基于多层次模板的脑功能网络特征选择及分类

doi:10.11772/j.issn.1001-9081.2018112421

计算机应用 ›› 2019, Vol. 39 ›› Issue (7): 1948-1953.DOI: 10.11772/j.issn.1001-9081.2018112421

基于多层次模板的脑功能网络特征选择及分类

吴浩¹, 王昕璨², 李欣芸¹, 刘志芬³, 陈俊杰¹, 郭浩¹

1. 太原理工大学信息与计算机学院, 山西晋中 030600;
2. 太原理工大学艺术学院, 山西晋中 030600;
3. 山西医科大学第一医院精神卫生科, 太原 030000

收稿日期:2018-12-07 修回日期:2019-01-07 出版日期:2019-07-10 发布日期:2019-07-15
通讯作者: 郭浩
作者简介:吴浩(1994-),女,山西临汾人,硕士研究生,CCF会员,主要研究方向:人工智能、智能信息处理与脑影像学;王昕璨(1993-),女,山西太原人,硕士研究生,主要研究方向:人工智能、智能信息处理与脑影像学;李欣芸(1999-),女,山西太原人,主要研究方向:人工智能;刘志芬(1981-),女,山西太原人,副主任医师,博士,主要研究方向:情感障碍基础与临床研究;陈俊杰(1956-),男,河北定州人,教授,博士,主要研究方向:人工智能、智能信息处理与脑影像学;郭浩(1981-),男,山西祁县人,副教授,博士,CCF会员,主要研究方向:人工智能、智能信息处理与脑影像学。
基金资助:
国家自然科学基金资助项目（61672374，61741212，61876124，61873178）；山西省科技厅应用基础研究项目青年面上项目（201601D021073）；山西省教育厅高等学校科技创新研究项目（2016139）；教育部赛尔网络下一代互联网技术创新项目（NGII20170712）；国家留学基金资助出国留学项目（201708140216）。

Brain function network feature selection and classification based on multi-level template

WU Hao¹, WANG Xincan², LI Xinyun¹, LIU Zhifen³, CHEN Junjie¹, GUO Hao¹

1. College of Information and Computer Science, Taiyuan University of Technology, Jinzhong Shanxi 030600, China;
2. College of Art, Taiyuan University of Technology, Jinzhong Shanxi 030600, China;
3. Department of Mental Health, First Hospital of Shanxi Medical University, Taiyuan Shanxi 030000, China

Received:2018-12-07 Revised:2019-01-07 Online:2019-07-10 Published:2019-07-15
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61672374, 61741212, 61876124, 61873178), the Youth Surface Program of Shanxi Provincial Science and Technology Department Applied Basic Research Project(201601D021073), the Shanxi Provincial Department of Education Science and Technology Innovation Research Project for Universities (2016139), the CERNET Next Generation Internet Technology Innovation Project of Ministry of Education (NGII20170712), the National Funded Overseas Studying Program (201708140216).

摘要/Abstract

摘要：

基于单一脑图谱模板的功能连接网络中提取的特征表示不足以揭示患者组和正常对照组（NC）之间的复杂拓扑结构差异，而传统的基于多模板的功能脑网络定义多采用独立模板，缺乏模板间的关联，从而忽略了各模板构建的功能脑网络中潜在的拓扑关联信息。针对上述问题，提出了一种多层次脑图谱模板和一种使用关系诱导稀疏（RIS）特征选择模型的方法。首先定义了具有关联的多层次脑图谱模板，挖掘模板之间潜在关系和表征组间网络结构差异；然后用RIS特征选择模型进行参数优化，进而提取组间差异特征；最后利用支持向量机（SVM）方法构建分类模型，并应用于抑郁症患者的诊断。在山西大学第一医院抑郁症临床诊断数据库上的实验结果显示，基于多层次模板的功能脑网络通过使用具有RIS特征的选择方法取得了91.7%的分类准确率，相比传统多模板方法的准确率提高了3个百分点。

关键词: 多层次模板, 功能脑网络, 关系诱导稀疏, 机器学习, 抑郁症

Abstract:

The feature representation extracted from the functional connection network based on single brain map template is not sufficient to reveal complex topological differences between patient group and Normal Control (NC) group. However, the traditional multi-template-based functional brain network definitions mostly use independent templates, ignoring the potential topological association information in functional brain networks built with each template. Aiming at the above problems, a multi-level brain map template and a method of Relationship Induced Sparse (RIS) feature selection model were proposed. Firstly, an associated multi-level brain map template was defined, and the potential relationship between templates and network structure differences between groups were mined. Then, the RIS feature selection model was used to optimize the parameters and extract the differences between groups. Finally, the Support Vector Machine (SVM) method was used to construct classification model and was applied to the diagnosis of patients with depression. The experimental results on the clinical diagnosis database of depression in the First Hospital of Shanxi University show that the functional brain network based on multi-level template achieves 91.7% classification accuracy by using the RIS feature selection method, which is 3 percentage points higher than that of traditional multi-template method.

Key words: multi-level template, functional brain network, relationship induced sparse, machine learning, Major Depressive Disorder (MDD)

中图分类号:

TP181

吴浩, 王昕璨, 李欣芸, 刘志芬, 陈俊杰, 郭浩. 基于多层次模板的脑功能网络特征选择及分类[J]. 计算机应用, 2019, 39(7): 1948-1953.

WU Hao, WANG Xincan, LI Xinyun, LIU Zhifen, CHEN Junjie, GUO Hao. Brain function network feature selection and classification based on multi-level template[J]. Journal of Computer Applications, 2019, 39(7): 1948-1953.

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基于多层次模板的脑功能网络特征选择及分类

Brain function network feature selection and classification based on multi-level template

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