脑结构网络的非rich-club连接协同性与卒中后抑郁发生的关联性研究  

Association between the non-rich-club connectivity synergism of brain structural network and the occurrence of post-stroke depression

作　　者：蔡玉姣 李洋 谢恺 徐宇浩 朱彦[3] 罗一烽 曹志宏 李月峰 Cai Yujiao;Li Yang;Xie Kai;Xu Yuhao;Zhu Yan;Luo Yifeng;Cao Zhihong;Li Yuefeng(Department of Radiology,Yixing Hospital Affiliated to Jiangsu University,Yixing 214200,China;Department of Neurology,Nanjing Drum Tower Hospital,the Affiliated Hospital of Nanjing University Medical School,Nanjing 210008,China;Department of Radiology,the Affiliated Hospital of Jiangsu University,Zhenjiang 212001,China)

机构地区：[1]江苏大学附属宜兴医院影像科,宜兴214200 [2]南京大学医学院附属鼓楼医院神经科,南京210008 [3]江苏大学附属医院影像科,镇江212001

出　　处：《中华神经科杂志》2024年第5期481-487,共7页Chinese Journal of Neurology

基　　金：国家自然科学基金(81871343);江苏省重点研发计划项目(BE2021693)。

摘　　要：目的探究卒中事件后(康复早期)脑结构网络的变化与卒中后抑郁(PSD)发病的关联。方法前瞻性收集江苏大学附属宜兴医院2020年3月至2021年5月收治的急性脑梗死拟出院患者87例,同期收集与之匹配的健康对照者34名。对所有受试者进行系统磁共振成像扫描和量表评估,并实施2年的纵向随访。依据随访时是否发生抑郁将患者纳入PSD组和卒中后非抑郁(PSND)组。基于图论分析方法对脑结构网络的拓扑特征进行分析,采用方差分析探究脑结构网络属性的组间差异。以Logistic回归模型分析差异性脑网络属性对PSD的预测效能。以线性回归分析非rich-club区域的协同性与rich-club连接变化的关系。结果PSD组的rich-club连接和非rich-club区域协同性显著低于PSND组(rich-club连接,P<0.01;支线连接/外围连接协同性,P<0.001)。回归模型显示非rich-club区域协同性对PSD的发生具有良好的预测效能(OR=1.195,95%CI 1.073~1.471,P<0.001)。线性回归分析结果进一步显示非rich-club区域协同性与Δrich-club连接具有显著相关性(r=-0.691,P<0.001)。结论卒中康复早期良好的非rich-club区域的协同性可促进rich-club连接修复、抑制PSD发病。Objective To explore the association between changes in brain structural network during the early stage of stroke recovery and the onset of post-stroke depression(PSD).Methods A total of 87 acute ischemic stroke patients scheduled for discharge,who were admitted to the Yixing Hospital Affiliated to Jiangsu University from March 2020 to May 2021,were prospectively collected.During the same period,34 healthy control subjects matched with the stroke patients were also collected.All participants underwent systematic magnetic resonance imaging scans and scale assessments,and were followed up longitudinally for 2 years.Based on the occurrence of depression during follow-up,the stroke patients were divided into PSD group and post-stroke non-depression(PSND)group.Graph theoretical analysis was used to analyze the topological characteristics of brain structural network.Analysis of variance was used to explore the differences in brain structural network attributes among groups.Logistic regression model was used to analyze the predictive power of differential brain network attributes for PSD.Linear regression analysis was conducted to investigate the relationship between the synergism of non-rich-club regions and changes in rich-club connectivity.Results The rich-club connectivity and synergism of the non-rich-club regions were significantly lower in the PSD group than in the PSND group(rich-club connectivity,P<0.01;synergism of feeder/local,P<0.001).The regression model demonstrated that the synergism of non-rich-club regions had a good predictive power for the occurrence of PSD(OR=1.195,95%CI 1.073-1.471,P<0.001).Furthermore,linear regression analysis revealed a significant correlation between the synergism of non-rich-club regions andΔrich-club connectivity(r=-0.691,P<0.001).Conclusion The good synergism of non-rich-club regions during the early stage of stroke recovery promotes the repair of rich-club connectivity and inhibits the onset of PSD.

关 键 词：卒中 卒中后抑郁 结构网络 非rich-club区域 预测 

分 类 号：R743.3[医药卫生—神经病学与精神病学]