Abstracts

Rationale: Converging evidence suggests that schizophrenia is a neurodevelopmental disorder with abnormal functional connectivity. However, the underlying neurological mechanism for individuals with subthreshold psychotic symptoms of schizophrenia remains largely unknown. Methods: In the present study, resting-state 128-channel electroencephalogram (EEG) data were acquired from 28 clinically stable individuals with first-episode schizophrenia (FESz), 20 individuals at ultra high-risk (UHR) for schizophrenia, 14 individuals at high-risk (HR), and 16 healthy controls (HC). The microstate analysis was used to assess the dynamics of functional networks in these participants. Four features were extracted for each microstate class (A, B, C, D): duration, frequency, occurrence and time coverage. Results: We found that EEG microstates appeared to be different in the four groups of individuals (FESz, UHR, HR, and HC). Differences appeared to be greatest between the subjects with FESz and HC as expected. Microstate features computed on the basis of resting state EEG data can be used to distinguish the four groups of people.Furthermore, we also observed significant differences in clinical and cognitive examinations between the four groups. Conclusions: The results demonstrate that microstate-based indicators may act as biomarkers for early diagnosis and prediction of at-risk individuals of schizophrenia. Furthermore, our findings illustrate the potential use of resting-state EEG in clinical screening, classification and quantitative evaluation for patients with neurodevelopmental disorders. Funding: This research was supported by grants from the National Key Research and Development Program of the Ministry of Science and Technology of China (grant number 2016YFF0201002), and the Electrophysiological Biobiomarkers in Schizophrenia project of Beijing Key Laboratory (grant number Z161100002616017).