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

Chinese

作为两种最常见的神经退行性疾病(NDDs),阿尔茨海默病(AD)和帕金森病(PD)在症状和病理生理学方面都有共同点,但是,这些两种不同疾病的重叠程度和方式仍然未被探索。在这项研究中,我们提出了一个明确的、可解释的和空间适应的转移学习框架,以澄清与AD和PD相关的形态生物标记。我们将这种高层次的机器学习模型应用于两个最流行的开源神经成像数据集,即阿尔茨海默病神经成像倡议(ADNI)和帕金森进展标记倡议(PPMI)。

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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