자료유형  

 학위논문

Control Number  

0017161086

International Standard Book Number  

9798382214764

Dewey Decimal Classification Number  

574

Main Entry-Personal Name  

Yue, Kun.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of Washington., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

268 p.

General Note  

Source: Dissertations Abstracts International, Volume: 85-10, Section: B.

General Note  

Advisor: Shojaie, Ali;Lila, Eardi.

Dissertation Note  

Thesis (Ph.D.)--University of Washington, 2024.

Summary, Etc.  

요약This dissertation addresses the challenges associated with correlated data in diverse fields, emphasizing both statistical methodology and applications in the realms of genetics and neuroimaging. The overarching theme revolves around the development and refinement of statistical tools, particularly focusing on large-scale linear mixed models and brain connectivity networks.In the second chapter, we confront the computational inefficiencies and instability issues of standard methods for estimating variance components in linear mixed models, commonly used in genetic studies. Utilizing regularized estimation strategies, we propose the restricted Haseman-Elston (REHE) regression and its resampling variant (reREHE) estimators, along with an inference framework for REHE, as fast and robust alternatives that provide nonnegative estimates with comparable accuracy to REML. The merits of REHE are illustrated using real data and benchmark simulation studies.The third chapter is motivated by the problem of inferring the graph structure of functional connectivity networks from multi-level functional magnetic resonance imaging (fMRI) data. We develop a valid inference framework for high-dimensional graphical models that accounts for group-level heterogeneity. We introduce a neighborhood-based method to learn the graph structure and reframe the problem as that of inferring fixed effect parameters in a doubly high-dimensional linear mixed model. Specifically, we propose a LASSO-based estimator and a de-biased LASSO-based inference framework for the fixed effect parameters of the linear mixed model. Moreover, we introduce consistent estimators for the variance components in order to identify subject-specific edges in the inferred graph. We also adapt our method to account for serial correlation by learning heterogeneous graphs in the setting of a vector autoregressive model. We demonstrate the performance of the proposed framework using real data and benchmark simulation studies.The fourth chapter delves into the temporally dynamic brain connectivity of the default mode network as a potential biomarker for Alzheimer's Disease (AD). Existing amyloid beta (Aβ) biomarkers, though effective, confront practical limitations. Brain functional connectivity alterations linked to AD pathology propose a non-invasive avenue for Aβ detection. However, current FC measurements lack standalone sensitivity. We investigate temporally dynamic FC through resting-state functional MRI and introduce the Generalized Autoregressive Conditional Heteroscedastic Dynamic Conditional Correlation (DCC-GARCH) model. To fulfill the model assumptions, we employ whitening procedures to remove the serial correlations. Recognizing the limitations of traditional methods, we introduce an iterative data-adaptive autoregressive model (IDAR) capable of modeling complex serial correlation structures for both long- and short-TR datasets. We comprehensively illustrate IDAR's performance by assessing residual serial correlations post-whitening and type-I error rates in task testing. After applying the IDAR approach to pre-process fMRI signals, we estimate dynamic functional connectivity profiles with DCC-GARCH models. Our results demonstrate superior sensitivity to CSF Aβ status and provide crucial insights into dynamic functional connectivity analysis in AD.

Subject Added Entry-Topical Term  

Biostatistics.

Subject Added Entry-Topical Term  

Pathology.

Subject Added Entry-Topical Term  

Medical imaging.

Subject Added Entry-Topical Term  

Genetics.

Index Term-Uncontrolled  

Existing amyloid beta

Index Term-Uncontrolled  

Alzheimer's Disease

Index Term-Uncontrolled  

Graph structure

Index Term-Uncontrolled  

Functional magnetic resonance imaging

Index Term-Uncontrolled  

Correlation structures

Added Entry-Corporate Name  

University of Washington Biostatistics

Host Item Entry  

Dissertations Abstracts International. 85-10B.

Electronic Location and Access  

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Control Number  

joongbu:656552