[1]刘幔利,孟雅婧,魏 巍,等.生物节律相关脑功能连接异常与双相情感障碍的相关性[J].南方医科大学学报,2020,(06):822-827.[doi:10.12122/j.issn.1673-4254.2020.06.08]
 Relationship between circadian rhythm related brain dysfunction and bipolar disorder.[J].Journal of Southern Medical University,2020,(06):822-827.[doi:10.12122/j.issn.1673-4254.2020.06.08]
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生物节律相关脑功能连接异常与双相情感障碍的相关性()
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《南方医科大学学报》[ISSN:1673-4254/CN:44-1627/R]

卷:
期数:
2020年06期
页码:
822-827
栏目:
出版日期:
2020-06-17

文章信息/Info

作者:
刘幔利孟雅婧魏 巍李 涛
Author(s):
Relationship between circadian rhythm related brain dysfunction and bipolar disorder
关键词:
双相情感障碍生物节律视交叉上核功能连接机器学习
Keywords:
bipolar disorder biological rhythm suprachiasmatic nucleus functional connectivity machine learning
DOI:
10.12122/j.issn.1673-4254.2020.06.08
文献标志码:
A
摘要:
目的 探讨双相情感障碍患者视交叉上核功能连接(FC)的改变,以及生物节律异常是否可作为双相情感障碍生物亚型分类的一种指标。方法 研究共纳入符合美国精神疾病诊断与统计手册第四版诊断标准的双相情感障碍患者138例和正常对照 150例,所有被试均通过静息态脑功能磁共振扫描,使用DPARSF软件对磁共振数据分析视交叉上核脑通路功能连接图(FC值),进行t检验;并基于静息态FC值,使用Scikit-learn 0.20.1中的主成分分析和K-means对双相情感障碍患者聚类分析。结果 与正常对照相比,双相情感障碍患者右侧视交叉上核与室旁核、右侧视交叉上核与下丘脑背内侧核、左侧视交叉上核与室旁核、左侧视交叉上核与下丘脑背内侧核功能连接增强。基于以上FC值,使用无监督机器学习K-means方法对双相情感障碍的最佳聚类为2类,轮廓系数为0.49。结论 双相情感障碍患者视交叉上核节律通路功能连接比正常对照增强,且节律通路功能连接值可将双相情感障碍分为两种亚型,提示生物节律是潜在的双相情感障碍的生物标记物之一。
Abstract:
Objective To investigate the changes of functional connectivity (FC) in the suprachiasmatic nucleus (SCN) of patients with bipolar disorder and perform a cluster analysis of patients with bipolar disorder based on FC. Methods The study recruited 138 patients with bipolar disorder (BD) diagnosed according to the 4th edition of Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) and 150 healthy control subjects. All the participants underwent resting-state functional magnetic resonance brain scans. DPARSF software was used to generate the FC diagram of the SCN. Based on the FC data, principal components analysis (PCA) and k-means in scikit-learn 0.20.1 were used for cluster analysis of the patients with bipolar disorder. Results Compared with the healthy controls, the patients showed enhanced functional connections between the SCN and the paraventricular nucleus and between the SCN and the dorsomedial hypothalamus nucleus. Based on these FC values, the optimal cluster of unsupervised k-means machine learning for bipolar disorder was 2, and the Silhouette coefficient was 0.49. Conclusion Patients with bipolar disorder have changes in the FC of the SCN, and the FC of the rhythm pathway can divide bipolar disorder into two subtypes, suggesting that biological rhythm is one of the potential biomarkers of bipolar disorder.
更新日期/Last Update: 2020-06-17