Abstract #1731

Feature selection and classification of aMCI subjects using local fMRI activation patterns

Mingwu Jin¹, Xiaowei Zhuang², Tim Curran³, and Dietmar Cordes²

¹University of Texas at Arlington, Arlington, TX, United States, ²Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States, ³University of Colorado Boulder, Boulder, CO, United States

Two feature selection methods and four classification methods were applied to fMRI memory activation data obtained from two groups of amnestic MCI (aMCI) subjects and normal control subjects to investigate the classification effectiveness of the memory contrasts and subregions of medial temporal lobe. Least absolute shrinkage and selection operator (LASSO) is more effective than principle component analysis (PCA) for feature selection. The features selected by LASSO can be combined with non-linear classifiers for high classification accuracy. The face-occupation paradigm provides more discriminant power than the paradigms using outdoor pictures or word pairs.

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