The Mexican dataset of a repetitive transcranial magnetic stimulation clinical trial on cocaine use disorder patients: SUDMEX TMS.

Cocaine use disorder (CUD) is a global health problem with severe consequences, leading to behavioral, cognitive, and neurobiological disturbances. While consensus on treatments is still ongoing, repetitive transcranial magnetic stimulation (rTMS) has emerged as a promising approach for medication-resistant disorders, including substance use disorders. In this context, here we present the SUDMEX-TMS, a Mexican dataset from an rTMS clinical trial involving CUD patients. This longitudinal dataset comprises 54 CUD patients (including 8 females) with data collected at five time points: baseline (T0), two weeks (T1), three months (T2), six months (T3) follow-up, and twelve months (T4) follow-up. The clinical rTMS treatment followed a double-blinded randomized clinical trial design (n = 24 sham/30 active) for 2 weeks, followed by an open-label phase. The dataset includes demographic, clinical, and cognitive measures, as well as magnetic resonance imaging (MRI) data collected at all time points, encompassing structural (T1-weighted), functional (resting-state fMRI), and multishell diffusion-weighted (DWI-HARDI) sequences. This dataset offers the opportunity to investigate the impact of rTMS on CUD participants, considering clinical, cognitive, and multimodal MRI metrics in a longitudinal framework.

The Mexican magnetic resonance imaging dataset of patients with cocaine use disorder: SUDMEX CONN.

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.

Clinical and Functional Connectivity Outcomes of 5-Hz Repetitive Transcranial Magnetic Stimulation as an Add-on Treatment in Cocaine Use Disorder: A Double-Blind Randomized Controlled Trial.

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.

Identifying cognitive deficits in cocaine dependence using standard tests and machine learning.

There is a growing need to address the variability in detecting cognitive deficits with standard tests in cocaine dependence (CD). The aim of the current study was to identify cognitive deficits by means of Machine Learning (ML) algorithms: Generalized Linear Model (Glm), Random forest (Rf) and Elastic Net (GlmNet), to allow more effective categorization of CD and Non-dependent controls (NDC and to address common methodological problems. For our validation, we used two independent datasets, the first consisted of 87 participants (53 CD and 34 NDC) and the second of 40 participants (20 CD and 20 NDC). All participants were evaluated with neuropsychological tests that included 40 variables assessing cognitive domains. Using results from the cognitive evaluation, the three ML algorithms were trained in the first dataset and tested on the second to classify participants into CD and NDC. While the three algorithms had a receiver operating curve (ROC) performance over 50%, the GlmNet was superior in both the training (ROC = 0.71) and testing datasets (ROC = 0.85) compared to Rf and Glm. Furthermore, GlmNet was capable of identifying the eight main predictors of group assignment (CD or NCD) from all the cognitive domains assessed. Specific variables from each cognitive test resulted in robust predictors for accurate classification of new cases, such as those from cognitive flexibility and inhibition domains. These findings provide evidence of the effectiveness of ML as an approach to highlight relevant sections of standard cognitive tests in CD, and for the identification of generalizable cognitive markers.