[1]马宁欣,王瑜,肖洪兵,等.基于图论分析的抑郁症与赌博行为相关性分析[J].中国医学物理学杂志,2024,41(11):1374-1382.[doi:DOI:10.3969/j.issn.1005-202X.2024.11.009]
　MA Ningxin,WANG Yu,XIAO Hongbing,et al.Correlation analysis between depression and gambling behavior using graph theory[J].Chinese Journal of Medical Physics,2024,41(11):1374-1382.[doi:DOI:10.3969/j.issn.1005-202X.2024.11.009]
点击复制

基于图论分析的抑郁症与赌博行为相关性分析()
分享到：

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

卷:
41卷
期数:
2024年第11期
页码:
1374-1382
栏目:
医学影像物理
出版日期:
2024-11-26

文章信息/Info

Title:
Correlation analysis between depression and gambling behavior using graph theory
文章编号:
1005-202X（2024）11-1374-09
作者:
马宁欣; 王瑜; 肖洪兵; 邢素霞; 徐冉
北京工商大学计算机与人工智能学院， 北京 100048
Author(s):
MA Ningxin;  WANG Yu;  XIAO Hongbing;  XING Suxia;  XU Ran
School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
关键词:
抑郁症; 功能脑网络; 功能磁共振成像; 图论分析
Keywords:
Keywords: depression functional brain network functional magnetic resonance imaging graph theory analysis
分类号:
R318；TP181
DOI:
DOI:10.3969/j.issn.1005-202X.2024.11.009
文献标志码:
A
摘要:
探究抑郁症患者脑网络的全局属性和局部属性与赌博行为学量表的相关性。对24名赌博行为抑郁症患者和24名健康对照组的任务态脑功能磁共振成像数据进行分析，利用SPM软件对数据进行预处理，采用图论分析方法，构建功能脑网络，计算脑网络的局部属性和全局属性。将不同病程抑郁症组（重度抑郁症患者8例、中度抑郁症患者8例及轻度抑郁症患者8例）和健康对照组的局部属性指标节点度和节点效率进行连边分析，并对比不同病程抑郁症组和健康对照组全局属性指标的变化，最后将全局属性中的小世界属性、全局效率、局部效率分别与赌博行为相关评分量表进行相关性分析。对抑郁症组和健康对照组进行双样本t检验，得到脑区之间的显著性连接（P<0.05），脑网络全局属性指标与不同行为学量表之间存在显著的负相关性，充分验证了赌博行为与抑郁症存在相关性，为探讨个体行为属性和抑郁症的相关性研究提供科学依据，进而辅助抑郁症患者的临床诊断和治疗。
Abstract:
Abstract: The correlations of global/local properties of brain networks with gambling behavioral scales in depression are explored. The task-state brain functional magnetic resonance imaging data of 24 patients with gambling behavior and depression and 24 healthy controls are analyzed, and preprocessed by SPM software. Graph theory analysis method is used to establish the functional brain networks in which local and global properties are calculated. Two sets of local attribute index including node degree and node efficiency are used to make edge analysis in different depression groups (major, moderate and mild depression groups, with 8 patients in each group) and healthy control group, and the changes in global properties are also discussed. Additionally, the correlations of scoring scale related to gambling behavior with 3 criteria on the global properties (small world attribute, global efficiency and local efficiency) are analyzed. The two-sample t-tests on depression groups and healthy control group confirm the significant connections among brain regions (P<0.05), and reveal the significant negative correlations between the global brain network attribute indexes and different behavioral scales, which fully verifies the correlation between gambling behavior and depression, and provides the basis for further exploring correlation between the individual behavior attribute and depression, thereby assisting clinical diagnosis and treatment of depression patients.

相似文献/References:

