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: ΔRcondition = ⟨Rₙo control (t) ⟩ − ⟨Rcondition (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.