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Oxycodone-Mediated Activation of the Mu Opioid Receptor Reduces Whole Brain Functional Connectivity in Mice
Md Taufiq Nasseef1, Jai Puneet Singh1, Aliza T Ehrlich1
1Douglas Hospital Research Center, Department of Psychiatry, School of Medicine, McGill University, Montreal, Quebec H4H 1R3, Canada.
Abstract:
Oxycodone is a potent medicinal opioid analgesic to treat pain. It is also addictive and a main cause for the current opioid crisis. At present, the impact of oxycodone on coordinated brain network activities, and contribution of the mu opioid receptor (MOR) to these effects, is unknown. We used pharmacological magnetic resonance imaging in mice to characterize MOR-mediated oxycodone effects on whole-brain functional connectivity (FC). Control (CTL) and MOR knockout (KO) animals were imaged under dexmedetomidine in a 7Tesla scanner. Acquisition was performed continuously before and after 2 mg/kg oxycodone administration (analgesic in CTL mice). Independent component analysis (data-driven) produced a correlation matrix, showing widespread oxycodone-induced reduction of FC across 71 components. Isocortex, nucleus accumbens (NAc), pontine reticular nucleus, and periacqueducal gray (PAG) components showed the highest number of significant changes. Seed-to-voxel FC analysis (hypothesis-driven) was then focused on PAG and NAc considered key pain and reward centers. The two seeds showed reduced FC with 8 and 22 Allen Brain Atlas-based regions, respectively, in CTL but not KO mice. Further seed-to-seed quantification showed highest FC modifications of both PAG and NAc seeds with hypothalamic and amygdalar areas, as well as between them, revealing the strongest impact across reward and aversion/pain centers of the brain. In conclusion, we demonstrate that oxycodone reduces brain communication in a MOR-dependent manner, and establish a preliminary whole-brain FC signature of oxycodone. This proof-of-principle study provides a unique platform and reference data set to test other MOR opioid agonists and perhaps discover new mechanisms and FC biomarkers predicting safer analgesics.
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