LARS NETWORK FILTRATION IN THE STUDY OF EEG BRAIN CONNECTIVITY

In a brain network, weak and non-significant edge weights between nodes signal spurious connections and are often discarded by thresholding the weights. Traditional practice of thresholding edge weights at an arbitrary value can be problematic. Network filtration provides an alternative by summarizing the changes in the network topology with respect to a broad range of thresholds. A well established network filtration approach depends on the graphical-LASSO (least absolute shrinkage and selection operator) model, where a sequence of binary networks are obtained based on non-zero sparse inverse covariance (IC) estimates of partial correlations at a range of sparsity parameters. The limitation of the graphical-LASSO network model is that it relies on the structural information rather than actual entries of the sparse IC matrices and therefore can only yield approximate dynamic topological changes in the network. In the current study, we propose a new network filtration approach based on least angle regression (LARS) that gives exact filtration values at which the filtration changes and apply it to study brain connectivity in response to emotional stimuli across different age groups via electroencephalographic (EEG) data.