Explainable machine learning identifies candidate shared neuroanatomical features in Alzheimer's and Parkinson's via importance inversion transfer

Abstract

Despite significant neurobiological and pathological overlaps, Alzheimer's (AD) and Parkinson's (PD)-the primary threats to healthy aging-are still managed as distinct clinical entities. Standard machine learning exacerbates this fragmentation by prioritizing divergent markers over shared traits, obscuring the invariant foundations of neurodegeneration. This study introduces an explainable framework leveraging Importance Inversion Transfer (IIT) to identify candidate neuroanatomical features that show relative stability across both disorders. By prioritizing structural anchors invariant across the neurodegenerative spectrum, IIT isolates candidate shared structural features. Analysis of multi-regional brain volumes identifies eight shared anchors, confirmed via an inductive validation protocol with high diagnostic robustness (AUC = 0.894). Findings reveal a morphological continuum between healthy aging and neurodegeneration, suggesting the presence of partially shared structural substrates. These results are consistent with-though do not demonstrate-a potential common early-phase vulnerability across neurodegenerative conditions, as conceptualized by the Neurodegenerative Elderly Syndrome (NES) framework, establishing a possible paradigm for early, system-level diagnosis.