Context-Dependent Activation of the Left Nucleus Accumbens in Reward Anticipation Predicts Clinical Outcome in Adolescents with Subthreshold Depression.

Adolescence is a critical developmental period during which subthreshold depression (StD) is common yet highly heterogeneous in its outcomes. Dysfunctional neural processing of reward and loss anticipation has been implicated in adolescent depression. However, it remains unclear whether these neural markers predict clinical trajectories in pre/early adolescents with StD.

Using data from the longitudinal cohort of Adolescent Brain Cognitive Development (ABCD) Study, we tracked pre/early adolescents over three years and classified them into four outcome groups: conversion to probable major depressive disorder (MDD), persistence of StD, remission of StD, and stable healthy controls (HC). Baseline functional magnetic resonance imaging (fMRI) data from the Monetary Incentive Delay (MID) task were used to quantify activation in key motivational regions during reward and loss anticipation of varying salience. Multinomial logistic regression models assessed whether baseline activation predicted later outcomes, with odds ratios (ORs) estimating the risk of MDD onset and symptom persistence.

The results showed a 6.6-fold higher risk of developing probable MDD in pre/early adolescents with StD, as compared to HC. Relative to HC, pre/early adolescents with StD showed altered activation in striatal and midbrain regions during reward anticipation. Specifically, left nucleus accumbens (NAcc) activation showed a context-dependent predictive pattern: lower activation during high-reward anticipation predicted conversion to probable MDD, whereas lower activation during loss anticipation predicted symptom remission.

Left NAcc activation may have context-dependent prognostic relevance for divergent parent-reported depressive symptom trajectories in pre/early adolescents.
Mental Health
Care/Management

Authors

Niu Niu, Dai Dai, Zeng Zeng, Zhu Zhu, Yin Yin, Liang Liang, Ying Ying, Wang Wang, Zhou Zhou, Li Li, Yu Yu, Ma Ma, Zhang Zhang
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