The Mathematics of Neurological Failure: Predicting Disease Onset through a Simulation of Delay-Induced Fragility

Current neurological diagnostic frameworks rely on probabilistic biomarkers that provide a static "snapshot" of pathology but lack the temporal resolution required to predict clinical onset. This research presents a deterministic approach to neuro-prognostics by modeling neurodegeneration as a systemic collapse governed by the laws of delay-induced fragility. By introducing the Srivastava Constant (v_s) and utilizing a high-dimensional digital twin framework, we simulate the kinetic decay of neuronal stability. Our findings indicate that neurological failure is a predictable phase transition that occurs when signal delays exceed a critical biological threshold. This model provides a robust mathematical foundation for forecasting the 15-year prophylactic window, shifting the clinical paradigm from reactive diagnosis to deterministic prevention.