  Recent studies found that in voxel-based neuroimage analysis, detecting and
differentiating "procedural bias" that are introduced during the preprocessing
steps from lesion features, not only can help boost accuracy but also can
improve interpretability. To the best of our knowledge, GSplit LBI is the first
model proposed in the literature to simultaneously capture both procedural bias
and lesion features. Despite the fact that it can improve prediction power by
leveraging the procedural bias, it may select spurious features due to the
multicollinearity in high dimensional space. Moreover, it does not take into
account the heterogeneity of these two types of features. In fact, the
procedural bias and lesion features differ in terms of volumetric change and
spatial correlation pattern. To address these issues, we propose a "two-groups"
Empirical-Bayes method called "FDR-HS" (False-Discovery-Rate Heterogenous
Smoothing). Such method is able to not only avoid multicollinearity, but also
exploit the heterogenous spatial patterns of features. In addition, it enjoys
the simplicity in implementation by introducing hidden variables, which turns
the problem into a convex optimization scheme and can be solved efficiently by
the expectation-maximum (EM) algorithm. Empirical experiments have been
evaluated on the Alzheimer's Disease Neuroimage Initiative (ADNI) database. The
advantage of the proposed model is verified by improved interpretability and
prediction power using selected features by FDR-HS.
