A Unified Dynamical-Structural Theory of Psychiatric Disorders

This repository presents a unified dynamical-structural framework for understanding major psychiatric disorders as emergent properties of large-scale brain network dysfunction. Rather than conceptualizing these disorders as isolated chemical imbalances or region-specific abnormalities, this work proposes a system-level model grounded in neural dynamics. At the core of the framework is a three-stage mechanism: the formation of a pathological node, its propagation across interconnected neural circuits, and the emergence of a network lock—a self-sustaining, closed dynamical state characterized by persistent activity, loss of flexibility, and resistance to external modulation. Within this model, diverse psychiatric conditions—including obsessive-compulsive disorder (OCD), treatment-resistant depression (TRD), and schizophrenia—are reinterpreted as distinct manifestations of the same underlying dynamical principle. Differences between disorders arise from variations in node localization, network topology, and oscillatory characteristics, rather than fundamentally different etiologies. The framework integrates converging evidence across multiple domains, including electrophysiology (EEG/MEG), functional and structural neuroimaging (fMRI, MRI), and neurobiological processes. It further introduces four fundamental pathological patterns—closed-loop circuits, pathological reentrant loops, aberrant oscillatory dynamics, and dysfunctional neurocircuits—as unified expressions of network-level lock states. By shifting the focus from static abnormalities to dynamic processes, this work provides a mechanistic explanation for symptom persistence, progressive structural changes, and treatment resistance. It also generates testable predictions and establishes a theoretical foundation for next-generation interventions, including targeted neuromodulation and closed-loop therapeutic systems. This repository includes a master theoretical framework alongside disorder-specific applications, offering a scalable model for future research in neuroscience, computational modeling, and precision psychiatry.