Anticipating depression trajectories by measuring plasticity and change through symptom network dynamics.

ABSTRACT:

Background

Network analysis is a promising approach for elucidating the dynamics of the transition from psychopathology to well-being. Recently, symptom connectivity strength has been proposed as a measure of plasticity - the capacity to change disease severity. Yet, empirical findings remain inconsistent. We propose that this inconsistency can be resolved by recognizing that the interpretation of connectivity strength varies along the recovery process from depression, whether at baseline or during clinical change.

Methods

We analyzed 2,710 depressed patients from the STAR*D dataset, grouped by the magnitude of change in depressive score. Symptom network connectivity was estimated from QIDS-C items at three time points: (i) baseline, (ii) change - defined as when clinical ch

SUBMITTER: Delli Colli C 

PROVIDER: S-EPMC12438987 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature