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Towards a network-based operationalization of plasticity for predicting the transition from depression to mental health

Nature Mental Health volume 2, pages 200–208 (2024)Cite this article

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Abstract

Plasticity is the ability to modify brain and behavior and allows the transition from psychopathology to mental wellbeing. High plasticity has been associated with high susceptibility to contextual factors, for example, living conditions, which ultimately drive the plasticity outcome. Here we exploited network analysis to show that plasticity—in this case, the susceptibility to modify the depression score—can be measured by assessing the symptom network connectivity: the weaker the connectivity, the higher the plasticity, resulting in a greater modification in mood symptoms. We analyzed the STAR*D dataset and found that baseline connectivity strength was weaker in responder patients than non-responder patients. Moreover, connectivity strength was inversely correlated with improvement in depression score (ρ = –0.88, P = 0.002) and susceptibility to change mood according to context (ρ = 0.78, P = 0.028). This operationalization of plasticity provides a mathematical tool to predict resilience, vulnerability and recovery, and to develop novel approaches for the prevention and treatment of major depressive disorder.

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Fig. 1: Flow diagram of the study.
Fig. 2: Connectivity strength of the symptom network is weaker in responder than in non-responder patients.
Fig. 3: Connectivity strength of the symptom network is inversely correlated to improvement.
Fig. 4: Connectivity strength of the symptom network is correlated to the susceptibility to change mood according to context.

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Data availability

The dataset used in the present study is available through the NIMH Data Archive. Researchers interested in using the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) dataset should request it from the NIMH (https://nda.nih.gov/).

Code availability

The R codes used for the analyses are publicly available and can be downloaded at https://figshare.com/s/a7874b5986b9a27aa030.

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Acknowledgements

The research was supported by the grant from the Italian Ministry of Health Ricerca Finalizzata RF-2018-12367249 and the Istituto Superiore di Sanità ISS20-4f4dcc4bflbc to I.B. We thank F. Cirulli (Istituto Superiore di Sanità) and J. Niedda (Sapienza University, Rome, Italy) for critical reading and inputs on data interpretation, and A. Maione (Istituto Superiore di Sanità) for editorial support.

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Authors and Affiliations

  1. Center for Behavioral Sciences and Mental Health, Istituto Superiore di Sanità, Rome, Italy

    Claudia Delli Colli, Flavia Chiarotti & Igor Branchi

  2. PhD program in Pharmacology and Toxicology, Department of Physiology and Pharmacology ‘Vittorio Erspamer’, Sapienza University of Rome, Rome, Italy

    Claudia Delli Colli

  3. Department of Physiology and Pharmacology ‘Vittorio Erspamer’, Sapienza University of Rome, Rome, Italy

    Patrizia Campolongo

  4. Neuropsychopharmacology Laboratory, European Center for Brain Research (CERC), IRCCS Santa Lucia Foundation, Rome, Italy

    Patrizia Campolongo

  5. Environment and Health Department, Istituto Superiore di Sanità, Rome, Italy

    Alessandro Giuliani

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Contributions

I.B. conceptualized theoretical background underpinning the study and wrote the manuscript in collaboration with C.D.C., A.G. and F.C. C.D.C. wrote the R codes and supervised the analyses in consultation with F.C., A.G. and I.B. The manuscript was reviewed and edited by F.C., A.G. and P.C. All of the authors of this study have fulfilled the criteria for authorship required by Nature Portfolio journals as their participation was essential for the design and implementation of the study. Roles and responsibilities were agreed among authors. All authors have agreed to all of the content within the manuscript.

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Correspondence to Igor Branchi.

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Nature Mental Health thanks Mayank Jog, Claus Normann and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Delli Colli, C., Chiarotti, F., Campolongo, P. et al. Towards a network-based operationalization of plasticity for predicting the transition from depression to mental health. Nat. Mental Health 2, 200–208 (2024). https://doi.org/10.1038/s44220-023-00192-z

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