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

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.

This work was supported by the National Research Council (CNR), Italy. The authors acknowledge the Institute of Cognitive Sciences and Technologies (ISTC-CNR) for providing the necessary research infrastructure. Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (https://adni.loni.usc.edu/) and the Parkinson’s Progression Markers Initiative (PPMI) database (https://www.ppmi-info.org/data).

As such, the investigators within the ADNI and PPMI contributed to the design and implementation of their respective studies and/or provided data but did not participate in the analysis or writing of this report. ADNI data collection and sharing was funded by the Alzheimer’s Disease Neuroimaging Initiative (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). PPMI – a public-private partnership – is funded by the Michael J.

Fox Foundation for Parkinson’s Research and funding partners (a full list of partners is available at https://www.ppmi-info.org/about-ppmi/who-we-are/study-sponsors). A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. This research was funded by the FISM - Fondazione Italiana Sclerosi Multipla - cod. 2022/R-Multi/040 and financed or co-financed with the “5 per mille” public funding.

Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via Gian Domenico Romagnosi, 18A, Rome, 00196, Italy AI2Life s.r.l., Innovative Start-Up, ISTC-CNR Spin-Off, Via Sebino, 32, Rome, 00199, Italy Department of Human Sciences, Communication, Education and Psychology, Libera Università Maria Ss. Assunta (LUMSA), Via della Traspontina, 21, Rome, 00193, Italy D.C. is a founding partner of AI2Life s.r.l. His primary employment is with the National Research Council (CNR), Italy.