  This paper compares three feature representation techniques used to represent
resting state functional magnetic resonance (fMRI) scans. The proposed models
of feature representation consider the time averaged fMRI scans as raw
representation of image data. The effectiveness of the representation is
evaluated by using these features for classification of Attention Deficit
Hyperactivity Disorder (ADHD) patients from healthy controls. The
dimensionality reduction methods used for feature representation are
maximum-variance unfolding, locally linear embedding and auto-encoders. The
classifiers tested for classification purpose were neural net and support
vector machine. Using auto-encoders with four hidden layers along with a
support vector machine classifier yielded a classification accuracy of 61.25%
along with 65.69% sensitivity and 52.20% specificity.
