Motivated by the needs of selecting important features from big neu-roimaging data,we develop a Bayesian variable screening algorithm for ultra-high dimensional data consisting of two steps: Step 1: compute a multivariate variable screening statistic based on marginal posterior moments; Step 2: perform the mixture model-based cluster analysis on screening statistics to identify the unimportant vari-ables.