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SO047

Computational decision-making phenotypes: latent profile analysis

COMPUTATIONAL DECISION-MAKING PHENOTYPES: A LATENT PROFILE ANALYSIS

Three latent profiles were identified in a nonclinical sample using computational decision-making parameters; these profiles differed in mania, behavioral activation, and social motivation.

The aim was to identify latent profiles in a nonclinical sample that could inform risk stratification by decomposing impaired decision-making, a core feature of mood disorders, using parameters of dopamine-dependent processes: reinforcement learning (RL) and vigor. A total of 162 participants recruited from the general population via Prolific.com completed cognitive tasks measuring reinforcement learning, working memory, response inhibition, effort discounting, and foraging; participants were phenotyped using computational modeling, and latent profile analysis (LPA) was performed. Models with one to six profiles were estimated and selected using the Bayesian Information Criterion (BIC); the best-fitting model had three profiles with freely estimated variances and zero covariances (BIC = 2099 in the abstract). Class differences were examined using the Bolck-Croon-Hagenaars (BCH) method across the Altman Self-Rating Mania Scale, the CES-D depression screening measure, the Snaith-Hamilton Pleasure Scale, and the Apathy Motivation Index. In the poster, Profile 1 (n = 61) was characterized by high vigor and low RL, indicating impaired reward learning accompanied by increased motor drive; Profile 2 (n = 48) was characterized by low RL and low vigor, representing a poor-performance group with reduced reward sensitivity and motor engagement; and Profile 3 (n = 52) was characterized by greater reinforcement learning ability relative to the sample mean. In the BCH analysis, scores on the mania dimension were 0.42 for Profile 1, -0.02 for Profile 2, and -0.48 for Profile 3; behavioral activation scores were 0.34, -0.003, and 0.40, respectively; and social motivation scores were -0.19, -0.08, and 0.30, respectively. Significant differences were indicated among all three profiles for mania, between Profiles 1–3 and 2–3 for behavioral activation, and between Profiles 2–3 for social motivation. In conclusion, computational phenotyping may identify meaningful behavioral subgroups beyond traditional symptom-based measures, and further studies will determine whether these phenotypes are useful in differentiating mood disorders.

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