Motivated by recent work studying massive imaging data in the nemroimaging literature, we propose various multiscale adaptive smoothing models (MARM) for spatially modeling the relation between high-dimensional imaging measures on a three-dimensional volume with a set of covariates.We develop several statistical inference procedures for SVCM and systematically study their theoretical properties.We conduct Monte Carlo simulation and real data analyses to examine the finite-sample performance of the proposed procedures.