Assessment of the Spatial and Temporal Distribution of Functional Connectivity in Resting-State BOLD fMRI

<p>Recent research into resting-state functional magnetic resonance imaging (fMRI) has shown that the brain is very active during rest. This thesis work utilizes blood oxygenation level dependent (BOLD) signals to investigate the spatial and temporal functional network information found within resting-state data, and aims to investigate the feasibility of extracting functional connectivity networks using different methods as well as the dynamic variability within some of the methods. Furthermore, this work looks into producing valid networks using a sparsely-sampled sub-set of the original data. </p><p>In this work we utilize four main methods: independent component analysis (ICA), principal component analysis (PCA), correlation, and a point-processing technique. Each method comes with unique assumptions, as well as strengths and limitations into exploring how the resting state components interact in space and time. </p><p>Correlation is perhaps the simplest technique. Using this technique, resting-state patterns can be identified based on how similar the time profile is to a seed region’s time profile. However, this method requires a seed region and can only identify one resting state network at a time. This simple correlation technique is able to reproduce the resting state network using subject data from one subject’s scan session as well as with 16 subjects.</p><p> Independent component analysis, the second technique, has established software programs that can be used to implement this technique. ICA can extract multiple components from a data set in a single analysis. The disadvantage is that the resting state networks it produces are all independent of each other, making the assumption that the spatial pattern of functional connectivity is the same across all the time points. ICA is successfully able to reproduce resting state connectivity patterns for both one subject and a 16 subject concatenated data set.</p><p>Using principal component analysis, the dimensionality of the data is compressed to find the directions in which the variance of the data is most significant. This method utilizes the same basic matrix math as ICA with a few important differences that will be outlined later in this text. Using this method, sometimes different functional connectivity patterns are identifiable but with a large amount of noise and variability. </p><p>To begin to investigate the dynamics of the functional connectivity, the correlation technique is used to compare the first and second halves of a scan session. Minor differences are discernable between the correlation results of the scan session halves. Further, a sliding window technique is implemented to study the correlation coefficients through different sizes of correlation windows throughout time. From this technique it is apparent that the correlation level with the seed region is not static throughout the scan length.</p><p> </p><p>The last method introduced, a point processing method, is one of the more novel techniques because it does not require analysis of the continuous time points. Here, network information is extracted based on brief occurrences of high or low amplitude signals within a seed region. Because point processing utilizes less time points from the data, the statistical power of the results is lower. There are also larger variations in DMN patterns between subjects. In addition to boosted computational efficiency, the benefit of using a point-process method is that the patterns produced for different seed regions do not have to be independent of one another.</p><p>This work compares four unique methods of identifying functional connectivity patterns. ICA is a technique that is currently used by many scientists studying functional connectivity patterns. The PCA technique is not optimal for the level of noise and the distribution of the data sets. The correlation technique is simple and obtains good results, however a seed region is needed and the method assumes that the DMN regions is correlated throughout the entire scan. Looking at the more dynamic aspects of correlation changing patterns of correlation were evident. The last point-processing method produces a promising results of identifying functional connectivity networks using only low and high amplitude BOLD signals.</p>

