Neural oscillations in the ventral striatum reveal differences between the encoding of palatable food and ethanol consumption

Abstract

Background Across multiple levels of investigation, there appear to be convergent neuronal processes underlying substance use and other motivated behaviors (i.e., the pursuit and consumption of rewarding substances). The consumption of alcohol and sweet, high‐fat food engages many of the same brain regions, especially, the ventral striatum. In the current study, we hypothesized that ventral striatal local field potentials (LFPs) recorded during self‐administration sessions could be used to detect when the consumption of 10% ethanol or sweet‐fat food (SF) was occurring compared to all other behaviors, including naturalistic controls (i.e., water or house‐chow). Methods We used an intermittent limited access approach to condition Sprague–Dawley rats to consume either ethanol or SF while we recorded LFPs. We used machine learning and simple logistic regressions to determine whether LFP features could classify when consumption of each substance was occurring, and whether a general model could predict consumption of both substances. We report performance as the average area under the receiver operator characteristic curve (AUROC). Results Consumption of a single substance was differentiable from all other behaviors, as evidenced by the AUROC (ethanol = 0.84 and SF = 0.83, p < 0.01). Models built from the combined dataset (general) did modestly overall (general → general = 0.68, p < 0.05), and did not detect the consumption of the two substances similarly (general → SF = 0.5 and general → ethanol = 0.63, p > 0.05). Conclusions Models successfully classified ethanol and SF consumption versus all other behavior/naturalistic controls. However, the findings highlight differences in how the ventral striatum represents the consumption of ethanol and SF and show that, although there is potential for finding biomarkers related to substance use, it may be difficult to build a model that performs well detecting multiple substances.