An explainable ML framework (Importance Inversion Transfer) isolates ten shared regional volumetric brain markers between Alzheimer's and Parkinson's and validates a morphological continuum with AUC=0.894.

Although it lacks molecular or intervention insights, the study offers potentially useful cross-disease neuroimaging biomarkers for early detection and cohort stratification that could improve Parkinson's trial design and biomarker-driven therapeutic development.

Despite significant neurobiological and pathological overlaps, Alzheimer's and Parkinson's diseases-the primary threats to healthy aging-are still managed as distinct clinical entities. Standard machine learning exacerbates this diagnostic fragmentation by prioritizing divergent markers over shared traits, thereby obscuring the invariant foundations of neurodegeneration. This study introduces Importance Inversion Transfer, an explainable machine learning framework designed to identify neuroanatomical invariants across the neurodegenerative spectrum. Prioritizing structural stability over discriminative utility isolates a shared pathological core consisting of ten regional volumetric anchors, validated through an inductive protocol with high diagnostic fidelity (AUC = 0.894). The identified morphological continuum between healthy aging and neurodegeneration delineates shared structural substrates consistent with-though not demonstrative of-a potential common early-phase vulnerability. Aligned with the Neurodegenerative Elderly Syndrome hypothesis, this evidence establishes a possible paradigm for early, system-level diagnosis.