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

• Published in: Biology of sex differences Vol. 10; no. 1; p. 61
• Main Authors: Henricks, Angela M, Sullivan, Emily D K, Dwiel, Lucas L, Keus, Karina M, Adner, Ethan D, Green, Alan I, Doucette, Wilder T
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 18.12.2019; BioMed Central; BMC
• Subjects: Alcohol; Alcohol Drinking - physiopathology; Alcohol use; Animals; Brain; Corticostriatal circuits; Drinking (Alcoholic beverages); Drinking behavior; Electrophysiological recording; Estrus; Female; Females; Gender differences; Learning algorithms; Local field potentials; Machine Learning; Male; Males; Neural circuitry; Nucleus accumbens; Nucleus Accumbens - physiology; Oscillations; Prediction models; Prefrontal cortex; Prefrontal Cortex - physiology; Rats, Sprague-Dawley; Rodents; Sex; Sex Characteristics; Sex differences; United States > US; Alcohol; Sex differences; Local field potentials; Corticostriatal circuits; Machine learning
• ISSN: 2042-6410; 2042-6410
• DOI: 10.1186/s13293-019-0276-0

Although male and female rats differ in their patterns of alcohol use, little is known regarding the neural circuit activity that underlies these differences in behavior. The current study used a machine learning approach to characterize sex differences in local field potential (LFP) oscillations that may relate to sex differences in alcohol-drinking behavior. LFP oscillations were recorded from the nucleus accumbens shell and the rodent medial prefrontal cortex of adult male and female Sprague-Dawley rats. Recordings occurred before rats were exposed to alcohol (n = 10/sex × 2 recordings/rat) and during sessions of limited access to alcohol (n = 5/sex × 5 recordings/rat). Oscillations were also recorded from each female rat in each phase of estrous prior to alcohol exposure. Using machine learning, we built predictive models with oscillation data to classify rats based on: (1) biological sex, (2) phase of estrous, and (3) alcohol intake levels. We evaluated model performance from real data by comparing it to the performance of models built and tested on permutations of the data. Our data demonstrate that corticostriatal oscillations were able to predict alcohol intake levels in males (p < 0.01), but not in females (p = 0.45). The accuracies of models predicting biological sex and phase of estrous were related to fluctuations observed in alcohol drinking levels; females in diestrus drank more alcohol than males (p = 0.052), and the male vs. diestrus female model had the highest accuracy (71.01%) compared to chance estimates. Conversely, females in estrus drank very similar amounts of alcohol to males (p = 0.702), and the male vs. estrus female model had the lowest accuracy (56.14%) compared to chance estimates. The current data demonstrate that oscillations recorded from corticostriatal circuits contain significant information regarding alcohol drinking in males, but not alcohol drinking in females. Future work will focus on identifying where to record LFP oscillations in order to predict alcohol drinking in females, which may help elucidate sex-specific neural targets for future therapeutic development.