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<p>Predicting disease trajectories in patients with major depressive disorder (MDD) can allow designing personalized therapeutic strategies. In this study, we aimed to show that measuring patients’ plasticity – that is the susceptibility to modify the mental state – identifies at baseline who will recover, anticipating the time to transition to wellbeing. We conducted a secondary analysis in two randomized clinical trials, STAR*D and CO-MED. Symptom severity was assessed using the Quick Inventory of Depressive Symptomatology while the context was measured at enrollment with the Quality-of-Life Enjoyment and Satisfaction Questionnaire. Patients were retrospectively grouped based on both their time to response or remission and their plasticity levels at baseline assessed through a network-based mathematical approach that operationalizes plasticity as the inverse of the symptom network connectivity strength. The results show that plasticity levels at baseline anticipate time to response and time to remission. Connectivity strength among symptoms is significantly lower – and thus plasticity higher – in patients experiencing a fast recovery. When the interplay between plasticity and context is considered, plasticity levels are predictive of disease trajectories only in subjects experiencing a favorable context, confirming that plasticity magnifies the influence of the context on mood. In conclusion, the assessment of plasticity levels at baseline holds promise for predicting MDD trajectories, potentially informing the design of personalized treatments and interventions. The combination of high plasticity and the experience of a favorable context emerges as critical to achieve recovery.</p>

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A network-based analysis anticipates time to recovery from major depression revealing a plasticity by context interplay

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月28日

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  • Claudia Delli Colli,
  • Aurelia Viglione,
  • Silvia Poggini,
  • Francesca Cirulli,
  • Flavia Chiarotti,
  • Alessandro Giuliani,
  • Igor Branchi

摘要

Predicting disease trajectories in patients with major depressive disorder (MDD) can allow designing personalized therapeutic strategies. In this study, we aimed to show that measuring patients’ plasticity – that is the susceptibility to modify the mental state – identifies at baseline who will recover, anticipating the time to transition to wellbeing. We conducted a secondary analysis in two randomized clinical trials, STAR*D and CO-MED. Symptom severity was assessed using the Quick Inventory of Depressive Symptomatology while the context was measured at enrollment with the Quality-of-Life Enjoyment and Satisfaction Questionnaire. Patients were retrospectively grouped based on both their time to response or remission and their plasticity levels at baseline assessed through a network-based mathematical approach that operationalizes plasticity as the inverse of the symptom network connectivity strength. The results show that plasticity levels at baseline anticipate time to response and time to remission. Connectivity strength among symptoms is significantly lower – and thus plasticity higher – in patients experiencing a fast recovery. When the interplay between plasticity and context is considered, plasticity levels are predictive of disease trajectories only in subjects experiencing a favorable context, confirming that plasticity magnifies the influence of the context on mood. In conclusion, the assessment of plasticity levels at baseline holds promise for predicting MDD trajectories, potentially informing the design of personalized treatments and interventions. The combination of high plasticity and the experience of a favorable context emerges as critical to achieve recovery.