Mind the gap: quantifying individual-population gap in depressive symptom dynamics through energy landscapes

Abstract

People do not always feel as they appear. Someone who seems stable may struggle internally, whereas someone who appears distressed may experience it differently. This gap matters in psychiatry, where assessment relies on symptom scales and external evaluation. Here we developed mindGAP (Measuring INDividual-population GAPs in psychiatric energy landscapes), a hierarchical variational Bayesian framework that uses longitudinal questionnaire data to estimate population-level and individual-level pairwise maximum entropy models (pMEMs). We applied mindGAP to time-series PHQ-9 data from 248 participants. The population landscape contained three major states, whereas individual-level landscapes often diverged from this shared structure. We quantified this gap as individual-population 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). Thus, mindGAP opens a route to quantifying a previously unquantified gap between population-level and individual-level symptom organization.