Multi-Modal Data Analysis for Alzheimer's Disease Diagnosis: An Ensemble Model Using Imagery and Genetic Features.
Multi-Modal Data Analysis for Alzheimer's Disease Diagnosis: An Ensemble Model Using Imagery and Genetic Features.
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用于阿尔茨海默病诊断的多模态数据分析：使用图像和遗传特征的集成模型。
DOI：
10.1109/embc46164.2021.9630174
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Liang,Gongbo
中科院分区：
文献类型：
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作者：
Ying,Qi;Xing,Xin;Liu,Liangliang;Lin,Ai-Ling;Jacobs,Nathan;Liang,Gongbo
阿尔茨海默病（AD）是一种主要影响老年人的破坏性神经系统疾病。据估计，今天有620万65岁及以上的美国人患有阿尔茨海默氏症。脑磁共振成像（MRI）被广泛用于AD的临床诊断。与此同时，在过去的几十年里，医学研究人员利用全基因组关联研究（GWAS）中的单核苷酸多态性（SNPs）信息确定了40个风险位点。然而，现有的研究通常将MRI和GWAS分开处理。例如，卷积神经网络通常使用MRI进行AD诊断。GWAS和SNP经常用于鉴定基因组性状。在这项研究中，我们提出了一个多模态AD诊断神经网络，同时使用MRI和SNP。所提出的方法展示了一种新的方式来使用GWAS的结果，直接包括SNP的预测模型。我们测试所提出的方法对阿尔茨海默病神经影像倡议数据集。评估结果表明，所提出的方法提高了模型的AD诊断性能，并达到93.5%的AUC和96.1%的AP，分别当患者有MRI和SNP数据。我们相信这项工作为GWAS应用带来了令人兴奋的新见解，并为未来的研究方向提供了光明。
Alzheimer’s disease (AD) is a devastating neurological disorder primarily affecting the elderly. An estimated 6.2 million Americans age 65 and older are suffering from Alzheimer’s dementia today. Brain magnetic resonance imaging (MRI) is widely used for the clinical diagnosis of AD. In the meanwhile, medical researchers have identified 40 risk locus using single-nucleotide polymorphisms (SNPs) information from Genome-wide association study (GWAS) in the past decades. However, existing studies usually treat MRI and GWAS separately. For instance, convolutional neural networks are often trained using MRI for AD diagnosis. GWAS and SNPs are frequently used to identify genomic traits. In this study, we propose a multi-modal AD diagnosis neural network that uses both MRIs and SNPs. The proposed method demonstrates a novel way to use GWAS findings by directly including SNPs in predictive models. We test the proposed methods on the Alzheimer’s Disease Neuroimaging Initiative dataset. The evaluation results show that the proposed method improves the model performance on AD diagnosis and achieves 93.5% AUC and 96.1% AP, respectively, when patients have both MRI and SNP data. We believe this work brings exciting new insights to GWAS applications and sheds light on future research directions.