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May 3, 2026Open Access

Structural Medicine v2.0.1: Neurodegeneration as a Latent Critical Structural Transition with Predictive Signatures

Key Points

This work aims to provide a structural framework for analyzing neurodegenerative progression through longitudinal cognitive data.
Introduced three core quantities: structural persistence, local structural decay rate, and directional asymmetry.
Analyzed time-to-conversion using Kaplan-Meier analysis and hazard risk with Cox proportional hazards modeling.
Examined pre-conversion variance instability and lag-1 autocorrelation as indicators of critical transitions.
Increased directional asymmetry is associated with shorter time-to-conversion.
Structural asymmetry predicts elevated hazard risk (HR ≈ 2.75).
Variance and autocorrelation increase prior to conversion.

Abstract

This repository accompanies the preprint: "Structural Medicine v2.0.1: Neurodegeneration as a Latent Critical Structural Transition with Predictive Signatures" This work presents a time-dependent structural framework for analyzing neurodegenerative progression using longitudinal cognitive data. Rather than modeling disease progression as a purely monotonic decline, the framework interprets neurodegeneration as a potential loss of structural resilience leading toward a latent critical transition. The model introduces three core quantities:- Structural persistence F(t)- Local structural decay rate λ(t)- Directional asymmetry A Using these variables, the study analyzes:- Time-to-conversion- Conversion-free probability (Kaplan–Meier analysis)- Hazard risk (Cox proportional hazards modeling)- Pre-conversion variance instability- Lag-1 autocorrelation (critical slowing down) The results show:- Increased directional asymmetry is associated with shorter time-to-conversion- Structural asymmetry predicts elevated hazard risk (HR ≈ 2.75)- Variance and autocorrelation increase prior to conversion These findings are consistent with early warning signals observed in complex systems approaching critical transitions. A schematic asymmetric potential model is proposed to connect statistical indicators with dynamical transition behavior. Version v2.0.1 is a maintenance and reproducibility update of v2.0, improving:- Consistency between manuscript, figures, and code- Alignment of figure filenames and LaTeX references- Reproducibility via a unified Python figure-generation script No core theoretical or analytical claims are modified from v2.0. This repository includes:- Full preprint (PDF)- Python script for figure generation (synthetic demonstration)- Complete figure set (Fig1–Fig9) Data used in this study were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI):https://adni.loni.usc.edu The included code does not redistribute ADNI data and uses synthetic data for demonstration purposes.

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