Correlation structure in micro-ECoG recordings is described by spatially coherent components

Electrocorticography (ECoG) is becoming more prevalent due to improvements in fabrication and recording technology as well as its ease of implantation compared to intracortical electrophysiology, larger cortical coverage, and potential advantages for use in long term chronic implantation. Given the flexibility in the design of ECoG grids, which is only increasing, it remains an open question what geometry of the electrodes is optimal for an application. Conductive polymer, PEDOT:PSS, coated microelectrodes have an advantage that they can be made very small without losing low impedance. This makes them suitable for evaluating the required granularity of ECoG recording in humans and experimental animals. We used two-dimensional (2D) micro-ECoG grids to record intra-operatively in humans and during acute implantations in mouse with separation distance between neighboring electrodes (i.e., pitch) of 0.4 mm and 0.2/0.25 mm respectively. To assess the spatial properties of the signals, we used the average correlation between electrodes as a function of the pitch. In agreement with prior studies, we find a strong frequency dependence in the spatial scale of correlation. By applying independent component analysis (ICA), we find that the spatial pattern of correlation is largely due to contributions from multiple spatially extended, time-locked sources present at any given time. Our analysis indicates the presence of spatially structured activity down to the sub-millimeter spatial scale in ECoG despite the effects of volume conduction, justifying the use of dense micro-ECoG grids.Published versio

Doctor of Philosophy

dissertationThis dissertation describes the use of cortical surface potentials, recorded with dense grids of microelectrodes, for brain-computer interfaces (BCIs). The work presented herein is an in-depth treatment of a broad and interdisciplinary topic, covering issues from electronics to electrodes, signals, and applications. Within the scope of this dissertation are several significant contributions. First, this work was the first to demonstrate that speech and arm movements could be decoded from surface local field potentials (LFPs) recorded in human subjects. Using surface LFPs recorded over face-motor cortex and Wernickes area, 150 trials comprising vocalized articulations of ten different words were classified on a trial-by-trial basis with 86% accuracy. Surface LFPs recorded over the hand and arm area of motor cortex were used to decode continuous hand movements, with correlation of 0.54 between the actual and predicted position over 70 seconds of movement. Second, this work is the first to make a detailed comparison of cortical field potentials recorded intracortically with microelectrodes and at the cortical surface with both micro- and macroelectrodes. Whereas coherence in macroelectrocorticography (ECoG) decayed to half its maximum at 5.1 mm separation in high frequencies, spatial constants of micro-ECoG signals were 530-700 ?m-much closer to the 110-160 ?m calculated for intracortical field potentials than to the macro-ECoG. These findings confirm that cortical surface potentials contain millimeter-scale dynamics. Moreover, these fine spatiotemporal features were important for the performance of speech and arm movement decoding. In addition to contributions in the areas of signals and applications, this dissertation includes a full characterization of the microelectrodes as well as collaborative work in which a custom, low-power microcontroller, with features optimized for biomedical implants, was taped out, fabricated in 65 nm CMOS technology, and tested. A new instruction was implemented in this microcontroller which reduced energy consumption when moving large amounts of data into memory by as much as 44%. This dissertation represents a comprehensive investigation of surface LFPs as an interfacing medium between man and machine. The nature of this work, in both the breadth of topics and depth of interdisciplinary effort, demonstrates an important and developing branch of engineering

Monitoring of cerebellar injury using micro ECoG signals in ketamine/xylazine treated rats

Much of the cerebellar research has been conducted in anesthetized animals, particularly using ketamine/xylazine combination in rats, and yet the absolute impact of the anesthesia on the neural circuit remains unanswered . In the current study, spontaneous electrical activity and sensory evoked potentials from the cerebellar surface with chronically implanted, flexible-substrate, multielectrode arrays in rats were collected and analyzed with the motor cortex signals. The power spectra and the intercontact coherence plots of the spontaneous activity in the awake-quiet animals extended up to 800 Hz in the cerebellum and only up to 200 Hz in the motor cortex. Ketamine/xylazine anesthesia suppressed most of the activity in the cerebellar cortex, which was in clear contrast to the motor cortex. In the awake cerebellum, large coherence values were observed between contact pairs as far apart as ∼2 mm. Otherwise, there was not a discernable relation between the coherence and the intercontact distance. These results have suggested that the surface electrodes could provide much more detailed information about the state of neural circuits when they were used on the cerebellar cortex compared with the cerebral areas. Findings in ketamine/xylazine treated rats by using micro ECoG signals extracted the baseline information in the cerebellum to investigate the altered electrophysiology in the damaged neural circuitry. The temporal course of excitability change in selected neural networks was used as a method to study traumatic brain injury (TBI). This research demonstrated the use of cerebellar evoked potentials (EPs), which was characterized in the first phase of the study, for monitoring the injury progression in a rat model of fluid percussion injury (FPI). A mechanical tap on the dorsal hand was used as a stimulus, and EPs were recorded from the paramedian lobule (PML) of the posterior cerebellum via multi-electrode arrays (MEA). Evoked response amplitudes (EPAs) were analyzed immediate after the injury and on a daily basis for one week thereafter. This data indicated a trend of consistently decreasing EPAs in all nine animals, losing as much as 75% of baseline amplitudes measured before the injury. Notably, it was highlighted that there was two particular time windows; the first 24 hours of injury in the acute period and day-3 to day-7 in the delayed period where the largest drops (~40% and 30%) were observed in the EPAs. Immunohistochemical analysis supported electrophysiological findings that there was severity dependent Purkinje cell (PC) loss under the implant site. Current research has presented the evidences that sensory evoked potentials recorded from the cerebellar surface can be a useful technique to monitor the course of cerebellar injury and identify the phases of injury progression even at mild levels

