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This paper compares three feature representation techniques used to representresting state functional magnetic resonance (fMRI) scans. The proposed modelsof feature representation consider the time averaged fMRI scans as rawrepresentation of image data. The effectiveness of the representation isevaluated by using these features for classification of Attention DeficitHyperactivity Disorder (ADHD) patients from healthy controls. Thedimensionality reduction methods used for feature representation aremaximum-variance unfolding, locally linear embedding and auto-encoders. Theclassifiers tested for classification purpose were neural net and supportvector machine. Using auto-encoders with four hidden layers along with asupport vector machine classifier yielded a classification accuracy of 61.25%along with 65.69% sensitivity and 52.20% specificity.

A Comparison of Representation Learning Methods for Dimensionality Reduction of fMRI Scans for Classification of ADHD