[1]刘越,王梦星,杜小霞,等.基于独立成分分析的遗尿症儿童脑功能网络研究[J].中国医学物理学杂志,2021,38(3):382-386.[doi:DOI:10.3969/j.issn.1005-202X.2021.03.021]
　LIU Yue,WANG Mengxing,DU Xiaoxia,et al.Brain functional network of pediatric patients with enuresis: a research based on independent component analysis[J].Chinese Journal of Medical Physics,2021,38(3):382-386.[doi:DOI:10.3969/j.issn.1005-202X.2021.03.021]
点击复制

基于独立成分分析的遗尿症儿童脑功能网络研究()
分享到：

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
38卷
期数:
2021年第3期
页码:
382-386
栏目:
脑科学与神经物理
出版日期:
2021-03-30

文章信息/Info

Title:
Brain functional network of pediatric patients with enuresis: a research based on independent component analysis
文章编号:
1005-202X（2021）03-0382-05
作者:
刘越1; 王梦星2; 杜小霞3; 张安易4; 马骏4; 何培忠2
1.上海理工大学医疗器械与食品学院， 上海 200093； 2.上海健康医学院医学影像学院， 上海 200237； 3.上海体育学院心理学院， 上海 200438； 4.国家儿童医学中心/上海交通大学医学院附属上海儿童医学中心发育行为儿科， 上海 200127
Author(s):
LIU Yue1;  WANG Mengxing2;  DU Xiaoxia3;  ZHANG Anyi4;  MA Jun4;  HE Peizhong2
1. School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 2. College of Medical Imaging, Shanghai University of Medicine & Health Sciences, Shanghai 200237, China 3. School of Psychology, Shanghai University of Sport, Shanghai 200438, China 4. Department of Developmental-Behavioral Pediatrics, Shanghai Childrens Medical Center (Childrens National Medical Center), Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
关键词:
遗尿症; 独立成分分析; 脑功能网络成分; 功能连接
Keywords:
Keywords: enuresis independent component analysis brain functional network component functional connectivity
分类号:
R318；R748
DOI:
DOI:10.3969/j.issn.1005-202X.2021.03.021
文献标志码:
A
摘要:
目的：用独立成分分析（ICA）的方法对原发性单症状夜间遗尿症（PMNE）儿童的脑功能网络成分间连接进行研究。方法：采集35例PMNE儿童和25例健康儿童脑功能磁共振图像，通过ICA获得每个被试的脑功能网络成分，然后计算每个被试脑功能网络成分间的功能连接强度，比较PMNE儿童与健康对照组的强度差异。结果：与对照组对比，PMNE患者的右侧执行控制网络与默认模式网络、左侧执行控制网络均存在功能连接异常（FDR, P<0.05）。结论：PMNE儿童存在脑功能网络成分间的连接异常，这可能为理解PMNE儿童的病理机制提供一些新的影像学依据。
Abstract:
Abstract: Objective To study the connectivity of the brain functional network components in pediatric patients with primary monosymptomatic nocturnal enuresis (PMNE) using independent component analysis (ICA). Methods The brain functional magnetic resonance images of 35 PMNE children and 25 healthy children were collected in the study. ICA was used to obtain the brain functional network components of each subject. The functional connectivity strength between the brain functional network components of each subject was calculated, and the differences in functional connectivity strength between PMNE children and healthy controls were compared. Results Compared with those of control group, the functional connectivity between the right executive control network and the left executive control network, and the connectivity between the right executive control network and the default mode network of PMNE patients both showed abnormalities (FDR, P<0.05). Conclusion: There is abnormal connectivity between the components of the brain functional network in PMNE children, which may provide some new imaging evidence for understanding the pathological mechanism of PMNE.

相似文献/References:

[1]袁野,王夏天,张子辰,等.基于小波变换和改进的瞬态独立成分分析融合算法的心电信号降噪方法[J].中国医学物理学杂志,2016,33(4):415.[doi:10.3969/j.issn.1005-202X.2016.04.019]
　[J].Chinese Journal of Medical Physics,2016,33(3):415.[doi:10.3969/j.issn.1005-202X.2016.04.019]
[2]刘迎军,杨志景,李淑龙.静息态功能磁共振成像数据中几个常用指标的相关性[J].中国医学物理学杂志,2017,34(1):26.[doi:10.3969/j.issn.1005-202X.2017.01.006]
　[J].Chinese Journal of Medical Physics,2017,34(3):26.[doi:10.3969/j.issn.1005-202X.2017.01.006]
[3]付令,武杰.独立成分分析在视觉运动核磁共振数据处理中的应用[J].中国医学物理学杂志,2017,34(7):676.[doi:10.3969/j.issn.1005-202X.2017.07.005]
　[J].Chinese Journal of Medical Physics,2017,34(3):676.[doi:10.3969/j.issn.1005-202X.2017.07.005]
[4]许光,吴宏,江桂华,等. 原发性失眠患者大脑额顶网络的异常功能连接[J].中国医学物理学杂志,2018,35(8):988.[doi:DOI:10.3969/j.issn.1005-202X.2018.08.023]
　XU Guang,WU Hong,JIANG Guihua,et al. Abnormal functional connectivity in frontoparietal network in primary insomnia patients[J].Chinese Journal of Medical Physics,2018,35(3):988.[doi:DOI:10.3969/j.issn.1005-202X.2018.08.023]
[5]王宪福,谢东东,刘畅,等.基于独立成分分析的青年吸烟者大脑静息态执行网络功能连接变化研究[J].中国医学物理学杂志,2020,37(3):289.[doi:DOI:10.3969/j.issn.1005-202X.2020.03.006]
　WANG Xianfu,XIE Dongdong,LIU Chang,et al.Changes of functional connectivity in executive network during the resting state in adolescent smokers: a research based on independent component analysis[J].Chinese Journal of Medical Physics,2020,37(3):289.[doi:DOI:10.3969/j.issn.1005-202X.2020.03.006]
[6]解晓燕,薛婷,张云淼,等.基于动态功能连接分析的青少年吸烟者大脑静息态默认模式网络[J].中国医学物理学杂志,2022,39(6):690.[doi:DOI:10.3969/j.issn.1005-202X.2022.06.006]
　XIE Xiaoyan,XUE Ting,ZHANG Yunmiao,et al.Resting-state default mode network in adolescent smokers: a research based on dynamic functional connectivity analysis[J].Chinese Journal of Medical Physics,2022,39(3):690.[doi:DOI:10.3969/j.issn.1005-202X.2022.06.006]
[7]张思河,曹乐,王金玮,等.基于表面肌电信号的BiLSTM-SA双臂肌力估计[J].中国医学物理学杂志,2023,40(11):1383.[doi:DOI:10.3969/j.issn.1005-202X.2023.11.011]
　ZHANG Sihe,CAO Le,WANG Jinwei,et al.BiLSTM-SA model for muscle strength estimation from sEMG[J].Chinese Journal of Medical Physics,2023,40(3):1383.[doi:DOI:10.3969/j.issn.1005-202X.2023.11.011]

备注/Memo

备注/Memo:
【收稿日期】2021-01-20 【基金项目】国家自然科学基金（81571658，81901720） 【作者简介】刘越，在读硕士，研究方向：医学成像及图像处理技术，E-mail: 2994215719@qq.com 【通信作者】何培忠，博士，教授，研究方向：磁共振成像设备关键技术，E-mail: hepz@sumhs.edu.cn
更新日期/Last Update: 2021-03-30