Characterizing Alzheimer's Disease With Image and Genetic Biomarkers Using Supervised Topic Models.
Characterizing Alzheimer's Disease With Image and Genetic Biomarkers Using Supervised Topic Models.
复制标题
DOI：
10.1109/jbhi.2019.2928831
复制
发表时间：
2020-04
影响因子：
7.7
通讯作者：
Angelini ED
中科院分区：
文献类型：
--
作者：
Yang J;Feng X;Laine AF;Angelini ED
神经影像和遗传生物标记物是阿尔茨海默病(AD)诊断的关键指标，神经解剖学模式和遗传变异是AD诊断的重要指标，已被广泛用于阿尔茨海默病(AD)分类研究。旨在模拟常见发生模式的发生方法可能会促进对这种疾病的理解，但尚未充分探索AD的特征。此外，在生成过程中引入监督成分可以约束模型以进行更具区别性的表征。在这项研究中，我们提出了一种基于有监督主题建模的独创性方法，从生成的角度来描述AD，同时保持了区分疾病人群的区分能力。我们的主题建模联合使用了离散化的图像特征和分类遗传特征。诊断信息-认知正常(CN)、轻度认知障碍(MCI)和阿尔茨海默病(AD)-被引入作为监督变量。在ADNI队列上的实验结果表明，我们的模型在获得竞争性区分性能的同时，可以发现揭示已知和新的神经解剖模式的主题，包括颞区、顶区和额区；以及遗传因素和神经解剖模式之间的关联。
Neuroimaging and genetic biomarkers have been widely studied from discriminative perspectives towards Alzheimer’s disease (AD) classification, since neuroanatomical patterns and genetic variants are jointly critical indicators for AD diagnosis. Generative methods, designed to model common occurring patterns, could potentially advance the understanding of this disease, but have not been fully explored for AD characterization. Moreover, the introduction of a supervised component into the generative process can constrain the model for more discriminative characterization. In this study, we propose an original method based on supervised topic modeling to characterize AD from a generative perspective, yet maintaining discriminative power at differentiating disease populations. Our topic modeling jointly exploits discretized image features and categorical genetic features. Diagnostic information - cognitively normal (CN), mild cognitive impairment (MCI) and AD - is introduced as a supervision variable. Experimental results on the ADNI cohort demonstrate that our model, while achieving competitive discriminative performance, can discover topics revealing both well-known and novel neuroanatomical patterns including temporal, parietal and frontal regions; as well as associations between genetic factors and neuroanatomical patterns.