2026 ECNP Congress

PS02-2097

A network-based measure of plasticity in major depressive disorder: investigation of contextual and biological moderators

Giulia Petruccioli — C. Delli Colli, A. Viglione, S. Poggini, B.W. Penninx, I. Branchi · Istituto Superiore di Sanità, Centre for Behavioral Sciences and Mental Health, Rome, Italy

2026 ECNP Congress

Introduction: Plasticity, the capacity to modify mental state, plays a crucial role in the transition from psychopathological condition to well-being. Recently, a novel framework, the Network theory of plasticity [1], proposed that individual plasticity can be operationalized as the inverse of global network connectivity strength (i.e., symptoms co-occurrence), where a weak connectivity reflects high plasticity, and vice versa [1-2].

Aims: The present study aims to investigate whether the individual’s plasticity levels interact with contextual factors in determining disease trajectories [3]. In addition, given the reported interplay between immune functioning and plasticity [4], the study aims to investigate whether an increase in inflammatory markers reduces plasticity in individuals with major depressive disorder (MDD).

Methods: Data derived from the Netherlands Study of Depression and Anxiety-Ecological Momentary Assessment (NESDA-EMA) study. Of 384 enrolled participants, 353 met the inclusion criteria after excluding those with more than 4 missing assessments and zero variance in mood responses. A subsample of 337 was used for inflammatory analyses. Participants completed a 31-item questionnaire 5 times per day for 14 consecutive days, including 16 momentary affect states rated on a 7-point scale, plus contextual items. The main outcomes capture the mood change in the second week (days 9-14): absolute and directional net-change, and maximum-change. Connectivity Strength was estimated from affect states collected at baseline (days 2-8) using the dynamic time warping (DTW) algorithm, quantifying their temporal covariation. The resulting DTW distance matrices were converted into similarity matrices via Radial Basis Function kernel (σ=0.5), and Connectivity Strength was computed as the sum of all edge weights. Inflammatory markers included C-reactive protein (CRP), Interleukin (IL)-6, IL-1β, and Tumor Necrosis Factor (TNF)-⍺, as well as a composite index of the first two markers. A one-month current depression diagnosis was established via the Composite International Diagnostic Interview (CIDI) and the severity of depressive symptoms via the Inventory of Depressive Symptomatology self-reported (IDS-SR).

Results: Higher Connectivity Strength (i.e., lower plasticity) significantly predicted smaller mood changes (|Maximum-change|: R=−0.47; |Net-Change|: R=−0.31, both p<0.001***). The interaction between Connectivity Strength and contextual factor, measured with an event-related stress index, in predicting the directional net-change was consistent with a “for better and for worse” pattern, but not statistically significant (p=0.09). The composite inflammatory index was positively associated with Connectivity Strength in the MDD (R=0.53, p<0.001***), but not in the no-MDD group. When analysed separately, CRP and IL-6 were also significantly associated with reduced plasticity (respectively R=0.47 and R=0.50, both p<0.01**). Accordingly, in MDD, higher inflammation was associated with reduced mood change capacity (|Maximum change|: R=−0.34, p<0.05*; |Net change|: R=−0.27, p=0.09).

Conclusions: These findings confirm the network-based plasticity as a predictor of individual capacity to change [1-2], offering a promising tool to anticipate disease trajectories [3]. Moreover, inflammation reduces plasticity in MDD participants, consistent with the inflammatory trap hypothesis [4]. Replication in larger longitudinal samples is warranted.

References

  1. I. Branchi, ‘Plasticity in mental health: A network theory.’, Neurosci Biobehav Rev, vol. 138, p. 104691, Jul. 2022, doi: 10.1016/j.neubiorev.2022.104691.
  2. C. Delli Colli, F. Chiarotti, P. Campolongo, A. Giuliani, and I. Branchi, ‘Towards a network-based operationalization of plasticity for predicting the transition from depression to mental health’, Nature Mental Health, vol. 2, no. 2, pp. 200–208, Jan. 2024, doi: 10.1038/s44220-023-00192-z.
  3. C. Delli Colli et al., ‘A network-based analysis anticipates time to recovery from major depression revealing a plasticity by context interplay’, Transl Psychiatry, vol. 15, no. 1, p. 32, Jan. 2025, doi: 10.1038/s41398-025-03246-1.
  4. I. Branchi, A. Viglione, B. Vai, F. Cirulli, F. Benedetti, and S. Poggini, ‘Breaking free from the inflammatory trap of depression: Regulating the interplay between immune activation and plasticity to foster mental health’, Neuroscience Applied, vol. 3, p. 103923, 2024, doi: 10.1016/j.nsa.2023.103923.