In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 17, No. 5 ( 2021-5-10), p. e1008955-
Abstract:
Adaptive behavior requires balancing approach and avoidance based on the rewarding and aversive consequences of actions. Imbalances in this evaluation are thought to characterize mood disorders such as major depressive disorder (MDD). We present a novel application of the drift diffusion model (DDM) suited to quantify how offers of reward and aversiveness, and neural correlates thereof, are dynamically integrated to form decisions, and how such processes are altered in MDD. Hierarchical parameter estimation from the DDM demonstrated that the MDD group differed in three distinct reward-related parameters driving approach-based decision making. First, MDD was associated with reduced reward sensitivity, measured as the impact of offered reward on evidence accumulation. Notably, this effect was replicated in a follow-up study. Second, the MDD group showed lower starting point bias towards approaching offers. Third, this starting point was influenced in opposite directions by Pavlovian effects and by nucleus accumbens activity across the groups: greater accumbens activity was related to approach bias in controls but avoid bias in MDD. Cross-validation revealed that the combination of these computational biomarkers were diagnostic of patient status, with accumbens influences being particularly diagnostic. Finally, within the MDD group, reward sensitivity and nucleus accumbens parameters were differentially related to symptoms of perceived stress and depression. Collectively, these findings establish the promise of computational psychiatry approaches to dissecting approach-avoidance decision dynamics relevant for affective disorders.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1008955
DOI:
10.1371/journal.pcbi.1008955.g001
DOI:
10.1371/journal.pcbi.1008955.g002
DOI:
10.1371/journal.pcbi.1008955.g003
DOI:
10.1371/journal.pcbi.1008955.g004
DOI:
10.1371/journal.pcbi.1008955.g005
DOI:
10.1371/journal.pcbi.1008955.t001
DOI:
10.1371/journal.pcbi.1008955.s001
DOI:
10.1371/journal.pcbi.1008955.s002
DOI:
10.1371/journal.pcbi.1008955.s003
DOI:
10.1371/journal.pcbi.1008955.r001
DOI:
10.1371/journal.pcbi.1008955.r002
DOI:
10.1371/journal.pcbi.1008955.r003
DOI:
10.1371/journal.pcbi.1008955.r004
DOI:
10.1371/journal.pcbi.1008955.r005
DOI:
10.1371/journal.pcbi.1008955.r006
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2193340-6
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