Abstract：
:Despite being considered primarily a mood disorder, major depressive disorder (MDD) is characterized by cognitive and decision making deficits. Recent research has employed computational models of reinforcement learning (RL) to address these deficits. The computational approach has the advantage in making explicit predictions about learning and behavior, specifying the process parameters of RL, differentiating between model-free and model-based RL, and the computational model-based functional magnetic resonance imaging and electroencephalography. With these merits there has been an emerging field of computational psychiatry and here we review specific studies that focused on MDD. Considerable evidence suggests that MDD is associated with impaired brain signals of reward prediction error and expected value ('wanting'), decreased reward sensitivity ('liking') and/or learning (be it model-free or model-based), etc., although the causality remains unclear. These parameters may serve as valuable intermediate phenotypes of MDD, linking general clinical symptoms to underlying molecular dysfunctions. We believe future computational research at clinical, systems, and cellular/molecular/genetic levels will propel us toward a better understanding of the disease.
journal_name
Neurosci Biobehav Revjournal_title
Neuroscience and biobehavioral reviewsauthors
Chen C,Takahashi T,Nakagawa S,Inoue T,Kusumi Idoi
10.1016/j.neubiorev.2015.05.005subject
Has Abstractpub_date
2015-08-01 00:00:00pages
247-67eissn
0149-7634issn
1873-7528pii
S0149-7634(15)00131-1journal_volume
55pub_type
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