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Maternal immune activation (MIA) is strongly associated with an increased risk of developing mental illness in adulthood, which often co-occurs with alcohol misuse. The current study aimed to begin to determine whether MIA, combined with adolescent alcohol exposure (AE), could be used as a model with which we could study the neurobiological mechanisms behind such co-occurring disorders. Pregnant Sprague-Dawley rats were treated with PolyI:C or saline on gestational day 15. Half of the offspring were given continuous access to alcohol during adolescence, leading to four experimental groups: controls, MIA, AE, and Dual (MIA + AE). We then evaluated whether MIA and/or AE alters: 1) alcohol consumption; and 2) cortical-striatal-hippocampal oscillations in adult offspring. Dual rats, particularly females, drank significantly more alcohol in adulthood compared to all other groups. Using machine learning to build predictive models from oscillations, we were able to differentiate Dual rats from control rats and AE rats in both sexes, and Dual rats from MIA rats in females. The current data suggest that MIA+AE (Dual “hits”) is a valuable model that we can use to study the neurobiological underpinnings of co-occurring disorders. Our future work aims to extend these findings to other addictive substances to enhance the translational relevance of this model.

Substance use disorders occur commonly in patients with schizophrenia and dramatically worsen their overall clinical course. While the exact mechanisms contributing to substance use in schizophrenia are not known, a number of theories have been put forward to explain the basis of the co-occurrence of these disorders. We propose here a unifying hypothesis that combines recent evidence from epidemiological and genetic association studies with brain imaging and pre-clinical studies to provide an updated formulation regarding the basis of substance use in patients with schizophrenia. We suggest that the genetic determinants of risk for schizophrenia (especially within neural systems that contribute to the risk for both psychosis and addiction) make patients vulnerable to substance use. Since this vulnerability may arise prior to the appearance of psychotic symptoms, an increased use of substances in adolescence may both enhance the risk for developing a later substance use disorder, and also serve as an additional risk factor for the appearance of psychotic symptoms. Future studies that assess brain circuitry in a prospective longitudinal manner during adolescence prior to the appearance of psychotic symptoms could shed further light on the mechanistic underpinnings of these co-occurring disorders while identifying potential points of intervention for these difficult-to-treat co-occurring disorders.

BackgroundAlthough 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.MethodsLFP 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.ResultsOur data demonstrate that corticostriatal oscillations were able to predict alcohol intake levels in males (p 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.ConclusionsThe 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.

Individuals differ in their vulnerability to develop alcohol dependence that are 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. Rats 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) of male Sprague-Dawley rats (n=13). 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 predict HD vs. LD group classification with greater accuracy than expected by chance (>80% accuracy). Additionally, NAcSh stimulation significantly reduced alcohol consumption in HD, but not LD (p0.05). 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.

Background Cross-frequency coupling (CFC) occurs when non-identical frequency components entrain one another. A ubiquitous example from neuroscience is low frequency phase to high frequency amplitude coupling in electrophysiological signals. Seminal work by Canolty revealed CFC in human ECoG data. Established methods band-pass the data into component frequencies then convert the band-passed signals into the analytic representation, from which we infer the instantaneous amplitude and phase of each component. Though powerful, such methods resolve signals with respect to time and frequency without addressing the multiresolution problem. New method We build upon the ground-breaking work of Canolty and others and derive a wavelet-based CFC detection algorithm that efficiently searches a range of frequencies using a sequence of filters with optimal trade-off between time and frequency resolution. We validate our method using simulated data and analyze CFC within and between the primary motor cortex and dorsal striatum of rats under ketamine-xylazine anesthesia. Results Our method detects the correct CFC in simulated data and reveals CFC between frequency bands that were previously shown to participate in corticostriatal effective connectivity. Comparison with existing methods Other CFC detection methods address the need to increase bandwidth when analyzing high frequency components but none to date permit rigorous bandwidth selection with no a priori knowledge of underlying CFC. Our method is thus particularly useful for exploratory studies. Conclusions The method developed here permits rigorous and efficient exploration of a hypothesis space and is particularly useful when the frequencies participating in CFC are unknown.

It has been notoriously difficult to understand interactions in the basal ganglia because of multiple recurrent loops. Another complication is that activity there is strongly dependent on behavior, suggesting that directional interactions, or effective connections, can dynamically change. A simplifying approach would be to examine just the direct, monosynaptic projections from cortex to striatum and contrast this with the polysynaptic feedback connections from striatum to cortex. Previous work by others on effective connectivity in this pathway indicated that activity in cortex could be used to predict activity in striatum, but that striatal activity could not predict cortical activity. However, this work was conducted in anesthetized or seizing animals, making it impossible to know how free behavior might influence effective connectivity. To address this issue, we applied Granger causality to local field potential signals from cortex and striatum in freely behaving rats. Consistent with previous results, we found that effective connectivity was largely unidirectional, from cortex to striatum, during anesthetized and resting states. Interestingly, we found that effective connectivity became bidirectional during free behaviors. These results are the first to our knowledge to show that striatal influence on cortex can be as strong as cortical influence on striatum. In addition, these findings highlight how behavioral states can affect basal ganglia interactions. Finally, we suggest that this approach may be useful for studies of Parkinson's or Huntington's diseases, in which effective connectivity may change during movement.