Dynamical System Modeling in Schizophrenia: A Narrative Review of Computational Psychiatry Frameworks

The symptom dynamics in schizophrenia often exhibit complex, non-linear temporal patterns, alternating between relatively predictable and disorganized states. Conventional diagnostic frameworks often struggle to capture these evolving dynamics, prompting the development of diverse modeling approaches aimed at describing symptom trajectories more faithfully. Among these approaches, dynamical systems theory provides a particularly powerful framework for addressing temporal dependencies, non-linear interactions, and state-dependent transitions. Recent advances in longitudinal data collection and analysis, including improved temporal resolution and computational methods, now enable both the construction of interpretable models that distinguish distinct trajectories of symptom evolution and the extraction of informative dynamical features. This narrative review synthesizes the growing body of work applying dynamical systems theory to schizophrenia spectrum. It highlights convergent insights across studies while explicitly distinguishing between conceptually motivated models, emerging computational approaches, and empirically validated frameworks. This narrative review also addresses current limitations, including methodological constraints, issues of feasibility, and the need for further longitudinal and clinical 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.