Zolmitriptan and human aggression: interaction with alcohol

Abstract Rationale The serotonin 1 B/D (5-HT1 B/D ) receptor has shown potential as a target for decreasing aggression. The 5-HT1 B/D agonist zolmitriptan's ability to reduce aggressive behavior in humans and its interaction with the well-known aggression-enhancing drug alcohol were examined. Objectives Our objective was to investigate zolmitriptan's potential to modify human aggression in a laboratory paradigm across a range of alcohol doses. Alcohol has been consistently associated with aggression and violence, thus we hoped to expand current understanding of alcohol's role in aggressive behavior via manipulation of the serotonin (5-HT) system. Methods Eleven social drinkers, seven male, were recruited to participate in a research study lasting 3-4 weeks. Aggression was measured using the point-subtraction aggression paradigm (PSAP), a laboratory model widely used in human aggression studies. Subjects were administered 5-mg zolmitriptan and placebo capsules along with alcohol doses of 0.0, 0.4 and 0.8 g/kg in a within-subject, counterbalanced dosing design. Data were analyzed as the ratio of aggressive/monetary-earning responses, to account for possible changes in overall motor function due to alcohol. Results There was a significant alcohol by zolmitriptan interaction on the aggressive/monetary response ratio. Specifically, compared to placebo, zolmitriptan decreased the aggressive/monetary ratio at the 0.4-and 0.8-g/kg alcohol doses. Conclusions A 5-mg dose of zolmitriptan effectively reduced alcohol-related aggression in an acute dosing protocol, demonstrating an interaction of 5-HT and alcohol in human aggressive behavior

Sketching the Power of Machine Learning to Decrypt a Neural Systems Model of Behavior

Uncovering brain-behavior mechanisms is the ultimate goal of neuroscience. A formidable amount of discoveries has been made in the past 50 years, but the very essence of brain-behavior mechanisms still escapes us. The recent exploitation of machine learning (ML) tools in neuroscience opens new avenues for illuminating these mechanisms. A key advantage of ML is to enable the treatment of large data, combing highly complex processes. This essay provides a glimpse of how ML tools could test a heuristic neural systems model of motivated behavior, the triadic neural systems model, which was designed to understand behavioral transitions in adolescence. This essay previews analytic strategies, using fictitious examples, to demonstrate the potential power of ML to decrypt the neural networks of motivated behavior, generically and across development. Of note, our intent is not to provide a tutorial for these analyses nor a pipeline. The ultimate objective is to relate, as simply as possible, how complex neuroscience constructs can benefit from ML methods for validation and further discovery. By extension, the present work provides a guide that can serve to query the mechanisms underlying the contributions of prefrontal circuits to emotion regulation. The target audience concerns mainly clinical neuroscientists. As a caveat, this broad approach leaves gaps, for which references to comprehensive publications are provided

Individualized relapse prediction: Personality measures and striatal and insular activity during reward-processing robustly predict relapse.

BackgroundNearly half of individuals with substance use disorders relapse in the year after treatment. A diagnostic tool to help clinicians make decisions regarding treatment does not exist for psychiatric conditions. Identifying individuals with high risk for relapse to substance use following abstinence has profound clinical consequences. This study aimed to develop neuroimaging as a robust tool to predict relapse.Methods68 methamphetamine-dependent adults (15 female) were recruited from 28-day inpatient treatment. During treatment, participants completed a functional MRI scan that examined brain activation during reward processing. Patients were followed 1 year later to assess abstinence. We examined brain activation during reward processing between relapsing and abstaining individuals and employed three random forest prediction models (clinical and personality measures, neuroimaging measures, a combined model) to generate predictions for each participant regarding their relapse likelihood.Results18 individuals relapsed. There were significant group by reward-size interactions for neural activation in the left insula and right striatum for rewards. Abstaining individuals showed increased activation for large, risky relative to small, safe rewards, whereas relapsing individuals failed to show differential activation between reward types. All three random forest models yielded good test characteristics such that a positive test for relapse yielded a likelihood ratio 2.63, whereas a negative test had a likelihood ratio of 0.48.ConclusionsThese findings suggest that neuroimaging can be developed in combination with other measures as an instrument to predict relapse, advancing tools providers can use to make decisions about individualized treatment of substance use disorders

Doubling down: increased risk-taking behavior following a loss by individuals with cocaine use disorder is associated with striatal and anterior cingulate dysfunction.

BackgroundCocaine use disorders (CUDs) have been associated with increased risk-taking behavior. Neuroimaging studies have suggested that altered activity in reward and decision-making circuitry may underlie cocaine user's heightened risk-taking. It remains unclear if this behavior is driven by greater reward salience, lack of appreciation of danger, or another deficit in risk-related processing.MethodsTwenty-nine CUD participants and forty healthy comparison participants completed the Risky Gains Task during a functional magnetic resonance imaging scan. During the Risky Gains Task, participants choose between a safe option for a small, guaranteed monetary reward and risky options with larger rewards but also the chance to lose money. Frequency of risky choice overall and following a win versus a loss were compared. Neural activity during the decision and outcome phase were examined using linear mixed effects models.ResultsAlthough the groups did not differ in overall risk-taking frequency, the CUD group chose a risky option more often following a loss. Neuroimaging analyses revealed that the comparison group showed increasing activity in the bilateral ventral striatum as they chose higher-value, risky options, but the CUD group failed to show this increase. During the outcome phase, the CUD group showed a greater decrease in bilateral striatal activity relative to the comparison group when losing the large amount, and this response was correlated with risk-taking frequency after a loss.ConclusionsThe brains of CUD individuals are hypersensitive to losses, leading to increased risk-taking behaviors, and this may help explain why these individuals take drugs despite aversive outcomes