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Bayesian Clustering and Modeling Approaches for the Analysis of Brain-Imaging Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian Clustering and Modeling Approaches for the Analysis of Brain-Imaging Data./
作者:	Fu, Haoyi.
面頁冊數:	1 online resource (169 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-10, Section: B.
Contained By:	Dissertations Abstracts International84-10B.
標題:	Mean square errors. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30359510click for full text (PQDT)
ISBN:	9798377685715

Bayesian Clustering and Modeling Approaches for the Analysis of Brain-Imaging Data.
Fu, Haoyi.

Bayesian Clustering and Modeling Approaches for the Analysis of Brain-Imaging Data. - 1 online resource (169 pages)

Source: Dissertations Abstracts International, Volume: 84-10, Section: B.

Thesis (Ph.D.)--University of Pittsburgh, 2023.

Includes bibliographical references

With the rapid development of modern techniques to measure functions and structures of the brain, statistical methods for analyzing brain-imaging data have become increasingly important to the advancement of science. My dissertation focuses on developing Bayesian clustering and modeling approaches for brain-imaging data with application to a brain imaging technique in particular: functional near-infrared spectroscopy (fNIRS).In the first part, I propose a group-based approach to clustering univariate time series via a mixture of smoothing splines experts. Time-independent covariates are incorporated via the logistic weights of a mixture-of-experts model. I formulate the approach in a fully Bayesian framework and conduct inference via reversible jump Markov chain Monte Carlo (RJMCMC) where the number of mixture components is assumed unknown. The superior performance of the approach in terms of subgroup detection and estimation is demonstrated through both simulation studies and applications to the analysis of fNIRS data.In the second part, the approach proposed in the first part is extended to the multivariate time series setting. Parameter estimation and inference are performed by Gibbs sampling, and the number of multivariate components is selected based on the deviance information criterion (DIC). The superior performance of the approach in terms of subgroup detection and estimation is demonstrated by simulation studies and applications to the analysis fNIRS data.In the final part, I propose a horseshoe prior-based generalized lasso for interpretable scalar on function regression. The approach is able to penalize regression coefficients with selected orders of differences by specifying appropriate prior structures. The horseshoe prior is able to control both the global and local shrinkage levels of each coefficient simultaneously. The proposed method is demonstrated to have superior performance in terms of signal detection and prediction accuracy through simulation studies, and is applied to the analysis of fNIRS data.Public Health Significance:Developing model-based clustering and modeling approaches provides innovative statistical methods for the analysis of brain-imaging data, which overcome the challenges of heterogeneity and high dimensionality. My proposed methods facilitate the discovery of underlying patterns of brain-imaging signals, as well as associations between these functional signals and clinical outcomes.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798377685715Subjects--Topical Terms:

3562318
Mean square errors.
Index Terms--Genre/Form:

542853
Electronic books.

Bayesian Clustering and Modeling Approaches for the Analysis of Brain-Imaging Data.
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520 $a With the rapid development of modern techniques to measure functions and structures of the brain, statistical methods for analyzing brain-imaging data have become increasingly important to the advancement of science. My dissertation focuses on developing Bayesian clustering and modeling approaches for brain-imaging data with application to a brain imaging technique in particular: functional near-infrared spectroscopy (fNIRS).In the first part, I propose a group-based approach to clustering univariate time series via a mixture of smoothing splines experts. Time-independent covariates are incorporated via the logistic weights of a mixture-of-experts model. I formulate the approach in a fully Bayesian framework and conduct inference via reversible jump Markov chain Monte Carlo (RJMCMC) where the number of mixture components is assumed unknown. The superior performance of the approach in terms of subgroup detection and estimation is demonstrated through both simulation studies and applications to the analysis of fNIRS data.In the second part, the approach proposed in the first part is extended to the multivariate time series setting. Parameter estimation and inference are performed by Gibbs sampling, and the number of multivariate components is selected based on the deviance information criterion (DIC). The superior performance of the approach in terms of subgroup detection and estimation is demonstrated by simulation studies and applications to the analysis fNIRS data.In the final part, I propose a horseshoe prior-based generalized lasso for interpretable scalar on function regression. The approach is able to penalize regression coefficients with selected orders of differences by specifying appropriate prior structures. The horseshoe prior is able to control both the global and local shrinkage levels of each coefficient simultaneously. The proposed method is demonstrated to have superior performance in terms of signal detection and prediction accuracy through simulation studies, and is applied to the analysis of fNIRS data.Public Health Significance:Developing model-based clustering and modeling approaches provides innovative statistical methods for the analysis of brain-imaging data, which overcome the challenges of heterogeneity and high dimensionality. My proposed methods facilitate the discovery of underlying patterns of brain-imaging signals, as well as associations between these functional signals and clinical outcomes.
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