What question did this study set out to answer?

The goal is to evolve from a predictive framework to a control-oriented model for neurodegenerative disease progression.

May 5, 2026Open Access

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

Key Points

The goal is to evolve from a predictive framework to a control-oriented model for neurodegenerative disease progression.
Developed a framework integrating structural persistence, decay rates, and asymmetry.
Analyzed cognitive trajectories from the Alzheimer’s Disease Neuroimaging Initiative (ADNI).
Introduced a multidimensional instability score and control formulation to assess intervention effects.
Increased directional asymmetry is linked to shorter time-to-conversion.
Higher structural asymmetry predicts a hazard risk of approximately 2.75 (HR).
Variance and lag-1 autocorrelation increase before conversion, signaling critical transitions.

Abstract

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: Rₘulti (t) = Σ wᵢ Varλᵢ (t) ρ₁λᵢ (t) Aᵢ (t) and extends the framework to a control formulation: Vcontrol (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.

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