A new science of mental disorders: Using personalised, transdiagnostic, dynamical systems to understand, model, diagnose and treat psychopathology

The core ideas of a 10-year research program 'New Science of Mental Disorders' are outlined. This research program moves away from the disorder-based 'one-model-fits-all' approach to treating mental disorders, and adopts the network approach to psychopathology as its foundation of research. Its core assumption is that dynamically interacting symptoms constitute the disorder. Our goal is to further develop the network approach by studying (1) dynamic networks of symptoms and other variables (i.e., elements) in a large number of individuals with a wide range of mental disorders from a transdiagnostic perspective (network-based diagnosis; mapping), including both Ecological Momentary Assessment (EMA) and digital phenotyping, (2) the transdiagnostic mechanisms reflecting potential causal relations among elements of the networks by performing experimental (pre-)clinical studies (zooming), and (3) the effectiveness of personalised network-informed interventions (tar-geting). Challenges to overcome in this research program are discussed, which relate to data collection (e.g., selection of EMA variables) and data analyses (e.g., power considerations), the development and application of network-informed diagnoses and network-informed interventions (e.g., what characteristic(s) of the network to target in interventions), and the implementation in clinical practice (e.g., train therapists in the use of networks in therapy).

Automated summary

This article outlines the core ideas of a 10-year research program called 'New Science of Mental Disorders', which moves away from the traditional disorder-based approach and adopts a network-based perspective in studying mental disorders. The program aims to develop the network approach by studying dynamic networks of symptoms and variables, understanding the underlying mechanisms, and evaluating personalized network-informed interventions. The challenges discussed in this program include data collection and analysis, development and application of network-informed diagnosis and interventions, and implementation in clinical practice.