High-dimensional multivariate mediation with application to neuroimaging data
Year: 2017
Type: article
Abstract: 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, with components ranked based on 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 single nucleotide polymorphisms (SNPs), or vectors of thousands of variables in large-scale epidemiological studies. We demonstrate the method using a functional magnetic resonance imaging 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.
Source: Biostatistics
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, Johns Hopkins University, National Institutes of Health, Johns Hopkins Bloomberg School of Public Health, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Institute on Drug Abuse, National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering
Awards: Multilevel mediation techniques for fMRI, G1420189321, G2139073708, G3090714985, G3545918724, Resource for Quantitative Functional FMRI, G4519768034, Statistical methods for biosignals with varying domains, Causal Inference for Neuroimaging, Large-Scale Semiparametric Graphical Models with Applications to Neuroscience, G6157362695
OpenAlex ID: W2285799064