Structural Medicine v3.0: From Predictive Signatures to Structural Stabilization in Neurodegenerative Progression

This repository accompanies the preprint: "Structural Medicine v3.0: From Predictive Signatures to Structural Stabilization in Neurodegenerative Progression" Structural Medicine v3.0 extends a previously proposed predictive framework (v2.0) toward a control-oriented formulation for neurodegenerative dynamics. Rather than modeling disease progression as a purely monotonic decline, the framework interprets neurodegeneration as a transition from structural persistence to structural instability. The model introduces three core quantities:- Structural persistence F(t)- Local structural decay rate λ(t)- Directional asymmetry A Empirical analysis based on longitudinal ADNI-derived cognitive trajectories demonstrates:- Increased directional asymmetry is associated with shorter time-to-conversion- Structural asymmetry predicts elevated hazard risk (HR ≈ 2.75)- Variance and lag-1 autocorrelation increase prior to conversion (critical slowing down) These findings are consistent with early warning signals observed in complex systems approaching critical transitions. Structural Medicine v3.0 further introduces a multidimensional instability score: R_multi(t) = Σ w_i Var[λ_i(t)] ρ_1[λ_i](t) A_i(t) and extends the framework to a control formulation: V_control(λ) = V(λ) + U(λ, t) where U(λ,t) represents interventions affecting effective recovery dynamics (e.g., pharmacological treatment, cognitive training, rehabilitation, or multimodal clinical intervention). While the included figures are synthetic demonstration visualizations for reproducibility, the reported summary statistics (hazard ratios, log-rank tests, and AUC values) are derived from empirical analysis of ADNI-based data. This repository includes:- Full preprint (PDF)- Python figure-generation script- Complete figure set (Fig1–Fig10) Data source:Alzheimer’s Disease Neuroimaging Initiative (ADNI)https://adni.loni.usc.edu This work provides a unified conceptual bridge between statistical observables, dynamical systems theory, and clinical intervention, forming a basis for predictive and interventional structural medicine.