[1]谢忠翔,武杰,项华中.基于稀疏表示变量选择方法的影像遗传学数据分析[J].中国医学物理学杂志,2020,37(5):584-588.[doi:10.3969/j.issn.1005-202X.2020.05.010]
　XIE Zhongxiang,WU Jie,XIANG Huazhong.Sparse representation-based variable selection algorithm for analysis of imaging genetics data[J].Chinese Journal of Medical Physics,2020,37(5):584-588.[doi:10.3969/j.issn.1005-202X.2020.05.010]
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基于稀疏表示变量选择方法的影像遗传学数据分析()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
37
期数:
2020年第5期
页码:
584-588
栏目:
医学影像物理
出版日期:
2020-05-25

文章信息/Info

Title:
Sparse representation-based variable selection algorithm for analysis of imaging genetics data
文章编号:
1005-202X（2020）05-0584-05
作者:
谢忠翔; 武杰; 项华中
上海理工大学医疗器械与食品学院，上海200093
Author(s):
XIE Zhongxiang;  WU Jie;  XIANG Huazhong
School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
关键词:
精神分裂症; 稀疏表示; 变量选择方法; 单核苷酸多态性; 功能磁共振成像
Keywords:
schizophrenia sparse representation variable selection algorithm single nucleotide polymorphisms functional magnetic resonance imaging
分类号:
R318
DOI:
10.3969/j.issn.1005-202X.2020.05.010
文献标志码:
A
摘要:
目的：采用影像遗传学研究方法探索精神分裂症的影像遗传学特征。方法：在传统稀疏回归模型的基础上，改进 了其在不同范数条件下进行变量选择的能力，形成一种基于稀疏表示变量选择算法，并将该算法应用于208 个受试者的 41 236个功能磁共振成像数据和722 177个单核苷酸多态性数据的综合分析。通过对两类数据施加不同的权重因子，并 使用不同的Lp （p=0、0.5、1）范数分别对模型进行求解，筛选出两类数据在不同条件下的显著特征。结果：基因DAOA和 HTR2A在3种范数下均被筛选出。此外，在影像学数据方面，发现中央前回、枕上回、顶下缘角回、角回、内侧和旁扣带脑 回、后扣带回脑区与精神分裂症相关，此发现与先前精神分裂症的临床医学研究结果一致。结论：基于稀疏表示变量选择 方法应用于影像遗传学数据分析是一个有效可行的途径，为今后精神分裂症的影像遗传学研究提供了一种新的研究 思路。
Abstract:
Objective To explore the imaging genetic characteristics of schizophrenia using imaging genetics method. Methods A sparse representation-based variable selection algorithm with improved ability of variable selection under different norm conditions based on traditional sparse regression model is proposed. The proposed algorithm was applied for the comprehensive analysis of 41 236 functional magnetic resonance imaging data and 722 177 single nucleotide polymorphisms data of 208 subjects. By applying different weight factors to the two types of data and using different Lp (p=0, 0.5, 1) norms for solving the models, the significant features of the two types of data were extracted. Results DAOA and HTR2A genes were extracted under 3 different Lp norms. In addition, the results of imaging data suggested that precentral, occipital_sup, parietal_inf, angular, cingulum_mid, cingulum_post were associated with schizophrenia, which was consistent with previous clinical studies on schizophrenia. Conclusion Sparse representation-based variable selection algorithm is an effective and feasible approach for the analysis of image genetics data, providing a new direction for the image genetics study on schizophrenia.

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备注/Memo

备注/Memo:
【收稿日期】2019-12-17 【基金项目】国家自然科学基金（61605114）；上海理工大学微创基金 （YS30810175） 【作者简介】谢忠翔，在读硕士，研究方向：医学影像技术，E-mail: 2572237304@qq.com 【通信作者】武杰，博士，讲师，研究方向：医学影像技术，E-mail: jieusst@ 163.com
更新日期/Last Update: 2020-06-03