Measuring directional coupling between EEG sources

Directional connectivity in the brain has been typically computed between scalp electroencephalographic (EEG) signals, neglecting the fact that correlations between scalp measurements are partly caused by electrical conduction through the head volume. Although recently proposed techniques are able to identify causality relationships between EEG sources rather than between recording sites, most of them need a priori assumptions about the cerebral regions involved in the EEG generation. We present a novel methodology based on multivariate autoregressive (MVAR) modeling and Independent Component Analysis (ICA) able to determine the temporal activation of the intracerebral EEG sources as well as their approximate locations. The direction of synaptic flow between these EEG sources is then estimated using the directed transfer function (DTF), and the significance of directional coupling strength evaluated with surrogated data. The reliability of this approach was assessed with simulations manipulating the number of data samples, the depth and orientation of the equivalent source dipoles, the presence of different noise sources, and the violation of the non-Gaussianity assumption inherent to the proposed technique. The simulations showed the superior accuracy of the proposed approach over other traditional techniques in most tested scenarios. Its validity was also evaluated analyzing the generation mechanisms of the EEG-alpha rhythm recorded from 20 volunteers under resting conditions. Results suggested that the major generation mechanism underlying EEG-alpha oscillations consists of a strong bidirectional feedback between thalamus and cuneus. The precuneus also seemed to actively participate in the generation of the alpha rhythm although it did not exert a significant causal influence neither on the thalamus nor on the cuneus. All together, these results suggest that the proposed methodology is a promising non-invasive approach for studying directional coupling between mutually interconnected neural populations.