[1]郭家梁,等.基于振幅-周期二维分布的脑电复杂度分析[J].中国医学物理学杂志,2016,33(6):633.[doi:10.3969/j.issn.1005-202X.2016.06.019]
[2]刘岩,李幼军,陈萌. 基于固有模态分解和深度学习的抑郁症脑电信号分类分析[J].中国医学物理学杂志,2017,34(9):963.[doi:DOI:10.3969/j.issn.1005-202X.2017.09.021]
　[J].Chinese Journal of Medical Physics,2017,34(11):963.[doi:DOI:10.3969/j.issn.1005-202X.2017.09.021]
[3]王静,孔令茵,雷炳业,等.抑郁症的脑复杂网络研究进展[J].中国医学物理学杂志,2020,37(6):780.[doi:DOI:10.3969/j.issn.1005-202X.2020.06.023]
　WANG Jing,KONG Lingyin,LEI Bingye,et al.Advances in research on complex brain networks in depression[J].Chinese Journal of Medical Physics,2020,37(11):780.[doi:DOI:10.3969/j.issn.1005-202X.2020.06.023]
[4]吴华旺,佘生林,郑伟,等.基于脑网络组图谱的首发-未服药抑郁症白质结构网络研究[J].中国医学物理学杂志,2021,38(7):909.[doi:DOI:10.3969/j.issn.1005-202X.2021.07.023]
　WU Huawang,SHE Shenglin,et al.Brainnetome atlas-based investigation of white matter structural networks in drug-na?e first-episode major depressive disorder[J].Chinese Journal of Medical Physics,2021,38(11):909.[doi:DOI:10.3969/j.issn.1005-202X.2021.07.023]
[5]计亚荣,王瑜,付常洋,等.基于典型相关分析与双模态数据融合的抑郁症辅助诊断[J].中国医学物理学杂志,2021,38(10):1316.[doi:DOI:10.3969/j.issn.1005-202X.2021.10.024]
　JI Yarong,WANG Yu,FU Changyang,et al.Aided diagnosis of major depressive disorder based on canonical correlation analysis and bimodal data fusion[J].Chinese Journal of Medical Physics,2021,38(11):1316.[doi:DOI:10.3969/j.issn.1005-202X.2021.10.024]
[6]刁云恒,王慧颖,董娇,等.机器学习在抑郁症辅助诊断中的应用进展[J].中国医学物理学杂志,2022,39(2):257.[doi:DOI:10.3969/j.issn.1005-202X.2022.02.021]
　DIAO Yunheng,WANG Huiying,et al.Advances in the application of machine learning in auxiliary diagnosis of depression[J].Chinese Journal of Medical Physics,2022,39(11):257.[doi:DOI:10.3969/j.issn.1005-202X.2022.02.021]
[7]段逸凡,王瑜,付常洋,等.基于双模态磁共振成像和决策层融合的抑郁症辅助诊断[J].中国医学物理学杂志,2022,39(3):378.[doi:DOI:10.3969/j.issn.1005-202X.2022.03.019]
　DUAN Yifan,WANG Yu,FU Changyang,et al.Auxiliary diagnosis of depression based on bimodal magnetic resonance imaging and decision level fusion[J].Chinese Journal of Medical Physics,2022,39(11):378.[doi:DOI:10.3969/j.issn.1005-202X.2022.03.019]
[8]郭朝晖,王瑜,马慧鋆,等.基于迁移学习和3D-WGMobileNet的青年抑郁症辅助诊断[J].中国医学物理学杂志,2024,41(4):455.[doi:DOI:10.3969/j.issn.1005-202X.2024.04.010]
　GUO Zhaohui,WANG Yu,MA Huijun,et al.Diagnosis of youth depression based on transfer learning and 3D-WGMobileNet[J].Chinese Journal of Medical Physics,2024,41(11):455.[doi:DOI:10.3969/j.issn.1005-202X.2024.04.010]
[9]龚旻炜,石佳琪,吴健.机器学习方法预测人群中抑郁症发病风险的研究进展[J].中国医学物理学杂志,2024,41(6):776.[doi:DOI:10.3969/j.issn.1005-202X.2024.06.017]
　GONG Minwei,,et al.Review on machine learning methods in predicting the risk of depression[J].Chinese Journal of Medical Physics,2024,41(11):776.[doi:DOI:10.3969/j.issn.1005-202X.2024.06.017]
[10]王建尚,张冰涛,王小敏,等.基于频空融合与3D-CNN-Attention的抑郁症识别[J].中国医学物理学杂志,2024,41(10):1307.[doi:DOI:10.3969/j.issn.1005-202X.2024.10.016]
　WANG Jianshang,ZHANG Bingtao,WANG Xiaomin,et al.Depression recognition based on frequency-space domain fusion and 3D-CNN-Attention[J].Chinese Journal of Medical Physics,2024,41(11):1307.[doi:DOI:10.3969/j.issn.1005-202X.2024.10.016]
[11]付常洋,王瑜,肖洪兵,等.基于多尺度功能脑网络融合特征的抑郁症分类算法[J].中国医学物理学杂志,2020,37(4):439.[doi:DOI:10.3969/j.issn.1005-202X.2020.04.008]
　FU Changyang,WANG Yu,XIAO Hongbing,et al.Classification of depression using fusion features based on multi-scale functional brain network[J].Chinese Journal of Medical Physics,2020,37(11):439.[doi:DOI:10.3969/j.issn.1005-202X.2020.04.008]

备注/Memo

备注/Memo:
【收稿日期】2024-07-03 【基金项目】北京市自然科学基金-北京市教育委员会科技计划重点项目（KZ202110011015） 【作者简介】马宁欣，硕士研究生，研究方向：图像处理与模式识别，E-mail: 1641338973@qq.com 【通信作者】王瑜，博士后，教授，研究方向：图像处理与模式识别，E-mail: wangyu@btbu.edu.cn
更新日期/Last Update: 2024-11-26