Resting-state brain oscillations predict cognitive function in psychiatric disorders: A transdiagnostic machine...

Area of Science:

• Neuroscience
• Psychiatry
• Cognitive Science

Background:

• Cognitive dysfunction is prevalent in psychiatric disorders, impacting quality of life.
• Deficits transcend diagnostic categories, highlighting the need for transdiagnostic research.
• Understanding the link between brain activity and cognition is crucial for developing new therapeutic strategies.

Purpose of the Study:

• To identify transdiagnostic patterns connecting cognitive function with resting-state electroencephalography (EEG) oscillations.
• To investigate the predictive power of EEG power spectrum features for cognitive performance.

Main Methods:

• Utilized random forest regression models to predict cognitive test performance using resting-state EEG power spectrum data.
• Analyzed data from 216 psychiatric outpatients who completed cognitive assessments and EEG recordings.
• Identified predictive EEG features by comparing model performance against chance levels.

Main Results:

• Random forest models successfully predicted performance in episodic memory (PAL), processing speed (CRT), and executive function (IED).
• Upper alpha oscillations correlated with better PAL and CRT performance; low alpha with worse CRT.
• Beta power predicted poorer performance across all tested cognitive domains.
• Theta and delta oscillations predicted better performance on IED and PAL.

Conclusions:

• Resting-state EEG oscillations are significant predictors of cognitive function dimensions in psychiatric disorders.
• These findings suggest potential for EEG-based biomarkers and novel treatment targets for cognitive deficits.