Characterizing dynamically evolving functional networks in humans with application to speech

Understanding how communication between brain areas evolves to support dynamic function remains a fundamental challenge in neuroscience. One approach to this question is functional connectivity analysis, in which statistical coupling measures are employed to detect signatures of interactions between brain regions. Because the brain uses multiple communication mechanisms at different temporal and spatial scales, and because the neuronal signatures of communication are often weak, powerful connectivity inference methodologies require continued development specific to these challenges. Here we address the challenge of inferring task-related functional connectivity in brain voltage recordings. We first develop a framework for detecting changes in statistical coupling that occur reliably in a task relative to a baseline period. The framework characterizes the dynamics of connectivity changes, allows inference on multiple spatial scales, and assesses statistical uncertainty. This general framework is modular and applicable to a wide range of tasks and research questions. We demonstrate the flexibility of the framework in the second part of this thesis, in which we refine the coupling statistics and hypothesis tests to improve statistical power and test different proposed connectivity mechanisms. In particular, we introduce frequency domain coupling measures and define test statistics that exploit theoretical properties and capture known sampling variability. The resulting test statistics use correlation, coherence, canonical correlation, and canonical coherence to infer task-related changes in coupling. Because canonical correlation and canonical coherence are not commonly used in functional connectivity analyses, we derive the theoretical values and statistical estimators for these measures. In the third part of this thesis, we present a sample application of these techniques to electrocorticography data collected during an overt reading task. We discuss the challenges that arise with task-related human data, which is often noisy and underpowered, and present functional connectivity results in the context of traditional and contemporary within-electrode analytics. In two of nine subjects we observe time-domain and frequency-domain network changes that accord with theoretical models of information routing during motor processing. Taken together, this work contributes a methodological framework for inferring task-related functional connectivity across spatial and temporal scales, and supports insight into the rapid, dynamic functional coupling of human speech

The ictal wavefront is the spatiotemporal source of discharges during spontaneous human seizures

The extensive distribution and simultaneous termination of seizures across cortical areas has led to the hypothesis that seizures are caused by large-scale coordinated networks spanning these areas. This view, however, is difficult to reconcile with most proposed mechanisms of seizure spread and termination, which operate on a cellular scale. We hypothesize that seizures evolve into self-organized structures wherein a small seizing territory projects high-intensity electrical signals over a broad cortical area. Here we investigate human seizures on both small and large electrophysiological scales. We show that the migrating edge of the seizing territory is the source of travelling waves of synaptic activity into adjacent cortical areas. As the seizure progresses, slow dynamics in induced activity from these waves indicate a weakening and eventual failure of their source. These observations support a parsimonious theory for how large-scale evolution and termination of seizures are driven from a small, migrating cortical area

The ictal wavefront is the spatiotemporal source of discharges during spontaneous human seizures

abstract: The extensive distribution and simultaneous termination of seizures across cortical areas has led to the hypothesis that seizures are caused by large-scale coordinated networks spanning these areas. This view, however, is difficult to reconcile with most proposed mechanisms of seizure spread and termination, which operate on a cellular scale. We hypothesize that seizures evolve into self-organized structures wherein a small seizing territory projects high-intensity electrical signals over a broad cortical area. Here we investigate human seizures on both small and large electrophysiological scales. We show that the migrating edge of the seizing territory is the source of travelling waves of synaptic activity into adjacent cortical areas. As the seizure progresses, slow dynamics in induced activity from these waves indicate a weakening and eventual failure of their source. These observations support a parsimonious theory for how large-scale evolution and termination of seizures are driven from a small, migrating cortical area.The final version of this article, as published in Nature Communications, can be viewed online at: https://www.nature.com/articles/ncomms1109

The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes

Neuronal activity in the brain gives rise to transmembrane currents that can be measured in the extracellular medium. Although the major contributor of the extracellular signal is the synaptic transmembrane current, other sources — including Na+ and Ca2+ spikes, ionic fluxes through voltage- and ligand-gated channels, and intrinsic membrane oscillations — can substantially shape the extracellular field. High-density recordings of field activity in animals and subdural grid recordings in humans, combined with recently developed data processing tools and computational modelling, can provide insight into the cooperative behaviour of neurons, their average synaptic input and their spiking output, and can increase our understanding of how these processes contribute to the extracellular signal

Psyche, Signals and Systems

For a century or so, the multidisciplinary nature of neuroscience has left the field fractured into distinct areas of research. In particular, the subjects of consciousness and perception present unique challenges in the attempt to build a unifying understanding bridging between the micro-, meso-, and macro-scales of the brain and psychology. This chapter outlines an integrated view of the neurophysiological systems, psychophysical signals, and theoretical considerations related to consciousness. First, we review the signals that correlate to consciousness during psychophysics experiments. We then review the underlying neural mechanisms giving rise to these signals. Finally, we discuss the computational and theoretical functions of such neural mechanisms, and begin to outline means in which these are related to ongoing theoretical research

Investigating large-scale brain dynamics using field potential recordings: Analysis and interpretation

New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (EEG, MEG, ECoG and LFP) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide best-practice recommendations for the analyses and interpretations using a forward model and an inverse model. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems