Structural Medicine v3.3: Adaptive Resonant Control in Non-Stationary Neurodegenerative Dynamics

This repository accompanies the preprint: "Structural Medicine v3.3: Adaptive Resonant Control in Non-Stationary Neurodegenerative Dynamics" Structural Medicine v3.3 extends the previous framework (v2.0–v3.2) by introducing adaptive resonant control to address the non-stationary nature of neurodegenerative dynamics. In v3.2, fixed-frequency resonant control was shown to fail when applied to longitudinal cognitive trajectories derived from ADNI data, revealing that structural decay dynamics λ(t) do not exhibit a stable intrinsic frequency. In v3.3, this limitation is addressed by estimating a time-dependent dominant frequency ω(t) using short-time Fourier transform (STFT). The adaptive control signal is defined as: U(t) = A(t) sin(ω(t)t + φ(t)) where A(t) scales with local instability amplitude and φ(t) is fixed as an anti-phase offset. An instability proxy R(t), defined as a rolling-window variance of λ(t), is used to evaluate control performance. The control effect is quantified as: ΔR_condition = ⟨R_no control(t)⟩ − ⟨R_condition(t)⟩ Results show that adaptive control partially reduces structural instability relative to both fixed-frequency control and no control, with stronger suppression observed in later stages where instability becomes dominant. The repository includes:- Full preprint (PDF)- Python figure generation script- Figure set (Fig15–Fig16) Figures are based on synthetic trajectories consistent with ADNI-derived structural decay modeling. Data source:Alzheimer’s Disease Neuroimaging Initiative (ADNI)https://adni.loni.usc.edu This work establishes a transition from:Predictable but not controllable (v3.2)toPartially controllable under adaptive dynamics (v3.3) and provides a foundation for future work connecting structural control signals to real-world intervention mechanisms.