Individuals differ in their vulnerability to develop alcohol dependence, which is determined by innate and environmental factors. The corticostriatal circuit is heavily involved in the development of alcohol dependence and may contain neural information regarding vulnerability to drink excessively. In the current experiment, we hypothesized that we could characterize high and low alcohol-drinking rats (HD and LD, respectively) based on corticostriatal oscillations and that these subgroups would differentially respond to corticostriatal brain stimulation. Male Sprague–Dawley rats ( n = 13) were trained to drink 10% alcohol in a limited access paradigm. In separate sessions, local field potentials (LFPs) were recorded from the nucleus accumbens shell (NAcSh) and medial prefrontal cortex (mPFC). Based on training alcohol consumption levels, we classified rats using a median split as HD or LD. Then, using machine-learning, we built predictive models to classify rats as HD or LD by corticostriatal LFPs and compared the model performance from real data to the performance of models built on data permutations. Additionally, we explored the impact of NAcSh or mPFC stimulation on alcohol consumption in HD vs. LD. Corticostriatal LFPs were able to predict HD vs. LD group classification with greater accuracy than expected by chance (>80% accuracy). Moreover, NAcSh stimulation significantly reduced alcohol consumption in HD, but not LD ( p < 0.05), while mPFC stimulation did not alter drinking behavior in either HD or LD ( p > 0.05). These data collectively show that the corticostriatal circuit is differentially involved in regulating alcohol intake in HD vs. LD rats, and suggests that corticostriatal activity may have the potential to predict a vulnerability to develop alcohol dependence in a clinical population.

Alcohol use disorders (AUDs) affect a large proportion of individuals in the United States.Unfortunately, the current therapies to treat AUDs are largely ineffective. Deep brain stimulation (DBS) has been proposed as a promising therapy for AUDs, but the high individual variability in treatment response limits broader implementation of this approach.The corticostriatal circuit is key in processing the rewarding properties of alcohol and we hypothesize that we can identify neural features within this circuit that will predict individual response to DBS in order to maximize treatment outcomes. In the current experiment, local field potentials (LFPs) were recorded from the nucleus accumbens shell (NAcSh) and medial prefrontal cortex (mPFC) of male Sprague-Dawley rats (n=13). The impact of NAcSh and mPFC DBS on levels of alcohol consumption was subsequently evaluated. On a population level, DBS in either region failed to significantly alter alcohol drinking behavior due to high individual variability in response. However, when individual features of corticostriatal LFPs were used as predictors of alcohol drinking outcomes (decreasers, increasers, or nonresponders to DBS) for each rat using a machine-learning approach (lasso), response to DBS could be predicted with greater accuracy than expected by chance. These data suggest that individual differences in corticostriatal circuit activity prior to stimulation likely contributes to the variable responses to DBS therapy. Ultimately, we hope to use knowledge gained from these data to tailor DBS therapies to individuals, further enhancing treatment efficacy.All rights reserved. No reuse allowed without permission.was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.