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中国生物医学工程学报  2018, Vol. 37 Issue (3): 283-289    DOI: 10.3969/j.issn.0258-8021.2018.03.004
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抑郁症的客观判别:基于光学脑成像的静息态功能性连接检测和分析
朱绘霖1,2*,许洁2,3,李江雪4,彭红军5
1 中山大学第三附属医院儿童发育行为中心,广州 510630
2 华南师范大学华南先进光电子研究院,光及电磁波研究中心,广州 510006
3 中国联合网络通信有限公司广东省分公司,广州 510627
4 华南师范大学心理咨询研究中心,广州 510631
5 广州脑科医院临床心理部,广州 510170
Objective Discrimination of Depression: Detection and Analysis of Resting State Functional Connectivity Based on Optical Brain Imaging
Zhu Huilin1, 2*, Xu Jie2,3, Li Jiangxue4, Peng Hongjun5
1 Children Developmental & Behavioral Center, Third Affiliated Hospital of Sun Yet-Sen University, Guangzhou 510630, China
2 Centre for Optical and Electromagnetic Research, South China Academy of Advanced Optoelectronics,South China Normal University, Guangzhou 510006, China
3 Guangdong Branch, China Unicom Co., Ltd, Guangzhou 510627, China
4 The Research Center of Psychological Counseling, South China Normal University, Guangzhou 510631, China
5 The Department of Clinical Psychology, Guangzhou Brain Hospital Guangzhou Huiai Hospital, the Affiliated Brain Hospital of Guangzhou Medical School, Guangzhou 510170, China
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摘要 近年来,基于功能性近红外光谱的静息态功能性连接逐渐用于精神疾病的研究。然而,由功能性近红外光谱得到的静息态功能性连接是否可以用于抑郁症的客观判别仍然是一个未知数。采用42通道的功能性近红外光谱技术,测量28个抑郁症患者和30个健康对照组的8 min前额皮层的自发血液动力活动。在独立成分分析和0.008~0.09 Hz的带通滤波器滤除不相关的成分后,计算前额皮层3个区域(额下回、额中回和额上回)左右半球连接性。然后,选择其中两个有显著性差异的参数作为样本的两个特征维度,并采用线性判别分析和支持向量机对随机抽取的75%样本进行训练,并对剩余的25%样本进行预测。最终均获得73%~74%的预测正确率和83%~87%的辨别率。这个结果支持由功能性近红外光谱技术得到的大脑静息态功能性连接在客观辨别抑郁症患者的可行性和有效性。
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朱绘霖
许洁
李江雪
彭红军
关键词 功能性近红外光谱静息态功能性连接抑郁症线性判别分析支持向量机    
Abstract:Recently, resting-state functional connectivity (RSFC) has gradually been studied in patients with mental disorders by functional near-infrared spectroscopy (fNIRS). However, it is still unknown whether RSFC derived from fNIRS is predictable for depressive disorders. In this work, we employed fNIRS(42 channels) to measure 8-minute spontaneous hemodynamic activity in the prefrontal cortex (PFC) of 28 patients having depressive disorders and 30 healthy controls. After filtering irrelative components by independent component and band-pass filter (0.008-0.09 Hz), we calculated left-right correlations in the prefrontal cortex which included inferior prefrontal cortex (IFG), middle prefrontal cortex (MFG) and superior prefrontal cortex (SFG).Then we selected two significant parameters (left-right correlations in the IFG and MFG as a participant’s two features for further classification (75% of the participants) and prediction (25% of the participants) using linear discriminant analysis (LDA) and support vector machine (SVM). Finally, a sensitivity of 73-74% and specificity of 83-87%was yielded. These results supported that RSFC derived from fNIRS is a feasible and effective technique to identify whether someone is suffered from depressive disorders.
Key wordsfunctional near-infrared spectroscopy    resting-state functional connectivity    major depressive disorders    linear discriminant analysis    support vector machine
收稿日期: 2017-04-06     
PACS:  R318  
基金资助:国家自然科学基金(81601533);广东省自然科学基金(2014A030310502);中国博士后科学基金(2015M580725 & 2016T90791)
通讯作者: E-mail:huilin.zhu@m.scnu.edu.cn   
引用本文:   
朱绘霖,许洁,李江雪,彭红军. 抑郁症的客观判别:基于光学脑成像的静息态功能性连接检测和分析[J]. 中国生物医学工程学报, 2018, 37(3): 283-289.
Zhu Huilin, Xu Jie, Li Jiangxue, Peng Hongjun. Objective Discrimination of Depression: Detection and Analysis of Resting State Functional Connectivity Based on Optical Brain Imaging. Chinese Journal of Biomedical Engineering, 2018, 37(3): 283-289.
链接本文:  
http://cjbme.csbme.org/CN/10.3969/j.issn.0258-8021.2018.03.004     或     http://cjbme.csbme.org/CN/Y2018/V37/I3/283
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