Randomized Structural Sparsity-Based Support Identification with Applications to Locating Activated or Discriminative Brain Areas: A Multicenter Reproducibility Study


During this paper, we specialize in a way to locate the relevant or discriminative brain regions connected with external stimulus or sure mental decease, which is additionally known as support identification, primarily based on the neuroimaging information. The main problem lies in the very high dimensional voxel area and comparatively few training samples, simply resulting in an unstable brain region discovery (or known as feature choice in context of pattern recognition). When the coaching samples are from totally different centers and have between-center variations, it can be even tougher to get a reliable and consistent result. Corresponding, we have a tendency to revisit our recently proposed algorithm primarily based on stability choice and structural sparsity. It is applied to the multicenter MRI data analysis for the primary time. A consistent and stable result is achieved across different centers despite the between-center data variation whereas several other state-of-the-art ways such as 2 sample t-check fail. Moreover, we have empirically showed that the performance of this algorithm is strong and insensitive to many of its key parameters. Additionally, the support identification results on both functional MRI and structural MRI are interpretable and will be the potential biomarkers.

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