2026 ECNP Congress

PS01-1094

Network-based plasticity across neural and symptom levels reflects the capacity for change in depression

Aurelia Viglione — C. Delli Colli, G. Petruccioli, F. van der Kuy, S. Poggini, M. Gatica, I. Branchi · Istituto Superiore di Sanità, Center for Behavioral Sciences and Mental Health, Rome, Italy

2026 ECNP Congress

Background

Plasticity is a fundamental property of the brain, enabling adaptive changes in neural organization and psychological functioning in response to experience, learning, and treatment [1]. In psychiatry, plasticity is increasingly conceptualized as a determinant of recovery trajectories [2]. A recent network-based framework operationalizes plasticity as the inverse of connectivity strength among elements of a system, such that lower connectivity reflects a greater capacity for change [3]. While this framework has been validated at the symptom level [4-5], evidence linking neural and psychological levels within a unified quantitative model remains limited.

Methods

We analyzed data from the UK Biobank (N = 25,161 individuals with a history of depression and antidepressant use). Treatment response was defined based on self-reported improvement following antidepressant treatment. Resting-state fMRI data (responders N = 4,019; non-responders N = 627) were used to compute whole-brain functional connectivity (FC) across 116 regions. FC was defined as the mean absolute Pearson correlation between regional time series. Group differences were assessed using Mann–Whitney U tests and confirmed via permutation testing (10,000 iterations) and balanced bootstrap resampling. Linear and logistic regression models tested associations between FC and treatment response while adjusting for age and sex. Symptom networks were estimated using Patient Health Questionnaire-9 data (PHQ-9 scores ≥ 10, indicating clinically relevant depressive symptoms) at two independent follow-ups. Global symptom connectivity strength was computed as the sum of absolute Spearman correlations among symptoms. Group differences were evaluated using the Network Comparison Test, complemented by bootstrap procedures to account for unequal sample sizes. Additionally, a mood–context concordance index was computed from normalized depressive symptoms (Recent Depressive Symptoms, RDS-4) and life satisfaction scores, and its association with FC and treatment response was evaluated.

Results

Responders exhibited significantly lower global FC compared to non-responders (Mann–Whitney U = 1,349,202, p = 0.0043; permutation p < 0.001). This effect remained significant after adjusting for age and sex (β = −0.097 ± 0.042, p < 0.05). Logistic regression indicated that higher FC predicted lower odds of treatment response (OR = 0.91, 95% CI 0.84–0.99, p < 0.05). Connectivity differences were widespread across the Default Mode, Ventral Attention, Subcortical, and Somatomotor networks. At the symptom level, responders showed significantly lower connectivity strength than non-responders at both follow-ups (NCT p = 0.002 and p = 0.0009). Bootstrap analyses confirmed the robustness of these findings. Furthermore, individuals with higher mood–context concordance exhibited lower FC (p < 0.05) and were more likely to be treatment responders (χ² p = 0.029).

Conclusions

Across both neural and symptom domains, lower connectivity—reflecting higher plasticity—was associated with greater capacity for psychological change and better treatment outcomes. These findings support plasticity as a cross-level system property measurable through network connectivity, providing a unified quantitative framework linking brain function and symptom dynamics. This approach may inform predictive models of disease trajectories and advance precision psychiatry by identifying individuals with greater potential for clinical improvement.

This research has been conducted using the UK Biobank (Application 102831) and has been supported by ERA-NET Neuron (EnviroMood) and Istituto Superiore di Sanità (NeuroDynamics ISS20-6bbea6c1e02b).

References

  1. Viglione A., Delli Colli C., Poggini S., Mobasher M., Vai B., Poletti S., Benedetti F., Branchi I. A network-based approach reveals higher plasticity levels in bipolar than major depressive disorder. J Affect Disord., 387:119549 (2025).
  2. Branchi, I. Integrating plasticity into precision psychiatry. European psychiatry : the journal of the Association of European Psychiatrists, 1-12 (2025).
  3. Branchi, I. Plasticity in mental health: A network theory. Neurosci Biobehav Rev 138, 104691 (2022).
  4. Delli Colli, C., Chiarotti, F., Campolongo, P., Giuliani, A., Branchi I. Towards a network-based operationalization of plasticity for predicting the transition from depression to mental health. Nat. Mental Health 2, 200–208 (2024).
  5. Delli Colli, C., Viglione, A., Poggini, S., Cirulli, F., Chiarotti, F., Giuliani, A., Branchi, I. A network-based analysis anticipates time to recovery from major depression revealing a plasticity by context interplay. Translational psychiatry 15, 32 (2025).