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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

Indonesian

Sebagai dua penyakit neurodegeneratif yang paling umum (NDDs), penyakit Alzheimer (AD) dan penyakit Parkinson (PD) berbagi kesamaan baik dalam gejala dan pathophysiology. Namun, sejauh mana dan bagaimana dua gangguan yang berbeda ini bertepatan tetap belum dijelaskan. Dalam penelitian ini, kami mengusulkan kerangka kerja pembelajaran transfer eksplisit, dapat diinterpretasikan dan dapat diadaptasi secara spasial untuk mengklarifikasi biomarker morfometrik yang terkait dengan AD dan PD. Kami menerapkan model pembelajaran mesin dimensi tinggi seperti itu ke dua set data neuroimaging sumber terbuka yang paling populer, yaitu, Alzheimer's Disease Neuroimaging Initiative (ADNI) dan Parkinson's Progression Markers Initiative (PPMI). Di satu sisi, klasifikasi disorder cross-order kami mengungkapkan pola perubahan neuroanatomik yang sesuai dengan

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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