Longitudinal computational phenotyping reveals a stable marker for depression symptoms
Year: 2026
Type: preprint
Study design: Observational Study
Abstract: Major depression is associated with reinforcement learning deficits, but it is unclear whether model parameters are stable behavioral markers for specific symptoms. We collected longitudinal smartphone task and questionnaire data from 300 depressed and 124 non-depressed participants over six months (3,461 sessions) with diagnosis confirmed by clinical interviews. Orthogonal general depression and anhedonia dimensions were extracted from questionnaires. Using computational modeling, we found that reward sensitivity, quantifying the blunted impact of small rewards on learning, was associated with elevated anhedonia and not general depression or diagnosis. In contrast, low baseline mood was associated with increased general depression but not anhedonia. Longitudinal analysis showed that baseline mood tracks changes in general depression over time, while reward sensitivity is a stable trait-like marker that predicts anhedonia. Using smartphone-based longitudinal computational phenotyping, we find distinct relationships between reinforcement learning and heterogeneous symptoms, identifying behavioral markers that represent potential targets for personalized medicine.
Source: PsyArXiv (OSF Preprints)
Authors: Chang-Hao Kao, Joseph Heffner, Gloria Feng, Jihyun K. Hur, Huw Jarvis, Chelsea Uddenberg, Millie Lawrence, Ellen Martin, Rachel E. Thomas, Madeleine Wallis, Katie Myerscough, Connor Lawhead, Sharina Hamm, Stephanie C. Lazzaro, Margaret T. Davis, Matilde M. Vaghi, John Krystal, Robb Rutledge
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7208740713