Time-significant wavelet coherence for the evaluation of schizophrenic brain activity using a graph theory approach

Among the various frameworks in which electroencephalographic (EEG) signal synchronization has been traditionally formulated, the most widely studied and used is the coherence that is entirely based on frequency analysis. However, at present time it is possible to capture information about the temporal profile of coherence, which is particularly helpful in studying non-stationary time-varying brain dynamics, like the wavelet coherence (WC). In this paper we propose a new approach of studying brain synchronization dynamics by extending the use of WC to include certain statistically significant (in terms of signal coherence) time segments, to study and characterize any disturbances present in the functional connectivity network of schizophrenia patients. Graph theoretical measures and visualization provide the tools to study the "disconnection syndrome" as proposed for schizophrenia. Specifically, we analyzed multichannel EEG data from twenty stabilized patients with schizophrenia and controls in an experiment of working memory (WM) using the gamma band (i.e., the EEG frequency of ca. 40 Hz), which is activated during the connecting activity (i.e., the "binding" of the neurons). The results are in accordance with the disturbance of connections between the neurons giving additional information related to the localization of most prominent disconnection.