Glutamine and GABA alterations in cingulate cortex may underlie alcohol drinking in a rat model of co-occurring alcohol use disorder and schizophrenia: an 1H-MRS study.

Alcohol use disorder commonly occurs in patients with schizophrenia and significantly worsens the clinical course of the disorder. The neurobiological underpinnings of alcohol drinking are not well understood. Magnetic resonance spectroscopy (MRS) has been used to assess the neurochemical substrates that may be associated with alcohol drinking in patients; however, the causal impact of these findings remains elusive, highlighting the need for studies in animal models. This study performed MRS in the neonatal ventral hippocampal lesioned (NVHL) rat model, a model of co-occurring schizophrenia and substance use disorders. NVHL lesions (or sham surgeries) were performed on post-natal day 7 and animals were given brief exposure to alcohol during adolescence (10% v/v in a 2-bottle choice design). Animals were re-exposed to alcohol during adulthood (20% v/v) until a stable drinking baseline was established, and then forced into abstinence to control for the effects of differential alcohol drinking. Animals were scanned for MRS after one month of abstinence. NVHL rats consumed significantly more alcohol than sham rats and in the cingulate cortex showed significantly higher levels of GABA and glutamine. Significantly lower GABA levels were observed in the nucleus accumbens. No differences between the NVHL and sham animals were observed in the hippocampus. Correlation analysis revealed that GABA and glutamine concentrations in the cingulate cortex significantly correlated with the rats' alcohol drinking prior to 30 days of forced abstinence. These findings suggest that a potential dysfunction in the glutamate/GABA-glutamine cycle may contribute to alcohol drinking in a rat model of schizophrenia, and this dysfunction could be targeted in future treatment-focused studies.

Building capacity for collaborative research on opioid and other substance use disorders through the Clinical and Translational Science Award Program.

The opioid crisis in the USA requires immediate action through clinical and translational research. Already built network infrastructure through funding by the National Institute on Drug Abuse (NIDA) and National Center for Advancing Translational Sciences (NCATS) provides a major advantage to implement opioid-focused research which together could address this crisis. NIDA supports training grants and clinical trial networks; NCATS funds the Clinical and Translational Science Award (CTSA) Program with over 50 NCATS academic research hubs for regional clinical and translational research. Together, there is unique capacity for clinical research, bioinformatics, data science, community engagement, regulatory science, institutional partnerships, training and career development, and other key translational elements. The CTSA hubs provide unprecedented and timely response to local, regional, and national health crises to address research gaps [Clinical and Translational Science Awards Program, Center for Leading Innovation and Collaboration, Synergy paper request for applications]. This paper describes opportunities for collaborative opioid research at CTSA hubs and NIDA-NCATS opportunities that build capacity for best practices as this crisis evolves. Results of a Landscape Survey (among 63 hubs) are provided with descriptions of best practices and ideas for collaborations, with research conducted by hubs also involved in premier NIDA initiatives. Such collaborations could provide a rapid response to the opioid epidemic while advancing science in multiple disciplinary areas.

Olanzapine Plus Samidorphan (ALKS 3831) in Schizophrenia and Comorbid Alcohol Use Disorder: A Phase 2, Randomized Clinical Trial.

