Robust feature selection in resting-state fMRI connectivity based on population studies

Abstract

We propose an alternative to univariate statistics for identifying population differences in functional connectivity. Our feature selection method is based on a procedure that searches across subsets of the data to isolate a set of robust, predictive functional connections. The metric, known as the Gini Importance, also summarizes multivariate patterns of interaction, which cannot be captured by univariate techniques. We compare the Gini Importance with univariate statistical tests to evaluate functional connectivity changes induced by schizophrenia. Our empirical results indicate that univariate features vary dramatically across subsets of the data and have little classification power. In contrast, relevant features based on Gini Importance are considerably more stable and allow us to accurately predict the diagnosis of a test subject.