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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

Korean

두 가지 가장 흔한 신경발생성 질환 (NDDs) 인 알츠하이머 병 (AD) 및 파킨슨 병 (PD)은 증상 및 병 생리학 모두에서 공통점을 공유합니다. 그러나, 이 두 가지 분리 된 질환이 어떠한 방식으로 덮여 있는지 여전히 탐구되지 않습니다. 이 연구에서 우리는 AD와 PD와 관련된 모르포메틱 바이오 마커를 밝히기 위해 명시적이고 해석 가능한 공간 적응형 전송 학습 프레임 워크를 제안했습니다. 그러나 우리는 두 가지 가장 인기있는 오픈 소스 신경 이미징 데이터 세트, 즉 알츠하이머 질병 Neuroimaging Initiative (ADNI) 및 파킨슨의 Progression Markers Initiative (PPMI)를 적용했습니다. 한편으로, 우리의 교차 장애 분류는

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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