Lu Jiaming, Chen Qian, Li Danyan, Zhang Wen, Xing Siyan, Wang Junxia, Zhang Xin, Liu Jiani, Qing Zhao, Dai Yutian, Zhang Bing
Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.
Department of Radiology, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China.
Front Neurosci. 2021 Sep 13;15:721236. doi: 10.3389/fnins.2021.721236. eCollection 2021.
Neuroimaging has demonstrated altered static functional connectivity in patients with premature ejaculation (PE), while studies examining dynamic changes in spontaneous brain activity in PE patients are still lacking. We aimed to explore the reconfiguration of dynamic functional connectivity (DFC) states in lifelong PE (LPE) patients and to distinguish LPE patients from normal controls (NCs) using a machine learning method based on DFC state features. Thirty-six LPE patients and 23 NCs were recruited. Resting-state functional magnetic resonance imaging (fMRI) data, the clinical rating scores on the Chinese Index of PE (CIPE), and intravaginal ejaculatory latency time (IELT) were collected from each participant. DFC was calculated by the sliding window approach. Finally, the Lagrangian support vector machine (LSVM) classifier was applied to distinguish LPE patients from NCs using the DFC parameters. Two DFC state metrics (reoccurrence times and transition frequencies) were introduced and we assessed the correlations between DFC state metrics and clinical variables, and the accuracy, sensitivity, and specificity of the LSVM classifier. By k-means clustering, four distinct DFC states were identified. The LPE patients showed an increase in the reoccurrence times for state 3 ( < 0.05, Bonferroni corrected) but a decrease for state 1 ( < 0.05, Bonferroni corrected) compared to the NCs. Moreover, the LPE patients had significantly less frequent transitions between state 1 and state 4 ( < 0.05, uncorrected) while more frequent transitions between state 3 and state 4 ( < 0.05, uncorrected) than the NCs. The reoccurrence times and transition frequencies showed significant associations with the CIPE scores and IELTs. The accuracy, sensitivity, and specificity of the LSVM classifier were 90.35, 87.59, and 85.59%, respectively. LPE patients were more inclined to be in DFC states reinforced intra-network and inter-network connection. These features correlated with clinical syndromes and can classify the LPE patients from NCs. Our results of reconfiguration of DFC states may provide novel insights for the understanding of central etiology underlying LPE, indicate neuroimaging biomarkers for the evaluation of clinical severity of LPE.
神经影像学研究表明早泄（PE）患者的静态功能连接存在改变，而关于PE患者自发脑活动动态变化的研究仍较为缺乏。我们旨在探索终身早泄（LPE）患者动态功能连接（DFC）状态的重新配置，并使用基于DFC状态特征的机器学习方法将LPE患者与正常对照（NC）区分开来。招募了36例LPE患者和23例NC。收集了每位参与者的静息态功能磁共振成像（fMRI）数据、中国早泄指数（CIPE）的临床评分以及阴道内射精潜伏期（IELT）。通过滑动窗口法计算DFC。最后，应用拉格朗日支持向量机（LSVM）分类器，利用DFC参数将LPE患者与NC区分开来。引入了两个DFC状态指标（重现次数和转换频率），并评估了DFC状态指标与临床变量之间的相关性，以及LSVM分类器的准确性、敏感性和特异性。通过k均值聚类，识别出四种不同的DFC状态。与NC相比，LPE患者状态3的重现次数增加（<0.05，Bonferroni校正），而状态1的重现次数减少（<0.05，Bonferroni校正）。此外，与NC相比，LPE患者在状态1和状态4之间的转换频率显著降低（<0.05，未校正），而在状态3和状态4之间的转换频率更高（<0.05，未校正）。重现次数和转换频率与CIPE评分和IELT显著相关。LSVM分类器的准确性、敏感性和特异性分别为90.35%、87.59%和85.59%。LPE患者更倾向于处于增强网络内和网络间连接的DFC状态。这些特征与临床综合征相关，能够将LPE患者与NC区分开来。我们关于DFC状态重新配置的结果可能为理解LPE的中枢病因提供新的见解，为评估LPE临床严重程度指明神经影像学生物标志物。
