Pain
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Decoding pain: uncovering the factors that affect the performance of neuroimaging-based pain models.
Neuroimaging-based pain biomarkers, when combined with machine learning techniques, have demonstrated potential in decoding pain intensity and diagnosing clinical pain conditions. However, a systematic evaluation of how different modeling options affect model performance remains unexplored. This study presents the results from a comprehensive literature survey and benchmark analysis. ⋯ Specifically, incorporating more pain-related brain regions, increasing sample sizes, and averaging less data during training and more data during testing improved performance. These findings offer useful guidance for developing neuroimaging-based biomarkers, underscoring the importance of strategic selection of modeling approaches to build better-performing neuroimaging pain biomarkers. However, the generalizability of these findings to clinical pain requires further investigation.
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Translational models of the sensitized pain system are needed to progress the understanding of involved mechanisms. In this study, long-term potentiation was used to develop a mechanism-based large-animal pain model. Event-related potentials to electrical stimulation of the ulnar nerve were recorded by intracranial recordings in pigs, 3 weeks before, immediately before and after, and 3 weeks after peripheral high-frequency stimulation (HFS) applied to the ulnar nerve in the right forelimb (7 pigs) or in control animals (5 pigs). ⋯ The relative increase in N1 30 minutes after HFS and the degree of mechanical hyperalgesia 2 weeks post-HFS was correlated ( P < 0.033). These results show for the first time that the pig HFS model resembles the human HFS model closely where the profile of sensitization is comparable. Interestingly, the degree of sensitization was associated with the cortical signs of hyperexcitability at HFS induction.
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Meta Analysis
Is personalization of psychological pain treatments necessary? Evidence from a Bayesian variance ratio meta-analysis.
This is the first study to empirically determine the potential for data-driven personalization in the context of chronic primary pain (CPP). Effect sizes of psychological treatments for individuals with CPP are small to moderate on average. Aiming for better treatment outcomes for the individual patient, the call to personalize CPP treatment increased over time. ⋯ However, this result warrants careful consideration. Further research is needed to shed light on the heterogeneity of psychological treatment studies and thus to uncover the full potential of data-driven personalized psychotherapy for patients with CPP. A Bayesian variance ratio meta-regression indicates empirical evidence that data-driven personalized psychotherapy for patients with chronic primary pain could increase effects of cognitive behavioral therapy.
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Chronic pain is a pervasive and debilitating condition with increasing implications for public health, affecting millions of individuals worldwide. Despite its high prevalence, the underlying neural mechanisms and pathophysiology remain only partly understood. Since its introduction 35 years ago, brain diffusion magnetic resonance imaging (MRI) has emerged as a powerful tool to investigate changes in white matter microstructure and connectivity associated with chronic pain. ⋯ We conclude by highlighting emerging approaches and prospective avenues in the field that may provide new insights into the pathophysiology of chronic pain and potential new therapeutic targets. Because of the limited current body of research and unidentified targeted therapeutic strategies, we are forced to conclude that further research is required. However, we believe that brain diffusion MRI presents a promising opportunity for enhancing our understanding of chronic pain and improving clinical outcomes.