Knowledge Commons of Institute of Automation，CAS
| 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
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| 发表期刊 | COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
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| 2017 | |
| 卷号 | 2017期号:2017页码:1 |
| 文章类型 | Article |
| 摘要 | Study 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. |
| 关键词 | Depression Classification Fmri Sparse Low-rank |
| WOS标题词 | Science & Technology ; Life Sciences & Biomedicine |
| DOI | 10.1155/2017/3609821 |
| 关键词[WOS] | RESTING-STATE FMRI ; MAJOR DEPRESSION ; TREATMENT-NAIVE ; THEORETICAL ANALYSIS ; ALZHEIMERS-DISEASE ; CINGULATE CORTEX ; CONNECTIVITY ; 1ST-EPISODE ; PATTERN |
| 收录类别 | SCI |
| 语种 | 英语 |
| 项目资助者 | National Natural Science Foundation of China(61305018 ; 61432008 ; 61472423 ; 61532006) |
| WOS研究方向 | Mathematical & Computational Biology |
| WOS类目 | Mathematical & Computational Biology |
| WOS记录号 | WOS:000405747000001 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/14844 |
| 专题 | 多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队 |
| 通讯作者 | Zhang Wensheng |
| 作者单位 | 1.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 |
| 第一作者单位 | 中国科学院自动化研究所 |
| 推荐引用方式 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. |
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| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Depression Disorder （4790KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 | |
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