点击次数：
所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：BIOINFORMATICS
关键字：FUNCTIONAL NETWORKS PARIETAL CORTEX ATROPHY
摘要：Motivation: Neuroimaging genetics is an emerging field to identify the associations between genetic variants [e.g. single-nucleotide polymorphisms (SNPs)] and quantitative traits (QTs) such as brain imaging phenotypes. However, most of the current studies focus only on the associations between brain structure imaging and genetic variants, while neglecting the connectivity information between brain regions. In addition, the brain itself is a complex network, and the higher-order interaction may contain useful information for the mechanistic understanding of diseases [i.e. Alzheimer's disease (AD)]. Results: A general framework is proposed to exploit network voxel information and network connectivity information as intermediate traits that bridge genetic risk factors and disease status. Specifically, we first use the sparse representation (SR) model to build hyper-network to express the connectivity features of the brain. The network voxel node features and network connectivity edge features are extracted from the structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (fMRI), respectively. Second, a diagnosis-aligned multi-modality regression method is adopted to fully explore the relationships among modalities of different subjects, which can help further mine the relation between the risk genetics and brain network features. In experiments, all methods are tested on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results not only verify the effectiveness of our proposed framework but also discover some brain regions and connectivity features that are highly related to diseases.
ISSN号：1367-4803
是否译文：否
发表时间：2019-06-01
合写作者：Wang, Meiling,Hao, Xiaoke,Huang, Jiashuang,Shao, Wei,葛少卫,张道强
通讯作者：王美玲,张道强