OBJECTIVE: Alcohol use disorder (AUD) is a common comorbidity of schizophrenia. No effective pharmacologic treatment is available for both disorders to date. METHODS: In a phase 2, double-blind study, patients with schizophrenia and AUD experiencing ≥ 10 drinking and ≥ 2 heavy-drinking days in the previous month and recent (≤ 6 mo) disease symptom exacerbation were recruited between June 2014 and March 2017. DSM-IV-TR and DSM-5 criteria were used to assign the diagnoses of schizophrenia and AUD, respectively. After a 6-week lead-in period, 234 eligible patients were randomized (1:1) to olanzapine + 10 mg samidorphan tablets (OLZ/SAM) or olanzapine + placebo tablets (olanzapine) for 36-60 weeks of treatment. The primary outcome of time to the first event of exacerbation of disease symptoms (EEDS) was evaluated using the log rank test for treatment comparison, and the Cox proportional-hazards model was used to estimate hazard ratio. Safety was assessed as adverse events and laboratory measures. RESULTS: No significant difference was observed between groups in the time to first EEDS (hazard ratio = 0.91; 95% CI, 0.53-1.56; P = .746). Patients treated with OLZ/SAM vs olanzapine had numerically lower rates in 6 of 8 criteria to evaluate EEDS. Change from baseline in percentage of heavy-drinking days during the double-blind treatment period was similar in OLZ/SAM- vs olanzapine-treated patients. OLZ/SAM was generally well tolerated with a safety profile similar to olanzapine. CONCLUSIONS: OLZ/SAM was not superior to olanzapine in the time to EEDS and was well tolerated in patients with schizophrenia and AUD. Further research is needed to identify effective treatments for this difficult-to-treat population. TRIAL REGISTRATIONS: ClinicalTrials.gov identifier: NCT02161718; EudraCT number: 2014-001211-39 ​.

Alcohol Use Disorder and Schizophrenia or Schizoaffective Disorder.

Schizophrenia and schizoaffective disorder are schizophrenia spectrum disorders that cause significant disability. Among individuals who have schizophrenia or schizoaffective disorder, alcohol use disorder (AUD) is common, and it contributes to worse outcomes than for those who do not have co-occurring substance use disorder. Common neurobiological mechanisms, including dysfunction in brain reward circuitry, may explain the high rates of co-occurrence of schizophrenia and AUD or other substance use disorders. Optimal treatment combines pharmacologic intervention and other therapeutic modalities to address both the psychotic disorder and AUD. Further research on the etiology of these co-occurring disorders and on treatment of affected individuals is needed.

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

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

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

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.

Finding the balance between model complexity and performance: Using ventral striatal oscillations to classify feeding behavior in rats.

The ventral striatum (VS) is a central node within a distributed network that controls appetitive behavior, and neuromodulation of the VS has demonstrated therapeutic potential for appetitive disorders. Local field potential (LFP) oscillations recorded from deep brain stimulation (DBS) electrodes within the VS are a pragmatic source of neural systems-level information about appetitive behavior that could be used in responsive neuromodulation systems. Here, we recorded LFPs from the bilateral nucleus accumbens core and shell (subregions of the VS) during limited access to palatable food across varying conditions of hunger and food palatability in male rats. We used standard statistical methods (logistic regression) as well as the machine learning algorithm lasso to predict aspects of feeding behavior using VS LFPs. We were able to predict the amount of food eaten, the increase in consumption following food deprivation, and the type of food eaten. Further, we were able to predict whether the initiation of feeding was imminent up to 42.5 seconds before feeding began and classify current behavior as either feeding or not-feeding. In classifying feeding behavior, we found an optimal balance between model complexity and performance with models using 3 LFP features primarily from the alpha and high gamma frequencies. As shown here, unbiased methods can identify systems-level neural activity linked to domains of mental illness with potential application to the development and personalization of novel treatments.

Potent Dopamine D2 Antagonists Block the Reward-Enhancing Effects of Nicotine in Smokers With Schizophrenia.

Antipsychotics that are potent dopamine (DA) D2 receptor antagonists have been linked to elevated levels of nicotine dependence in smokers with schizophrenia. Because activation of D2 receptors mediates motivation for nicotine, we examined whether potent D2 antagonists would diminish nicotine's ability to stimulate reward processing-a mechanism that may drive compensatory increases in smoking. Smokers with schizophrenia (n = 184) were recruited and stratified into medication groups based on D2 receptor antagonist potency. The effects of smoking on reward function were assessed using a probabilistic reward task (PRT), administered pre- and post-smoking. The PRT used an asymmetrical reinforcement schedule to produce a behavioral response bias, previously found to increase under conditions (including smoking) that enhance mesolimbic DA signaling. Among the 98 participants with valid PRT data and pharmacotherapy that could be stratified into D2 receptor antagonism potency, a medication × smoking × block interaction emerged (P = .005). Post-hoc tests revealed a smoking × block interaction only for those not taking potent D2 antagonists (P = .007). This group exhibited smoking-related increases in response bias (P < .001) that were absent in those taking potent D2 antagonists (P > .05). Our findings suggest that potent D2 antagonists diminish the reward-enhancing effects of nicotine in smokers with schizophrenia. This may be a mechanism implicated in the increased rate of smoking often observed in patients prescribed these medications. These findings have important clinical implications for the treatment of nicotine dependence in schizophrenia.

