Back

Synaptic Plasticity in the Agranular Insular Cortex Predicts Escalated Ethanol Consumption

Shillinglaw, J. E.; Aziz, H. C.; Carrizales, D. G.; Morrisett, R. A.; Mangieri, R. A.

2021-05-18 neuroscience
10.1101/2021.05.17.444509 bioRxiv
Show abstract

The Agranular Insular Cortex (AIC) is implicated in alcohol use disorder and pharmacologically relevant concentrations of acute ethanol inhibit N-methyl-D-aspartate receptor (NMDAR)-mediated glutamatergic synaptic transmission and plasticity onto layer 2/3 AIC pyramidal neurons. However, it is not known whether the actions of ethanol on glutamatergic synapses are means by which chronic ethanol alters mechanisms of learning and memory in AIC as alcohol drinking transitions from controlled to problematic. We utilized the chronic intermittent ethanol (CIE) vapor model of ethanol exposure in adult male mice, alone or in combination with voluntary ethanol consumption, to determine whether glutamatergic synapses on layer 2/3 AIC pyramidal neurons are differentially regulated by different durations and intensities of chronic ethanol exposure. We observed evidence of both ethanol- and age-related metaplasticity of AIC layer 2/3 glutamatergic synapses, as only young adult, ethanol-naive mice exhibited NMDAR-dependent long term depression ex vivo. Our findings also indicated that voluntary ethanol consumption alone can elicit glutamatergic plasticity in vivo. We found that the ratio of NMDAR- to AMPAR-mediated postsynaptic currents was reduced not only in CIE-treated, but also in air-treated, chronically drinking mice relative to ethanol-naive controls. Furthermore, lower NMDA/AMPA ratios were predictive of greater escalation of ethanol consumption. These findings suggest that even moderate exposure to ethanol may elicit plasticity in the agranular insular cortex that contributes to the progression toward uncontrolled drinking.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"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.