Analysis of the Neural Networks Dynamics Associated to Resting-State Electroencephalographic Activity in Schizophrenia Patients

The objective of this study is to characterize the dynamic changes in the global node strength of the neuronal network in patients with schizophrenia (SZ) during the resting-state. Previous studies suggest that cognitive impairments associated with SZ could be linked to functional alterations in neuronal networks, leading to a “disconnection syndrome”. Therefore, electroencephalographic (EEG) activity was recorded from 11 SZ patients and 20 control subjects. Time-varying functional connectivity was estimated using the instantaneous amplitude correlation (IAC) derived from EEG data in the conventional EEG frequency bands. Subsequently, using the time-resolved adjacency matrices in each frequency band, the distribution of global node strength was computed to analyze the alterations that SZ elicits in the properties of IAC fluctuations. To ascertain differences in global node strength between control subjects and SZ patients, we computed the mean, standard deviation, skewness, and kurtosis from the distribution of these values along time. All four cumulants showed statistically significant differences in the gamma frequency band (p-values < 0.05). In addition, beta-1 and beta-2 bands showed statistically significant differences in skewness and kurtosis (p-values < 0.05). These findings suggest that the dynamical organization of the functional network during the resting-state in SZ patients is altered, which can be observed in changes in the fluctuations of global connectivity patterns in fast frequency bands. These results provide valuable insights into understanding of the disruptions of brain functional networks in SZ patients.