Gabapentin Enacarbil Extended-Release for Alcohol Use Disorder: A Randomized, Double-Blind, Placebo-Controlled, Multisite Trial Assessing Efficacy and Safety.

BACKGROUND: Several single-site alcohol treatment clinical trials have demonstrated efficacy for immediate-release (IR) gabapentin in reducing drinking outcomes among individuals with alcohol dependence. The purpose of this study was to conduct a large, multisite clinical trial of gabapentin enacarbil extended-release (GE-XR) (HORIZANT® ), a gabapentin prodrug formulation, to determine its safety and efficacy in treating alcohol use disorder (AUD). METHODS: Men and women (n = 346) who met DSM-5 criteria for at least moderate AUD were recruited across 10 U.S. clinical sites. Participants received double-blind GE-XR (600 mg twice a day) or placebo and a computerized behavioral intervention (Take Control) for 6 months. Efficacy analyses were prespecified for the last 4 weeks of the treatment period. RESULTS: The GE-XR and placebo groups did not differ significantly on the primary outcome measure, percentage of subjects with no heavy drinking days (28.3 vs. 21.5, respectively, p = 0.157). Similarly, no clinical benefit was found for other drinking measures (percent subjects abstinent, percent days abstinent, percent heavy drinking days, drinks per week, drinks per drinking day), alcohol craving, alcohol-related consequences, sleep problems, smoking, and depression/anxiety symptoms. Common side-effects were fatigue, dizziness, and somnolence. A population pharmacokinetics analysis revealed that patients had lower gabapentin exposure levels compared with those in other studies using a similar dose but for other indications. CONCLUSIONS: Overall, GE-XR at 600 mg twice a day did not reduce alcohol consumption or craving in individuals with AUD. It is possible that, unlike the IR formulation of gabapentin, which showed efficacy in smaller Phase 2 trials at a higher dose, GE-XR is not effective in treating AUD, at least not at doses approved by the U.S. Food and Drug Administration for treating other medical conditions.

Machine Learning Based Classification of Deep Brain Stimulation Outcomes in a Rat Model of Binge Eating Using Ventral Striatal Oscillations.

Neuromodulation-based interventions continue to be evaluated across an array of appetitive disorders but broader implementation of these approaches remains limited due to variable treatment outcomes. We hypothesize that individual variation in treatment outcomes may be linked to differences in the networks underlying these disorders. Here, Sprague-Dawley rats received deep brain stimulation separately within each nucleus accumbens (NAc) sub-region (core and shell) using a within-animal crossover design in a rat model of binge eating. Significant reductions in binge size were observed with stimulation of either target but with significant variation in effectiveness across individuals. When features of local field potentials (LFPs) recorded from the NAc were used to classify the pre-defined stimulation outcomes (response or non-response) from each rat using a machine-learning approach (lasso), stimulation outcomes could be classified with greater accuracy than expected by chance (effect sizes: core = 1.13, shell = 1.05). Further, these LFP features could be used to identify the best stimulation target for each animal (core vs. shell) with an effect size = 0.96. These data suggest that individual differences in underlying network activity may relate to the variable outcomes of circuit based interventions, and measures of network activity could have the potential to individually guide the selection of an optimal stimulation target to improve overall treatment response rates.

Unique Effects of Clozapine: A Pharmacological Perspective.

