Impaired reward prediction error encoding and striatal-midbrain connectivity in depression

Poornima Kumar,Andrew L Cohen,Miranda L Beltzer,Diego A Pizzagalli,Franziska Goer,Nancy H Brooks,Daniel G Dillon,Laura Murray

doi:10.1038/s41386-018-0032-x

Poornima Kumar, Andrew L Cohen + Show 6 more

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https://doi.org/10.1038/s41386-018-0032-x

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Abstract

Anhedonia (hyposensitivity to rewards) and negative bias (hypersensitivity to punishments) are core features of major depressive disorder (MDD), which could stem from abnormal reinforcement learning. Emerging evidence highlights blunted reward learning and reward prediction error (RPE) signaling in the striatum in MDD, although inconsistencies exist. Preclinical studies have clarified that ventral tegmental area (VTA) neurons encode RPE and habenular neurons encode punishment prediction error (PPE), which are then transmitted to the striatum and cortex to guide goal-directed behavior. However, few studies have probed striatal activation, and functional connectivity between VTA-striatum and VTA-habenula during reward and punishment learning respectively, in unmedicated MDD. To fill this gap, we acquired fMRI data from 25 unmedicated MDD and 26 healthy individuals during a monetary instrumental learning task and utilized a computational modeling approach to characterize underlying neural correlates of RPE and PPE. Relative to controls, MDD individuals showed impaired reward learning, blunted RPE signal in the striatum and overall reduced VTA-striatal connectivity to feedback. Critically, striatal RPE signal was increasingly blunted with more major depressive episodes (MDEs). No group differences emerged in PPE signals in the habenula and VTA or in connectivity between these regions. However, PPE signals in the habenula correlated positively with number of MDEs. These results highlight impaired reward learning, disrupted RPE signaling in the striatum (particularly among individuals with more lifetime MDEs) as well as reduced VTA-striatal connectivity in MDD. Collectively, these findings highlight reward-related learning deficits in MDD and their underlying pathophysiology.

Highlights

Major depressive disorder (MDD) is a complex, heterogenous psychiatric disorder [1] and despite decades of research, its pathophysiology remains incompletely understood
We examined reinforcement learning (RL) in unmedicated individuals with MDD using a well-established instrumental learning task in conjunction with a Q-learning computational model and a region of interest (ROI) approach
MATERIALS AND METHODS Participants Twenty-six healthy controls and 28 unmedicated individuals with MDD recruited from the community were enrolled and screened using the Structured Clinical Interview for the DSM-IV (SCID; [34]) and Hamilton Depression Rating Scale (HDRS; [35])

Summary

Introduction

Major depressive disorder (MDD) is a complex, heterogenous psychiatric disorder [1] and despite decades of research, its pathophysiology remains incompletely understood. There has been a burgeoning interest in applying computational algorithms to dissect RL in healthy and psychiatric populations Using these models, individual differences can be captured by tracking trial-by-trial variability in learning. Non-human primate findings have shown that phasic firing of dopamine (DA) neurons in the ventral tegmental area (VTA) encodes reward prediction error (RPE). These midbrain DA RPE signals are transmitted to the striatum and cortex and used to update stimulus-action values and guide goaldirected behavior [4, 5]. Consistent with this, human fMRI studies have described RPE signals in cortico-striatal circuits including the striatum, midbrain and prefrontal cortex [6, 7], and these signals are altered by manipulations that affect phasic DA signaling [8,9,10]

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