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

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          Abstract

          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.

          Abstract

          Measurement(s) functional brain measurement • Diffusion Weighted Imaging • Abnormality of brain morphology • Alteration Of Cognitive Function • Clinical Study
          Technology Type(s) Functional Magnetic Resonance Imaging • Diffusion Weighted Imaging • Turbo Field Echo MRI • neuropsychological test • Clinical Evaluation
          Factor Type(s) Cocaine Dependence
          Sample Characteristic - Organism Homo
          Sample Characteristic - Location Mexico City

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          Most cited references74

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          The assessment and analysis of handedness: The Edinburgh inventory

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            Cortical surface-based analysis. I. Segmentation and surface reconstruction.

            Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging. Copyright 1999 Academic Press.
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              Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.

              Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements. Copyright © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                egarza@comunidad.unam.mx
                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group UK (London )
                2052-4463
                31 March 2022
                31 March 2022
                2022
                : 9
                : 133
                Affiliations
                [1 ]GRID grid.9486.3, ISNI 0000 0001 2159 0001, Instituto de Neurobiología, , Universidad Nacional Autónoma de México campus Juriquilla, ; Querétaro, Mexico
                [2 ]GRID grid.419886.a, ISNI 0000 0001 2203 4701, Escuela de Medicina y Ciencias de la Salud TecSalud, , Tecnológico de Monterrey, ; Monterrey, Mexico
                [3 ]GRID grid.4494.d, ISNI 0000 0000 9558 4598, University of Groningen, Department of Biomedical Sciences of Cells and Systems, Cognitive Neuroscience Center, , University Medical Center Groningen, ; Groningen, the Netherlands
                [4 ]GRID grid.9486.3, ISNI 0000 0001 2159 0001, División de estudios de posgrado de la Facultad de Medicina, , Universidad Nacional Autónoma de México, ; Mexico City, Mexico
                [5 ]GRID grid.412041.2, ISNI 0000 0001 2106 639X, Université de Bordeaux, ; Bordeaux, France
                [6 ]GRID grid.419154.c, ISNI 0000 0004 1776 9908, Subdirección de Investigaciones Clínicas, Instituto Nacional de Psiquiatría Ramón de la Fuente Muñız, ; Mexico City, Mexico
                [7 ]Comisión Nacional para la Prevención de Adicciones, Mexico City, Mexico
                [8 ]GRID grid.440977.9, ISNI 0000 0004 0483 7094, Faculty of Psychology, , Universidad Anáhuac México Sur, ; Mexico City, Mexico
                Author information
                http://orcid.org/0000-0001-8029-8369
                http://orcid.org/0000-0001-5216-7177
                http://orcid.org/0000-0002-9596-988X
                http://orcid.org/0000-0003-1381-8648
                Article
                1251
                10.1038/s41597-022-01251-3
                8971535
                35361781
                1fb27212-39df-4754-aefa-d62d193eca8b
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 8 September 2021
                : 10 March 2022
                Funding
                Funded by: CONACYT FOSISS - 0201493 CONACYT CATEDRAS - 2358948
                Categories
                Data Descriptor
                Custom metadata
                © The Author(s) 2022

                drug development,preclinical research
                drug development, preclinical research

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