Corticostriatal Oscillations Predict High vs. Low Drinkers in a Rat Model of Limited Access Alcohol Consumption

Abstract

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.

Sex differences in the ability of corticostriatal oscillations to predict rodent alcohol consumption

“…Custom electrodes were implanted bilaterally targeting the NAc shell (NAcSh; from bregma: DV − 8 mm; AP + 1.2 mm; ML ± 1.0 mm) and PL/IL junction of the mPFC (from bregma: DV − 5 mm; AP + 3.7 mm; ML ± 0.75 mm). The NAcSh was targeted based on previous work demonstrating that deep brain stimulation of the NAcSh could reduce alcohol intake in male P rats and high-alcohol drinking outbred rats [28, 29]. Thus, we hypothesized that LFPs recorded from the NAcSh would contain information regarding alcohol intake in rats.…”

Maternal immune activation and adolescent alcohol exposure increases alcohol drinking and disrupts cortical-striatal-hippocampal oscillations in adult offspring

“…LFP oscillation analysis: Similar to our previous publications [20,21,22] we built general linear models to classify rats based on group assignment (i.e., control, MIA, AE, or Dual) using LFP oscillation data. Since each rat underwent two baseline recording sessions, we used data from both sessions to build baseline models.…”

“…These data help us identify which neural features to target to reduce alcohol misuse in future studies. For example, we have previously shown that deep brain stimulation to the NAc shell has the capacity to reduce alcohol consumption in high-drinking male rats [21], and these data are only a small portion of the literature suggesting the neuromodulation-based therapies can be efficacious for addiction and other neuropsychiatric disorders [40]. However, these studies have almost exclusively been done in male animals.…”

“…Electrodes were designed and constructed in-house and were similar to those used in our previous publications [20,21,22]. Animals were anesthetized with isoflurane gas (4% induction, 2% maintenance) and mounted in a stereotaxic frame.…”

“…Data from each recording were analyzed using frequency ranges from the rodent literature (δ =1-4 Hz, θ = 5-10 Hz, α = 11-14 Hz, β = 15-30 Hz, low γ = 45-65 Hz, and high γ = 70-90 Hz). LFP signal processing to characterize the power spectral densities (PSD) within, and coherence between brain regions, for each rat was calculated using custom code written for Matlab, as we have previously published [20,21,22]…”