Abstract
Schizophrenia is a devastating disease with a prevalence of 1% in populations around the world. Current diagnostic techniques of schizophrenia and high-risk population are based on subjective psychiatric interviews. Early diagnosis and intervention can mitigate progression and improve treatment outcomes. However, the lack of biomarkers that support objective examinations has been a long-term bottleneck in clinical diagnosis and assessment of schizophrenia and its high-risk state. In the present study, resting-state 128-channel electroencephalogram (EEG) data were acquired from 65 participants, including clinically-stable individuals with first-episode schizophrenia (FESZ), individuals at ultra-high-risk (UHR) and high-risk (HR), and healthy controls (HC). Microstate analysis was used to assess the dynamics of functional networks in these participants. Three features were extracted for each class of microstate (A, B, C, D, E, F): duration, occurrence and time coverage. Furthermore, clinical examinations and cognitive tests were performed. Behavioral results showed poorer performances in the participants as the disease progressed. Moreover, microstate features computed from resting-state EEG microstates (especially microstate class D) were capable of distinguishing the four groups of individuals. Combined biomarkers including clinical examinations, cognitive tests and EEG microstate parameters were identified as a potential effective diagnostic tool, achieving the highest classification performance using the random forest model compared with the support vector machine (SVM) and long short term memory (LSTM) networks, with an average classification of 92%, mean sensitivity of 91.8%, and specificity of 90.8% among the four groups, which were much higher than that only using behavioral features. The results demonstrate that microstate-based indicators together with behavioral results 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 of patients with neurodevelopmental disorders.
Highlights
Schizophrenia is a devastating disease with a prevalence of 1% in populations around the world [1]
In the present study we examined whether microstates in resting-state EEG together with clinical examination and cognitive tests could be used as biomarkers for prediction of schizophrenia
The biomarkers used for the prediction of schizophrenia are combined biomarkers including behavioral examinations and EEG microstates parameters
Summary
Schizophrenia is a devastating disease with a prevalence of 1% in populations around the world [1]. Individuals are usually affected in their late teens or early twenties with an enormous variety of psychiatric characteristics and comorbid conditions, which include depression, affecting almost 50% of schizophrenic patients; substance abuse in approximately. 47%; posttraumatic stress disorder in approximately 29%; obsessive-compulsive disorder in approximately 23%; and panic disorder in approximately 15% of psychotic individuals [2]. Emerging evidence points to genetic factors, in combination with environmental factors, as possibly leading to risk of schizophrenia [1], [4]. These findings have generated a broad consensus that schizophrenia requires robust diagnostic, prophylactic and therapeutic strategies
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
