Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院
Journal:Zidonghua Xuebao Acta Auto. Sin.
Abstract:The past decade has witnessed the increasing development of multimodal neuroimaging and genomic techniques. 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 modelling. 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 reviews 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. Copyright © 2018 Acta Automatica Sinica. All rights reserved.
ISSN No.:0254-4156
Translation or Not:no
Date of Publication:2018-01-01
Co-author:Hao, Xiao-Ke,Li, Chan-Xiu,Yan, Jing-Wen,Shen, Li
Correspondence Author:zdq