People do not always feel the way they appear. Someone who seems emotionally stable may be struggling internally, whereas someone who appears distressed may experience their state very differently. This gap matters in psychiatry, where assessment often relies on symptom scales and external evaluation. Here we developed a hierarchical variational Bayesian framework to jointly estimate population-derived dynamics, reflecting an external reference based on pooled data, and individual-derived dynamics, reflecting each participant's subjective symptom organization. We applied this framework to time-series PHQ-9 data of depressive states from 248 participants during the COVID-19 pandemic. The population landscape contained three major stable states, whereas individualized landscapes often diverged substantially from this shared structure. We quantified this mismatch as individual-group landscape divergence, which was associated not only with depressive severity but also with modern-type depression-related traits (TACS-22) and interpersonal sensitivity-self traits (IPS-22). These findings reveal a previously unquantified gap between population-level and individual-level symptom organization.
Tsutsumi, M., Kubo, T., Kato, T. A., Naoki, H.
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