Functional and Effective Connectivity Characterization of Absence Seizures

Abstract

The human brain is a complex system and many approaches are currently being used to study its disorders. Functional Connectivity is a well-known approach which characterizes the relationship between two or more regions of the brain. This relationship is inferred from measuring and correlating activity from the brain. On the other hand, Effective Connectivity quantifies activities instead of correlating them. For Functional Connectivity, Phase Locked Value (PLV) and other measures are used. For Effective Connectivity, Transfer Entropy is the main measure used. The problem is: Functional Connectivity describes the flow of information in the complex network of the brain in terms of correlation, and allows time delays computations for better correlations - but gives no information regarding causality; Transfer Entropy is very accurate regarding causality, but has assessed interactions at only one time delay. This paper will address these issues by analysing a data set of absence seizures on both connectivity analyses. PLV (Phase Locking Value) and Transfer Entropy (TE) algorithms will be performed on EEG data from 11 subjects diagnosed with Absence Seizures. Afterwards, both networks will be described in terms of each connectivity type. The main contribution of this paper is that the network specification of both Functional and Effective Connectivity are complementary when characterizing absence seizures from EEG data.