Essays on Energy Economics and Industrial Organization

<p>The dissertation consists of three chapters related to the low-price guarantee marketing strategy and energy efficiency analysis. The low-price guarantee is a marketing strategy in which firms promise to charge consumers the lowest price among their competitors. Chapter 1 addresses the research question "Does a Low-Price Guarantee Induce Lower Prices'' by looking into the retail gasoline industry in Quebec where there was a major branded firm which started a low-price guarantee back in 1996. Chapter 2 does a consumer welfare analysis of low-price guarantees to drive police indications and offers a new explanation of the firms' incentives to adopt a low-price guarantee. Chapter 3 develops the energy performance indicators (EPIs) to measure energy efficiency of the manufacturing plants in pulp, paper and paperboard industry.</p><p>Chapter 1 revisits the traditional view that a low-price guarantee results in higher prices by facilitating collusion. Using accurate market definitions and station-level data from the retail gasoline industry in Quebec, I conducted a descriptive analysis based on stations and price zones to compare the price and sales movement before and after the guarantee was adopted. I find that, contrary to the traditional view, the stores that offered the guarantee significantly decreased their prices and increased their sales. I also build a difference-in-difference model to quantify the decrease in posted price of the stores that offered the guarantee to be 0.7 cents per liter. While this change is significant, I do not find the response in comeptitors' prices to be significant. The sales of the stores that offered the guarantee increased significantly while the competitors' sales decreased significantly. However, the significance vanishes if I use the station clustered standard errors. Comparing my observations and the predictions of different theories of modeling low-price guarantees, I conclude the empirical evidence here supports that the low-price guarantee is a simple commitment device and induces lower prices. </p><p>Chapter 2 conducts a consumer welfare analysis of low-price guarantees to address the antitrust concerns and potential regulations from the government; explains the firms' potential incentives to adopt a low-price guarantee. Using station-level data from the retail gasoline industry in Quebec, I estimated consumers' demand of gasoline by a structural model with spatial competition incorporating the low-price guarantee as a commitment device, which allows firms to pre-commit to charge the lowest price among their competitors. The counterfactual analysis under the Bertrand competition setting shows that the stores that offered the guarantee attracted a lot more consumers and decreased their posted price by 0.6 cents per liter. Although the matching stores suffered a decrease in profits from gasoline sales, they are incentivized to adopt the low-price guarantee to attract more consumers to visit the store likely increasing profits at attached convenience stores. Firms have strong incentives to adopt a low-price guarantee on the product that their consumers are most price-sensitive about, while earning a profit from the products that are not covered in the guarantee. I estimate that consumers earn about 0.3% more surplus when the low-price guarantee is in place, which suggests that the authorities should not be concerned and regulate low-price guarantees. In Appendix B, I also propose an empirical model to look into how low-price guarantees would change consumer search behavior and whether consumer search plays an important role in estimating consumer surplus accurately.</p><p>Chapter 3, joint with Gale Boyd, describes work with the pulp, paper, and paperboard (PP&PB) industry to provide a plant-level indicator of energy efficiency for facilities that produce various types of paper products in the United States. Organizations that implement strategic energy management programs undertake a set of activities that, if carried out properly, have the potential to deliver sustained energy savings. Energy performance benchmarking is a key activity of strategic energy management and one way to enable companies to set energy efficiency targets for manufacturing facilities. The opportunity to assess plant energy performance through a comparison with similar plants in its industry is a highly desirable and strategic method of benchmarking for industrial energy managers. However, access to energy performance data for conducting industry benchmarking is usually unavailable to most industrial energy managers. The U.S. Environmental Protection Agency (EPA), through its ENERGY STAR program, seeks to overcome this barrier through the development of manufacturing sector-based plant energy performance indicators (EPIs) that encourage U.S. industries to use energy more efficiently. In the development of the energy performance indicator tools, consideration is given to the role that performance-based indicators play in motivating change; the steps necessary for indicator development, from interacting with an industry in securing adequate data for the indicator; and actual application and use of an indicator when complete. How indicators are employed in EPA’s efforts to encourage industries to voluntarily improve their use of energy is discussed as well. The chapter describes the data and statistical methods used to construct the EPI for plants within selected segments of the pulp, paper, and paperboard industry: specifically pulp mills and integrated paper & paperboard mills. The individual equations are presented, as are the instructions for using those equations as implemented in an associated Microsoft Excel-based spreadsheet tool.</p>

Bridging the Scale Gap from Leaf to Canopy in Biosphere-Atmosphere Gas and Particle Exchanges

