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Mood computational mechanisms underlying increased risk behavior in adolescent suicidal patients

Wang, Z.; Nan, T.; Wang, T.; Liu, Y.; Lu, F.; Yu, Y.; Cai, X.; He, Z.; Luo, Y.; Blain, B.

2023-11-01 psychiatry and clinical psychology
10.1101/2023.10.31.23297870 medRxiv
Show abstract

Suicidal thoughts and behaviors (STB) rank among the foremost causes of death globally. While literature consistently highlights increased risk behavior in individuals with STB and identifies mood issues as central to STB, precise cognitive and affective computational mechanisms driving this increased risky behavior remain elusive. Here, we asked 83 adolescent inpatients with affective disorders, where 58 patients with STB (S+) and 25 without STB (S-), and 118 sex/age-matched healthy control (HC) to make decisions between certain vs. gamble option with momentary mood ratings. Choice data analyses revealed more risk behavior in S+ compared to S- and HC. Using a prospect theory model enhanced with value-insensitive approach-avoidance parameters revealed that this rise in risky behavior resulted only from a heightened approach parameter in S+. Furthermore, approach strength mediated the rise in gambling choices with STB severity. Altogether, model-based choice data analysis indicated dysfunction in the approach system in S+, leading to greater propensity for gambling in the gain domain regardless of the lottery expected value. Additionally, mood model-based analyses revealed reduced sensitivity to certain rewards in S+ compared to S- and HC. Importantly, these computational markers generalized to general population (n = 747). In S+, mood sensitivity to certain rewards was negatively correlated with gambling, offering a mood computational account for increased risk behavior in STB. These findings remained significant even after adjusting for demographic, clinical, and medication-related variables. Overall, our study uncovers cognitive and affective mechanisms contributing to increased risk behavior in STB, with significant implications for suicide prevention.

Published in eLife (predicted rank #17) · training set

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