基于统计学习的影像遗传学方法综述

郝小可; 李蝉秀; 严景文; 沈理; 张道强

doi:10.16383/j.aas.2018.c160696

基于统计学习的影像遗传学方法综述

doi: 10.16383/j.aas.2018.c160696

郝小可^1,,
李蝉秀^1,,
严景文^2,,
沈理^2,,
张道强^1, ,

1.
南京航空航天大学计算机科学与技术学院南京 211106 中国
2.
印第安纳大学医学院印第安纳波利斯 46202 美国

基金项目:

国家自然科学基金 61422204

国家自然科学基金 61732006

国家自然科学基金 61473149

详细信息

作者简介:
郝小可河北工业大学计算机科学与软件学院讲师.于2017年在南京航空航天大学计算机科学与技术学院获得博士学位.分别于2009年和2012年在南京信息工程大学计算机与软件学院获得学士学位和硕士学位.主要研究方向为机器学习, 影像遗传学.E-mail:robinhc@163.com

李蝉秀南京航空航天大学计算机科学与技术学院硕士研究生.2015年在南京航空航天大学计算机科学与技术学院获得学士学位.主要研究方向为机器学习, 影像遗传学.E-mail:lcx_show@nuaa.edu.cn

严景文印第安纳大学普渡大学印第安纳波利斯联合分校信息学与计算学院生物健康信息学系助理教授.曾分别在南京航空航天大学和华中科技大学获得学士学位和硕士学位.2015年获得印第安纳大学信息学与计算学院博士学位.主要研究方向为机器学习, 影像遗传学.E-mail:jingyan@iupui.edu

沈理印第安纳大学医学院放射学与影像科学系副教授.曾分别在西安交通大学和上海交通大学获得学士和硕士学位, 在达特茅斯学院获得博士学位, 专业均为计算机科学.主要研究方向为医学影像计算, 信息生物学, 影像遗传学, 脑连接组.E-mail:shenli@iu.edu

通讯作者:
张道强南京航空航天大学计算机科学与技术学院教授.分别于1999年和2004年在南京航空航天大学获得学士学位和博士学位.主要研究方向为机器学习, 模式识别, 数据挖掘以及医学影像分析.本文通信作者.E-mail:dqzhang@nuaa.edu.cn

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出版历程
- 收稿日期: 2016-09-30
- 录用日期: 2017-04-10
- 刊出日期: 2018-01-01

A Review of Statistical-learning Imaging Genetics

1.
School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2.
School of Medicine, Indiana University, Indianapolis, IN 46202, USA

Funds:

National Natural Science Foundation of China 61422204

National Natural Science Foundation of China 61732006

National Natural Science Foundation of China 61473149

More Information

Author Bio:
Lecturer at the School of Computer Science and Engineering, Hebei University of Technology. He received his Ph. D. degree from the School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics in 2017. He received his bachelor degree and master degree from the School of Computer and Software, Nanjing University of Information Science and Technology in 2009 and 2012, respectively. His research interest covers machine learning and imaging genetics

Master student at the School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics. She received her bachelor degree from the School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics in 2015. Her research interest covers machine learning and imaging genetics

Assistant professor in the Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University Indianapolis, USA. She received her bachelor degree from Nanjing University of Aeronautics and Astronautics. She received her master degree from Huazhong University of Science and Technology. She received her Ph. D. degree from the School of Informatics and Computing, Indiana University. Her research interest covers machine learning and imaging genetics

Associate professor of Radiology and Imaging Sciences at Indiana University School of Medicine. He received his bachelor degree from Xi0an Jiao Tong University, master degree from Shanghai Jiao Tong University, and Ph. D. degree from Dartmouth College, all in computer science. His research interest covers medical image computing, bioinformatics, imaging genomics, and brain connectomics

Corresponding author: ZHANG Dao-Qiang Professor at the School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics. He received his bachelor degree and Ph. D. degree in computer science from Nanjing University of Aeronautics and Astronautics, in 1999 and 2004, respectively. His research interest covers machine learning, pattern recognition, data mining, and medical image analysis. Corresponding author of this paper

摘要

摘要: 近年来随着多模态神经影像技术和基因检测技术的发展，影像遗传学这一交叉学科的研究能够运用脑影像技术将人类大脑的结构与功能作为表型来评价基因对个体的影响，使得人们可以在脑的宏观结构上以更客观的测量手段理解基因对行为或精神疾病的影响.而统计学习方法作为基于数据驱动的关联分析强有力工具，能够充分利用生物标志数据内在的结构信息构建模型来分析易感基因与大脑结构或者功能的相关性，从而更好地揭示脑认知行为或者相关疾病的产生机制.本文首先简要介绍了影像遗传学的研究背景和基本原理，然后回顾了单变量方法在影像遗传学研究中的应用，随后对基于多变量统计学习的基因-影像关联的研究思路和建模方法进行了归纳总结，最后对遗传影像学的未来研究发展方向进行了分析和展望.
- 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
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.
- Imaging genetics /
- statistical learning /
- structured sparse learning /
- multivariate analysis /
- association analysis
注释:

1) 本文责任编委朱朝喆

HTML全文

图 1 基于统计学习的影像遗传学关联分析研究方法

Fig. 1 Association analysis in imaging genetics based on statistical learning

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图 2 树型结构引导稀疏回归模型^[54]

Fig. 2 Tree-guided sparse regression model ^[54]

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图 3 任务相关的纵向稀疏回归模型^[56]

Fig. 3 Task-correlated longitudinal sparse regression model ^[56]

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图 4 多模态关联模型^[57]

Fig. 4 Multi-modality association model ^[57]

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图 5 组稀疏多任务回归和特征选择模型^[58]

Fig. 5 Group-sparse multi-task regression and feature selection model ^[58]

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图 6 稀疏低秩回归模型^[59-60]

Fig. 6 Sparse reduced rank regression model ^[59-60]

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图 7 稀结构化稀疏的双多变量关联模型^[73]

Fig. 7 Structured sparse bi-multivariate correlation model ^[73]

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