Major Problems in Clinical Psychological Science and How to Address them. Introducing a Multimodal Dynamical Network Approach

Abstract

Abstract Background Despite impressive dissemination programs of best-practice therapies, clinical psychology faces obstacles in developing more efficacious treatments for mental disorders. In contrast to other medical disciplines, psychotherapy has made only slow progress in improving treatment outcomes. Improvements in the classification of mental disorders could enhance the tailoring of treatments to improve effectiveness. We introduce a multimodal dynamical network approach, to address some of the challenges faced by clinical research. These challenges include the absence of a comprehensive meta-theory, comorbidity, substantial diagnostic heterogeneity, violations of ergodicity assumptions, and a limited understanding of causal processes. Methods Through the application of multimodal dynamical network analysis, we describe how to advance clinical research by addressing central problems in the field. By utilizing dynamic network analysis techniques (e.g., Group Iterative Multiple Model Estimation, multivariate Granger causality), multimodal measurements (i.e., psychological, psychopathological, and neurobiological data), intensive longitudinal data collection (e.g., Ecological Momentary Assessment), and causal inference methods (e.g., GIMME), our approach could improve the comprehension and treatment of mental disorders. Under the umbrella of the systems approach and utilizing e.g., graph theory and control theory, we aim to integrate data from longitudinal, multimodal measurements. Results The multimodal dynamical network approach enables a comprehensive understanding of mental disorders as dynamic networks of interconnected symptoms. It dismantles artificial diagnostic boundaries, facilitating a transdiagnostic view of psychopathology. The integration of longitudinal data and causal inference techniques enhances our ability to identify influential nodes, prioritize interventions, and predict the impact of therapeutic strategies. Conclusion The proposed approach could improve psychological treatment by providing individualized models of psychopathology and by suggesting individual treatment angles.