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Computational and Mathematical Methods in Medicine Volume 2017 ,2017-04-12
Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features
Research Article
Xin Wang 1 Yanshuang Ren 2 Wensheng Zhang 1
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DOI:10.1155/2017/3609821
Received 2016-12-11, accepted for publication 2017-03-20, Published 2017-03-20
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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.

授权许可

Copyright © 2017 Xin Wang et al. 2017
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

图表

The schematic diagram of our method for depression disorder classification. SVM: support vector machine.

The classification accuracy with different sets of parameters.

FC matrix and topology structure of FBN from one patient with depression. (a) and (b) are the FC matrix and topology structure of sparse low-rank FBN, (c) and (d) are those of Pearson correlation FBN, and (e) and (f) are those of sparse FBN.

FC matrix and topology structure of FBN from one patient with depression. (a) and (b) are the FC matrix and topology structure of sparse low-rank FBN, (c) and (d) are those of Pearson correlation FBN, and (e) and (f) are those of sparse FBN.

FC matrix and topology structure of FBN from one patient with depression. (a) and (b) are the FC matrix and topology structure of sparse low-rank FBN, (c) and (d) are those of Pearson correlation FBN, and (e) and (f) are those of sparse FBN.

FC matrix and topology structure of FBN from one patient with depression. (a) and (b) are the FC matrix and topology structure of sparse low-rank FBN, (c) and (d) are those of Pearson correlation FBN, and (e) and (f) are those of sparse FBN.

FC matrix and topology structure of FBN from one patient with depression. (a) and (b) are the FC matrix and topology structure of sparse low-rank FBN, (c) and (d) are those of Pearson correlation FBN, and (e) and (f) are those of sparse FBN.

FC matrix and topology structure of FBN from one patient with depression. (a) and (b) are the FC matrix and topology structure of sparse low-rank FBN, (c) and (d) are those of Pearson correlation FBN, and (e) and (f) are those of sparse FBN.

Average modularity scores of different FBN with different thresholds. PC: Pearson correlation model; SR: sparse representation model; and SLR: sparse low-rank model.

Proportion of each kind of selected features in the three methods. PC: Pearson correlation model; SR: sparse representation model; and SLR: sparse low-rank model.

The discriminative brain regions of patients with depression compared with healthy controls. The color bar indicates the index of displayed brain regions.

通讯作者

Wensheng Zhang.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China, cas.cn.wszhang_casia@hotmail.com

推荐引用方式

Xin Wang,Yanshuang Ren,Wensheng Zhang. Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features. Computational and Mathematical Methods in Medicine ,Vol.2017(2017)

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