Brain rhythms of depression: A predictive processing perspective

Area of Science:

• Neuroscience
• Psychiatry
• Computational Psychiatry

Background:

• Depression symptoms like anhedonia and social withdrawal can be understood through predictive processing frameworks.
• Predictive processing models suggest the brain generates predictions and updates them based on incoming sensory information.

Purpose of the Study:

• To review human electroencephalography (EEG) findings on the moment-to-moment dynamics of inference in depression.
• To explore how altered information processing, specifically inference precision, contributes to depressive states.

Main Methods:

• Review of human electroencephalography (EEG) studies.
• Analysis of evoked, oscillatory, and aperiodic EEG measures to assess information processing dynamics.
• Framing findings within the context of predictive processing theory.

Main Results:

• Incoming information in depression appears registered but may lack sufficient precision to update higher-level beliefs.
• This processing imbalance may favor "model maintenance" over cognitive flexibility.
• Rumination is suggested as a potential correlate of this relative state stability.

Conclusions:

• Altered inference precision is a potential mechanism in depression.
• Proposes "inference phenotypes" as a novel way to understand depression heterogeneity.
• Suggests EEG-based approaches for stratifying patients and guiding targeted interventions.