Simple fully automated group classification on brain fMRI

We propose a simple, well grounded classification technique which is suited for group classification on brain fMRI datasets that have high dimensionality, small number of subjects, high noise level, high subject variability, imperfect registration and capture subtle cognitive effects. We propose threshold-split region as a new feature selection method and majority vote as the classification technique. Our method does not require a predefined set of regions of interest. We use average across sessions, only one feature per experimental condition, feature independence assumption, and simple classifiers. The seeming counter-intuitive approach of using a simple design is supported by signal processing and statistical theory. Experimental results in two block design datasets that capture brain function under distinct monetary rewards for cocaine addicted and control subjects, show that our method exhibits increased generalization accuracy compared to commonly used feature selection and classification techniques.