Alterations in regional homogeneity of resting-state cerebral activity in patients with chronic prostatitis/chronic pelvic pain syndrome

Autonomic Nervous System Dysfunction Is Related to Chronic Prostatitis/Chronic Pelvic Pain Syndrome

Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) is a common and non-lethal urological condition with painful symptoms. The complexity of CP/CPPS's pathogenesis and lack of efficient etiological diagnosis results in incomplete treatment and recurrent episodes, causing long-term mental and psychological suffering in patients. Recent findings indicate that the autonomic nervous system involves in CP/CPPS, including sensory, sympathetic, parasympathetic, and central nervous systems. Neuro-inflammation and sensitization of sensory nerves lead to persistent inflammation and pain. Sympathetic and parasympathetic alterations affect the cardiovascular and reproductive systems and the development of prostatitis. Central sensitization lowers pain thresholds and increases pelvic pain perception in chronic prostatitis. Therefore, this review summarized the detailed processes and mechanisms of the critical role of the autonomic nervous system in developing CP/CPPS. Furthermore, it describes the neurologically relevant substances and channels or receptors involved in this process, which provides new perspectives for new therapeutic approaches to CP/CPPS.

Implicit Motor Imagery for Chronic Pelvic Pain: A Cross-Sectional Case-Control Study

Implicit motor imagery (IMI), with an image laterality discrimination (LD) task, has been proposed as a useful therapeutic tool to restore body schema in patients with chronic pelvic pain (CPP). The aim of this study was to analyse the existence of differences between patients with CPP and healthy individuals in order to justify the use of IMI. An observational, cross-sectional study with non-probabilistic sampling was designed as a one-to-one matched case-control study. Through a web link designed for this purpose, a total of 40 abdominoperineal images were shown to 130 participants during the laterality task. Outcome measures were pain intensity (visual analogue scale, VAS), accuracy, response time (RT), and CPPQ-Mohedo score (Chronic Pelvic Pain Questionnaire-Mohedo). This was an observational, cross-sectional study with a total of 64 CPP patients and 66 healthy individuals. The comparative analysis between groups revealed significant differences in accuracy, CPPQ-Mohedo and VAS (p < 0.001), but not in RT; in patients with CPP, accuracy was correlated with a lower CPPQ-Mohedo score and RT and, the greater the pain intensity, the higher the CPPQ-Mohedo score and RT, and the lower the accuracy. In the LD task, the patients with CPP made more mistakes than the healthy individuals. IMI could be a useful and complementary tool in the therapeutic approach for patients with CPP.

Altered local connectivity in chronic pain: A voxel-wise meta-analysis of resting-state functional magnetic resonance imaging studies

BACKGROUND

A number of studies have used regional homogeneity (ReHo) to depict local functional connectivity in chronic pain (CP). However, the findings from these studies were mixed and inconsistent.

METHODS

A computerized literature search will be performed in PubMed, Web of Science, Embase, China National Knowledge Infrastructure (CNKI), WanFang, and SinoMed databases until June 15, 2019 and updated on March 20, 2020. This protocol will follow the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P). The Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) software will be used for this voxel-wise meta-analysis.

RESULTS

This meta-analysis will identify the most consistent ReHo alterations in CP.

CONCLUSIONS

To our knowledge, this will be the first voxel-wise meta-analysis that integrates ReHo findings in CP. This meta-analysis will offer the quantitative evidence of ReHo alterations that characterize brain local functional connectivity of CP.

PROSPERO REGISTRATION NUMBER

CRD42019148523.

Using Deep Learning and Resting-State fMRI to Classify Chronic Pain Conditions

Chronic pain is known as a complex disease due to its comorbidities with other symptoms and the lack of effective treatments. As a consequence, chronic pain seems to be under-diagnosed in more than 75% of patients. At the same time, the advance in brain imaging, the popularization of machine learning techniques and the development of new diagnostic tools based on these technologies have shown that these tools could be an option in supporting decision-making of healthcare professionals. In this study, we computed functional brain connectivity using resting-state fMRI data from one hundred and fifty participants to assess the performance of different machine learning models, including deep learning (DL) neural networks in classifying chronic pain patients and pain-free controls. The best result was obtained by training a convolutional neural network fed with data preprocessed using the MSDL probabilistic atlas and using the dynamic time warping (DTW) as connectivity measure. DL models had a better performance compared to other less costly models such as support vector machine (SVM) and RFC, with balanced accuracy ranged from 69 to 86%, while the area under the curve (ROC) ranged from 0.84 to 0.93. Also, DTW overperformed correlation as connectivity measure. These findings support the notion that resting-state fMRI data could be used as a potential biomarker of chronic pain conditions.

Chronic Pelvic Pain: Assessment, Evaluation, and Objectivation

Chronic Pelvic Pain (CPP) and Chronic Pelvic Pain Syndrome (CPPS) have a significant impact on men and women of reproductive and nonreproductive age, with a considerable burden on overall quality of life (QoL) and on psychological, functional, and behavioural status. Moreover, diagnostic and therapeutic difficulties are remarkable features in many patients. Therefore evaluation, assessment and objectivation tools are often necessary to properly address each patient and consequently his/her clinical needs. Here we review the different tools for pain assessment, evaluation, and objectivation; specific features regarding CPP/CPPS will be highlighted. Also, recent findings disclosed with neuroimaging investigations will be reviewed as they provide new insights into CPP/CPPS pathophysiology and may serve as a tool for CPP assessment and objectivation.