Drug and Alcohol Dependence

Data from our study that examined the effects of alcohol on disgust sensitivity (TDD).

Longitudinal data set for night time entertainment district patrons in Brisbane.

This data set includes all data collected to test the hypothesis that extinction of cocaine-associated cues would be additive or synergistic with the effects of ceftriaxone on reduction of cue-primed cocaine-seeking.

This data represents the responses that 768 students at a large, public mid-Western University who were lifelong abstainers from prescription stimulants, prescription opioids, cocaine, and heroin gave to an anonymous online survey. The file contains demographic information, the students' ratings of the importance of 17 reasons for abstinence from each of the four substances, the students' responses to the mini-IPIP, and the students' responses to the Brief Self-Control Survey.

This file contains an excel document with all of the relevant data discussed in our publication, along with summaries of said data.

In a randomized, placebo-controlled, between subject study, we applied tDCS bilaterally with the anodal electrode targeting the right DLPFC (https://clinicaltrials.gov/ct2/show/NCT03025321). Patients with cocaine use disorder were allocated to ten sessions of either active tDCS (n = 29) or sham tDCS (n = 30) on five consecutive days. Inhibitory control and risky decision-making were measured via a Go-NoGo task and a two-choice gambling task, respectively, each at baseline, one day after all tDCS sessions and a follow up after three months. In addition, relapse at follow-up and craving were measured. An independent samples t-test was performed in SPSS to analyse the average number of days participants had relapsed in the 3 months after the tDCS intervention. An additional analysis was performed to test whether relapse rates differed for the active and sham tDCS group using the chi-square fisher exact test. For craving a multilevel analysis was performed in R (R Core Team, 2018) using the lme4 package (Bates et al., 2014) to fit the nested data structure of Time within individuals (Level 1), and Group (sham vs. active tDCS) at Level 2. Multilevel modelling also allowed us to include data points of individuals with missing data. Missing data is almost inevitable in EMA studies, since most participants miss at least some prompts. For behavioural data generated during the Go-NoGo task and the TCGT, multilevel analyses were again performed in R using the lme4 package . For all outcome measures, Time (pre, post, and follow-up) was defined at Level 1 and patients were defined at Level 2 with Group (sham tDCS vs active tDCS) as predictor variable. Similar to the outcome of craving, models M0, M1, M2, M3 and M4 were fitted to behavioural outcomes.

A recipe and detailed calculations for creating edible THC for self-administration in mice.

EMA dataset

I-STDDS Data

SPSS files for behavior