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Updated: May 30, 2026

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery
Published on: April 14, 2016
Imaging drugs with and without clinical analgesic efficacy
Jaymin Upadhyay1, Julie Anderson, Adam J Schwarz
1Imaging Consortium for Drug Development, Brain Imaging Center, McLean Hospital, Belmont, MA 02478, USA.
Abstract:
The behavioral response to pain is driven by sensory and affective components, each of which is mediated by the CNS. Subjective pain ratings are used as readouts when appraising potential analgesics; however, pain ratings alone cannot enable a characterization of CNS pain circuitry during pain processing or how this circuitry is modulated pharmacologically. Having a more objective readout of potential analgesic effects may allow improved understanding and detection of pharmacological efficacy for pain. The pharmacological/functional magnetic resonance imaging (phMRI/fMRI) methodology can be used to objectively evaluate drug action on the CNS. In this context, we aimed to evaluate two drugs that had been developed as analgesics: one that is efficacious for pain (buprenorphine (BUP)) and one that failed as an analgesic in clinical trials aprepitant (APREP). Using phMRI, we observed that activation induced solely by BUP was present in regions with μ-opioid receptors, whereas APREP-induced activation was seen in regions expressing NK(1) receptors. However, significant pharmacological modulation of functional connectivity in pain-processing pathways was only observed following BUP administration. By implementing an evoked pain fMRI paradigm, these drugs could also be differentiated by comparing the respective fMRI signals in CNS circuits mediating sensory and affective components of pain. We report a correlation of functional connectivity and evoked pain fMRI measures with pain ratings as well as peak drug concentration. This investigation demonstrates how CNS-acting drugs can be compared, and how the phMRI/fMRI methodology may be used with conventional measures to better evaluate candidate analgesics in small subject cohorts.
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