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Unifying treatments for depression: an application of the Free Energy Principle

Authors: Adam Mourad Chekroud; Adam Mourad Chekroud;

Unifying treatments for depression: an application of the Free Energy Principle

Abstract

Major Depressive Disorder is a debilitating and increasingly prevalent psychiatric condition (Compton et al., 2006; Andersen et al., 2011). At present, its primary treatments are antidepressant medications and psychotherapy. Curiously, although the pharmacological effects of antidepressants manifest within hours, remission of clinical symptoms takes a number of weeks-if at all. Independently, support has grown for an idea-proposed as early as Helmholtz (von Helmholtz, 1924)-that the brain is a prediction machine, holding generative models for the purpose of inferring causes of sensory information (Dayan et al., 1995; Rao and Ballard, 1999; Knill and Pouget, 2004; Friston et al., 2006; Friston, 2010). If the brain does indeed represent a collection of beliefs about the causal structure of the world, then the depressed phenotype may emerge from a collection of depressive beliefs. These beliefs are modified gradually through successive combinations of expectations with observations. As a result, phenotypic remission ought to take some time as the brain's relevant statistical structures become less pessimistic.

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Keywords

major depressive disorder, generative models, free-energy principle, computational psychiatry, Major Depressive Disorder, Computational Psychiatry, Generative models, Antidepressants, BF1-990, antidepressants efficacy, antidepressants, Antidepressants Efficacy, Psychology, predictive coding

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
46
Top 10%
Top 10%
Top 10%
Green
gold