Multivariate Pattern Analysis of Lifelong Premature Ejaculation Based on Multiple Kernel Support Vector Machine

Bowen Geng; Ming Gao; Ruiqing Piao; Chengxiang Liu; Ke Xu; Shuming Zhang; Xiao Zeng; Peng Liu; Yanzhu Wang

doi:10.3389/fpsyt.2022.906404

Multivariate Pattern Analysis of Lifelong Premature Ejaculation Based on Multiple Kernel Support Vector Machine

Front Psychiatry. 2022 Jul 25:13:906404. doi: 10.3389/fpsyt.2022.906404. eCollection 2022.

Authors

Bowen Geng^{1

2}, Ming Gao³, Ruiqing Piao^{1

2}, Chengxiang Liu^{1

2}, Ke Xu^{1

2}, Shuming Zhang^{1

2}, Xiao Zeng^{1

2}, Peng Liu^{1

2}, Yanzhu Wang³

Affiliations

¹ Life Science Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China.
² Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China.
³ Department of Urology, Xi'An Daxing Hospital Affiliated to Yan'an University, Xi'an, China.

Abstract

Objective: This study aimed to develop an effective support vector machine (SVM) classifier based on the multi-modal data for detecting the main brain networks involved in group separation of premature ejaculation (PE).

Methods: A total of fifty-two patients with lifelong PE and 36 matched healthy controls were enrolled in this study. Structural MRI data, functional MRI data, and diffusion tensor imaging (DTI) data were used to process SPM12, DPABI4.5, and PANDA, respectively. A total of 12,735 features were reduced by the Mann-Whitney U test. The resilience nets method was further used to select features.

Results: Finally, 36 features (3 structural MRI, 7 functional MRI, and 26 DTI) were chosen in the training dataset. We got the best SVM model with an accuracy of 97.5% and an area under the curve (AUC) of 0.986 in the training dataset as well as an accuracy of 91.4% and an AUC of 0.966 in the testing dataset.

Conclusion: Our findings showed that the majority of the brain abnormalities for the classification was located within or across several networks. This study may contribute to the neural mechanisms of PE and provide new insights into the pathophysiology of patients with lifelong PE.

Keywords: MRI; diffusion tensor imaging (DTI); lifelong premature ejaculation (lifelong PE); machine learning; multivariate pattern analysis (MVPA); neuroimaging; support vector machine (SVM).