Abnormal brain structure as a potential biomarker for venous erectile dysfunction: evidence from multimodal MRI and machine learning
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Abstract
Objectives
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.
Methods
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.
Results
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%.
Conclusions
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.
Key Points
• 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.
Keywords
Venous erectile dysfunction Multimode magnetic resonance imaging VBM TBSS Machine-learning classificationAbbreviations
- AD
Axial diffusivity
- BPRS
Brief Psychiatric Rating Scale
- FA
Fractional anisotropy
- GM
Grey matter
- HAMA
Hamilton Anxiety Rating Scale
- HAMD
Hamilton Depression Rating Scale
- IIEF-5
International Index of Erectile Function
- MD
Mean diffusivity
- NIH-CPSI
National Institutes of Health Chronic Prostatitis Symptom Index
- PED
Psychogenic ED
- PEDT
Premature Ejaculation Diagnostic Tool
- RD
Radial diffusivity
- SAS
Self-Rating Anxiety Scale
- SDS
Self-Rating Depression Scale
- TBSS
Tract-based spatial statistics
- VBM
Voxel-based morphometry
- VED
Venous erectile dysfunction
- WM
White matter
Notes
Acknowledgements
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
Guarantor
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.
Informed consent
Written informed consent was obtained from all subjects (patients) in this study.
Ethical approval
Institutional Review Board approval was obtained by the Medical Ethics Committee of the Union Hospital.
Methodology
• prospective
• case-control study/diagnostic study
• performed at one institution
Supplementary material
References
- 1.Wespes E, Amar E, Hatzichristou D et al (2006) EAU Guidelines on Erectile Dysfunction: An Update. European Urology 49:806–815CrossRefPubMedGoogle Scholar
- 2.Fabbri A, Caprio M, Aversa A (2003) Pathology of erection. Journal of endocrinological investigation 26:87–91PubMedGoogle Scholar
- 3.Vicenzini E, Altieri M, Michetti PM et al (2008) Cerebral vasomotor reactivity is reduced in patients with erectile dysfunction. European neurology 60:85–88CrossRefPubMedGoogle Scholar
- 4.Rajkumar RP (2015) The impact of disrupted childhood attachment on the presentation of psychogenic erectile dysfunction: An exploratory study. The journal of sexual medicine 12:798–803CrossRefPubMedGoogle Scholar
- 5.Glina S, Cohen DJ, Vieira M (2014) Diagnosis of erectile dysfunction. Current opinion in psychiatry 27:394–399CrossRefPubMedGoogle Scholar
- 6.Argiolas A, Melis MR (2005) Central control of penile erection: role of the paraventricular nucleus of the hypothalamus. Progress in neurobiology 76:1–21CrossRefPubMedGoogle Scholar
- 7.Stoléru S, Fonteille V, Corneill C, Joyal C, Moulier V (2012) Functional neuroimaging studies of sexual arousal and orgasm in healthy men and women: a review and meta-analysis. Neuroscience & Biobehavioral Reviews 36:1481–1509CrossRefGoogle Scholar
- 8.Redoutc J, Stolutc S, Pugeat M et al (2005) Brain processing of visual sexual stimuli in treated and untreated hypogonadal patients. Psychoneuroendocrinology 30:461–482CrossRefGoogle Scholar
- 9.Montorsi F, Perani D, Anchisi D et al (2003) Brain activation patterns during video sexual stimulation following the administration of apomorphine: results of a placebo-controlled study. European Urology 43:405–411CrossRefPubMedGoogle Scholar
- 10.Mouras H, Stolasa S, Bittoun J et al (2003) Brain processing of visual sexual stimuli in healthy men: a functional magnetic resonance imaging study. Neuroimage 20:855–869CrossRefPubMedGoogle Scholar
- 11.Arnow BA, Desmond JE, Banner LL et al (2002) Brain activation and sexual arousal in healthy, heterosexual males. Brain 125:1014–1023CrossRefPubMedGoogle Scholar
- 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
