Mood computational mechanisms underlying increased risk behavior in adolescent suicidal patients

Area of Science:

• Neuroscience
• Computational Psychiatry
• Developmental Psychology

Background:

• Suicidal thoughts and behaviors (STB) are a major global health concern.
• Elevated risk-taking is a known correlate of STB, but underlying computational mechanisms are unclear.
• Mood disturbances are central to suicidality, impacting decision-making.

Purpose of the Study:

• To investigate the cognitive and affective computational mechanisms driving increased risk-taking in adolescents with STB.
• To model decision-making processes in relation to mood and risk preference.
• To identify computational signatures predictive of suicidal symptom severity.

Main Methods:

• Utilized a decision-making task with certain and gamble options in adolescent inpatients with affective disorders (with and without STB) and healthy controls.
• Employed computational modeling (prospect-theory framework) to analyze choice behavior and mood sensitivity.
• Validated computational signatures in an independent general-population sample.

Main Results:

• Adolescents with STB (S+) showed significantly greater risk-taking compared to those without STB (S-) and healthy controls (HC).
• Increased risk-taking in S+ was driven by an elevated approach parameter and reduced sensitivity to certain rewards.
• Computational signatures predicted suicidal symptom severity and generalized to the broader population.

Conclusions:

• Identified specific cognitive (elevated approach) and affective (reduced reward sensitivity) computational mechanisms underlying risk-taking in STB.
• Findings suggest that altered reward processing contributes to maladaptive decision-making in individuals with suicidal ideation.
• Highlights the potential of computational psychiatry for early identification and prevention of suicidality.