Fully Bayesian modeling for fMRI group analysis

• Ano de defesa: 2019
• Autor(a) principal: Jiménez, Johnatan Cardona
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Análise Bayesiana; Integração de Monte Carlo; Modelos lineares dinâmicos
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/45/45133/tde-17012020-155414/

Functional magnetic resonance imaging or functional MRI (fMRI) is a non-invasive way to assess brain activity by detecting changes associated with blood flow. In this thesis, we propose a fully Bayesian procedure to analyze fMRI data for individual and group stages. For the individual stage, we use a Matrix-Variate Dynamic Linear Model (MDLM), where the temporal dependence is modeled through the state parameters and the spatial dependence is modeled only locally, taking the nearest neighbors of each voxel location. For the group stage, we take advantage of the posterior distribution of the state parameters obtained at the individual stage and create a new posterior distribution that represents the updated beliefs for the group analysis. Since the posterior distribution for the state parameters is indexed by the time t, we propose three options for algorithms that allow on-line estimated curves for the state parameters to be drawn and posterior probabilities to be computed in order to assess brain activation for both individual and group stages. We illustrate our method through two practical examples and offer an assessment using real resting-state data to compute empirical false-positive brain activation rates. Finally, we make available the R package BayesDLMfMRI to perform task-based fMRI data analysis for individual and group stages using the method proposed in this thesis.