High-Dimensional Multivariate Mediation Analysis for Brain Imaging: A Dimension Reduction Approach

Music City Center 

Causal mediation analysis is critical for understanding how changes in the brain mediate the effects of environmental and genetic factors on neurological outcomes in neuroimaging studies. However, traditional mediation methods often face challenges when dealing with high-dimensional multivariate mediators, such as complex brain imaging data, due to the curse of dimensionality and reduced statistical power. This study introduces a novel methodology leveraging envelope methods to enhance dimensionality reduction, pathway identification, and statistical power in detecting indirect effects. The proposed approach is applied to Alzheimer's Disease Neuroimaging Initiative (ADNI) data to examine how structural changes in brain regions mediate the impact of genetic factors on cognitive decline. Simulation studies validate the asymptotic properties of the estimators and demonstrate that the method outperforms existing techniques in improved power and reduced variance in estimation. This approach advances mediation analysis in neuroimaging and extends to other high-dimensional multivariate contexts, offering a robust framework for disease detection and intervention strategies.