%0 Journal Article
%T Reward-Related Brain Activity Mediates the relationship between Decision-Making Deficits and Pediatric Depression Symptom Severity.
%A Pitliya RJ
%A Burani K
%A Nelson BD
%A Hajcak G
%A Jin J
%J Biol Psychiatry Cogn Neurosci Neuroimaging
%V 0
%N 0
%D 2024 Jun 26
%M 38942146
%F 6.05
%R 10.1016/j.bpsc.2024.06.007
%X <B>BACKGROUND: </B>The mechanisms linking neural and behavioral indices of reduced reward sensitivity in depression, particularly in children, remain unclear. Reward positivity (RewP), a neural index of reward processing, has been consistently associated with depression. Separately, recent studies using the drift-diffusion model (DDM) on behavioral data have delineated computational indices of reward sensitivity. Therefore, the present study examined whether RewP is a neural mediator of DDM-based indices of reward processing in predicting pediatric depression across varying levels of symptom severity.<BR><B>METHODS: </B>A community sample of 166 girls, aged 8 to 14 years, completed two tasks. The first was a reward guessing task from which RewP was computed using electroencephalography; the second was a probabilistic reward-based decision-making task. On this second task, DDM analysis was applied to behavioral data to quantify the efficiency of accumulating reward-related evidence (drift rate) and potential baseline bias (starting point) towards the differently rewarded choices. Depression severity was measured using the self-report Children's Depression Inventory (CDI).<BR><B>RESULTS: </B>RewP was correlated with drift rate, but not starting point bias, towards the more rewarded choice. Furthermore, RewP completely mediated the association between a slower drift rate towards the more rewarded option and higher depression symptom severity.<BR><B>CONCLUSIONS: </B>Our findings suggest that reduced neural sensitivity to reward feedback might be a neural mechanism underscoring behavioral insensitivity to reward in children and adolescents with higher depression symptom severity, offering novel insights into the relationship between neural and computational indices of reward processing in this context.