Thesis title: Measuring plasticity in mental health: a network-based approach
Any dynamic process in biology that produces changes is enabled by plasticity. In neuroscience and psychiatry, plasticity – defined as the ability to modify brain functioning, behavior and mental process – can be assessed as the rate of change in response to environmental stimuli over time. In clinical practice, plasticity is increasingly recognized as a crucial process as it enables the transition from psychopathology to well-being by reorganizing brain functioning and mental processes. It is noteworthy that the definition of plasticity implies that plasticity is neither inherently beneficial nor detrimental as enhancing plasticity increases the likelihood of mental state transitions without determining the direction in which these transitions occur. The trajectory of such transitions is determined by contextual factors, such as living conditions or subjective appraisal of quality of life. The varying degrees of plasticity – and thus of ability to change – may in part explain why some people are capable to recover while others not, as well as why individuals may exhibit different responses to pharmacological treatments and therapeutic strategies involving environmental and psychotherapeutic interventions. Although plasticity can provide valuable insights into individual's ability to change, there is currently no comprehensive measure that can quantitatively assess it. Growing evidence suggests that the network-based approach in mental health holds promise for advancing the field. This approach conceptualizes mental disorders as networks in which symptoms act as interconnected nodes that directly influence one another. The ground-breaking aspect of this theoretical framework is its ability to explore the onset, progression and recovery of psychopathology by leveraging the general properties of networks. Building on this perspective, the recent network theory of plasticity proposes an operationalization of plasticity, positing that the network connectivity strength among symptoms serves as a measure of individual’s plasticity and therefore of their ability to transition from psychopathology to wellbeing. In particular, the weaker the connectivity, the higher the plasticity and thus the greater the capability of the individual to modify their mental state. During my PhD, I analyzed clinical datasets to validate this operationalization. To this aim, I exploited mathematical tools derived from network analysis well implemented in R statistical software. In Study 1, published in 2024 in Nature Mental Health, we analyzed the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) dataset, showing that the network connectivity strength is significantly weaker in responder than in non-responder patients, and is inversely correlated with improvement in depression scores (i.e., the greater the improvement, the lower the connectivity). Additionally, as high plasticity promotes changes in mental state that are shaped by contextual factors, we found that baseline connectivity strength correlates with the susceptibility to change depression score according to the quality of context. Therefore, individuals with weak connectivity – that is high plasticity – are those who improve their mental state when exposed to favorable contextual factors. In Study 2, submitted to Translational Psychiatry, we further validated the operationalization of plasticity, exploring the hypothesis that the time required to shift from one state to another, such as from psychopathology to wellbeing, is determined by plasticity levels – where higher plasticity corresponds to faster transitions – in two independent datasets, the STAR*D and the Combining medications to enhance depression outcomes (CO-MED). We found a proportional relationship between connectivity strength at baseline and the time needed to achieve response. In particular, patients showing a fast recovery are those characterized by lower connectivity at baseline. When considering the interplay between plasticity and context, plasticity levels were predictive of disease trajectories mainly in subjects experiencing a favorable context, confirming that plasticity magnifies the influence of the context on mood. Overall, these results demonstrate the reliability and applicability of the operationalization of plasticity. This approach holds promise to advance clinical practice in psychiatry, allowing to stratify patients at baseline for personalized treatments and interventions based on plasticity and context.