Bayesian nonparametric models for functional magnetic resonance imaging (fMRI) data

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dc.contributor.advisorGuindani, Micheleen_US
dc.contributor.committeeMemberVannucci, Marinaen_US
dc.contributor.committeeMemberSchweinberger, Michaelen_US
dc.contributor.committeeMemberCox, Stevenen_US
dc.creatorZhang, Linlinen_US
dc.date.accessioned2016-02-05T21:26:29Zen_US
dc.date.available2016-02-05T21:26:29Zen_US
dc.date.created2015-05en_US
dc.date.issued2015-04-24en_US
dc.date.submittedMay 2015en_US
dc.date.updated2016-02-05T21:26:29Zen_US
dc.description.abstractIn this research work, I propose Bayesian nonparametric approaches to model functional magnetic resonance imaging (fMRI) data. Due to the complex spatial and temporal correlation structure as well as the high dimensionality of fMRI data, statistical methods play a crucial role in the analysis of fMRI data. My research focuses on developing novel methods that incorporate both temporal and spatial correlations into a single modeling framework and simultaneously capture brain connectivity via appropriate priors. First, I propose a spatio-temporal nonparametric Bayesian variable selection model of single-subject fMRI data. The method provides a joint analytical framework that allows to detect activated brain regions in response to a stimulus and infer the clustering of spatially remote voxels that exhibit fMRI time series with similar characteristics. I show good performance of the model on inference through simulations, and demonstrate via synthetic data analysis that the model outperforms methods implemented in the SPM8, a standard software for fMRI data analysis. I also apply the model to a fMRI study on attention to visual motion, and illustrate the results of activation detection and clustering. Then I propose a Bayesian modeling approach to the analysis of multiple-subject fMRI data. The proposed method provides a unified, single stage, and probabilistically coherent Bayesian framework for the inference of task-related brain activity. Furthermore, I employ with advanced Bayesian nonparametric priors to tie the activation strengths within and across subjects, and graphical network priors to model the complex spatio-temporal correlation structure observed in fMRI scans from multiple subjects. I develop a variational Bayesian method for inference, in addition to a Markov Chain Monte Carlo (MCMC) method. I investigate the performance of the proposed model on simulated data, and compare its performance to competing methods on synthetic data. In an application to data from a fMRI study on breast cancer survivors, the model demonstrates the excellent estimation performance.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationZhang, Linlin. "Bayesian nonparametric models for functional magnetic resonance imaging (fMRI) data." (2015) Diss., Rice University. <a href="https://hdl.handle.net/1911/88406">https://hdl.handle.net/1911/88406</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/88406en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectBayesian nonparametric approachesen_US
dc.subjectfunctional magnetic resonance imaging (fMRI) dataen_US
dc.subjectspatio-temporal correlationen_US
dc.subjectactivation detectionen_US
dc.subjectbrain connectivityen_US
dc.subjectgraphical network priorsen_US
dc.subjectvariational Bayesianen_US
dc.subjectMarkov Chain Monte Carlo (MCMC)en_US
dc.titleBayesian nonparametric models for functional magnetic resonance imaging (fMRI) dataen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentStatisticsen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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