Some Novel Spatial Stochastic Models for Functional Neuroimaging Analysis.

Bayesian Methods;Spatial Point Processes;Functional Neuroimaging;Meta Analysis;Public Health;Health Sciences;Biostatistics

Kang, JianTaylor, Stephan F. ;

University of Michigan

关键词: Bayesian Methods;    Spatial Point Processes;    Functional Neuroimaging;    Meta Analysis;    Public Health;    Health Sciences;    Biostatistics;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/89729/jiankang_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

In this dissertation, we develop some new spatial point process models and discuss their applications to the analysis of functional neuroimaging data.In the first project, we propose a Bayesian spatial hierarchical model using a marked independent cluster process for functional neuroimaging meta analysis. In contrast to the current approaches, our hierarchical model accounts for intra-study variation in location (if any), inter-study variation, and idiosyncratic foci that do not cluster between studies. A defining feature of our model is its ability to dissociate inter-study spread of foci from the spatial uncertainty in population centers. Our model is illustrated on a meta analysis consisting of 437 studies from 164 publications.In the second project, motivated by joint analysis of multi type functional neuroimaging meta analysis data, we propose a non-parametric Bayesian modeling approach that extends the Poisson/Gamma random field model for multivariate point processes. In particular, each type of point patterns is modeled as a Poisson point process driven by a random intensity that is modeled as a kernel convolution of a gamma random field. The type-level gamma random fields are linked and modeled as a realization of a common gamma random field shared by all groups.We propose a hybrid algorithm with adaptive rejection sampling (ARS) embedded in a Markov chain Monte Carlo (MCMC) algorithm for posterior inference. We illustrate our models on simulated examples and two real data sets. The aim of the third project is to produce ;;reverse inference” on psychological states given functional neuroimaging meta analysis data. Given type labels that classify each study, we construct a Bayesian spatial point process classifier based on the posterior predictive probability of class membership. We measure performance via leave-one-out cross validation using an importance sampling approach that avoids multiple posterior simulations.We demonstrate our method on the meta analysis of emotions, classifying different sub-types of emotions.Our method attains a much higher prediction accuracy compared with a comparable naive Bayesian classifier.