CASIA OpenIR  > 精密感知与控制研究中心  > 人工智能与机器学习
Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features
Wang, Xin1; Ren, Yanshuang2; Zhang, Wensheng1; Zhang Wensheng
Source PublicationCOMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
2017
Volume2017Issue:2017Pages:1
SubtypeArticle
AbstractStudy of functional brain network (FBN) based on functional magnetic resonance imaging (fMRI) has proved successful in depression disorder classification. One popular approach to construct FBN is Pearson correlation. However, it only captures pairwise relationship between brain regions, while it ignores the influence of other brain regions. Another common issue existing in many depression disorder classification methods is applying only single local feature extracted from constructed FBN. To address these issues, we develop a new method to classify fMRI data of patients with depression and healthy controls. First, we construct the FBN using a sparse low-rank model, which considers the relationship between two brain regions given all the other brain regions. Moreover, it can automatically remove weak relationship and retain the modular structure of FBN. Secondly, FBN are effectively measured by eight graph-based features from different aspects. Tested on fMRI data of 31 patients with depression and 29 healthy controls, our method achieves 95% accuracy, 96.77% sensitivity, and 93.10% specificity, which outperforms the Pearson correlation FBN and sparse FBN. In addition, the combination of graph-based features in our method further improves classification performance. Moreover, we explore the discriminative brain regions that contribute to depression disorder classification, which can help understand the pathogenesis of depression disorder.
KeywordDepression Classification Fmri Sparse Low-rank
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
DOI10.1155/2017/3609821
WOS KeywordRESTING-STATE FMRI ; MAJOR DEPRESSION ; TREATMENT-NAIVE ; THEORETICAL ANALYSIS ; ALZHEIMERS-DISEASE ; CINGULATE CORTEX ; CONNECTIVITY ; 1ST-EPISODE ; PATTERN
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61305018 ; 61432008 ; 61472423 ; 61532006)
WOS Research AreaMathematical & Computational Biology
WOS SubjectMathematical & Computational Biology
WOS IDWOS:000405747000001
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14844
Collection精密感知与控制研究中心_人工智能与机器学习
Corresponding AuthorZhang Wensheng
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.China Acad Chinese Med Sci, Guanganmen Hosp, Dept Radiol, Beijing 100053, Peoples R China
Recommended Citation
GB/T 7714
Wang, Xin,Ren, Yanshuang,Zhang, Wensheng,et al. Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features[J]. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE,2017,2017(2017):1.
APA Wang, Xin,Ren, Yanshuang,Zhang, Wensheng,&Zhang Wensheng.(2017).Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features.COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE,2017(2017),1.
MLA Wang, Xin,et al."Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features".COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2017.2017(2017):1.
Files in This Item: Download All
File Name/Size DocType Version Access License
Depression Disorder (4790KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Xin]'s Articles
[Ren, Yanshuang]'s Articles
[Zhang, Wensheng]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Xin]'s Articles
[Ren, Yanshuang]'s Articles
[Zhang, Wensheng]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Xin]'s Articles
[Ren, Yanshuang]'s Articles
[Zhang, Wensheng]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features(SCI).pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.