The updating process, which consists of building mental models and adapting them to the changes occurring in the environment, is impaired in neglect patients. A simple rock-paper-scissors experiment was conducted in our lab to examine updating impairments in neglect patients. The results of this experiment demonstrate a significant difference between the performance of healthy and brain damaged participants. While healthy controls did not show any difficulty learning the computer’s strategy, right brain damaged patients failed to learn the computer’s strategy. A computational modeling approach is employed to help us better understand the reason behind this difference and thus learn more about the updating process in healthy people and its impairment in right brain damaged patients. Broadly, we hope to learn more about the nature of the updating process, in general. Also the hope is that knowing what must be changed in the model to “brain-damage” it can shed light on the updating deficit in right brain damaged patients. To do so I adapted a pattern detection method named “ELPH” to a reinforcement-learning human decision making model called “RELPH”. This model is capable of capturing the behavior of both healthy and right brain damaged participants in our task according to our defined measures. Indeed, this thesis is an effort to discuss the possible differences among these groups employing this computational model.

Cell shape is important to understanding the mechanics of three-dimensional (3D) cell aggregates. When an aggregate of embryonic cells is compressed between parallel plates, the cell mass and the cells of which it is composed flatten. Over time, the cells typically move past one another and return to their original, spherical shapes, even during sustained compression, although the profile of the aggregate changes little once plate motion stops. Although the surface and interfacial tensions of cells have been attributed to driving these internal movements, measurements of these properties have largely eluded researchers. Here, an existing 3D finite element model, designed specifically for the mechanics of cell-cell interactions, is enhanced so that it can be used to investigate aggregate compression. The formulation of that model is briefly presented and enhancements made to its rearrangement algorithms discussed. Simulations run using the model show that the rounding of interior cells is governed by the ratio between the interfacial tension and cell viscosity, whereas the shape of cells in contact with the medium or the compression plates is dominated by their respective cell-medium or cell-plate surface tensions. The model also shows that as an aggregate compresses, its cells elongate more in the circumferential direction than the radial direction. Since experimental data from compressed aggregates are anticipated to consist of confocal sections, geometric characterization methods are devised to quantify the anisotropy of cells and to relate cross sections to 3D properties. The average anisotropy of interior cells as found using radial cross sections corresponds more closely with the 3D properties of the cells than data from series of parallel sections. A basis is presented for estimating cell-cell interfacial tensions from the cell shape histories they exhibit during the cell reshaping phase of an aggregate compression test.

The pre-Botzinger complex (pBC) is a sub-circuit of the respiratory central pattern generator. The pBC is required for eupnea and is contained in a transverse slice of the ventrolateral medulla. In the slice, pBC cells are responsible for generating the respiratory rhythm, and hypoglossal motoneurons (HMs) are responsible for transmitting the rhythm out of the brainstem to the muscles. Understanding how the transverse slice rhythm is generated and transmitted is a first step in understanding how this process occurs in vivo. To understand this network, we developed ionic current models of the individual network components and explored how the various ion channels affect single-cell firing characteristics and network dynamics. First, we used the considerable amounts of experimental data from neonatal HMs to develop an HM model. The model was used to explore the roles of ion channels in shaping the complex dynamics of the neonatal HM action potential (AP) and to investigate the age-dependent changes in HMs. We used a genetic algorithm to optimize the HM model to more closely fit experimental measures of AP shape. A comparison of feature-based and template-based fitness functions revealed that a feature-based fitness function performs best when optimizing the HM model to fit characteristics of the neonatal HM AP. Next, we used our existing pBC models to understand how different ionic currents affect rhythmogenesis in the pBC. Our results indicate that intrinsic bursters increase the robustness of rhythm generation in the pBC. Finally, we developed an improved pBC neuron model and explored how various ion channels affect bursting dynamics at the single-cell level. The HM and pBC models developed in this study will be used in future network models of the transverse slice.

While there is continuing interest in bioresorbable materials for orthopedic fixation devices, the major challenge in utilizing these materials in load-bearing applications is creating materials sufficiently stiff and strong to sustain loads throughout healing while maintaining fracture stability. The primary aim of this study is to quantify the degradation rate of a bioresorbable material system, then use this degradation rate to determine the material response of an orthopedic device made of the same material as healing progresses. The present research focuses on the development and characterization of a material system consisting of carbon foam infiltrated with hydroxyapatite (HA) reinforced poly(D,L-lactide)-co-poly(glycolide) (PDLGA). A processing technique is developed to infiltrate carbon foam with HA/PDLGA and material morphology is investigated. Additionally, short-term rat osteoblast cell studies are undertaken to establish a starting point for material biocompatibility. Degradation experiments are conducted to elicit the time-dependent properties of the material system at the material scale. These properties are then incorporated into computational models of an internal plate attached to a fractured human femur to design and predict the material response to applied physiological loads. Results from this work demonstrate the importance of material dissolution rate as well as material strength when designing internal fixation plates.

