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The effectiveness of cognitive strategies to attenuate pain has been reported in various behavioural studies, however the underlying neuronal mechanisms are only now beginning to be understood. Using a 7 T fMRI, we investigated three different pain attenuation strategies in 20 healthy subjects via: (a) non-imaginal distraction by counting backwards in steps of seven; (b) imaginal distraction by imagining a safe place; and (c) reinterpretation of the pain valence (reappraisal). Although we found considerable variability in the performances, all strategies exhibited a significant relief of pain compared to an unmodulated pain condition. Our finding argues against a subject's potential predisposition for a certain attenuation approach, as some of the subjects performed well on all attenuation tasks yet others performed low on all attenuation tasks. We further investigated the variability of performance within-subjects and explored the cortical regions that contribute to successful single attempts of pain attenuation at trial level. For each of the three tasks, we found a different pattern of brain activity that reflects the performance of pain attenuation. The more successful trials are related to reduced activity of different parts of the insular cortex. Behavioural data suggest that distraction is the preferable cognitive strategy to modulate pain perception. For three different cognitive strategies we revealed brain regions that are suggested to reliably modulate the perception of pain. The findings could be of utmost benefit for future attempts to integrate neuroscientific techniques into the treatment of pain. Further studies are necessary to investigate whether the present results are transferable to patients as an essential part of the multimodal therapy for chronic pain. These patients may also benefit from additional neurofeedback techniques by combining the strategies with the cortical feedback in order to modulate pain-related brain activity.
Pubmed ID: 30711854
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Multi paradigm numerical computing environment and fourth generation programming language developed by MathWorks. Allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Used to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.
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