Abnormal Resting-State Functional Connectivity in the Whole Brain in Lifelong Premature Ejaculation Patients Based on Machine Learning Approach
Recent neuroimaging studies have indicated that abnormalities in brain structure and function may play an important role in the etiology of lifelong premature ejaculation (LPE). LPE patients have exhibited aberrant cortical structure, altered brain network function and abnormal brain activation in r...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-05-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fnins.2019.00448/full |
| _version_ | 1819017320773713920 |
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| author | Ziliang Xu Xuejuan Yang Ming Gao Ming Gao Ming Gao Lin Liu Jinbo Sun Peng Liu Wei Qin |
| author_facet | Ziliang Xu Xuejuan Yang Ming Gao Ming Gao Ming Gao Lin Liu Jinbo Sun Peng Liu Wei Qin |
| author_sort | Ziliang Xu |
| collection | DOAJ |
| description | Recent neuroimaging studies have indicated that abnormalities in brain structure and function may play an important role in the etiology of lifelong premature ejaculation (LPE). LPE patients have exhibited aberrant cortical structure, altered brain network function and abnormal brain activation in response to erotic pictures. However, it remains unclear whether resting-state whole brain functional connectivity (FC) is altered in LPE patients. Machine learning analysis has the advantage of screening the best classification features from high-throughput data (such as FC), which has the potential to identify the pathophysiological targets of disease by establishing classification indicators for patients and healthy controls (HCs). Therefore, the supported vector machine based classification model using FC as features was used in the present study to confirm the most specific FCs that distinguish LPE patients from healthy controls. After feature selection, the remained features were used to build the classification model, with an accuracy 0.85 ± 0.14, sensitivity of 0.92 ± 0.18, specificity of 0.72 ± 0.30, and recall index of 0.85 ± 0.17 across 1000 testing groups (100 times 10-folds cross validation). After that, two-sample t-tests with family-wise error correction were used to compare these features that occur more than 500 times during training steps between LPE patients and HCs. Four FCs, (1) between left medial part of orbital frontal cortex (mOFC) and right mOFC, (2) between the left rectus and right postcentral gyrus, (3) between the right insula and left pallidum, and (4) between the right middle part of temporal pole and right inferior part of temporal gyrus showed significant group difference. These results demonstrate that resting-state brain FC might be a discriminating feature to distinguish LPE patients from HCs. These classification features, especially the FC between bilateral mOFC, provide underlying abnormal central functional targets in LPE etiology, which offers a novel alternative target for future intervention in LPE treatment. |
| first_indexed | 2024-12-21T03:01:39Z |
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| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-21T03:01:39Z |
| publishDate | 2019-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neuroscience |
| spelling | doaj.art-92ffb8a1c06841dfb1a8f3f2cd3450f22022-12-21T19:18:10ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2019-05-011310.3389/fnins.2019.00448442650Abnormal Resting-State Functional Connectivity in the Whole Brain in Lifelong Premature Ejaculation Patients Based on Machine Learning ApproachZiliang Xu0Xuejuan Yang1Ming Gao2Ming Gao3Ming Gao4Lin Liu5Jinbo Sun6Peng Liu7Wei Qin8Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, ChinaEngineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, ChinaAssisted Reproduction Center, Northwest Women’s and Children’s Hospital, Xi’an, ChinaDepartment of Urology, Xijing Hospital, Fourth Military Medical University, Xi’an, ChinaDepartment of Andrology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, ChinaEngineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, ChinaEngineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, ChinaEngineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, ChinaEngineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, ChinaRecent neuroimaging studies have indicated that abnormalities in brain structure and function may play an important role in the etiology of lifelong premature ejaculation (LPE). LPE patients have exhibited aberrant cortical structure, altered brain network function and abnormal brain activation in response to erotic pictures. However, it remains unclear whether resting-state whole brain functional connectivity (FC) is altered in LPE patients. Machine learning analysis has the advantage of screening the best classification features from high-throughput data (such as FC), which has the potential to identify the pathophysiological targets of disease by establishing classification indicators for patients and healthy controls (HCs). Therefore, the supported vector machine based classification model using FC as features was used in the present study to confirm the most specific FCs that distinguish LPE patients from healthy controls. After feature selection, the remained features were used to build the classification model, with an accuracy 0.85 ± 0.14, sensitivity of 0.92 ± 0.18, specificity of 0.72 ± 0.30, and recall index of 0.85 ± 0.17 across 1000 testing groups (100 times 10-folds cross validation). After that, two-sample t-tests with family-wise error correction were used to compare these features that occur more than 500 times during training steps between LPE patients and HCs. Four FCs, (1) between left medial part of orbital frontal cortex (mOFC) and right mOFC, (2) between the left rectus and right postcentral gyrus, (3) between the right insula and left pallidum, and (4) between the right middle part of temporal pole and right inferior part of temporal gyrus showed significant group difference. These results demonstrate that resting-state brain FC might be a discriminating feature to distinguish LPE patients from HCs. These classification features, especially the FC between bilateral mOFC, provide underlying abnormal central functional targets in LPE etiology, which offers a novel alternative target for future intervention in LPE treatment.https://www.frontiersin.org/article/10.3389/fnins.2019.00448/fulllifelong premature ejaculationfeature selectionfunctional magnetic resonance imagingsupport vector machinefunctional connectivity |
| spellingShingle | Ziliang Xu Xuejuan Yang Ming Gao Ming Gao Ming Gao Lin Liu Jinbo Sun Peng Liu Wei Qin Abnormal Resting-State Functional Connectivity in the Whole Brain in Lifelong Premature Ejaculation Patients Based on Machine Learning Approach Frontiers in Neuroscience lifelong premature ejaculation feature selection functional magnetic resonance imaging support vector machine functional connectivity |
| title | Abnormal Resting-State Functional Connectivity in the Whole Brain in Lifelong Premature Ejaculation Patients Based on Machine Learning Approach |
| title_full | Abnormal Resting-State Functional Connectivity in the Whole Brain in Lifelong Premature Ejaculation Patients Based on Machine Learning Approach |
| title_fullStr | Abnormal Resting-State Functional Connectivity in the Whole Brain in Lifelong Premature Ejaculation Patients Based on Machine Learning Approach |
| title_full_unstemmed | Abnormal Resting-State Functional Connectivity in the Whole Brain in Lifelong Premature Ejaculation Patients Based on Machine Learning Approach |
| title_short | Abnormal Resting-State Functional Connectivity in the Whole Brain in Lifelong Premature Ejaculation Patients Based on Machine Learning Approach |
| title_sort | abnormal resting state functional connectivity in the whole brain in lifelong premature ejaculation patients based on machine learning approach |
| topic | lifelong premature ejaculation feature selection functional magnetic resonance imaging support vector machine functional connectivity |
| url | https://www.frontiersin.org/article/10.3389/fnins.2019.00448/full |
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