Coupling beams are structural elements used to connect two or more shear walls. The most common material used in the construction of coupling beam is reinforced concrete. The use of coupling beams along with shear walls require them to resist large shear forces, while possessing sufficient ductility to dissipate the energy produced due to the lateral loads. This study has been undertaken to produce a computational model to replicate the behavior of conventionally reinforced coupling beams subjected to cyclic loading. The model is developed in the finite element analysis software ABAQUS. The concrete damaged plasticity model was used to simulate the behavior of concrete. A calibration model using a cantilever beam was produced to generate key parameters in the model that are later adapted into modeling of two coupling beams with aspect ratios: 1.5 and 3.6. The geometrical, material, and loading values are adapted from experimental specimens reported in the literature, and the experimental results are then used to validate the computational models. The results like evolution of damage parameter and crack propagation from this study are intended to provide guidance on finite element modeling of conventionally reinforced concrete coupling beams under cyclic lateral loading.

Elevated levels of reactive oxygen species (ROS) cause or aggravate a variety of pathological conditions such as cardiovascular disease, cancer and rheumatoid arthritis. Despite known links between oxidative stress and disease, years of clinical studies have failed to show clear benefits of antioxidant therapy. It is now recognized that ROS such as hydrogen peroxide can act as signaling molecules and are required for physiological functioning of a number of signaling pathways. Therefore, a mechanistic basis of ROS-mediated regulation of cell signaling must be established to enable rational design of antioxidant-based therapies. The challenges in quantification of transient changes mediated by ROS during cell signaling have impeded investigation of redox-regulated signaling. In the present work, computational modeling is used to circumvent these technical challenges and to test competing hypotheses of redox regulation. Using a quantitative, systems level approach to study interactions between ROS dependent and independent regulatory mechanisms, the most comprehensive model of the IL-4 signaling pathway to date has been developed and validated with experimental data. The model is capable of predicting kinase phosphorylation dynamics under new oxidative conditions, and our analyses suggest that reversible oxidation of tyrosine phosphatases is the primary mechanism of redox regulation in this pathway. Additional computational methods have been developed to study ROS as mediators of crosstalk between signaling pathways, to optimize model parameters, and to interrogate model dynamics for the purpose of model selection. Collectively, these modeling tools provide a new systems-level perspective for investigating reversible protein oxidation as a means of control over cellular signal transduction.

This dissertation focuses on the development of computational models and algorithms related to diffuse reflectance spectroscopy. Specifically, this work aims to advance diffuse reflectance spectroscopy to a technique that is capable of measuring depth dependent properties in tissue. First, we introduce the Monte Carlo lookup table (MCLUT) method for extracting optical properties from diffuse reflectance spectra. Next, we extend this method to a two-layer tissue geometry so that it can extract depth dependent properties in tissue. We then develop a computational model that relates photon sampling depth to optical properties and probe geometry. This model can be used to aid in design of application specific diffuse reflectance probes. In order to provide justification for using a two-layer model for extracting tissue properties, we show that the use of a one-layer model can lead to significant errors in the extracted optical properties. Lastly, we use our two-layer MCLUT model and a probe that was designed based on our sampling depth model to extract tissue properties from the skin of 80 subjects at 5 anatomical locations. The results agree with previously published values for skin properties and show that can diffuse reflectance spectroscopy can be used to measured depth dependent properties in tissue. / text

