Longitudinal computational phenotyping reveals a stable marker for depression symptoms
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.
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BibTeX
@misc{kao2026longitudinal,
title = {{Longitudinal computational phenotyping reveals a stable marker for depression symptoms}},
author = {Chang-Hao Kao and Joseph Heffner and Gloria Feng and Jihyun K. Hur and Huw Jarvis and Chelsea Uddenberg and Millie Lawrence and Ellen Martin and Rachel E. Thomas and Madeleine Wallis},
howpublished = {PsyArXiv},
year = {2026},
month = sep,
doi = {10.31234/osf.io/c3qbj_v1},
url = {https://osf.io/preprints/psyarxiv/c3qbj_v1/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Longitudinal computational phenotyping reveals a stable marker for depression symptoms
AU - Chang-Hao Kao
AU - Joseph Heffner
AU - Gloria Feng
AU - Jihyun K. Hur
AU - Huw Jarvis
AU - Chelsea Uddenberg
AU - Millie Lawrence
AU - Ellen Martin
AU - Rachel E. Thomas
AU - Madeleine Wallis
PY - 2026
DA - 2026/09/04
PB - PsyArXiv
DO - 10.31234/osf.io/c3qbj_v1
UR - https://osf.io/preprints/psyarxiv/c3qbj_v1/
AB - 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.
ER -