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B1025
Title: Time course modeling for brain imaging data Authors:  Atsushi Kawaguchi - Saga University (Japan) [presenting]
Abstract: Brain time varying information is useful for identifying biomarkers that can be used for diagnosis of brain disorders. This can be measured as longitudinal or time series Magnetic Resonance Imaging (MRI) data. We propose a dimension-reduction method using supervised (multi-block) sparse component analysis. The method is first implemented through basis expansion of spatial brain images, and the scores are then reduced through regularized matrix decomposition to produce simultaneous data-driven selections of related brain regions, supervised by univariate composite scores representing linear combinations of covariates. Two advantages of the proposed method are that it identifies the associations between brain regions at the voxel level, and that supervision is helpful for interpretation. This also regards the functional data analysis approach, which can be applied to the time course modeling. The proposed method was applied to the real data and was compared with the existing methods.