A Review of Statistical-learning Imaging Genetics

Xiao Ke Hao, Chan Xiu Li, Jing Wen Yan, Li Shen, Dao Qiang Zhang

Research output: Contribution to journalReview article

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.

Original languageEnglish (US)
Pages (from-to)13-24
Number of pages12
JournalZidonghua Xuebao/Acta Automatica Sinica
Volume44
Issue number1
DOIs
StatePublished - Jan 2018

Keywords

  • Association analysis
  • Imaging genetics
  • Multivariate analysis
  • Statistical learning
  • Structured sparse learning

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Computer Graphics and Computer-Aided Design

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