自动化学报2018，Vol.44Issue(1)：13-24,12.DOI:10.16383/j.aas.2018.c160696
基于统计学习的影像遗传学方法综述
A Review of Statistical-learning Imaging Genetics
摘要
Abstract
The past decade has witnessed the increasing development of multimodal neuroimaging and genomic tech-niques. Imaging genetics,an interdisciplinary field,aims to evaluate and characterize genetic variants in individuals that influence phenotypic measures derived from structural and functional brain images. This strategy is able to reveal the complex mechanisms via macroscopic intermediates from genetic level to cognition and psychiatric disorders in humans. On the other hand,statistical learning methods,as a powerful tool in the data-driven based association study,can make full use of priori-knowledge(inter correlated structure information among imaging and genetic data)for correlation mod-elling. Therefore,the association study can address the correlations between risk gene and brain structure or function,so as to help explore a better mechanistic understanding of behaviors or disordered brain functions. This paper firstly re-views the related background and fundamental work in imaging genetics and then shows the univariate statistical learning approaches for correlation analysis. Subsequently,it summarizes the main idea and modeling in gene-imaging association studies based on multivariate statistical learning. Finally,this paper presents some prospects of future work.关键词
影像遗传学/统计学习/结构化稀疏学习/多变量分析/关联分析Key words
Imaging genetics/statistical learning/structured sparse learning/multivariate analysis/association analysis引用本文复制引用
郝小可,李蝉秀,严景文,沈理,张道强..基于统计学习的影像遗传学方法综述[J].自动化学报,2018,44(1):13-24,12.基金项目
国家自然科学基金(61422204,61473149,61732006)资助Supported by National Natural Science Foundation of China(61422204,61473149,61732006) （61422204,61473149,61732006）
