Statistical methods for neuroimaging data analysis and cognitive science

dc.contributor.authorSong, Yin
dc.contributor.supervisorNathoo, Farouk
dc.date.accessioned2019-05-29T18:16:10Z
dc.date.available2019-05-29T18:16:10Z
dc.date.copyright2019en_US
dc.date.issued2019-05-29
dc.degree.departmentDepartment of Mathematics and Statisticsen_US
dc.degree.levelDoctor of Philosophy Ph.D.en_US
dc.description.abstractThis thesis presents research focused on developing statistical methods with emphasis on tools that can be used for the analysis of data in neuroimaging studies and cognitive science. The first contribution addresses the problem of determining the location and dynamics of brain activity when electromagnetic signals are collected using magnetoencephalography (MEG) and electroencephalography (EEG). We formulate a new spatiotemporal model that jointly models MEG and EEG data as a function of unobserved neuronal activation. To fit this model we derive an efficient procedure for simultaneous point estimation and model selection based on the iterated conditional modes algorithm combined with local polynomial smoothing. The methodology is evaluated through extensive simulation studies and an application examining the visual response to scrambled faces. In the second contribution we develop a Bayesian spatial model for imaging genetics developed for analyses examining the influence of genetics on brain structure as measured by MRI. We extend the recently developed regression model of Greenlaw et al. (\textit{Bioinformatics}, 2017) to accommodate more realistic correlation structures typically seen in structural brain imaging data. We allow for spatial correlation in the imaging phenotypes obtained from neighbouring regions in the same hemisphere of the brain and we also allow for correlation in the same phenotypes obtained from different hemispheres (left/right) of the brain. This correlation structure is incorporated through the use of a bivariate conditional autoregressive spatial model. Both Markov chain Monte Carlo (MCMC) and variational Bayes approaches are developed to approximate the posterior distribution and Bayesian false discovery rate (FDR) procedures are developed to select SNPs using the posterior distribution while accounting for multiplicity. The methodology is evaluated through an analysis of MRI and genetic data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and we show that the new spatial model exhibits improved performance on real data when compared to the non-spatial model of Greenlaw et al. (2017). In the third and final contribution we develop and investigate tools for the analysis of binary data arising from repeated measures designs. We propose a Bayesian approach for the mixed-effects analysis of accuracy studies using mixed binomial regression models and we investigate techniques for model selection.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.bibliographicCitationSong, Y., Nathoo, F.S., Babul A. (2018). A Potts-Mixture Spatiotemporal Joint Model for Combined MEG and EEG Data. Canadian Journal of Statistics, accepted for publicationen_US
dc.identifier.bibliographicCitationSong, Y., Ge, S., Cao, J., Wang, L., Nathoo, F.S (2019). A Bayesian Spatial Model for Imaging Genetics. Under revision for Biometrics. Revision invited May 16, 2019.en_US
dc.identifier.bibliographicCitationSong, Y., Nathoo, F.S., Masson, M.E.J. (2017). A Bayesian approach to the mixed effects analysis of repeated measures accuracy studies. Journal of Memory and Language, DOI: 10.1016/j.jml.2017.05.002.en_US
dc.identifier.urihttp://hdl.handle.net/1828/10906
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectBayesian statisticsen_US
dc.subjectNeuroimagingen_US
dc.subjectImaging geneticsen_US
dc.subjectCognitive scienceen_US
dc.titleStatistical methods for neuroimaging data analysis and cognitive scienceen_US
dc.typeThesisen_US

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