Parabrachial-amygdala circuit cooperates with a posterior striatal area to drive opioid withdrawal aversion
Lee, S.-C.; Shimoda, K. A.; Ross, J. D.; Coudriet, J. M.; Jhou, T.; Ikemoto, S.
Show abstract
Opioid addiction treatment is often hampered by the severe dysphoria of opioid withdrawal, but withdrawal treatments are limited by incomplete understanding of brain mechanisms involved. One area frequently implicated in withdrawal symptoms is the central amygdala, whose capsular portion (CeC) is particularly strongly activated during withdrawal. Additionally, a ventral posterior striatal region that resides near CeC, the interstitial nucleus of the posterior limb of the anterior commissure (IPACc), is also activated as strikingly as CeC. However, it is still unknown how these regions are activated, nor whether their activation explains the high intensity of withdrawal dysphoria. Using RNAscope, we found that c-fos expression is induced in the parabrachial nucleus (PB), a key glutamatergic afferent of CeC, after precipitated morphine withdrawal. Chemogenetic inhibition of PB glutamatergic neurons (VG2PB) nearly eliminated withdrawal-induced CeC c-Fos, without affecting IPACc c-Fos, indicating these two nuclei are activated by distinct sources. Furthermore, VG2PB inhibition markedly reduced somatic (jumping) and modestly reduced affective (place avoidance) withdrawal behavior. On the other hand, inhibition of CeC-projecting PB neuronal subtypes expressing calcitonin gene-related peptide (CGRP) or mu opioid receptor (MOR) reduced place avoidance without affecting jumping, indicating their specific role in withdrawal aversion. Strikingly, simultaneous inhibition of VG2PB and posterior striatal region containing IPACc robustly reduced withdrawal-induced place avoidance much more than the modest effects of either inhibition alone, suggesting their cooperative action in driving aversion. Our data suggests that PB-CeC circuit and posterior striatal area constitute a cooperative system driving opioid withdrawal aversion.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Chronic morphine induces adaptations in opioid receptor signaling in a thalamo-cortico-striatal circuit that are projection-dependent, sex-specific and regulated by mu opioid receptor phosphorylation 95%
- Complementary roles for ventral pallidum cell types and their projections in relapse 94%
- Mu-opioids suppress GABAergic synaptic transmission onto orbitofrontal cortex pyramidal neurons with subregional selectivity. 93%
Similar papers in this journal
- The ultrasonic vocalization (USV) syllable profile during neonatal opioid withdrawal and a kappa opioid receptor component to increased USV emissions in female mice 94%
- Chronic delivery of buprenorphine during abstinence decreases incubation of heroin seeking and neuronal activation in medial prefrontal cortex and striatum in male and female rats 93%
- Activation of Infralimbic cortex neurons projecting to the nucleus accumbens shell suppresses discriminative stimulus-triggered relapse to cocaine seeking in rats 92%
Similar papers in this journal
- Perinatal Fentanyl Exposure Drives Enduring Addiction Risk and Central Amygdala Gene Dysregulation 95%
- Anterior cingulate cortex activation of claustrum projection neuron subtypes is enhanced by alcohol 93%
- Domain-selective BET inhibition attenuates transcriptional and behavioral responses to cocaine 92%
Similar papers in this journal
- Alcohol Attenuates CRF-Induced Excitatory Effects from the Extended Amygdala to Dorsostriatal Cholinergic Interneurons 93%
- The influence of nucleus accumbens shell D1 and D2 neurons on outcome-specific Pavlovian instrumental transfer 93%
- Adolescent Alcohol Exposure Promotes Mechanical Allodynia and Alters Synaptic Function at Inputs from the Basolateral Amgydala to the Prelimbic Cortex 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.