Machine Learning for Brain Imaging Genomics Methods: A Review

In the past decade, multimodal neuroimaging and genomic techniques have been increasingly developed. As an interdisciplinary topic, brain imaging genomics is devoted to evaluating and characterizing genetic variants in individuals that influence phenotypic measures derived from structural and functional brain imaging. This technique is capable of revealing the complex mechanisms by macroscopic intermediates from the genetic level to cognition and psychiatric disorders in humans. It is well known that machine learning is a powerful tool in the data-driven association studies, which can fully utilize priori knowledge (intercorrelated structure information among imaging and genetic data) for association modelling. In addition, the association study is able to find the association between risk genes and brain structure or function so that a better mechanistic understanding of behaviors or disordered brain functions is explored. In this paper, the related background and fundamental work in imaging genomics are first reviewed. Then, we show the univariate learning approaches for association analysis, summarize the main idea and modelling in genetic-imaging association studies based on multivariate machine learning, and present methods for joint association analysis and outcome prediction. Finally, this paper discusses some prospects for future work.

Automated summary

Brain imaging genomics is an interdisciplinary field that aims to evaluate and characterize genetic variants that influence phenotypic measures derived from brain imaging data. This technique can reveal the complex mechanisms underlying cognition and psychiatric disorders. Machine learning is a powerful tool for data-driven association studies, utilizing intercorrelated structure information to identify the association between risk genes and brain structure or function. This paper provides a comprehensive review of the background, methods, and future prospects in imaging genomics.