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In this paper, we focus on how to locate the relevant or discriminative brainregions related with external stimulus or certain mental decease, which is alsocalled support identification, based on the neuroimaging data. The maindifficulty lies in the extremely high dimensional voxel space and relativelyfew training samples, easily resulting in an unstable brain region discovery(or called feature selection in context of pattern recognition). When thetraining 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 stabilityselection and structural sparsity. It is applied to the multi-center MRI dataanalysis for the first time. A consistent and stable result is achieved acrossdifferent centers despite the between-center data variation while many otherstate-of-the-art methods such as two sample t-test fail. Moreover, we haveempirically showed that the performance of this algorithm is robust andinsensitive to several of its key parameters. In addition, the supportidentification results on both functional MRI and structural MRI areinterpretable and can be the potential biomarkers.

Randomized Structural Sparsity based Support Identification with  Applications to Locating Activated or Discriminative Brain Areas: A  Multi-center Reproducibility Study