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：QUANTITATIVE TRAIT LOCI ALZHEIMERS-DISEASE IMAGING GENETICS BRAIN STRUCTURE ADNI COHORT REGRESSION SELECTION LASSO SHRINKAGE ALGORITHM
摘要：Motivation: Neuroimaging genetics identifies the relationships between genetic variants (i.e., the single nucleotide polymorphisms) and brain imaging data to reveal the associations from genotypes to phenotypes. So far, most existing machine-learning approaches are widely used to detect the effective associations between genetic variants and brain imaging data at one time-point. However, those associations are based on static phenotypes and ignore the temporal dynamics of the phenotypical changes. The phenotypes across multiple time-points may exhibit temporal patterns that can be used to facilitate the understanding of the degenerative process. In this article, we propose a novel temporally constrained group sparse canonical correlation analysis (TGSCCA) framework to identify genetic associations with longitudinal phenotypic markers. Results: The proposed TGSCCA method is able to capture the temporal changes in brain from longitudinal phenotypes by incorporating the fused penalty, which requires that the differences between two consecutive canonical weight vectors from adjacent time-points should be small. A new efficient optimization algorithm is designed to solve the objective function. Furthermore, we demonstrate the effectiveness of our algorithm on both synthetic and real data (i.e., the Alzheimer's Disease Neuroimaging Initiative cohort, including progressive mild cognitive impairment, stable MCI and Normal Control participants). In comparison with conventional SCCA, our proposed method can achieve strong associations and discover phenotypic biomarkers across multiple timepoints to guide disease-progressive interpretation.
ISSN号：1367-4803
是否译文：否
发表时间：2017-07-15
合写作者：Hao, Xiaoke,Li, Chanxiu,Yan, Jingwen,Yao, Xiaohui,Risacher, Shannon L.,Saykin, Andrew J.,Shen, Li
通讯作者：张道强
发表时间：2017-07-15