Reconfiguration of Dynamic Functional Connectivity States in Patients With Lifelong Premature Ejaculation.
Reconfiguration of Dynamic Functional Connectivity States in Patients With Lifelong Premature Ejaculation.
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DOI：
10.3389/fnins.2021.721236
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发表时间：
2021
影响因子：
4.3
通讯作者：
Zhang B
中科院分区：
文献类型：
--
作者：
Lu J;Chen Q;Li D;Zhang W;Xing S;Wang J;Zhang X;Liu J;Qing Z;Dai Y;Zhang B
目的：神经影像学已证明早泄 (PE) 患者的静态功能连接发生改变，但仍缺乏检查早泄患者自发大脑活动动态变化的研究。我们的目的是探索终身 PE (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区分 LPE 患者与正常对照 (NC)。方法：招募了 36 名 LPE 患者和 23 名 NC。收集每位参与者的静息态功能磁共振成像 (fMRI) 数据、中国 PE 指数 (CIPE) 的临床评分以及阴道内射精潜伏时间 (IELT)。 DFC是通过滑动窗口方法计算的。最后，应用拉格朗日支持向量机 (LSVM) 分类器使用 DFC 参数区分 LPE 患者和 NC。引入了两个 DFC 状态指标（复发时间和转换频率），我们评估了 DFC 状态指标和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。结果：通过 k 均值聚类，确定了四种不同的 DFC 状态。与 NC 相比，LPE 患者的状态 3 的复发时间增加（p < 0.05，Bonferroni 校正），但状态 1 的复发时间减少（p < 0.05，Bonferroni 校正）。此外，与 NC 患者相比，LPE 患者在状态 1 和状态 4 之间的转换频率明显较低（p < 0.05，未校正），而在状态 3 和状态 4 之间的转换频率较高（p < 0.05，未校正）。复发时间和转变频率与 CIPE 分数和 IELT 显着相关。 LSVM 分类器的准确度、灵敏度和特异性分别为 90.35、87.59 和 85.59%。结论：LPE患者更倾向于处于加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，可以将 LPE 患者与 NC 进行分类。我们的 DFC 状态重新配置结果可能为理解 LPE 的核心病因提供新的见解，表明神经影像生物标志物用于评估 LPE 的临床严重程度。
Purpose: 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. Methods: 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. Results: By k-means clustering, four distinct DFC states were identified. The LPE patients showed an increase in the reoccurrence times for state 3 (p < 0.05, Bonferroni corrected) but a decrease for state 1 (p < 0.05, Bonferroni corrected) compared to the NCs. Moreover, the LPE patients had significantly less frequent transitions between state 1 and state 4 (p < 0.05, uncorrected) while more frequent transitions between state 3 and state 4 (p < 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. Conclusion: 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.
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