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These digital collections include theses, dissertations, and faculty publications from the School of Biomedical Engineering.

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Browsing School of Biomedical Engineering by Subject "action potential"

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    Action potential initiation mechanisms: analysis and numerical study
    (Colorado State University. Libraries, 2022) Aldohbeyb, Ahmed A., author; Lear, Kevin L., advisor; Vigh, Jozsef, committee member; Prasad, Ashok, committee member; Venayagamoorthy, Karan, committee member
    Action potentials (AP) are the unitary elements of information processing in the nervous system. Understanding AP initiation mechanisms is a fundamental step in determining how neurons encode information. However, variation in neuronal response is a characteristic of mammalian neurons, which further complicate the analysis of neuronal firing dynamics. Several studies have associated the variation in AP onset with the type and densities of voltage-gated ion channels, diversity in synaptic inputs, neuron intrinsic properties, cooperative Na+ gating, or AP backpropagation. But the mechanisms that underlie the response variability remain unclear and subject to debate. Even though all these studies tried to answer the same question, the definition of AP onset and rapidity differs between them, highlighting the need for a more systematic and consistent method to quantify AP onset features, and hence analyzing the variation in AP onset. Two novel methods were developed to quantify AP rapidity. The proposed methods have lower relative variation, higher ability to classify neuron types, and higher sensitivity and specificity to voltage-gated Na+ channels parameters than current methods. AP rapidity was used to analyze different factors impacting the AP activation mechanism. However, the prior rapidity quantification methods are subjectively based on the researcher's judgment, which complicates the comparison between different studies. Thus, we proposed a more systematic and consistent method based on the full-width or half-width at half the rising phase peak of the membrane potential's second-time derivative (Vm). First, using an HH-type model, we showed that the peak width methods are sensitive to changes in the Na+ channel parameters and conductance and minimally impacted by changes in the K+ channel parameters compared to the phase slope, the standard quantification method. Second, we compared the peak width methods to the two prior methods, phase slope and error ratio, using recordings from cortical and hippocampal pyramidal neurons, hippocampal PVBCs, and FS cortical neurons found in online databases. The results showed that the new methods have the lowest variation between neurons within a specific type while significantly differentiating several neuron types. Together, the two studies showed that the peak width methods provide another sensitive tool to investigate the mechanisms impacting AP onset dynamics and provide a better tool to study Na+ channels kinetics and AP onset features. A conductance-based model that includes dynamics of ion concentration and cooperative Na+ channels was developed to investigate the mechanisms responsible for observed neuronal response variation. Random response variability has previously been observed in spike trains evoked from individual neurons by the same DC stimulus, but we observed systematic variation. The first APs' in a burst had attributes that were comparable regardless of the stimulus strength, while the subsequent APs' attributes monotonically change during bursts, and the magnitude of change increases with stimulus strength. These two spike train features were observed in three different neuron types (n = 51), indicating a shared mechanism is responsible for the spike train pattern. Various existing computational models fail to replicate the monotonic variation in AP attributes. We proposed incorporating ion concentration dynamics and cooperative gating to account for the missing behavior. A model with dynamic reversal potential but without cooperative Na+ channel gating reproduces the AP attribute's variation during bursts, but not the first APs' attributes. The first APs' attributes were reproduced only in the presence of a fraction of cooperative Na+ channels. Cooperative gating also enhanced the magnitude of modeled variation of some AP attributes to better match the electrophysiological recordings. Therefore, we conclude that changes in ion concentration dynamics could be responsible for the monotonic change in some AP's attributes during normal neuronal firing, and cooperative gating can enhance this effect. Thus, the two mechanisms contribute to the observed variability in neuronal response, especially the variation in AP rapidity.
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    ItemOpen Access
    Uncovering details of the electrical properties of cells
    (Colorado State University. Libraries, 2022) Nejad, Jasmine E., author; Lear, Kevin L., advisor; Tobet, Stuart, committee member; McGrew, Ashley K., committee member; Simske, Steve, committee member
    The electrical properties of cells have long been studied by scientists across many fields, yet there are still major gaps in our understanding of the intrinsic properties of many types of cells, such as parasite eggs, as well as the detailed electrical behavior of excitable cells, such as neurons. This work aims to provide insights into how these properties can be measured and how machine learning can be used to advance our understanding of these phenomena. The first part of this work discusses the development of a microfluidic impedance cytometer for the enumeration and classification of parasite eggs isolated from fecal samples. Current diagnostics in parasitology rely on the manual counting of eggs, cysts, and oocysts on microscope slides that have been isolated from fecal samples. These methods depend on trained technicians with expertise in the preparation of samples and detection of parasites on these slides, which increases cost and turnaround times for diagnosis. This leads many farmers and ranchers to opt to pool fecal samples from multiple animals to save time and labor. In cattle herds, resistance is often due to underdosing, which can be caused by treating all animals to an average weight or treating by the calendar instead of targeted deworming. This blanket use of anthelmintics, or anti-parasitic medication, is leading to concerns about anthelmintic resistance, which would cause major issues in the livestock industry, as well create unforeseen ecological imbalances. The developed microfluidic system provides a proof-of-concept for a microfluidic impedance cytometer capable of measuring the impedance of parasite eggs at multiple frequencies, simultaneously, as each of the eggs passes through a microfluidic channel past a sensing region. This region consists of parallel electrodes on the top and bottom of the channel, allowing for measurement of the voltage across the channel. When an egg passes through, the signal is interrupted, leaving a distinct profile of the electrical properties at each frequency over time. This system shows proof-of-concept of the impedance measurements at 500kHz and 10MHz and provides insights for further exploration of these properties, with the eventual use of machine learning algorithms for discrimination of parasite eggs from debris, and differentiation of parasite genera. The second part of this work discusses machine learning classification of neuronal subtypes based on features extracted from patch-clamp recordings from adult mice, using data acquired from publicly available databases. Classification of neuronal subtypes has been a continuously progressing area of neuroscience, building on advancements in our understanding of the morphology, physiology, and biochemistry of different neurons, and contributing to the accuracy and repeatability of action potential and neuronal circuit models. This work explores the use of k-nearest neighbors, support vector machine, decision tree, logistic regression, and naïve Bayes algorithms for classification of fast-spiking or regular-spiking neurons from the hippocampus or the primary somatosensory cortex. K-nearest neighbors shows the most accurate classification of these groups, using action potential width, amplitude, and onset potential as features (inputs into the algorithm), with the addition of a measure of rapidity (acceleration near action potential onset) showing major increases in classification accuracy. Of the three methods for measuring rapidity, inverse of the full width at half of the maximum of the second derivative of the membrane potential (V̈m) (IFWd2), inverse of the half width at half of the maximum of V̈m (IHWd2), and the slope of the phase plot (V̇m vs. Vm) near AP onset (phase slope), including the phase slope measure of rapidity increased the accuracy to nearly perfect (weighted f1-score > 0.9999). In addition, the use of phase slope and action potential width as the only features for classification produces measures of accuracy, weighted f1-scores, of >0.9996. The results show the value of rapidity in action potential dynamics, the distinct difference between rapidity in APs generated by hippocampal neurons relative to cortical neurons, and low standard deviations for rapidity values in cortical neurons (fast- and regular-spiking). These findings have potential implications for understanding the ion channel dynamics in action potential initiation and propagation, which can improve the modeling of action potentials and neuronal circuits.