Abnormal brain structure as a potential biomarker for venous erectile dysfunction: evidence from multimodal MRI and machine learning
- 34 Downloads
To investigate the cerebral structural changes related to venous erectile dysfunction (VED) and the relationship of these changes to clinical symptoms and disorder duration and distinguish patients with VED from healthy controls using a machine learning classification.
45 VED patients and 50 healthy controls were included. Voxel-based morphometry (VBM), tract-based spatial statistics (TBSS) and correlation analyses of VED patients and clinical variables were performed. The machine learning classification method was adopted to confirm its effectiveness in distinguishing VED patients from healthy controls.
Compared to healthy control subjects, VED patients showed significantly decreased cortical volumes in the left postcentral gyrus and precentral gyrus, while only the right middle temporal gyrus showed a significant increase in cortical volume. Increased axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) values were observed in widespread brain regions. Certain regions of these alterations related to VED patients showed significant correlations with clinical symptoms and disorder durations. Machine learning analyses discriminated patients from controls with overall accuracy 96.7%, sensitivity 93.3% and specificity 99.0%.
Cortical volume and white matter (WM) microstructural changes were observed in VED patients, and showed significant correlations with clinical symptoms and dysfunction durations. Various DTI-derived indices of some brain regions could be regarded as reliable discriminating features between VED patients and healthy control subjects, as shown by machine learning analyses.
• Multimodal magnetic resonance imaging helps clinicians to assess patients with VED.
• VED patients show cerebral structural alterations related to their clinical symptoms.
• Machine learning analyses discriminated VED patients from controls with an excellent performance.
• Machine learning classification provided a preliminary demonstration of DTI’s clinical use.
KeywordsVenous erectile dysfunction Multimode magnetic resonance imaging VBM TBSS Machine-learning classification
Brief Psychiatric Rating Scale
Hamilton Anxiety Rating Scale
Hamilton Depression Rating Scale
International Index of Erectile Function
National Institutes of Health Chronic Prostatitis Symptom Index
Premature Ejaculation Diagnostic Tool
Self-Rating Anxiety Scale
Self-Rating Depression Scale
Tract-based spatial statistics
Venous erectile dysfunction
We would like to thank the three anonymous reviewers for their helpful comments on an earlier version of this manuscript. We thank all participants in this study.
Compliance with ethical standards
The scientific guarantor of this publication is Lian Yang.
Conflict of interest
The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.
Statistics and biometry
No complex statistical methods were necessary for this paper.
Written informed consent was obtained from all subjects (patients) in this study.
Institutional Review Board approval was obtained by the Medical Ethics Committee of the Union Hospital.
• case-control study/diagnostic study
• performed at one institution
- 12.Cera N, Delli Pizzi S, Di Pierro E, Gambi F, Tartaro A, Zang Y-F (2012) Macrostructural Alterations of Subcortical Grey Matter in Psychogenic Erectile.Google Scholar
- 15.Zhao L, Guan M, Zhu X et al (2015) Aberrant topological patterns of structural cortical networks in psychogenic erectile dysfunction. Frontiers in human neuroscience 9Google Scholar
- 19.Arrigo A, Calamuneri A, Milardi D et al (2017) Visual System Involvement in Patients with Newly Diagnosed Parkinson Disease. Radiology:161732Google Scholar
- 25.Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12:2825–2830Google Scholar
- 28.Kader KO, Sanverdi E, Has A, Temuçin Ç, T mu S, Doerschner K (2013) Tract-based spatial statistics of diffusion tensor imaging in hereditary spastic paraplegia with thin corpus callosum reveals widespread white matter changes. Diagnostic and Interventional Radiology 19:181Google Scholar
- 35.Jellinger K (2007) Fiber Pathways of the Brain. European Journal of Neurology 14Google Scholar