Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item Collective dynamics of astrocyte and cytoskeletal systems(2024) Mennona, Nicholas John; Losert, Wolfgang; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Advances in imaging and biological sample preparations now allow researchersto study collective behavior in cellular networks with unprecedented detail. Imaging the electrical signaling of neuronal networks at the cellular level has generated exciting insights into the multiscale interactions within the brain. This thesis aims at a complementary view of the general information processing of the brain, focusing on other modes of non-electrical information. The modes discussed are the collective, dynamical characteristics of non-electrically active, non-neuronal brain cells, and mechanical systems. Astrocytes are the studied non-neuronal brain cells, and the cytoskeleton is the studied dynamic, mechanical system consisting of various filamentous networks. The two filamentous networks studied herein are the actin cytoskeleton and the microtubule network. Techniques from calcium imaging and cell mechanics are adapted to measure these often overlooked information channels, which operate at length scales and timescales distinct from electrical information transmission. Structural, astrocyte actin images, microtubule structural image sequences, and the calcium signals of collections of astrocytes are analyzed using computer vision and information theory. Filamentous alignment of actin with nearby boundaries reveals that stellate astrocytes have more perpendicularly oriented actin than undifferentiated astrocytes. Harnessing the larger length scale and slower dynamical time scale of microtubule filaments relative to actin filaments led to the creation of a computer vision tool to measure lateral filamentous fluctuations. Finally, we adapt information theory to the analog calcium (Ca2+) signals within astrocyte networks classified according to subtype. We find that, despite multiple physiological differences between immature and injured astrocytes, stellate (healthy) astrocytes have the same speed of information transport as these other astrocyte subtypes. This uniformity in speed persists when either the cytoskeleton (Latrunculin B) or energy state (ATP) is perturbed. Astrocytes, regardless of physiological subtype, tend to behave similarly when active under normal conditions. However, these healthy astrocytes respond most significantly to energy perturbation, relative to immature and injured astrocytes, as viewed through cross-correlation, mutual information, and partitioned entropy. These results indicate the value of drawing information from structure and dynamics. We developed and adapted tools across scales from nanometer scale alignment of actin filaments to hundreds of microns scale information dynamics in astrocyte networks. Including all potential modalities of information within complex biological systems, such as the collective dynamics of astrocytes and the cytoskeleton in brain networks is a step toward a fuller characterization of brain functioning and cognition.Item MULTISCALE MEASUREMENTS OF ELECTRICAL & MECHANICAL CELLULAR DYNAMICS(2023) Alvarez, Phillip; Losert, Wolfgang; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation focuses on the study and measurement of coupled electrical and mechanical responses in mammalian cells, tissues, and organs. Cellular biophysics often studies forces and their impact on biochemical pathways. These forces can be electrical, resulting in neuronal action potentials or cardiac cell contractions, or mechanical, driving e.g., a cell’s ability to recognize physical probing or surface texture. These forces and their responses, though, are frequently coupled through interlinked cellular mechanisms which result in emergent responses that take both electrical and mechanical signals into account. One challenge in capturing these emergent responses is that they occur on multiple scales, from the intracellular scale to the organ scale, limiting the ability of commercial microscopes to image these responses simultaneously. In this work I use surface texture, optical imaging, and multiscale-capable image analysis algorithms across these scales to elicit and measure electrical and mechanical responses. To image emergent responses from electrical and mechanical coupling, I developed two custom microscopes that can image at multiple length scales and timescales simultaneously. The Multiscale Microscope can capture slow intracellular mechanical dynamics concurrently with fast tissue scale electrical dynamics, while the BEAMM microscope links fast tissue scale electrical dynamics with both intracellular mechanical dynamics and slower organ-scale mechanical and electrical responses. Finally, I describe ongoing and future studies which exploit these new capabilities for multiscale measurements of electrical and mechanical dynamics.Item DEVELOPING MACHINE LEARNING TECHNIQUES FOR NETWORK CONNECTIVITY INFERENCE FROM TIME-SERIES DATA(2022) Banerjee, Amitava; Ott, Edward; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Inference of the connectivity structure of a network from the observed dynamics of the states of its nodes is a key issue in science, with wide-ranging applications such as determination of the synapses in nervous systems, mapping of interactions between genes and proteins in biochemical networks, distinguishing ecological relationships between different species in their habitats etc. In this thesis, we show that certain machine learning models, trained for the forecasting of experimental and synthetic time-series data from complex systems, can automatically learn the causal networks underlying such complex systems. Based on this observation, we develop new machine learning techniques for inference of causal interaction network connectivity structures underlying large, networked, noisy, complex dynamical systems, solely from the time-series of their nodal states. In particular, our approach is to first train a type of machine learning architecture, known as the ‘reservoir computer’, to mimic the measured dynamics of an unknown network. We then use the trained reservoir computer system as an in silico computational model of the unknown network to estimate how small changes in nodal states propagate in time across that network. Since small perturbations of network nodal states are expected to spread along the links of the network, the estimated propagation of nodal state perturbations reveal the connections of the unknown network. Our technique is noninvasive, but is motivated by the widely used invasive network