Examining clinical symptoms in schizophrenia based on visual-spatial working memory and resting-state EEG

Area of Science:

• Neuroscience
• Psychiatry
• Cognitive Science

Background:

• Schizophrenia (SZ) is a severe mental illness marked by disrupted brain network interactions.
• Understanding these network alterations is crucial for diagnosis and treatment.

Purpose of the Study:

• To explore associations between resting-state electroencephalogram (EEG) and cognitive traits in SZ patients.
• To predict clinical severity (PANSS scores) and working memory (WM) performance using EEG connectivity.

Main Methods:

• Analysis of resting-state EEG data in SZ patients and healthy controls (HC).
• Investigated frontal-parietal network connectivity in alpha and beta frequency bands.
• Utilized multivariable linear regression to predict PANSS scores based on network properties.

Main Results:

• SZ patients showed stronger frontal-parietal linkages associated with lower visual-spatial WM accuracy.
• Increased PANSS scores correlated with altered network efficiency and clustering coefficients.
• A predictive model accurately estimated PANSS scores from EEG network characteristics.

Conclusions:

• Resting-state EEG reveals distinct network patterns in SZ related to cognitive deficits and symptom severity.
• These findings support EEG as a potential tool for objective SZ assessment and intervention development.