Anticipating depression trajectories by measuring plasticity and change through symptom network dynamics
Year: 2025
Type: article
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 change in depression score occurs, (iii) post-change - corresponding to when the maximum clinical change is achieved. RESULTS: = 0.002). A direct and significant association between connectivity strength and depression severity emerges only at the change (ρ = 0.98, p = 0.0003) and post-change (ρ = 0.95, p = 0.001) time points. CONCLUSIONS: The interpretation of connectivity strength for predicting depression trajectories varies by timepoint: at baseline, it measures plasticity -- the capacity for change -- whereas during clinical change, it indicates the magnitude of change in symptom severity. This framework supports the reliability of this prognostic marker for designing personalized therapeutic interventions in psychiatry.
Source: European Psychiatry
Institutions: Istituto Superiore di Sanità, University of Amsterdam
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $3,300
OpenAlex ID: W4413137474