studies from other species 12,13 , we have freely released all data and the generated code.
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.
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.
Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows comparison with invasive or terminal procedures. To date, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. We introduce StandardRat, a consensus rat functional MRI acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired in rats from 46 centers. We developed a reproducible pipeline for the analysis of rat data acquired with diverse protocols and determined experimental and processing parameters associated with a more robust functional connectivity detection. We show that the standardized protocol enhances biologically plausible functional connectivity patterns, relative to pre-existing acquisitions. The protocol and processing pipeline described here are openly shared with the neuroimaging community to promote interoperability and cooperation towards tackling the most important challenges in neuroscience.
Background: Borderline personality disorder is present in 19% of cocaine dependence cases; however, this dual pathology is poorly understood. We wished to characterize the dual pathology and find its functional connectivity correlates to better understand it.Methods: We recruited 69 participants divided into 4 groups: dual pathology (n = 20), cocaine dependence without borderline personality disorder (n = 19), borderline personality without cocaine dependence (n = 10) and healthy controls (n = 20). We used self-reported instruments to measure impulsivity and emotional dysregulation. We acquired resting state fMRI and performed seed-based analyses of the functional connectivity of bilateral amygdala.Results: Borderline personality disorder and cocaine dependence as factors had opposing effects in impulsivity and emotional dysregulation, as well as on functional connectivity between left amygdala and medial prefrontal cortex. On the other hand, in the functional connectivity between right amygdala and left insula, the effect of having both disorders was instead additive, reducing functional connectivity strength. The significant functional connectivity clusters were correlated with impulsivity and emotional dysregulation.Conclusions: In this study, we found that clinical scores of dual pathology patients were closer to those of borderline personality disorder without cocaine dependence than to those of cocaine dependence without borderline personality disorder, while amygdala-medial prefrontal cortex functional connectivity patterns in dual pathology patients were closer to healthy controls than expected.
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