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The Negative Affect of Protracted Opioid Abstinence: Progress and Perspectives From Rodent Models
Lola Welsch1, Julie Bailly1, Emmanuel Darcq1
1Douglas Mental Health Institute, Department of Psychiatry, McGill University, Montreal, Quebec, Canada.
Abstract:
Opioid use disorder (OUD) is characterized by the development of a negative emotional state that develops after a history of long-term exposure to opioids. OUD represents a true challenge for treatment and relapse prevention. Human research has amply documented emotional disruption in individuals with an opioid substance use disorder, at both behavioral and brain activity levels; however, brain mechanisms underlying this particular facet of OUD are only partially understood. Animal research has been instrumental in elucidating genes and circuits that adapt to long-term opioid use or are modified by acute withdrawal, but research on long-term consequences of opioid exposure and their relevance to the negative affect of OUD remains scarce. In this article, we review the literature with a focus on two questions: 1) Do we have behavioral models in rodents, and what do they tell us? and 2) What do we know about the neuronal populations involved? Behavioral rodent models have successfully recapitulated behavioral signs of the OUD-related negative affect, and several neurotransmitter systems were identified (i.e., serotonin, dynorphin, corticotropin-releasing factor, oxytocin). Circuit mechanisms driving the negative mood of prolonged abstinence likely involve the 5 main reward-aversion brain centers (i.e., nucleus accumbens, bed nucleus of the stria terminalis, amygdala, habenula, and raphe nucleus), all of which express mu opioid receptors and directly respond to opioids. Future work will identify the nature of these mu opioid receptor-expressing neurons throughout reward-aversion networks, characterize their adapted phenotype in opioid abstinent animals, and hopefully position these primary events in the broader picture of mu opioid receptor-associated brain aversion networks.
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