Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models.

ABSTRACT: Pattern recognition models have been increasingly applied to neuroimaging data over the last two decades. These applications have ranged from cognitive neuroscience to clinical problems. A common limitation of these approaches is that they do not incorporate previous knowledge about the brain structure and function into the models. Previous knowledge can be embedded into pattern recognition models by imposing a grouping structure based on anatomically or functionally defined brain regions. In this work, we present a novel approach that uses group sparsity to model the whole brain multivariate pattern as a combination of regional patterns. More specifically, we use a sparse version of Multiple Kernel Learning (MKL) to simultaneously learn the contribution of each brain region, previously de

SUBMITTER: Schrouff J 

PROVIDER: S-EPMC5797202 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature