  Missingness is a common issue for neuroimaging data, and neglecting it in
downstream statistical analysis can introduce bias and lead to misguided
inferential conclusions. It is therefore crucial to conduct appropriate
statistical methods to address this issue. While multiple imputation is a
popular technique for handling missing data, its application to neuroimaging
data is hindered by high dimensionality and complex dependence structures of
multivariate neuroimaging variables. To tackle this challenge, we propose a
novel approach, named High Dimensional Multiple Imputation (HIMA), based on
Bayesian models. HIMA develops a new computational strategy for sampling large
covariance matrices based on a robustly estimated posterior mode, which
drastically enhances computational efficiency and numerical stability. To
assess the effectiveness of HIMA, we conducted extensive simulation studies and
real-data analysis using neuroimaging data from a Schizophrenia study. HIMA
showcases a computational efficiency improvement of over 2000 times when
compared to traditional approaches, while also producing imputed datasets with
improved precision and stability.
