Department of Statistics Seminar
North Carolina State University


Martin Lindquist

FROM Biostatistics  

FROM Johns Hopkins Bloomberg School of Public Health

High-dimensional Multivariate Mediation with Application to Neuroimaging Data

Mediation analysis is an important tool in the behavioral sciences for investigating the role of intermediate variables that lie in the path between a treatment and an outcome variable. The influence of the intermediate variable on the outcome is often explored using a linear structural equation model (LSEM), with model coefficients interpreted as possible effects. While there has been significant research on the topic, little work has been done when the intermediate variable (mediator) is a high-dimensional vector.  In this work we introduce a novel method for identifying potential mediators in this setting called the directions of mediation (DMs).  DMs linearly combine potential mediators into a smaller number of orthogonal components, ranked by the proportion of the LSEM likelihood each accounts for. This method is well suited for cases when many potential mediators are measured. Examples of high-dimensional potential mediators are brain images composed of hundreds of thousands of voxels, genetic variation measured at millions of SNPs, or vectors of thousands of variables in large-scale epidemiological studies. We demonstrate the method using a functional magnetic resonance imaging (fMRI) study of thermal pain where we are interested in determining which brain locations mediate the relationship between the application of a thermal stimulus and self-reported pain.

Friday, 24 March 2017
3:00pm - 4:00pm
2203 SAS Hall

Refreshments will be served in the 5th floor commons at 2:30pm.
NOTE: No food or drink is allowed in any of the classrooms in SAS Hall.