Depression as a disorder of distributional coding

TL;DR

This paper proposes a new theory linking depression to disrupted distributional coding of value in the brain, integrating dopamine, reinforcement learning, and AI insights to better understand its neural mechanisms.

Contribution

It introduces a novel model of depression's pathophysiology that combines three research areas into a comprehensive framework.

Findings

Links dopamine dysfunction to distributional coding deficits in depression

Proposes a unified model connecting reinforcement learning and neural coding

Suggests new research directions for understanding and treating depression

Abstract

Major depressive disorder persistently stands as a major public health problem. While some progress has been made toward effective treatments, the neural mechanisms that give rise to the disorder remain poorly understood. In this Perspective, we put forward a new theory of the pathophysiology of depression. More precisely, we spotlight three previously separate bodies of research, showing how they can be fit together into a previously overlooked larger picture. The first piece of the puzzle is provided by pathophysiology research implicating dopamine in depression. The second piece, coming from computational psychiatry, links depression with a special form of reinforcement learning. The third and final piece involves recent work at the intersection of artificial intelligence and basic neuroscience research, indicating that the brain may represent value using a distributional code. Fitting these three pieces together yields a new model of depression's pathophysiology, which spans circuit, systems, computational and behavioral levels, opening up new directions for research.