inference method, whereby the temporal propagation of active perturbations applied to the network nodes are observed and employed to infer the network links (e.g., tracing the effects of knocking down multiple genes, one at a time, can be used infer gene regulatory networks). We discuss how we can further apply this methodology to infer causal network structures underlying different time-series datasets and compare the inferred network with the ground truth whenever available. We shall demonstrate three practical applications of this network inference procedure in (1) inference of network link strengths from time-series data of coupled, noisy Lorenz oscillators, (2) inference of time-delayed feedback couplings in opto-electronic oscillator circuit networks designed the laboratory, and, (3) inference of the synaptic network from publicly-available calcium fluorescence time-series data of C. elegans neurons. In all examples, we also explain how experimental factors like noise level, sampling time, and measurement duration systematically affect causal inference from experimental data. The results show that synchronization and strong correlation among the dynamics of different nodal states are, in general, detrimental for causal network inference. Features that break synchrony among the nodal states, e.g., coupling strength, network topology, dynamical noise, and heterogeneity of the parameters of individual nodes, help the network inference. In fact, we show in this thesis that, for parameter regimes where the network nodal states are not synchronized, we can often achieve perfect causal network inference from simulated and experimental time-series data, using machine learning techniques, in a wide variety of physical systems. In cases where effects like observational noise, large sampling time, or small sampling duration hinder such perfect network inference, we show that it is possible to utilize specially-designed surrogate time-series data for assigning statistical confidence to individual inferred network links. Given the general applicability of our machine learning methodology in time-series prediction and network inference, we anticipate that such techniques can be used for better model-building, forecasting, and control of complex systems in nature and in the lab.Item Speech Segregation and Representation in the Ferret Auditory and Frontal Cortices(2022) Joshi, Neha Hemant; Shamma, Shihab; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The problem of separating overlapping streams of sound, and selectively attending to a sound of interest is ubiquitous in humans and animals alike as a means for survival and communication. This problem, known as the cocktail party problem, is the focus of this thesis, where we explore the neural correlates of two-speaker segregation in the auditory and frontal cortex, using the ferret as an animal model. While speech segregation has been studied extensively in humans using various non-invasive imaging as well as some restricted invasive techniques, these do not provide a way to obtain neural data at the single-unit level. In animal models, streaming studies have been limited to simple stimuli like tone streams, or sound in noise. In this thesis, we extend this work to understand how complex auditory stimuli such as human speech is encoded at the single-unit and population level in both the auditory cortex, as well as the frontal cortex of the ferret. In the first part of the thesis, we explore current literature in auditory streaming and design a behavioral task using the ferret as an animal model to perform stream segregation. We train ferrets to selectively listen to one speaker over another, and perform a task to indicate detection of the attended speaker. We show the validity of this behavioral task, and the reliability with which the animal performs this task of two speaker stream segregation. In the second part, we collect neurophysiological data which is post-processed to obtain data from single units in both the auditory cortex (the primary auditory cortex, and the secondary region which includes the dorsal posterior ectosylvian gyrus) as well as the dorsolateral aspect of the frontal cortex of the ferret. We analyse the data and present findings of how the auditory and frontal cortices encode the information required to reliably segregate the speaker of relevance from the mixture of two speakers, and the insights provided into stream segregation mechanisms and the cocktail party solved by animals using neural decoding approaches. We finally demonstrate that stream segregation has already begun at the level of the primary auditory cortex. In agreement with previous attention-modulated neural studies in the auditory cortex, we show that this stream segregation is more pronounced in the secondary cortex, where we see clear enhancement of the attended speaker, and suppression of the unattended speaker. We explore the contribution of various areas within the primary and secondary regions, and how it relates to speaker selectivity of individual neuronal units. We also study the neural encoding of top-down attention modulation in the ferret frontal cortex. Finally, we discuss the conclusions from these results in the broader context of their relevance to the field, and what future directions it may hold for the field.Item Developing New Experimental Techniques to Understand Neuronal Networks(2021) Aghayee, Samira Sadat; Losert, Wolfgang; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Studying the propagation of action potentials across neuronal networks and how information is stored and accessed in their dynamic firing patterns has always been the essence of neuroscience. Emerging evidence shows that information in the brain is encoded in the simultaneous or avalanche-like firing of multiple, spatially separated groups of neurons. Thus, understanding the collective behavior of neurons is essential for understanding how the brain processes information and encodes memory. Since its discovery, the advent of optogenetics has brought upon a revolution in neuroscience, where individual neuronal circuits are able to be selectively probed and their connections decoded. This ability has been used by many groups to great effect, with some groups even using optogenetic stimulation to create phantom sensations, which are typically encoded in the functional activity of distinct neuronal populations. However, in-vivo optogenetic excitation relies inherently on the quality and accuracy of the stimulation method, with many problems arising due to biological effects such as animal motion, the scattering nature of brain tissue, and