2026 ECNP Congress

PS02-2094

Network-based assessment of plasticity predicts vulnerability to depressive symptomatology in healthy individuals

Claudia Delli Colli — G. Petruccioli, E.Z. Xu, A. Viglione, S. Poggini, B. Igor · Istituto Superiore di Sanità, Center for Behavioral Sciences and Mental Health, Roma, Italy

2026 ECNP Congress

Background

Early identification of individuals at elevated risk of developing depressive symptoms is a critical public health priority for effective preventive strategies. However, the capacity to identify individuals vulnerable to depression is limited, as currently available biomarkers demonstrate weak prognostic value [1]. Plasticity, defined as the capacity to transition across mental states, is increasingly recognized as a key factor in mental health, as it enables the reorganization of the neural and mental processes underlying the transitions from psychopathology to wellbeing, and vice versa [2]. Building on the conceptualization of mental disorders as networks in which psychological features act as interconnected nodes [3], a network-based operationalization of plasticity has recently been proposed [2]. In this framework, plasticity is operationalized as the inverse of connectivity strength among psychological features, with weak connectivity indicating higher plasticity and vice versa [2]. Cross-sectional studies provide empirical support, showing that higher plasticity predicts greater and faster changes in depressive symptom severity [4-5]. This study aims to investigate at the individual level whether the network-based assessment of plasticity is a reliable predictor of subsequent changes in depression symptomatology.

Methods

Data were obtained from the Multi-Mood cohort study, ecological momentary assessment study within the Neureka Project (2019-2022). Two independent cohorts were included: 155 paid university students and 194 volunteer members of the general public. In the study 16 affective states were collected twice daily for 8 weeks and depressive symptoms were assessed weekly using the Center for Epidemiologic Studies Depression Scale. A total of 146 participants – without baseline depressive symptoms – were included in the analysis. The primary outcome was the maximum change in depressive symptoms. The secondary outcome was the time to onset depressive symptomatology. Individual plasticity was estimated at baseline (first week of the study) using network analysis and operationalized as the inverse of connectivity strength among affective states, such that higher connectivity reflects lower plasticity.

Results

Baseline connectivity strength was negatively correlated with subsequent maximal change in depressive symptoms [Spearman rank correlation coefficient (ρ) = -0.54, P < .001]. Kaplan–Meier survival analyses indicated that individuals with low connectivity strength reached the cut-offs for mild depressive symptoms significantly faster than those with high connectivity strength (χ² = 41, P < .0001). Cox hazards regression confirmed that high connectivity strength was associated with a significantly lower risk of developing mild depressive symptoms (HR = 0.20, 95% CI [0.12-0.34], P < .001). Results were consistent across the two cohorts and by gender.

Conclusions

This study provides the first evidence that an individual-level, network-based measure of plasticity can prospectively predict vulnerability to depressive symptoms in a healthy population. Extending prior group-level findings, our results show that lower plasticity is associated with both a greater increase in depressive symptoms and a faster onset of symptomatology. These findings support the relevance of plasticity as a quantifiable marker of mental health vulnerability, highlighting its potential utility for early identification and targeted prevention within precision psychiatry.

Funding: The research was supported by the grant from the ERANET Neuron and Istituto Superiore di Sanità, project EnviroMood.

References

  1. Carvalho, A. F et al (2020). “Evidence-based umbrella review of 162 peripheral biomarkers for major mental disorders.” Transl Psychiatry 10(1): 152
  2. Borsboom, D. (2017). “A network theory of mental disorders.” World Psychiatry 16(1): 5-13.
  3. Branchi, I. (2022). “Plasticity in mental health: A network theory.” Neurosci Biobehav Rev 138: 104691.
  4. Delli Colli, C., et al. (2025). “A network-based analysis anticipates time to recovery from major depression revealing a plasticity by context interplay”. Transl Psychiatry. 15(1): p. 32.
  5. Delli Colli, C et al., (2024). “Towards a network-based operationalization of plasticity for predicting the transition from depression to mental health.” Nat. Mental Health 2:200–208.