Brain Mechanisms of Virtual Reality Breathing Versus Traditional Mindful Breathing in Pain Modulation: Observational Functional Near-infrared Spectroscopy Study.

BACKGROUND: Pain is a complex experience that involves sensory-discriminative and cognitive-emotional neuronal processes. It has long been known across cultures that pain can be relieved by mindful breathing (MB). There is a common assumption that MB exerts its analgesic effect through interoception. Interoception refers to consciously refocusing the mind's attention to the physical sensation of internal organ function. OBJECTIVE: In this study, we dissect the cortical analgesic processes by imaging the brains of healthy subjects exposed to traditional MB (TMB) and compare them with another group for which we augmented MB to an outside sensory experience via virtual reality breathing (VRB). METHODS: The VRB protocol involved in-house-developed virtual reality 3D lungs that synchronized with the participants' breathing cycles in real time, providing them with an immersive visual-auditory exteroception of their breathing. RESULTS: We found that both breathing interventions led to a significant increase in pain thresholds after week-long practices, as measured by a thermal quantitative sensory test. However, the underlying analgesic brain mechanisms were opposite, as revealed by functional near-infrared spectroscopy data. In the TMB practice, the anterior prefrontal cortex uniquely modulated the premotor cortex. This increased its functional connection with the primary somatosensory cortex (S1), thereby facilitating the S1-based sensory-interoceptive processing of breathing but inhibiting its other role in sensory-discriminative pain processing. In contrast, virtual reality induced an immersive 3D exteroception with augmented visual-auditory cortical activations, which diminished the functional connection with the S1 and consequently weakened the pain processing function of the S1. CONCLUSIONS: In summary, our study suggested two analgesic neuromechanisms of VRB and TMB practices-exteroception and interoception-that distinctively modulated the S1 processing of the ascending noxious inputs. This is in line with the concept of dualism (Yin and Yang).

Radiographic interpretation using high-resolution Cbct to diagnose degenerative temporomandibular joint disease.

The objective of this study was to use high-resolution cone-beam computed images (hr- CBCT) to diagnose degenerative joint disease in asymptomatic and symptomatic subjects using the Diagnostic Criteria for Temporomandibular Disorders DC/TMD imaging criteria. This observational study comprised of 92 subjects age-sex matched and divided into two groups: clinical degenerative joint disease (c-DJD, n = 46) and asymptomatic control group (n = 46). Clinical assessment of the DJD and high-resolution CBCT images (isotropic voxel size of 0.08mm) of the temporomandibular joints were performed for each participant. An American Board of Oral and Maxillofacial Radiology certified radiologist and a maxillofacial radiologist used the DC/TMD imaging criteria to evaluate the radiographic findings, followed by a consensus of the radiographic evaluation. The two radiologists presented a high agreement (Cohen's Kappa ranging from 0.80 to 0.87) for all radiographic findings (osteophyte, erosion, cysts, flattening, and sclerosis). Five patients from the c- DJD group did not present radiographic findings, being then classified as arthralgia. In the asymptomatic control group, 82.6% of the patients presented radiographic findings determinant of DJD and were then classified as osteoarthrosis or overdiagnosis. In conclusion, our results showed a high number of radiographic findings in the asymptomatic control group, and for this reason, we suggest that there is a need for additional imaging criteria to classify DJD properly in hr-CBCT images.

Use of low-dose naltrexone in the management of chronic pain conditions: A systematic review.

BACKGROUND: The authors aimed to evaluate the efficacy of low-dose naltrexone in the management of chronic pain conditions and determine its potential use in orofacial pain management. METHODS: A comprehensive literature review was completed in the PubMed/MEDLINE, Embase, Cumulated Index to Nursing and Allied Health Literature, Dentistry and Oral Sciences Source Library databases up through June 17, 2019, using terms such as neurogenic, inflammation, naltrexone, temporomandibular, and chronic pain. The primary outcome was reduction in pain intensity and, secondarily, improvement in quality of life. RESULTS: A total of 793 studies were obtained with the initial search and 8 articles were selected for evaluation. Of these 8 articles, 4 were case reports, 3 were clinical studies, and 1 was a randomized controlled trial. Six studies included data on fibromyalgia, 2 studies included data on chronic regional pain syndrome, and 1 examined multiple diagnoses, including fibromyalgia, interstitial cystitis, and chronic pelvic pain. The primary outcome of all of the studies was pain intensity reduction. CONCLUSIONS AND PRACTICAL IMPLICATIONS: Low-dose naltrexone provides an alternative in medical management of chronic pain disorders as a novel anti-inflammatory and immunomodulator. It can offer additional management options, as orofacial pain conditions share characteristics with other chronic pain disorders. Owing to the size and heterogeneity of the studies, more large-scale studies are needed, along with additional studies assessing orofacial pain response to low-dose naltrexone.

Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning.

After chronic low back pain, Temporomandibular Joint (TMJ) disorders are the second most common musculoskeletal condition affecting 5 to 12% of the population, with an annual health cost estimated at $4 billion. Chronic disability in TMJ osteoarthritis (OA) increases with aging, and the main goal is to diagnosis before morphological degeneration occurs. Here, we address this challenge using advanced data science to capture, process and analyze 52 clinical, biological and high-resolution CBCT (radiomics) markers from TMJ OA patients and controls. We tested the diagnostic performance of four machine learning models: Logistic Regression, Random Forest, LightGBM, XGBoost. Headaches, Range of mouth opening without pain, Energy, Haralick Correlation, Entropy and interactions of TGF-ß1 in Saliva and Headaches, VE-cadherin in Serum and Angiogenin in Saliva, VE-cadherin in Saliva and Headaches, PA1 in Saliva and Headaches, PA1 in Saliva and Range of mouth opening without pain; Gender and Muscle Soreness; Short Run Low Grey Level Emphasis and Headaches, Inverse Difference Moment and Trabecular Separation accurately diagnose early stages of this clinical condition. Our results show the XGBoost + LightGBM model with these features and interactions achieves the accuracy of 0.823, AUC 0.870, and F1-score 0.823 to diagnose the TMJ OA status. Thus, we expect to boost future studies into osteoarthritis patient-specific therapeutic interventions, and thereby improve the health of articular joints.