Deep joint learning diagnosis of Alzheimer's disease based on multimodal feature fusion.

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Jingru Wang, Shipeng Wen, Wenjie Liu, Xianglian Meng, Zhuqing Jiao
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引用次数: 0

Abstract

Alzheimer's disease (AD) is an advanced and incurable neurodegenerative disease. Genetic variations are intrinsic etiological factors contributing to the abnormal expression of brain function and structure in AD patients. A new multimodal feature fusion called "magnetic resonance imaging (MRI)-p value" was proposed to construct 3D fusion images by introducing genes as a priori knowledge. Moreover, a new deep joint learning diagnostic model was constructed to fully learn images features. One branch trained a residual network (ResNet) to learn the features of local pathological regions. The other branch learned the position information of brain regions with different changes in the different categories of subjects' brains by introducing attention convolution, and then obtained the discriminative probability information from locations via convolution and global average pooling. The feature and position information of the two branches were linearly interacted to acquire the diagnostic basis for classifying the different categories of subjects. The diagnoses of AD and health control (HC), AD and mild cognitive impairment (MCI), HC and MCI were performed with data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The results showed that the proposed method achieved optimal results in AD-related diagnosis. The classification accuracy (ACC) and area under the curve (AUC) of the three experimental groups were 93.44% and 96.67%, 89.06% and 92%, and 84% and 81.84%, respectively. Moreover, a total of six novel genes were found to be significantly associated with AD, namely NTM, MAML2, NAALADL2, FHIT, TMEM132D and PCSK5, which provided new targets for the potential treatment of neurodegenerative diseases.

基于多模态特征融合的阿尔茨海默病深度联合学习诊断。
阿尔茨海默病(AD)是一种无法治愈的晚期神经退行性疾病。基因变异是导致阿尔茨海默病患者大脑功能和结构异常的内在病因。研究人员提出了一种名为 "磁共振成像(MRI)-p 值 "的新型多模态特征融合方法,通过引入基因作为先验知识来构建三维融合图像。此外,还构建了一个新的深度联合学习诊断模型,以全面学习图像特征。一个分支训练了一个残差网络(ResNet),以学习局部病理区域的特征。另一个分支通过引入注意力卷积,学习不同类别受试者大脑中发生不同变化的脑区的位置信息,然后通过卷积和全局平均池获得位置的判别概率信息。两个分支的特征信息和位置信息进行线性交互,从而获得对不同类别受试者进行分类的诊断依据。利用阿尔茨海默病神经影像学倡议(ADNI)的数据,对注意力缺失症和健康控制(HC)、注意力缺失症和轻度认知障碍(MCI)、轻度认知障碍和 MCI 进行了诊断。结果表明,所提出的方法在与阿兹海默症相关的诊断中取得了最佳效果。三个实验组的分类准确率(ACC)和曲线下面积(AUC)分别为93.44%和96.67%、89.06%和92%、84%和81.84%。此外,共发现6个新基因与AD显著相关,分别是NTM、MAML2、NAALADL2、FHIT、TMEM132D和PCSK5,为潜在的神经退行性疾病治疗提供了新靶点。
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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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