Automatic selection of epileptic independent fMRI components

EEG-correlated fMRI analysis has proven to be useful in localizing regions of BOLD activation related to epileptic activity. However, as EEG does not always provide reliable information, purely fMRI-based data-driven techniques are invaluable. Recently, we have shown that independent component analysis (ICA) can extract sources related to the epileptic network even in such EEG-negative cases [1]. Moreover, these sources were shown to be informative with respect to the seizure onset zone (SOZ). In order to utilize this concept in clinical practice in a prospective manner, this work aims at developing an automatic technique for selecting the epileptic sources. The proposed approach applies a cascade of two classifiers. In the first step artifact related sources are discarded. In the second step the sources are characterized by four discriminative features and epileptic sources are selected from among other BOLD-related components. Our technique reaches a promising 77% specificity and provides concordant sources with the EEG-correlated fMRI activation maps or with the SOZ in 71% of the cases.