Schizophrenia is a heterogenous and severe neuropsychiatric disorder that affects nearly 1% of the population worldwide. Antipsychotic drugs are the mainstay of treatment, but not all patients with schizophrenia respond to treatment with these agents. Clozapine, the first atypical antipsychotic, is a highly effective medication for patients with schizophrenia who do not respond to other antipsychotics. Although clozapine tends not to produce extrapyramidal symptoms, other side effects of the drug (e.g., agranulocytosis, myocarditis, seizures) limit its widespread use. This chapter reviews clozapine's unique clinical effects and unusual pharmacological profile. In addition to its effects in treatment-resistant schizophrenia, clozapine has been shown to decrease suicidality, which occurs at an increased rate in patients with schizophrenia. Still preliminary, but consistent data, also suggest that clozapine limits substance use in these patients, an important effect since substance use disorders are common in patients with schizophrenia and are associated with a poor outcome, including an increased risk for suicide and poor response to treatment. We have suggested, from animal studies, that clozapine's apparent ability to limit substance use may occur through its actions as a weak dopamine D2 receptor antagonist, a potent norepinephrine α-2 receptor antagonist and a norepinephrine reuptake inhibitor. Using animal models, we have built combinations of agents toward creation of safer clozapine-like drugs to reduce substance use in these patients. Future research into the mechanisms of action of clozapine toward the development of safe clozapine-like agents is of great public health importance.

The link between schizophrenia and substance use disorder: A unifying hypothesis.

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.

Understanding marijuana's effects on functional connectivity of the default mode network in patients with schizophrenia and co-occurring cannabis use disorder: A pilot investigation.

Nearly half of patients with schizophrenia (SCZ) have co-occurring cannabis use disorder (CUD), which has been associated with decreased treatment efficacy, increased risk of psychotic relapse, and poor global functioning. While reports on the effects of cannabis on cognitive performance in patients with SCZ have been mixed, study of brain networks related to executive function may clarify the relationship between cannabis use and cognition in these dual-diagnosis patients. In the present pilot study, patients with SCZ and CUD (n=12) and healthy controls (n=12) completed two functional magnetic resonance imaging (fMRI) resting scans. Prior to the second scan, patients smoked a 3.6% tetrahydrocannabinol (THC) cannabis cigarette or ingested a 15mg delta-9-tetrahydrocannabinol (THC) pill. We used resting-state functional connectivity to examine the default mode network (DMN) during both scans, as connectivity/activity within this network is negatively correlated with connectivity of the network involved in executive control and shows reduced activity during task performance in normal individuals. At baseline, relative to controls, patients exhibited DMN hyperconnectivity that correlated with positive symptom severity, and reduced anticorrelation between the DMN and the executive control network (ECN). Cannabinoid administration reduced DMN hyperconnectivity and increased DMN-ECN anticorrelation. Moreover, the magnitude of anticorrelation in the controls, and in the patients after cannabinoid administration, positively correlated with WM performance. The finding that DMN brain connectivity is plastic may have implications for future pharmacotherapeutic development, as treatment efficacy could be assessed through the ability of therapies to normalize underlying circuit-level dysfunction.

Applying novel technologies and methods to inform the ontology of self-regulation.

Self-regulation is a broad construct representing the general ability to recruit cognitive, motivational and emotional resources to achieve long-term goals. This construct has been implicated in a host of health-risk behaviors, and is a promising target for fostering beneficial behavior change. Despite its clear importance, the behavioral, psychological and neural components of self-regulation remain poorly understood, which contributes to theoretical inconsistencies and hinders maximally effective intervention development. We outline a research program that seeks to define a neuropsychological ontology of self-regulation, articulating the cognitive components that compose self-regulation, their relationships, and their associated measurements. The ontology will be informed by two large-scale approaches to assessing individual differences: first purely behaviorally using data collected via Amazon's Mechanical Turk, then coupled with neuroimaging data collected from a separate population. To validate the ontology and demonstrate its utility, we will then use it to contextualize health risk behaviors in two exemplar behavioral groups: overweight/obese adults who binge eat and smokers. After identifying ontological targets that precipitate maladaptive behavior, we will craft interventions that engage these targets. If successful, this work will provide a structured, holistic account of self-regulation in the form of an explicit ontology, which will better clarify the pattern of deficits related to maladaptive health behavior, and provide direction for more effective behavior change interventions.