Statistical Methods in Imaging Genomics and Brain Connectivity
Presenter: Farouk Nathoo
When: Thursday, July 14, 2016 Time: 9:00 AM - 10:30 AM
Room: Salon A Carson Hall (Level 2)
Session Synopsis:
A Bayesian Group Sparse Multi-Task Regression Model for Imaging Genomics
Recent advances in technology for brain imaging and high-throughput genotyping have motivated studies examining the influence of genetic variation on brain structure. In this setting, high-dimensional regression for multi-SNP association analysis is challenging as the response variables obtained through brain imaging comprise potentially interlinked endophenotypes, and there is a desire to incorporate a biological group structure among SNPs based on their belonging genes. Wang et al. (Bioinformatics, 2012) have recently developed an approach for the analysis of imaging genomic studies based on penalized regression with regularization based on a novel group l_{2,1}-norm penalty which encourages sparsity at the gene level. While incorporating a number of useful features, a shortcoming of the proposed approach is that it only furnishes a point estimate and techniques for obtaining valid standard errors or interval estimates are not provided. We solve this problem by developing a corresponding Bayesian formulation based on a three-level hierarchical model that allows for full posterior inference using Gibbs sampling. Techniques for the selection of tuning parameters are investigated thoroughly and we make comparisons between cross-validation, fully Bayes, and empirical Bayes approaches for the choice of tuning parameters. Our proposed methodology is investigated using simulation studies and is applied to the analysis of a large dataset collected as part of the Alzheimer's Disease Neuroimaging Initiative.
Statistical Methods in Imaging Genomics and Brain Connectivity
Presenter: Linglong Kong
When: Thursday, July 14, 2016 Time: 9:00 AM - 10:30 AM
Room: Salon A Carson Hall (Level 2)
Session Synopsis:
Estimation for bivariate quantile varying coefficient model and its application in DTI data analysis
We propose a bivariate quantile regression method for the bivariate varying coefficient model through a directional approach. The varying coefficients are approximated by the B-spline basis and an $L_{2}$-type penalty is imposed to achieve desired smoothness. We develop a multistage estimation procedure based on the Propagation-Separation~(PS) approach to borrow information from nearby directions. The PS method is capable of handling the computational complexity raised by simultaneously considering multiple directions to efficiently estimate varying coefficients while guaranteeing certain smoothness along directions. We reformulate the optimization problem to solve it by the Alternating Direction Method of Multipliers~(ADMM), which is implemented using R while the core is written in C to speed it up. Simulation studies are conducted to confirm the finite sample performance of our proposed method. A real data on Diffusion Tensor Imaging~(DTI) properties from a clinical study on neurodevelopment is analyzed.
Statistical Methods in Imaging Genomics and Brain Connectivity
Presenter: Marina Vannucci
When: Thursday, July 14, 2016 Time: 9:00 AM - 10:30 AM
Room: Salon A Carson Hall (Level 2)
Session Synopsis:
A Bayesian Approach to Dynamic Functional Connectivity Networks
FMRI studies have traditionally assumed stationarity of the connectivity patterns observed in a subject during an fMRI experiment. While the assumption has successfully allowed to study large-scale properties of brain functioning, it is generally recognized that functional connectivity varies with time and tasks performed. In this talk, we describe a novel Bayesian methodological framework for the analysis of temporal dynamics of functional networks in task-based fMRI data. Our proposed formulation allows joint modeling of the task-related activations in addition to the dynamics of individual functional connectivity. We illustrate the proposed approach by means of simulation and an analysis on a real fMRI dataset.
