Network States in the Basolateral Amygdala Predicts Voluntary Alcohol Consumption.


Journal

bioRxiv : the preprint server for biology
Titre abrégé: bioRxiv
Pays: United States
ID NLM: 101680187

Informations de publication

Date de publication:
03 Mar 2024
Historique:
medline: 11 3 2024
pubmed: 11 3 2024
entrez: 11 3 2024
Statut: epublish

Résumé

Although most adults in the United States will drink alcohol in their life, only about 6% will go on to develop an alcohol use disorder (AUD). While a great deal of work has furthered our understanding of the cycle of addiction, it remains unclear why certain people transition to disordered drinking. Altered activity in regions implicated in AUDs, like the basolateral amygdala (BLA), has been suggested to play a role in the pathophysiology of AUDs, but how these networks contribute to alcohol misuse remains unclear. Our recent work demonstrated that alcohol can modulate BLA network states and that GABAergic parvalbumin (PV) interneurons are crucial modulators of network activity in the BLA. Further, our lab has demonstrated that δ subunit-containing GABA Oscillatory states in the BLA have been demonstrated to drive behavioral states involved in emotional processing, including negative valence processing. Given that negative emotional states/hyperkatifeia contribute to the cycle of AUDs, our previous work demonstrating the ability of alcohol to modulate BLA network states and thereby behavioral states suggests that this mechanism may influence alcohol intake. Here we demonstrate a relationship between the ability of alcohol to modulate oscillations in the BLA and future alcohol intake such that the extent to which alcohol influences BLA network states predict the extent of future voluntary alcohol intake. These findings suggest that individual variability in the sensitivity of the BLA network to alcohol influences voluntary alcohol consumption.

Identifiants

pubmed: 38464012
doi: 10.1101/2023.06.21.545962
pmc: PMC10925084
pii:
doi:

Types de publication

Preprint

Langues

eng

Auteurs

Classifications MeSH