cell health. Typically, groups either use digital micromirror arrays or spatial light modulators, with the former lacking transmission efficiency and the latter having a high technical skill barrier due to its propensity to induce artifacts into intended patterns of light. This dissertation attempts to reduce the barrier towards the use of spatial light modulators in optogenetics by improving targeting accuracy, reducing the effects of unmodulated light and related artifacts, and developing new methods of stimulation which reduce the power density directed at neurons. To accomplish the first step, improving targeting accuracy, I created and demonstrated a real-time capable particle-based motion tracking algorithm to correct for animal motion. To reduce the effects of optical artifacts, I developed and patented a method of using Fresnel lenses convolved with intended light patterns to project higher orders of diffraction and un-diffracted light axially away from the object plane. To improve cell health during stimulation, I researched the use of optical vortices to stimulate neurons, allowing for ion channel activation with reduced power per unit area. Finally, I set the stage for new science by creating neuroimaging platforms integrating these techniques and capable of imaging activity across multiple scales. Other avenues for improvement are outlined as well in this dissertation, as well as new scientific questions that can be asked, leveraging these developments contained within.Item ANFA NYC: A Neuroethical Architecture(2015) Petrocelli, Luke Stephen; Rockcastle, Garth C; Architecture; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Our environments have a direct effect on our wellbeing. Science is starting to focus on the human brain and tell us why this is the case. This thesis concerns the integration of the neurosciences with architectural design. It is the stance of this thesis that architects have the responsibility to address the cognitive effects of the environments which they design in an effort to combat the so-called ‘diseases of civilization’. The chosen site, South Point Park on Roosevelt Island in New York City, is proposed to be developed as a NeuroArchitecture research incubator, providing interested groups a place to study the consilience of neuroscience and architecture. Research will be applied through four venues: The chosen site and landscape, a NeuroArchitecture Incubator, Blackwell Tower, and the ruin of a mid-19th Century Smallpox Hospital.Item Dendritic Integration and Reciprocal Inhibition in the Retina(2008-11-17) Grimes, William Norman; Walker, Robert; Chemical Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The mammalian retina is capable of signaling over a vast range of mean light levels (~10^10). Such a large dynamic range is achieved by segregating signals into contrasting pathways and utilizing excitatory and inhibitory neural circuits. The goal of this study was to elucidate subcellular mechanisms responsible for shaping dendritic computation and reciprocal inhibition within the retinal circuitry. Amacrine cells make up a unique class of inhibitory interneurons which lack anatomically distinct input and output structures. Although these interneurons clearly play important roles in complex visual processing, there is relatively little known about the ~30 subtypes. A17 amacrine cells have been shown to shape the time course of visual signaling in vivo. Intuition might suggest that a wide field (~400 µm) interneuron, such as A17, would provide long range lateral inhibition or center surround inhibition. However, using multi-disciplinary approaches, we have uncovered multiple mechanisms which underlie dendritic integration and synaptic transmission in A17 that allow it to respond with a high degree of synapse specificity. Additionally, these mechanisms work in concert with post-synaptic mechanisms to extend the dynamic range of reciprocal inhibition in the inner retina.Item The Relationship Between Physical Activity and Executive Control Functioning as Modified by Genotype(2007-08-03) Hearn, Joe W.; Hatfield, Bradley D.; Kinesiology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)As we age, the structure of the brain deteriorates and cognitive functioning declines. The region of the brain that begins to age the fastest is the frontal lobe, in which the dorsal-lateral prefrontal cortex is involved in executive control functions such as planning, organizing, initiating behaviors, and working memory. For some individuals, the brain declines more rapidly with age because of genetic factors. Apolipoprotein E (APOE) is a gene that assists in the transport of cholesterol and repair of the brain when it is damaged. Presence of the ε4 allele impairs cholesterol transport and puts its carriers at risk for increased cognitive decline and possibly dementia of the Alzheimer's type (DAT). Physical activity can slow the aging process of the brain and delay the onset and severity of cognitive decline and DAT as it increases oxygenation and blood flow, neuronal growth and synaptogenesis, and it increases the expression of genes helpful to the functioning of the brain such as brain-derived neurotrophic factor (BDNF). Therefore, individuals at greater genetic risk for age-related cognitive decline (i.e., ε4 carriers) should receive increased benefit from physical activity. Accordingly, this study examined the relationship between physical activity and executive control functioning, assessed by the Wisconsin Card Sort Test (WCST) in middle-aged APOE ε4 carriers and non-carriers. High-active participants were predicted to perform better than their low-active counterparts, and this difference should be even greater among APOE ε4 carriers. While most research studies on this topic have focused on general cognitive performance, the present study is specific in its focus on executive control functioning. Sixty-seven cognitively normal middle-aged adults between the ages of 50 - 70 years were assessed on medical history, overall cognitive functioning, APOE genotype, level of physical activity, and executive control functioning (WCST). Using hierarchical regression, seven WCST variables were regressed on age, genotype, physical activity, and the interaction between genotype and physical activity. Analysis revealed that as level of physical activity increased, performance significantly improved on all seven WCST variables for APOE ε4 carriers, but not for non-carriers. These results reveal that the benefits of physical activity to cognitive performance in this age group are specific to those who are genetically at-risk for cognitive decline.