ArticleFrontiers in pain research (Lausanne, Switzerland)2023
Quantitative sensory testing in a magnetic resonance environment: considerations for thermal sensitivity and patient safety.
Article in Frontiers in pain research (Lausanne, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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7 authors at 4 institutions in 2 countries.
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Abstract
Introduction: Quantitative sensory testing (QST) is often used to understand the perceptual basis of acute and chronic conditions, including pain. As the need grows for developing a mechanistic understanding of neurological pathways underlying perception in the basic and clinical sciences, there is a greater need to adapt techniques such as QST to the magnetic resonance (MR) environment. No studies have yet evaluated the impact of the MR environment on the perception of thermal stimuli. This study aimed to evaluate the differences in temperature sensitivity outside an MR environment and during an MRI scanning session. We hypothesized that there would be a difference in how participants reported their pain sensitivity between the two environments. Methods: Healthy participants underwent thermal QST outside the MR scanning environment, where they were asked to rate the temperature of a noxious stimulus at which they perceived their pain to be 7/10, using a Likert scale ranging from 0 to 10. Participants repeated this procedure inside a 3.0 T MRI approximately 30 min later. We repeated our investigation in a clinical cohort of participants with a chronic pain condition. Results: There were statistically significant changes of 1.1°C in thermal sensitivity between environments. This increase in pain threshold was found in healthy participants and replicated in the clinical cohort. Discussion: Findings can be applied toward improving MR safety, the resolution of brain pathways underlying pain mechanisms, and to more broadly comment on the impact of the MR environment on investigations that integrate perception-influenced processes.
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