A Predictive Coding Framework for Understanding Major Depression.

Area of Science:

• Neuroscience
• Psychiatry
• Cognitive Science

Background:

• Predictive coding models explain brain function via top-down predictions and bottom-up error signals.
• Depression is linked to negative biases and altered reward prediction error signaling.
• Evidence suggests impaired prediction signaling across sensory domains in depression.

Purpose of the Study:

• To review electrophysiological and neuroimaging evidence for disrupted predictive processing in depression.
• To frame disrupted generative predictions as a cause of depressive symptoms.
• To focus on sensory deviance and reward processing in depression.

Main Methods:

• Review of electrophysiological studies.
• Analysis of neuroimaging research.
• Examination of sensory deviance detection and reward processing studies.

Main Results:

• Depression is associated with impaired generative predictions and prediction error signaling.
• Disrupted predictive processing affects multiple sensory domains.
• Monoaminergic and glutamatergic systems play a role in predictive coding.

Conclusions:

• Disrupted predictive coding offers a novel framework for understanding depression.
• Impaired predictive signaling may underlie anhedonia and negative bias in depression.
• This review provides a roadmap for future research into depression and predictive coding.