Newest papers

Cross-disorder Learning of Imaging-derived Joint Mophometric Markers in Alzheimer's and Parkinson's Diseases

Yuji Zhao, Boris A. Gutman · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27150 · cited by 0 works (OpenAlex) · open-access PDF

German

Als die beiden häufigsten neurodegenerativen Erkrankungen (NDDs), die Alzheimer-Krankheit (AD) und die Parkinson-Krankheit (PD) teilen gemeinsame Gemeinsamkeiten sowohl in der Symptomologie als auch in der Pathophysiologie. Jedoch, in welchem Umfang und in welcher Weise diese beiden unterschiedlichen Erkrankungen überlappen, bleiben unerforscht. In dieser Studie schlagen wir einen expliziten, interpretierbaren und räumlich adaptiven Transfer-Lernrahmen vor, um die morphometrischen Biomarker im Zusammenhang mit AD und PD zu klären. Wir haben ein solches hochdimensionales maschinelles Lernmodell auf zwei beliebteste Open-Source-Neuroimaging-Datensätze angewendet, dh die Alzheimer-Krankheit Neuroimaging Initiative (ADNI)

Machine translation (Meta M2M100); the English original follows.

Abstract

As two most prevalent neurodegenerative diseases (NDDs), Alzheimer's disease (AD) and Parkinson's disease (PD) shared commonalities both in symptomology and pathophysiology. However, to what extent and in what way these two distinct disorders overlap remain underexplored. In this study, we propose an explicit, interpretable and spatially adaptive transfer learning framework to elucidate the morphometric biomarkers linked to AD and PD. We applied such high-dimensional machine learning model to two most popular open-source neuroimaging datasets, i.e., Alzheimer's Disease Neuroimaging Initiative (ADNI) and Parkinson's Progression Markers Initiative (PPMI). On the one hand, our cross-disorder classifier revealed neuro-anatomical alteration patterns well aligning with previous neuroimaging research findings and underscoring patients' clinical presentations. On the other hand, the spatially adaptive trained classifier using shared prior achieved statistically superior diagnostic performance compared to respective individual models, which further highlights the cross-disorder framework's efficacy on modeling limited-size datasets across disease domains.

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Topics: Neuroimaging, Disease, Classifier (UML), Artificial intelligence, Medicine, Machine learning

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