Computational markers of risky decision-making predict for relapse to alcohol.

BACKGROUND: Relapse remains the major challenge in treatment of alcohol use disorder (AUD). Aberrant decision-making has been found as important cognitive mechanism underlying relapse, but factors associated with relapse vulnerability are unclear. Here, we aim to identify potential computational markers of relapse vulnerability by investigating risky decision-making in individuals with AUD. METHODS: Forty-six healthy controls and fifty-two individuals with AUD were recruited for this study. The risk-taking propensity of these subjects was investigated using the balloon analog risk task (BART). After completion of clinical treatment, all individuals with AUD were followed up and divided into a non-relapse AUD group and a relapse AUD group according to their drinking status. RESULTS: The risk-taking propensity differed significantly among healthy controls, the non-relapse AUD group, and the relapse AUD group, and was negatively associated with the duration of abstinence in individuals with AUD. Logistic regression models showed that risk-taking propensity, as measured by the computational model, was a valid predictor of alcohol relapse, and higher risk-taking propensity was associated with greater risk of relapse to drink. CONCLUSION: Our study presents new insights into risk-taking measurement and identifies computational markers that provide prospective information for relapse to drink in individuals with AUD.

Clean-lifting transfer of large-area residual-free graphene films.

A unique "clean-lifting transfer" (CLT) technique that applies a controllable electrostatic force to transfer large-area and high-quality CVD-grown graphene onto various rigid or flexible substrates is reported. The CLT technique without using any organic support or adhesives can produce residual-free graphene films with large-area processability, and has great potential for future industrial production of graphene-based electronics or optoelectronics.