Detection of Mild Cognitive Impairment from Language Markers with Crossmodal Augmentation
Detection of Mild Cognitive Impairment from Language Markers with Crossmodal Augmentation
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DOI：
10.1142/9789811270611_0002
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发表时间：
2022-11
影响因子：
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通讯作者：
Guangliang Liu;Zhiyu Xue;L. Zhan;H. Dodge;Jiayu Zhou
中科院分区：
文献类型：
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作者：
Guangliang Liu;Zhiyu Xue;L. Zhan;H. Dodge;Jiayu Zhou
轻度认知障碍是阿尔茨海默病的前驱阶段。它的检测一直是建立队列研究和开发阿尔茨海默氏症治疗干预措施的关键任务。已经开发了各种类型的标记物用于检测。例如，来自神经成像的成像标记物已经显示出很高的灵敏度，而其成本对于早期痴呆的大规模筛查仍然是令人望而却步的。数字生物标志物（如语言标记）的最新进展提供了一种可获得且负担得起的替代方案。虽然成像标记给出了大脑的解剖学描述，但语言标记捕捉了早期痴呆受试者的行为特征。这种差异表明，从成像方式的辅助信息的好处，以提高预测能力的单峰预测模型的基础上单独的语言标记。然而，联合分析的一个重大障碍是，在典型的队列中，只有非常有限的受试者同时具有成像和语言模式。为了应对这一挑战，在本文中，我们开发了一种新的跨模态增强工具，它利用辅助成像信息来改善语言标记的特征空间，使只有语言标记的主体可以通过增强从成像信息中受益。我们的实验结果表明，使用语言标记和辅助成像信息训练的多模态预测模型显着优于单峰预测模型。
Mild cognitive impairment is the prodromal stage of Alzheimer’s disease. Its detection has been a critical task for establishing cohort studies and developing therapeutic interventions for Alzheimer’s. Various types of markers have been developed for detection. For example, imaging markers from neuroimaging have shown great sensitivity, while its cost is still prohibitive for large-scale screening of early dementia. Recent advances from digital biomarkers, such as language markers, have provided an accessible and affordable alternative. While imaging markers give anatomical descriptions of the brain, language markers capture the behavior characteristics of early dementia subjects. Such differences suggest the benefits of auxiliary information from the imaging modality to improve the predictive power of unimodal predictive models based on language markers alone. However, one significant barrier to the joint analysis is that in typical cohorts, there are only very limited subjects that have both imaging and language modalities. To tackle this challenge, in this paper, we develop a novel crossmodal augmentation tool, which leverages auxiliary imaging information to improve the feature space of language markers so that a subject with only language markers can benefit from imaging information through the augmentation. Our experimental results show that the multi-modal predictive model trained with language markers and auxiliary imaging information significantly outperforms unimodal predictive models.