Cognitive mechanisms of treatment in depression

Neuropsychopharmacology. 2012 Jan;37(1):117-36. doi: 10.1038/npp.2011.183. Epub 2011 Oct 5.


Cognitive abnormalities are a core feature of depression, and biases toward negatively toned emotional information are common, but are they a cause or a consequence of depressive symptoms? Here, we propose a 'cognitive neuropsychological' model of depression, suggesting that negative information processing biases have a central causal role in the development of symptoms of depression, and that treatments exert their beneficial effects by abolishing these biases. We review the evidence pertaining to this model: briefly with respect to currently depressed patients, and in more detail with respect to individuals at risk for depression and the effects of antidepressant treatments. As well as being present in currently depressed individuals, negative biases are detectable in those vulnerable for depression due to neuroticism, genetic risk, or previous depressive illness. Recent evidence provides strong support for the notion that both antidepressant drugs and psychological therapies modify negative biases, providing a common mechanism for understanding treatments for depression. Intriguingly, it may even be possible to predict which patients will benefit most from which treatments on the basis of neural responses to negative stimuli. However, further research is required to ascertain whether negative processing biases will be useful in predicting, detecting, and treating depression, and hence in preventing a chronic, relapsing course of illness.

Publication types

  • Review

MeSH terms

  • Antidepressive Agents / therapeutic use*
  • Cognition Disorders / drug therapy*
  • Cognition Disorders / physiopathology
  • Cognition Disorders / psychology
  • Depressive Disorder / drug therapy*
  • Depressive Disorder / physiopathology
  • Depressive Disorder / psychology
  • Humans
  • Predictive Value of Tests
  • Psychotherapy / methods*
  • Psychotherapy / trends


  • Antidepressive Agents