Representing the Cognitive Impairment Continuum of Alzheimer's Disease and Lewy Body Dementia with a Novel Finer‐Scale Cortical Representation via Disease Embedding Tree

作者：Tong Chen, Minheng Chen, Yan Zhuang, Jing Zhang, Lu Zhang, T. Liu, Andrew M. Blamire, John T O'Brien, Li Su, Dajiang Zhu · 发表于：Alzheimer s & Dementia · 年份：2025 · DOI：10.1002/alz70856_102179 · 被引用次数：2 · 研究领域：Dementia and Cognitive Impairment Research、Functional Brain Connectivity Studies、Alzheimer's disease research and treatments

BACKGROUND: Alzheimer's Disease (AD) and Lewy Body Dementia (LBD) often exhibit overlapping neuropathological features and symptoms, posing significant challenges for differential diagnosis. While many studies focus on leveraging machine learning with neuroimaging data for early dementia diagnosis, investigating the progression and interactions between AD and LBD offers an opportunity to uncover valuable insights into their shared features and hidden connections. METHOD: We propose the Disease Embedding Tree (DET) framework to model continuous relationships among AD, Cognitively Normal (CN), and LBD subjects on T1-weighted structural MRI data from 106 subjects (36 AD: 15 females, 21 males; 78.25 ± 5.76 years; 35 CN: 15 females, 20 males; 76.74 ± 5.15 years; 35 LBD: 8 females, 27 males; 78.37 ± 6.94 years). For each subject, we reconstructed cortical surfaces and adopted a novel cortical folding pattern representation, Gyral Network, to identify the potential cortical hubs, known as 3 hinge gyri (3HGs). Cortical features, including cortical thickness, curvature, sulcal depth, fractal dimension, and local gyrification index, were extracted from 3HGs and used to train the DET model. The DET model projects the extracted features of each subject to a high-dimensional embedding space. The order constraint conditions the inter-group relationship while the Mini-Mental State Examination (MMSE) scores are incorporated to model the inter-subject continuous relationship in embedding spac...