Neuroimaging biomarkers of psychogenic erectile dysfunction: protocol for a systematic review.

BMJ OPEN(2019)

Cited 11|Views21
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Abstract
Introduction Erectile dysfunction (ED) is the most common male sexual disorder that severely impacts the sexual performance and quality of life of men. As the main subtype of ED, psychogenic ED (pED) has been demonstrated to be a genitourinary disease and also associated with alterations in both brain structure and function. However, the scattered neuroimaging evidence from individual studies has not yet been integrated, and the central pathological alterations associated with pED remain unclear. The objective of this systematic review is to integrate and assess the evidence of the impact of pED on brain structure and function. Methods and analysis Five databases (PubMed, EMBASE, Web of Science, China Biology Medicine Database and China National Knowledge Infrastructure (CNKI)) will be systematically searched from inception to 1 October 2019 (the anticipated completion date of this review), with language restricted to English and Chinese. Studies focusing on the structural or functional alterations in patients with pED will be retrieved. The study selection process will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline and quality assessment will be conducted with a customised checklist. After data extraction, a qualitative review will be performed to synthesise the structural and functional brain alterations as well as the correlations between the altered cerebral structures and functions and the clinical characteristics of patients with pED. If the collected data make it feasible, an activation likelihood estimation meta-analysis will also be launched. Ethics and dissemination Ethical approval is not required as primary data will not be collected. This review will be published in a peer-reviewed journal and presented at conferences. PROSPERO registration number CRD42019117206.
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Key words
psychogenic erectile dysfunction,neuroimaging,MRI,activation likelihood estimation
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