demonstrating strong speech decoding with only ~2 minutes of training data. The decoding metrics were also applied to examine the spatial characteristics of µECoG. We demonstrated the importance of high channel count, high spatial coverage, high resolution, and micro-scale recording, to enable accurate speech decoding. The second section (chapter 3) extended these analytical techniques to identify neural mechanisms of speech for both planning and execution. Distinct neuro-anatomical networks that were identified were functionally specific to speech features. Further, the utilization of µECoG uncovered spatial mechanisms that facilitated the transition from speech planning to motor execution. These sets of results obtained from novel neural interfacing technologies and clinical recordings provide a robust analytical methodology to study neural mechanisms of human speech.