Good sleep is necessary for good health. Sleep health is increasingly recognized as important for physical and mental health by both the medical profession and the general public, and there is great interest in how to avoid and treat sleep disorders and problems. Recent research indicates that insufficient sleep, disrupted sleep, and sleep disorders affect many aspects of human health including sexual function. In fact, patients with urological disorders or erectile dysfunction (ED) may have a sleep disorder that contributes to their urological or sexual dysfunction. Obstructive sleep apnea, insomnia, shift work disorder, and restless legs syndrome are all common sleep disorders and are associated with ED and/or other urological disorders. Therefore, careful attention should be paid to the diagnosis and treatment of concomitant sleep disorders in patients with sexual dysfunction. In this review, we provide an overview of what sleep is and how it is assessed in the clinic or laboratory; our current understanding of the functions of sleep and sleep health; a description of common sleep disorders, as well as how they are diagnosed and treated; and how sleep and its disorders are associated with male sexual dysfunction. Sleep is considered to be a 'third pillar of health', along with diet and exercise. With an understanding of common sleep disorders and how they can impact male sexual function, the urologist can ensure that sleep disorders are considered as a contributor to sexual dysfunction in their patients in order to provide them with the optimal treatment for overall health.Keywords: Erectile dysfunction; Sexual dysfunctions, psychological; Sleep; Sleep wake disorders; Testosterone This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data. Amongst recent attempts to improve performance on NHP data, deep learning models appear to outperform the traditional tools. However, given the minimal sample size of most NHP studies and notable variations in data quality, the deep learning models are very rarely applied to multi-site samples in NHP imaging. To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e. U-Net Model), and then transferred this to NHP data using a small NHP training sample. The resulting transfer-learning model converged faster and achieved more accurate performance than a similar U-Net Model trained exclusively on NHP samples. We improved the generalizability of the model by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data-Exchange (PRIME-DE) consortium. Our final model outperformed brain extraction routines from popular MRI packages (AFNI, FSL, and FreeSurfer) across a heterogeneous sample from multiple sites in the PRIME-DE with less computational cost (20 s~10 min). We also demonstrated the transfer-learning process enables the macaque model to be updated for use with scans from chimpanzees, marmosets, and other mammals (e.g. pig). Our model, code, and the skull-stripped mask repository of 136 macaque monkeys are publicly available for unrestricted use by the neuroimaging community at https://github.com/HumanBrainED/NHP-BrainExtraction .
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