Dynamical system modeling in schizophrenia: a narrative review of computational psychiatry frameworks

Abstract

Abstract The temporal evolution of schizophrenia symptoms is inherently complex and non-linear. Clinical states fluctuate between relatively stable and highly disorganised phases. These dynamics are only partially captured by conventional diagnostic frameworks. This limitation has motivated the development of models that explicitly account for temporal structure and state transitions. Dynamical systems theory provides a rigorous framework for describing non-linear interactions and evolving symptom trajectories. Recent advances in longitudinal data acquisition and computational analysis have enabled the identification of informative dynamical features and the characterisation of patient-specific trajectories. This narrative review synthesises the growing body of work applying dynamical systems theory to schizophrenia spectrum disorders. It distinguishes theoretical models, data-driven approaches, and frameworks empirically evaluated against real patient data. It also examines methodological limitations, translational challenges, and the need for further longitudinal validation. The aim is to provide a coherent conceptual framework for understanding schizophrenia as a dynamic process, to promote cross-disciplinary dialogue, and to guide future research in translational computational psychiatry.