Xi Xiao, Li Qianfeng, Wood Lisa J, Bose Eliezer, Zeng Xi, Wang Jun, Luo Xun, Wang Qing Mei
Stroke Biological Recovery Laboratory, Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, The Teaching Affiliate of Harvard Medical School, Charlestown, MA 02129, USA.
Department of Rehabilitation Medicine, The First Affiliated Hospital of Zhengzhou University, Road No. 169 Nanyang Attachment 10, Zhengzhou 450054, China.
Brain Sci. 2022 Aug 11;12(8):1065. doi: 10.3390/brainsci12081065.
To estimate network structures to discover the interrelationships among variables and distinguish the difference between networks. Three hundred and forty-eight stroke patients were enrolled in this retrospective study. A network analysis was used to investigate the association between those variables. A Network Comparison Test was performed to compare the correlation of variables between networks. Three hundred and twenty-five connections were identified, and 22 of these differed significantly between the high- and low-Functional Independence Measurement (FIM) groups. In the high-FIM network structure, brain-derived neurotrophic factor (BDNF) and length of stay (LOS) had associations with other nodes. However, there was no association with BDNF and LOS in the low-FIM network. In addition, the use of amantadine was associated with shorter LOS and lower FIM motor subscores in the high-FIM network, but there was no such connection in the low-FIM network. Centrality indices revealed that amantadine use had high centrality with others in the high-FIM network but not the low-FIM network. Coronary artery disease (CAD) had high centrality in the low-FIM network structure but not the high-FIM network. Network analysis revealed a new correlation of variables associated with stroke recovery. This approach might be a promising method to facilitate the discovery of novel factors important for stroke recovery.
为估计网络结构以发现变量之间的相互关系并区分不同网络之间的差异。本回顾性研究纳入了348例中风患者。采用网络分析来研究这些变量之间的关联。进行网络比较测试以比较不同网络中变量的相关性。共识别出325个连接，其中22个在高功能独立性测量（FIM）组和低功能独立性测量组之间存在显著差异。在高FIM网络结构中，脑源性神经营养因子（BDNF）和住院时间（LOS）与其他节点存在关联。然而，在低FIM网络中，BDNF和LOS之间没有关联。此外，在高FIM网络中，金刚烷胺的使用与较短的住院时间和较低的FIM运动子评分相关，但在低FIM网络中没有这种联系。中心性指标显示，在高FIM网络中，金刚烷胺的使用与其他因素具有较高的中心性，但在低FIM网络中并非如此。冠状动脉疾病（CAD）在低FIM网络结构中具有较高的中心性，但在高FIM网络中并非如此。网络分析揭示了与中风恢复相关的变量之间的新关联。这种方法可能是一种有前景的方法，有助于发现对中风恢复重要的新因素。
