Inferring effective connectivity in the brain from EEG time series using dynamic bayesian networks

Abstract

Effective connectivity, defined as the influence of a neuronal population on another, is known to have great significance for understanding the organization of the brain. Disruptions in the effective connectivity patterns occur in the case of neurological and psychopathological diseases. Therefore, it is important to develop models of effective brain connectivity from non-invasive neuroimaging data. In this paper, we propose to use dynamic Bayesian networks (DBN) to learn effective brain connectivity from electroencephalogram (EEG) data. DBNs use first order Markov chain to model EEG time series obtained from multiple electrodes. We explore effective brain connectivity in healthy and schizophrenic subjects using this framework. Fourier bootstrapping technique is used to identify the statistically significant pairs of interactions among electrodes.