Structural Medicine v1.9.2: Data-Driven Reconstruction, Prediction, and Leakage-Controlled Validation of Structural Potential in Neurodegeneration

This work presents Structural Medicine v1. 9. 2, a data-driven framework for reconstructing, predicting, and validating structural dynamics in neurodegenerative progression using longitudinal cognitive data. Conventional models describe disease progression as a monotonic decline. In contrast, we demonstrate that neurodegeneration follows a two-stage dynamical process: (1) Variance instability — early-stage increase in structural fluctuations (2) Directional collapse bias — late-stage irreversible drift toward degradation Using longitudinal cognitive data (ADNI framework), we define structural persistence F (t) from normalized MMSE and compute the local structural decay rate λ (t). From these trajectories, we reconstruct: - empirical drift fields - effective structural force F_λ (λ) - reconstructed structural potential V (λ) The results reveal a consistent deformation of the underlying potential landscape across disease stages: balanced basin → flattening → directional deformation → collapse-biased dynamics In addition, we demonstrate that directional asymmetry enables individual-level prediction of structural collapse. A receiver operating characteristic (ROC) analysis shows strong predictive performance (AUC = 0. 908). To ensure robustness, we perform leakage-controlled validation by restricting predictors to pre-conversion data. The predictive performance remains strong under this constraint (AUC = 0. 850, 95% CI 0. 813, 0. 885), confirming that directional asymmetry captures genuine early structural instability signals rather than post-hoc information. This provides a unified structural interpretation linking statistical fluctuation, asymmetry emergence, dynamical instability, and predictive behavior within a single potential-based framework. All figures are reproducible using the provided Python scripts. Synthetic data generation is included for demonstration when original datasets are unavailable. This work bridges clinical longitudinal analysis and dynamical systems theory, offering a new perspective on neurodegenerative processes as structural transitions rather than simple decline.