Combining graph and machine learning methods to analyze differences in functional connectivity across sex

Open Neuroimag J. 2012:6:1-9. doi: 10.2174/1874440001206010001. Epub 2012 Jan 26.


In this work we combine machine learning methods and graph theoretical analysis to investigate gender associated differences in resting state brain network connectivity. The set of all correlations computed from the fMRI resting state data is used as input features for classification. Two ensemble learning methods are used to perform the detection of the set of discriminative edges between groups (males vs. females) of brain networks: 1) Random Forest and 2) an ensemble method based on least angle shrinkage and selection operator (lasso) regressors. Permutation testing is used not only to assess significance of classification accuracy but also to evaluate significance of feature selection. Finally, these methods are applied to data downloaded from the Connectome Project website. Our results suggest that gender differences in brain function may be related to sexually dimorphic regional connectivity between specific critical nodes via gender-discriminative edges.

Keywords: GLMNET; Graph theory; Machine learning; Random forest.; Regularization; Resting state fMRI.