  In this paper, we focus on how to locate the relevant or discriminative brain
regions related with external stimulus or certain mental decease, which is also
called support identification, based on the neuroimaging data. The main
difficulty lies in the extremely high dimensional voxel space and relatively
few training samples, easily resulting in an unstable brain region discovery
(or called feature selection in context of pattern recognition). When the
training samples are from different centers and have betweencenter variations,
it will be even harder to obtain a reliable and consistent result.
Corresponding, we revisit our recently proposed algorithm based on stability
selection and structural sparsity. It is applied to the multi-center MRI data
analysis for the first time. A consistent and stable result is achieved across
different centers despite the between-center data variation while many other
state-of-the-art methods such as two sample t-test fail. Moreover, we have
empirically showed that the performance of this algorithm is robust and
insensitive to several of its key parameters. In addition, the support
identification results on both functional MRI and structural MRI are
interpretable and can be the potential biomarkers.
