中国神经再生研究(英文版)

中国神经再生研究(英文版) ›› 2014, Vol. 9 ›› Issue (2): 153-163.doi: 10.4103/1673-5374.125344

• 原著:脑损伤修复保护与再生 • 上一篇    下一篇

首发未用药的抑郁症患者静息态功能脑网络可发生异常

  

  • 收稿日期:2013-11-02 出版日期:2014-01-10 发布日期:2014-01-10
  • 基金资助:

    国家自然基金（61070077, 61170136, 61373101,81171290），山西省自然基金（2010011020-2, 2011011015-4），山西省社会发展科技攻关项目（20130313012-2），山西省教育厅高校科技项目（20121003），太原理工大学青年基金项目（2012L014），太原理工大学青年团队项目（2013T047）

Resting-state functional connectivity abnormalities in first-onset unmedicated depression

Hao Guo1, Chen Cheng1, Xiaohua Cao2, Jie Xiang1, Junjie Chen1, Kerang Zhang2   

  1. 1 College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, Shanxi Province, China
    2 Department of Psychiatry, First Affiliated Hospital, Shanxi Medical University, Taiyuan, Shanxi Province, China
  • Received:2013-11-02 Online:2014-01-10 Published:2014-01-10
  • Contact: Junjie Chen, M.D., College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, Shanxi Province, China, chenjunjie_tyut@sina.com.
  • Supported by:

    This study was supported by the National Natural Science Foundation of China, No. 61070077, 61170136, 61373101, 81171290; the Natural Science Foundation of Shanxi Province in China, No. 2010011020-2, 2011011015-4; Programs for Science and Technology Social Development of Shanxi Province, No. 20130313012-2; Science and Technology Projects by Shanxi Provincial Education Ministry, No. 20121003; Youth Fund by Taiyuan University of Technology, No. 2012L014; Youth Team Fund by Taiyuan University of Technology, No. 2013T047.

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摘要：

抑郁症与多个脑区的形态、功能异常有关，但其在全脑范围内的拓扑结构变化却尚未明确。我们采集36例首发未用药抑郁症患者及26例正常对照者静息态功能磁共振数据。利用AAL模板及偏相关方法完成静息态功能脑网络构建。利用复杂网络理论，进行指标计算及统计分析，结果表明抑郁症患者及正常对照者均表现出典型的小世界属性。与正常对照相比，抑郁症患者特征路径长度显著缩短，意味着其向随机化方向发展的趋势。抑郁症患者表现出皮质-纹状体-苍白球-丘脑神经环路的关键区域的节点属性显著异常。此外还发现，右侧海马及右侧丘脑与抑郁症严重程度有关。研究选取270个局部属性为分类特征并以统计显著性P值为特征筛选标准，利用人工神经网络算法进行分类研究。结果证明了脑网络指标可以作为有效的特征应用在机器学习研究中，为脑网络指标给出了一个合理的应用方向。研究还对特征重要性与组间差异显著性间进行了相关分析，二者表现出明显正相关。即节点特征的组间差异越显著，其对分类的贡献度就越强。证明统计显著性可以作为有效的量化评价指标来指导脑网络指标的选择，可辅助临床诊断抑郁症。

关键词: 神经再生, 抑郁症, 功能磁共振, 图论, 复杂网络, 脑网络, 分类, 特征选择, 基金资助文章

Abstract:

Depression is closely linked to the morphology and functional abnormalities of multiple brain regions; however, its topological structure throughout the whole brain remains unclear. We collected resting-state functional MRI data from 36 first-onset unmedicated depression patients and 27 healthy controls. The resting-state functional connectivity was constructed using the Automated Anatomical Labeling template with a partial correlation method. The metrics calculation and statistical analysis were performed using complex network theory. The results showed that both depressive patients and healthy controls presented typical small-world attributes. Compared with healthy controls, characteristic path length was significantly shorter in depressive patients, suggesting development toward randomization. Patients with depression showed apparently abnormal node attributes at key areas in cortical-striatal-pallidal-thalamic circuits. In addition, right hippocampus and right thalamus were closely linked with the severity of depression. We selected 270 local attributes as the classification features and their P values were regarded as criteria for statistically significant differences. An artificial neural network algorithm was applied for classification research. The results showed that brain network metrics could be used as an effective feature in machine learning research, which brings about a reasonable application prospect for brain network metrics. The present study also highlighted a significant positive correlation between the importance of the attributes and the intergroup differences; that is, the more significant the differences in node attributes, the stronger their contribution to the classification. Experimental findings indicate that statistical significance is an effective quantitative indicator of the selection of brain network metrics and can assist the clinical diagnosis of depression.

Key words: nerve regeneration, depression, functional MRI, graph theory, complex networks, brain network, classification, feature selection, NSFC grant, neural regeneration