Meditation training has been shown to improve attention and emotion regulation. However, the mechanisms responsible for these effects are largely unknown. In order to make further progress, a rigorous interdisciplinary approach that combines both empirical and theoretical experiments is required. This dissertation uses such an approach to analyze electroencephalogram (EEG) data collected during two three-month long intensive meditation retreats in four steps. First, novel tools were developed for preprocessing the EEG data. These tools helped remove ocular artifacts, muscular artifacts, and interference from power lines in a semi-automatic fashion. Second, in order to identify the cortical correlates of meditation, longitudinal changes in the cortical activity were measured using spectral analysis. Three main longitudinal changes were observed in the retreat participants: (1) reduced individual alpha frequency after training, similar reduction has been consistently found in experienced meditators; (2) reduced alpha-band power in the midline frontal region, which correlated with improved vigilance performance; and (3) reduced beta-band power in the parietal-occipital regions, which correlated with daily time spent in meditation and enhanced self-reported psychological well-being. Third, a formal computational model was developed to provide a concrete and testable theory about the underlying mechanisms. Four theoretical experiments were run, which showed, (1) reduced intrathalamic gain after training, suggesting enhanced alertness; (2) increased cortico-thalamic delay, which strongly correlated with the reduction in individual alpha frequency (found during spectral analysis); (3) reduction in intrathalamic gain provided increased stability to the brain; and (4) anterior-posterior division in the modeled reticular nucleus of the thalamus (TRN) layer and increased connectivity in the posterior region of TRN after training. Fourth, correlation analysis was performed to ground the changes in cortical activity and model parameters into changes in behavior and self-reported psychological functions. Through these four steps, a concrete theory of the mechanisms underlying focused-attention meditation was constructed. This theory provides both mechanistic and teleological reasoning behind the changes observed during meditation training. The theory further leads to several predictions, including the possibility that customized meditation techniques can be used to treat patients suffering from neurodevelopmental disorders and epilepsy. Lastly, the dissertation attempts to link the theory to the long-held views that meditation improves awareness, attention, stability, and psychological well-being. / text

The updating process, which consists of building mental models and adapting them to the changes occurring in the environment, is impaired in neglect patients. A simple rock-paper-scissors experiment was conducted in our lab to examine updating impairments in neglect patients. The results of this experiment demonstrate a significant difference between the performance of healthy and brain damaged participants. While healthy controls did not show any difficulty learning the computer’s strategy, right brain damaged patients failed to learn the computer’s strategy. A computational modeling approach is employed to help us better understand the reason behind this difference and thus learn more about the updating process in healthy people and its impairment in right brain damaged patients. Broadly, we hope to learn more about the nature of the updating process, in general. Also the hope is that knowing what must be changed in the model to “brain-damage” it can shed light on the updating deficit in right brain damaged patients. To do so I adapted a pattern detection method named “ELPH” to a reinforcement-learning human decision making model called “RELPH”. This model is capable of capturing the behavior of both healthy and right brain damaged participants in our task according to our defined measures. Indeed, this thesis is an effort to discuss the possible differences among these groups employing this computational model.

The nature of visual properties used for object perception in mid- and high-level vision areas of the brain is poorly understood. Past studies have employed simplistic stimuli probing models limited in descriptive power and mathematical under-pinnings. Unfortunately, pursuit of more complex stimuli and properties requires searching through a wide, unknown space of models and of images. The difficulty of this pursuit is exacerbated in brain research by the limited number of stimulus responses that can be collected for a given human subject over the course of an experiment. To more quickly identify complex visual features underlying cortical object perception, I develop, test, and use a novel method in which stimuli for use in the ongoing study are selected in realtime based on fMRI-measured cortical responses to recently-selected and displayed stimuli. A variation of the simplex method controls this ongoing selection as part of a search in visual space for images producing maximal activity — measured in realtime — in a pre-determined 1 cm3 brain region. I probe cortical selectivities during this search using photographs of real-world objects and synthetic “Fribble” objects. Real-world objects are used to understand perception of naturally-occurring visual properties. These objects are characterized based on feature descriptors computed from the scale invariant feature transform (SIFT), a popular computer vision method that is well established in its utility for aiding in computer object recognition and that I recently found to account for intermediate-level representations in the visual object processing pathway in the brain. Fribble objects are used to study object perception in an arena in which visual properties are well defined a priori. They are constructed from multiple well-defined shapes, and variation of each of these component shapes produces a clear space of visual stimuli. I study the behavior of my novel realtime fMRI search method, to assess its value in the investigation of cortical visual perception, and I study the complex visual properties my method identifies as highly-activating selected brain regions in the visual object processing pathway. While there remain further technical and biological challenges to overcome, my method uncovers reliable and interesting cortical properties for most subjects — though only for selected searches performed for each subject. I identify brain regions selective for holistic and component object shapes and for varying surface properties, providing examples of more precise selectivities within classes of visual properties previously associated with cortical object representation. I also find examples of “surround suppression,” in which cortical activity is inhibited upon viewing stimuli slightly deviation from the visual properties preferred by a brain region, expanding on similar observations at lower levels of vision.