Pain
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Randomized Controlled Trial
The effects of virtual reality neuroscience-based therapy on clinical and neuroimaging outcomes in patients with chronic back pain: a randomized clinical trial.
Chronic pain remains poorly managed. The integration of immersive technologies (ie, virtual reality [VR]) with neuroscience-based principles may provide effective pain treatment by targeting cognitive and affective neural processes that maintain pain and therefore potentially changing neurobiological circuits associated with pain chronification and amplification. We tested the effectiveness of a novel VR neuroscience-based therapy (VRNT) to improve pain-related outcomes in n = 31 participants with chronic back pain, evaluated against usual care (waitlist control; n = 30) in a 2-arm randomized clinical trial ( NCT04468074). ⋯ Several secondary clinical outcomes were also improved by VRNT, including disability, quality of life, sleep, and fatigue. In addition, VRNT was associated with increases in dorsomedial prefrontal functional connectivity with the superior somatomotor, anterior prefrontal and visual cortices, and decreased white matter fractional anisotropy in the corpus callosum adjacent to the anterior cingulate, relative to the control condition. Thus, VRNT showed preliminary efficacy in significantly reducing pain and improving overall functioning, possibly through changes in somatosensory and prefrontal brain networks.
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Substantial interindividual variability characterizes osteoarthritis (OA) pain. Previous findings identify quantitative sensory testing (QST), psychological factors, and health-related quality of life as contributors to OA pain and predictors of treatment outcomes. This exploratory study aimed to explain baseline OA pain intensity and predict OA pain after administration of a nonsteroidal anti-inflammatory drug in combination with paracetamol for 3 weeks. ⋯ When assessed separately, PCS was the strongest predictor (32.2% of baseline and 24.1% of follow-up pain), but QST, symptoms of anxiety and depression, PCS, and quality of life also explained some variability in baseline and follow-up knee OA pain. Further analyses revealed that only TSP and PCS were not mediated by any other included variables, highlighting their role as unique contributors to OA pain presentation. This study emphasizes the importance of embracing a multimodal approach to OA pain and highlights PCS and TSP as major contributors to the baseline OA pain experience and the OA pain experience after OA treatment.
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Musculoskeletal (MSK) pain is a common reason for consultation in general practice and frequently reported in children and adolescents. This study examined the prevalence of MSK pain in 13-year-old children and assessed associations with physical and psychosocial factors. Data from the Generation R Study, a population-based birth cohort, was used. ⋯ Children with MSK pain were more likely to have reported MSK pain at 6 years. Multivariable analyses showed significant associations for male sex (OR 0.74, 95% CI 0.56-0.98), high maternal educational (OR 0.69, 95% CI 0.49-0.96), higher BMI (OR 1.19, 95% CI 1.05-1.35), being physically active (OR 1.41, 95% CI 1.03-1.91), and behavioral problems (OR 1.85, 95% CI 1.33-2.59) with the presence of MSK pain. The chronic nature of MSK pain in combination with the relatively high prevalence of MSK pain in this study shows that MSK pain is already an important problem at a young age.
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Pain anticipation during conditions of uncertainty can unveil intrinsic biases, and understanding these biases can guide pain treatment interventions. This study used machine learning and functional magnetic resonance imaging to predict anticipatory responses in a pain anticipation experiment. One hundred forty-seven participants that included healthy controls (n = 57) and individuals with current and/or past mental health diagnosis (n = 90) received cues indicating upcoming pain stimuli: 2 cues predicted high and low temperatures, while a third cue introduced uncertainty. ⋯ Three distinct response profiles emerged: subjects with a negative bias towards high pain anticipation, those with a positive bias towards low pain anticipation, and individuals whose predictions during uncertainty were unbiased. These profiles remained stable over one year, were consistent across diagnosed psychopathologies, and correlated with cognitive coping styles and underlying insula anatomy. The findings suggest that individualized and stable pain anticipation occurs in uncertain conditions.
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Facial grimacing is used to quantify spontaneous pain in mice and other mammals, but scoring relies on humans with different levels of proficiency. Here, we developed a cloud-based software platform called PainFace ( http://painface.net ) that uses machine learning to detect 4 facial action units of the mouse grimace scale (orbitals, nose, ears, whiskers) and score facial grimaces of black-coated C57BL/6 male and female mice on a 0 to 8 scale. ⋯ By analyzing the frequency distribution of grimace scores, we found that mice spent 7x more time in a "high grimace" state following laparotomy surgery relative to sham surgery controls. Our study shows that PainFace reproducibly quantifies facial grimaces indicative of nonevoked spontaneous pain and enables laboratories to standardize and scale-up facial grimace analyses.