<p>Terrestrial ecosystems, occupying more than 25% of the Earth's surface, can serve as</p><p>`biological valves' in regulating the anthropogenic emissions of atmospheric aerosol</p><p>particles and greenhouse gases (GHGs) as responses to their surrounding environments.</p><p>While the signicance of quantifying the exchange rates of GHGs and atmospheric</p><p>aerosol particles between the terrestrial biosphere and the atmosphere is</p><p>hardly questioned in many scientic elds, the progress in improving model predictability,</p><p>data interpretation or the combination of the two remains impeded by</p><p>the lack of precise framework elucidating their dynamic transport processes over a</p><p>wide range of spatiotemporal scales. The diculty in developing prognostic modeling</p><p>tools to quantify the source or sink strength of these atmospheric substances</p><p>can be further magnied by the fact that the climate system is also sensitive to the</p><p>feedback from terrestrial ecosystems forming the so-called `feedback cycle'. Hence,</p><p>the emergent need is to reduce uncertainties when assessing this complex and dynamic</p><p>feedback cycle that is necessary to support the decisions of mitigation and</p><p>adaptation policies associated with human activities (e.g., anthropogenic emission</p><p>controls and land use managements) under current and future climate regimes.</p><p>With the goal to improve the predictions for the biosphere-atmosphere exchange</p><p>of biologically active gases and atmospheric aerosol particles, the main focus of this</p><p>dissertation is on revising and up-scaling the biotic and abiotic transport processes</p><p>from leaf to canopy scales. The validity of previous modeling studies in determining</p><p>iv</p><p>the exchange rate of gases and particles is evaluated with detailed descriptions of their</p><p>limitations. Mechanistic-based modeling approaches along with empirical studies</p><p>across dierent scales are employed to rene the mathematical descriptions of surface</p><p>conductance responsible for gas and particle exchanges as commonly adopted by all</p><p>operational models. Specically, how variation in horizontal leaf area density within</p><p>the vegetated medium, leaf size and leaf microroughness impact the aerodynamic attributes</p><p>and thereby the ultrane particle collection eciency at the leaf/branch scale</p><p>is explored using wind tunnel experiments with interpretations by a porous media</p><p>model and a scaling analysis. A multi-layered and size-resolved second-order closure</p><p>model combined with particle </p><p>uxes and concentration measurements within and</p><p>above a forest is used to explore the particle transport processes within the canopy</p><p>sub-layer and the partitioning of particle deposition onto canopy medium and forest</p><p>oor. For gases, a modeling framework accounting for the leaf-level boundary layer</p><p>eects on the stomatal pathway for gas exchange is proposed and combined with sap</p><p>ux measurements in a wind tunnel to assess how leaf-level transpiration varies with</p><p>increasing wind speed. How exogenous environmental conditions and endogenous</p><p>soil-root-stem-leaf hydraulic and eco-physiological properties impact the above- and</p><p>below-ground water dynamics in the soil-plant system and shape plant responses</p><p>to droughts is assessed by a porous media model that accommodates the transient</p><p>water </p><p>ow within the plant vascular system and is coupled with the aforementioned</p><p>leaf-level gas exchange model and soil-root interaction model. It should be noted</p><p>that tackling all aspects of potential issues causing uncertainties in forecasting the</p><p>feedback cycle between terrestrial ecosystem and the climate is unrealistic in a single</p><p>dissertation but further research questions and opportunities based on the foundation</p><p>derived from this dissertation are also brie</p><p>y discussed.</p>

New Advancements of Scalable Statistical Methods for Learning Latent Structures in Big Data

<p>Constant technology advances have caused data explosion in recent years. Accord- ingly modern statistical and machine learning methods must be adapted to deal with complex and heterogeneous data types. This phenomenon is particularly true for an- alyzing biological data. For example DNA sequence data can be viewed as categorical variables with each nucleotide taking four different categories. The gene expression data, depending on the quantitative technology, could be continuous numbers or counts. With the advancement of high-throughput technology, the abundance of such data becomes unprecedentedly rich. Therefore efficient statistical approaches are crucial in this big data era.</p><p>Previous statistical methods for big data often aim to find low dimensional struc- tures in the observed data. For example in a factor analysis model a latent Gaussian distributed multivariate vector is assumed. With this assumption a factor model produces a low rank estimation of the covariance of the observed variables. Another example is the latent Dirichlet allocation model for documents. The mixture pro- portions of topics, represented by a Dirichlet distributed variable, is assumed. This dissertation proposes several novel extensions to the previous statistical methods that are developed to address challenges in big data. Those novel methods are applied in multiple real world applications including construction of condition specific gene co-expression networks, estimating shared topics among newsgroups, analysis of pro- moter sequences, analysis of political-economics risk data and estimating population structure from genotype data.</p>