Motion Correction for fMRI data using Conditional Transition Regime Switching General Autoregressive Conditional Heteroskedasticity Models

This dissertation develops the Conditional Transition Regime Switching General Autoregressive Conditional Heteroskedasticity Model (CTRS-GARCH) for motion correction of functional magnetic resonance imaging (fMRI) region of interest (ROI) time series. This methodology brings together finite mixtures, hidden Markov modeling, proportional odds modeling, and multivariate volatility analysis to develop a non-destructive (in the sense it does not remove time points) method for removing the influence of motion from the correlation matrices that result from functional connectivity analyses on fMRI data. This dissertation develops the analytics and estimation procedures for the CTRS-GARCH, evaluates the performance using simulations, and uses the CTRS-GARCH to evaluate motion artifacts on an empirical dataset.Doctor of Philosoph

Modeling Heterogeneous Peer Assortment Effects using Latent Class Pseudo-Maximum Likelihood Exponential Random Graph Models

This thesis develops a class of models for inference on networks called Sender/Receiver Latent Class Exponential Random Graph Models (SRLCERGMs). This class of models extends the existing Exponential Random Graph Modeling framework to allow analysts to model unobserved heterogeneity in the effects of nodal covariates and network features. Simulations across a variety of conditions are presented to evaluate the performance of this technique, and an empirical example regarding substance use among adolescents is also presented. Implications for the analysis of social networks in psychological science are discussed.Master of Art

Missing Data in Discrete Time State-Space Modeling of Ecological Momentary Assessment Data: A Monte-Carlo Study of Imputation Methods

When using ecological momentary assessment data (EMA), missing data is pervasive as participant attrition is a common issue. Thus, any EMA study must have a missing data plan. In this paper, we discuss missingness in time series analysis and the appropriate way to handle missing data when the data is modeled as a discrete time continuous measure state-space model. We found that Missing Completely At Random, Missing At Random, and Time-dependent Missing At Random data have less bias and variability than Autoregressive Time-dependent Missing At Random and Missing Not At Random. The Kalman filter excelled at handling missing data. Contrary to the literature, we found that, with either default package settings or a lag-1 imputation model, multiple imputation struggled to recover the parameters

A Monte Carlo Evaluation of Weighted Community Detection Algorithms

The past decade has been marked with a proliferation of community detection algorithms that aim to organize nodes (e.g., individuals, brain regions, variables) into modular structures that indicate subgroups, clusters, or communities. Motivated by the emergence of big data across many fields of inquiry, these methodological developments have primarily focused on the detection of communities of nodes from matrices that are very large. However, it remains unknown if the algorithms can reliably detect communities in smaller graph sizes (i.e., 1000 nodes and fewer) which are commonly used in brain research. More importantly, these algorithms have predominantly been tested only on binary or sparse count matrices and it remains unclear the degree to which the algorithms can recover community structure for different types of matrices, such as the often used cross-correlation matrices representing functional connectivity across predefined brain regions. Of the publicly available approaches for weighted graphs that can detect communities in graph sizes of at least 1000, prior research has demonstrated that Newman's spectral approach (i.e., Leading Eigenvalue), Walktrap, Fast Modularity, the Louvain method (i.e., multilevel community method), Label Propagation, and Infomap all recover communities exceptionally well in certain circumstances. The purpose of the present Monte Carlo simulation study is to test these methods across a large number of conditions, including varied graph sizes and types of matrix (sparse count, correlation, and reflected Euclidean distance), to identify which algorithm is optimal for specific types of data matrices. The results indicate that when the data are in the form of sparse count networks (such as those seen in diffusion tensor imaging), Label Propagation and Walktrap surfaced as the most reliable methods for community detection. For dense, weighted networks such as correlation matrices capturing functional connectivity, Walktrap consistently outperformed the other approaches for recovering communities

Ordinal Outcome State-Space Models for Intensive Longitudinal Data

Intensive longitudinal (IL) data are increasingly prevalent in psychological science, coinciding with technological advancements that make it simple to deploy study designs such as daily diary and ecological momentary assessments. IL data are characterized by a rapid rate of data collection (1+ collections per day), over a period of time, allowing for the capture of the dynamics that underlie psychological and behavioral processes. One powerful framework for analyzing IL data is state-space modeling, where observed variables are considered measurements for underlying states (i.e., latent variables) that change together over time. However, state-space modeling has typically relied on continuous measurements, whereas psychological data often comes in the form of ordinal measurements such as Likert scale items. In this manuscript, we develop a general estimating approach for state-space models with ordinal measurements, specifically focusing on a graded response model for Likert scale items. We evaluate the performance of our model and estimator against that of the commonly used ``linear approximation'' model, which treats ordinal measurements as though they are continuous. We find that our model resulted in unbiased estimates of the state dynamics, while the linear approximation resulted in strongly biased estimates of the state dynamicsComment: 28 pages, 6 figures, 7 pages supplementary material

An Exploratory Analysis of Personality, Attitudes, and Study Skills on the Learning Curve within a Team-based Learning Environment

Objective. To examine factors that determine the interindividual variability of learning within a team-based learning environment

Concert recording 2015-04-26

[Track 01]. Catching shadows / Ivan Trevino -- [Track 02]. Variation in F-sharp minor, op. 24. Theme : Andante cantabile ; Variation I : Allegretto scherzando ; Variations III : Andante molto sostenuto ; Variation V : Vivo scherzando / Léon Stekke -- [Track 03]. Concerto in E minor. Allegro apassionoto / Felix Mendelssohn -- [Track 04]. Cantabile et presto / George Enesco -- [Track 05]. Poem / Charles Griffes -- [Track 06]. Legende / George Enesco -- [Track 07]. Violin concerto in A minor, op. 53. Allegro ma non troppo / Antonin Dvorâk -- [Track 08]. Fantasie concertante / Jacques Casérède

Immunological Tolerance to Muscle Autoantigens Involves Peripheral Deletion of Autoreactive CD8+ T Cells

Muscle potentially represents the most abundant source of autoantigens of the body and can be targeted by a variety of severe autoimmune diseases. Yet, the mechanisms of immunological tolerance toward muscle autoantigens remain mostly unknown. We investigated this issue in transgenic SM-Ova mice that express an ovalbumin (Ova) neo-autoantigen specifically in skeletal muscle. We previously reported that antigen specific CD4+ T cell are immunologically ignorant to endogenous Ova in this model but can be stimulated upon immunization. In contrast, Ova-specific CD8+ T cells were suspected to be either unresponsive to Ova challenge or functionally defective. We now extend our investigations on the mechanisms governing CD8+ tolerance in SM-Ova mice. We show herein that Ova-specific CD8+ T cells are not detected upon challenge with strongly immunogenic Ova vaccines even after depletion of regulatory T cells. Ova-specific CD8+ T cells from OT-I mice adoptively transferred to SM-Ova mice started to proliferate in vivo, acquired CD69 and PD-1 but subsequently down-regulated Bcl-2 and disappeared from the periphery, suggesting a mechanism of peripheral deletion. Peripheral deletion of endogenous Ova-specific cells was formally demonstrated in chimeric SM-Ova mice engrafted with bone marrow cells containing T cell precursors from OT-I TCR-transgenic mice. Thus, the present findings demonstrate that immunological tolerance to muscle autoantigens involves peripheral deletion of autoreactive CD8+ T cells