- 13.Zhang P, Liu J, Li G et al (2014) White matter microstructural changes in psychogenic erectile dysfunction patients. Andrology 2:379–385CrossRefPubMedGoogle Scholar
- 14.Zhao L, Guan M, Zhang X et al (2015) Structural insights into aberrant cortical morphometry and network organization in psychogenic erectile dysfunction. Human brain mapping 36:4469–4482CrossRefPubMedGoogle 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
- 16.Ferretti A, Caulo M, Del Gratta C et al (2005) Dynamics of male sexual arousal: distinct components of brain activation revealed by fMRI. Neuroimage 26:1086–1096CrossRefPubMedGoogle Scholar
- 17.Ashburner J, Friston KJ (2000) Voxel-based morphometry—the methods. Neuroimage 11:805–821CrossRefPubMedGoogle Scholar
- 18.Ashburner J (2012) SPM: a history. Neuroimage 62:791–800CrossRefPubMedGoogle Scholar
- 19.Arrigo A, Calamuneri A, Milardi D et al (2017) Visual System Involvement in Patients with Newly Diagnosed Parkinson Disease. Radiology:161732Google Scholar
- 20.Fan W, Zhang W, Li J et al (2015) Altered contralateral auditory cortical morphology in unilateral sudden sensorineural hearing loss. Otology & Neurotology 36:1622CrossRefGoogle Scholar
- 21.Smith SM, Jenkinson M, Johansen-Berg H et al (2006) Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31:1487–1505CrossRefPubMedGoogle Scholar
- 22.Smith SM, Jenkinson M, Woolrich MW et al (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23:S208–S219CrossRefPubMedGoogle Scholar
- 23.Jenkinson M, Bannister P, Brady M, Smith S (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17:825–841CrossRefPubMedGoogle Scholar
- 24.Smith SM (2002) Fast robust automated brain extraction. Human brain mapping 17:143–155CrossRefPubMedGoogle 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
- 26.Mori S, Oishi K, Jiang H et al (2008) Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage 40:570–582CrossRefPubMedPubMedCentralGoogle Scholar
- 27.Poeppl TB, Langguth B, Laird AR, Eickhoff SB (2014) The functional neuroanatomy of male psychosexual and physiosexual arousal: A quantitative meta-analysis. Human brain mapping 35:1404–1421CrossRefPubMedGoogle 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
- 29.Alexander AL, Hurley SA, Samsonov AA et al (2011) Characterization of cerebral white matter properties using quantitative magnetic resonance imaging stains. Brain connectivity 1:423–446CrossRefPubMedPubMedCentralGoogle Scholar
- 30.Song S-K, Yoshino J, Le TQ et al (2005) Demyelination increases radial diffusivity in corpus callosum of mouse brain. Neuroimage 26:132–140CrossRefPubMedGoogle Scholar
- 31.Sun SW, Liang HF, Trinkaus K, Cross AH, Armstrong RC, Song SK (2006) Noninvasive detection of cuprizone induced axonal damage and demyelination in the mouse corpus callosum. Magnetic Resonance in Medicine 55:302–308CrossRefPubMedGoogle Scholar
- 32.Alexander AL, Lee JE, Lazar M, Field AS (2007) Diffusion tensor imaging of the brain. Neurotherapeutics 4:316–329CrossRefPubMedPubMedCentralGoogle Scholar
- 33.Rouw R, Scholte HS (2007) Increased structural connectivity in grapheme-color synesthesia. Nature neuroscience 10:792CrossRefPubMedGoogle Scholar
- 34.Scholz J, Klein MC, Behrens TE, Johansen-Berg H (2009) Training induces changes in white-matter architecture. Nature neuroscience 12:1370–1371CrossRefPubMedPubMedCentralGoogle Scholar
- 35.Jellinger K (2007) Fiber Pathways of the Brain. European Journal of Neurology 14Google Scholar
- 36.Henze R, Brunner R, Thiemann U et al (2012) White matter alterations in the corpus callosum of adolescents with first-admission schizophrenia. Neuroscience letters 513:178–182CrossRefPubMedGoogle Scholar
- 37.Hagemann JH, Berding G, Bergh S et al (2003) Effects of visual sexual stimuli and apomorphine SL on cerebral activity in men with erectile dysfunction. European Urology 43:412–420CrossRefPubMedGoogle Scholar