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Cocaine use disorder (CUD) is a substance use disorder (SUD) characterized by compulsion to seek, use and abuse of cocaine, with severe health and economic consequences for the patients, their families and society. Due to the lack of successful treatments and high relapse rate, more research is needed to understand this and other SUD. Here, we present the SUDMEX CONN dataset, a Mexican open dataset of 74 CUD patients (9 female) and matched 64 healthy controls (6 female) that includes demographic, cognitive, clinical, and magnetic resonance imaging (MRI) data. MRI data includes: 1) structural (T1-weighted), 2) multishell high-angular resolution diffusion-weighted (DWI-HARDI) and 3) functional (resting state fMRI) sequences. The repository contains unprocessed MRI data available in brain imaging data structure (BIDS) format with corresponding metadata available at the OpenNeuro data sharing platform. Researchers can pursue brain variability between these groups or use a single group for a larger population sample.

Background Cocaine use disorder (CUD) is a global condition lacking effective treatment. Repetitive transcranial magnetic stimulation (rTMS) may reduce craving and frequency of cocaine use, but little is known about its efficacy and neural effects. We sought to elucidate short- and long-term clinical benefits of 5-Hz rTMS as an add-on to standard treatment in patients with CUD and discern underlying functional connectivity effects using magnetic resonance imaging. Methods A total of 44 patients with CUD were randomly assigned to complete the 2-week double-blind randomized controlled trial (acute phase) (sham [n = 20, 2 female] and active [n = 24, 4 female]), in which they received two daily sessions of rTMS on the left dorsolateral prefrontal cortex (PFC). Subsequently, 20 patients with CUD continued to an open-label maintenance phase for 6 months (two weekly sessions for up to 6 mo). Results rTMS plus standard treatment for 2 weeks significantly reduced craving (baseline: 3.9 ± 3.6; 2 weeks: 1.5 ± 2.4, p = .013, d = 0.77) and impulsivity (baseline: 64.8 ± 16.8; 2 weeks: 53.1 ± 17.4, p = .011, d = 0.79) in the active group. We also found increased functional connectivity between the left dorsolateral PFC and ventromedial PFC and between the ventromedial PFC and right angular gyrus. Clinical and functional connectivity effects were maintained for 3 months, but they dissipated by 6 months. We did not observe reduction in positive results for cocaine in urine; however, self-reported frequency and grams consumed for 6 months were reduced. Conclusions With this randomized controlled trial, we show that 5-Hz rTMS has potential promise as an adjunctive treatment for CUD and merits further research.

Background and aims There is a growing need for detecting valid and generalizable markers due to a demand of accurate Cocaine Dependence diagnosis and treatment. Machine Learning (ML) is a modern statistical alternative to select from multiple observations the most reliable features, which allows precise and more effective categorization addressing the demand to improve diagnosis. The aim of the current study was to identify cognitive markers by using three ML algorithms, Elastic Net (GlmNet), Random forest (Rf) and Generalized Linear Model (Glm), with the purpose of classify Cocaine Dependence (CD) and Non-dependent controls (NDC) to make it generalizable for new samples. Methods Two independent samples were required, the first one consisted on 87 participants (53 CD and 34 NDC) and the second one conformed by 40 participants (20 CD and 20 NDC). All participants were evaluated with neuropsychological tests that included 40 variables assessing cognitive domains of flexibility, inhibition, working memory, problem solving, planning, decision making and theory of the mind. With the results of the cognitive evaluation the three ML algorithms were trained in the first sample and tested on the second one to classify into CD and NDC. Results Even though the three algorithms had a ROC performance over 50%, GlmNet was superior in both, training (ROC = 0.71) and testing set (ROC = 0.85) compared to Rf and Glm. Furthermore, GlmNet was capable of identifying eight predictors out of 40 from all the cognitive domains assessed. Conclusions ML is an effective approach for the identification of generalizable cognitive markers. Specific subsets resulted robust predictors for accurate classification of new cases, such as those from cognitive flexibility and inhibition domain. These findings are relevant in addictions field as they have highly beneficial potential for diagnosis and treatment improvement, not only for CD but also for other substances abuse.