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Model-based Investigations of Peripheral Nerve Stimulation Using Longitudinal Intrafascicular Electrodes

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Electrical stimulation of the human peripheral nervous system can be a powerful tool to treat various medical conditions and provide insight into nervous system processes. A critical challenge for many applications is to selectively activate neurons that have the desired effect while avoiding the activation of neurons that produce side…

Electrical stimulation of the human peripheral nervous system can be a powerful tool to treat various medical conditions and provide insight into nervous system processes. A critical challenge for many applications is to selectively activate neurons that have the desired effect while avoiding the activation of neurons that produce side effects. To stimulate peripheral fibers, the longitudinal intrafascicular electrode (LIFE) targets small groups of fibers inside the fascicle using low-amplitude pulses and is well-suited for chronic use. This work aims to understand better the ability to use intrafascicular stimulation with LIFEs to activate small groups of neurons within a fascicle selectively.A hybrid workflow was developed to simulate: 1) the production/propagation of the electric field induced by the stimulation pulse and 2) the effect of the electric field on fiber activation (recruitment). To create efficient and robust strategies for the selective recruitment of axons, recognizing the effect of each parameter on their recruitment and activation pattern is essential. Thus, using this hybrid workflow, the effects of various factors such as fascicular anatomy, electrode parameters, and stimulation pulse parameters on recruitment have been characterized, and the sensitivity of the recruitment patterns to these parameters has been explored. Results demonstrated the potential advantages of specific stimulation strategies and the sensitivity of recruitment patterns to electrode placement and tissue properties. For example, it is demonstrated: the significant effect of endoneurium conductivities on threshold levels; that a configuration with a LIFE as a local ground can be used to deselect its surrounding axons; the advantages of changing the delay between pulses in dual monopolar stimulation in targeting different axons clusters and increasing the activation frequency of some axons; how monopolar and bipolar configurations can be used to enhance spatial selectivity; the effect of longitudinal displacement of axons, electrode length and electrode movement on the recruitment and the activation pattern. In summary, this work forms the foundation for developing stimulation strategies to enhance the selectivity that can be achieved with intrafascicular stimulation.
ContributorsRouhani, Morteza (Author) / Abbas, James J (Thesis advisor) / Crook, Sharon M (Thesis advisor) / Baer, Steven M (Committee member) / Sadleir, Rosalind (Committee member) / Gardner, Carl (Committee member) / Arizona State University (Publisher)
Created2022
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Understanding Cortical Neuron Dynamics through Simulation-Based Applications of Machine Learning

Description
It is increasingly common to see machine learning techniques applied in conjunction with computational modeling for data-driven research in neuroscience. Such applications include using machine learning for model development, particularly for optimization of parameters based on electrophysiological constraints. Alternatively, machine learning can be used to validate and enhance techniques for…

It is increasingly common to see machine learning techniques applied in conjunction with computational modeling for data-driven research in neuroscience. Such applications include using machine learning for model development, particularly for optimization of parameters based on electrophysiological constraints. Alternatively, machine learning can be used to validate and enhance techniques for experimental data analysis or to analyze model simulation data in large-scale modeling studies, which is the approach I apply here. I use simulations of biophysically-realistic cortical neuron models to supplement a common feature-based technique for analysis of electrophysiological signals. I leverage these simulated electrophysiological signals to perform feature selection that provides an improved method for neuron-type classification. Additionally, I validate an unsupervised approach that extends this improved feature selection to discover signatures associated with neuron morphologies - performing in vivo histology in effect. The result is a simulation-based discovery of the underlying synaptic conditions responsible for patterns of extracellular signatures that can be applied to understand both simulation and experimental data. I also use unsupervised learning techniques to identify common channel mechanisms underlying electrophysiological behaviors of cortical neuron models. This work relies on an open-source database containing a large number of computational models for cortical neurons. I perform a quantitative data-driven analysis of these previously published ion channel and neuron models that uses information shared across models as opposed to information limited to individual models. The result is simulation-based discovery of model sub-types at two spatial scales which map functional relationships between activation/inactivation properties of channel family model sub-types to electrophysiological properties of cortical neuron model sub-types. Further, the combination of unsupervised learning techniques and parameter visualizations serve to integrate characterizations of model electrophysiological behavior across scales.
ContributorsHaynes, Reuben (Author) / Crook, Sharon M (Thesis advisor) / Gerkin, Richard C (Committee member) / Zhou, Yi (Committee member) / Baer, Steven (Committee member) / Armbruster, Hans D (Committee member) / Arizona State University (Publisher)
Created2020