Browsing by Author "Tian, Xinyuan"

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    Bayesian mixed model inference for genetic association under related samples with brain network phenotype
    (Oxford University Press, 2024) Tian, Xinyuan; Wang, Yiting; Wang, Selena; Zhao, Yi; Zhao, Yize; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Genetic association studies for brain connectivity phenotypes have gained prominence due to advances in noninvasive imaging techniques and quantitative genetics. Brain connectivity traits, characterized by network configurations and unique biological structures, present distinct challenges compared to other quantitative phenotypes. Furthermore, the presence of sample relatedness in the most imaging genetics studies limits the feasibility of adopting existing network-response modeling. In this article, we fill this gap by proposing a Bayesian network-response mixed-effect model that considers a network-variate phenotype and incorporates population structures including pedigrees and unknown sample relatedness. To accommodate the inherent topological architecture associated with the genetic contributions to the phenotype, we model the effect components via a set of effect network configurations and impose an inter-network sparsity and intra-network shrinkage to dissect the phenotypic network configurations affected by the risk genetic variant. A Markov chain Monte Carlo (MCMC) algorithm is further developed to facilitate uncertainty quantification. We evaluate the performance of our model through extensive simulations. By further applying the method to study, the genetic bases for brain structural connectivity using data from the Human Connectome Project with excessive family structures, we obtain plausible and interpretable results. Beyond brain connectivity genetic studies, our proposed model also provides a general linear mixed-effect regression framework for network-variate outcomes.
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    Sex-specific topological structure associated with dementia via latent space estimation
    (Wiley, 2024) Wang, Selena; Wang, Yiting; Xu, Frederick H.; Tian, Xinyuan; Fredericks, Carolyn A.; Shen, Li; Zhao, Yize; Alzheimer’s Disease Neuroimaging Initiative; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Introduction: We investigate sex-specific topological structures associated with typical Alzheimer's disease (AD) dementia using a novel state-of-the-art latent space estimation technique. Methods: This study applies a probabilistic approach for latent space estimation that extends current multiplex network modeling approaches and captures the higher-order dependence in functional connectomes by preserving transitivity and modularity structures. Results: We find sex differences in network topology with females showing more default mode network (DMN)-centered hyperactivity and males showing more limbic system (LS)-centered hyperactivity, while both show DMN-centered hypoactivity. We find that centrality plays an important role in dementia-related dysfunction with stronger association between connectivity changes and regional centrality in females than in males. Discussion: The study contributes to the current literature by providing a more comprehensive picture of dementia-related neurodegeneration linking centrality, network segregation, and DMN-centered changes in functional connectomes, and how these components of neurodegeneration differ between the sexes. Highlights: We find evidence supporting the active role network topology plays in neurodegeneration with an imbalance between the excitatory and inhibitory mechanisms that can lead to whole-brain destabilization in dementia patients. We find sex-based differences in network topology with females showing more default mode network (DMN)-centered hyperactivity, males showing more limbic system (LS)-centered hyperactivity, while both show DMN-centered hypoactivity. We find that brain region centrality plays an important role in dementia-related dysfunction with a stronger association between connectivity changes and regional centrality in females than in males. Females, compared to males, tend to exhibit stronger dementia-related changes in regions that are the central actors of the brain networks. Taken together, this research uniquely contributes to the current literature by providing a more comprehensive picture of dementia-related neurodegeneration linking centrality, network segregation, and DMN-centered changes in functional connectomes, and how these components of neurodegeneration differ between the sexes.