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      Gray-matter structure in long-term abstinent methamphetamine users

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          Previous studies of brain structure in methamphetamine users have yielded inconsistent findings, possibly reflecting small sample size and inconsistencies in duration of methamphetamine abstinence as well as sampling and analyses methods. Here we report on a relatively large sample of abstinent methamphetamine users at various stages of long-term abstinence.


          Chronic methamphetamine users ( n = 99), abstinent from the drug ranging from 12 to 621 days, and healthy controls ( n = 86) received T1-weighted structural magnetic resonance imaging brain scans. Subcortical and cortical gray-matter volumes and cortical thickness were measured and the effects of group, duration of abstinence, duration of methamphetamine use and onset age of methamphetamine use were investigated using the Freesurfer software package.


          Methamphetamine users did not differ from controls in gray-matter volumes, except for a cluster in the right lateral occipital cortex where gray-matter volume was smaller, and for regions mainly in the bilateral superior frontal gyrui where thickness was greater. Duration of abstinence correlated positively with gray-matter volumes in whole brain, bilateral accumbens nuclei and insulae clusters, and right hippocampus; and with thickness in a right insula cluster. Duration of methamphetamine use correlated negatively with gray-matter volume and cortical thickness of a cluster in the right lingual and pericalcarine cortex.


          Chronic methamphetamine use induces hard-to-recover cortical thickening in bilateral superior frontal gyri and recoverable volumetric reduction in right hippocampus, bilateral accumbens nuclei and bilateral cortical regions around insulae. These alternations might contribute to methamphetamine-induced neurocognitive disfunctions and reflect a regional specific response of the brain to methamphetamine.

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          Most cited references 44

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          Measuring the thickness of the human cerebral cortex from magnetic resonance images.

           B Fischl,  Anders Dale (2000)
          Accurate and automated methods for measuring the thickness of human cerebral cortex could provide powerful tools for diagnosing and studying a variety of neurodegenerative and psychiatric disorders. Manual methods for estimating cortical thickness from neuroimaging data are labor intensive, requiring several days of effort by a trained anatomist. Furthermore, the highly folded nature of the cortex is problematic for manual techniques, frequently resulting in measurement errors in regions in which the cortical surface is not perpendicular to any of the cardinal axes. As a consequence, it has been impractical to obtain accurate thickness estimates for the entire cortex in individual subjects, or group statistics for patient or control populations. Here, we present an automated method for accurately measuring the thickness of the cerebral cortex across the entire brain and for generating cross-subject statistics in a coordinate system based on cortical anatomy. The intersubject standard deviation of the thickness measures is shown to be less than 0.5 mm, implying the ability to detect focal atrophy in small populations or even individual subjects. The reliability and accuracy of this new method are assessed by within-subject test-retest studies, as well as by comparison of cross-subject regional thickness measures with published values.
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            A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks.

            Cognitively demanding tasks that evoke activation in the brain's central-executive network (CEN) have been consistently shown to evoke decreased activation (deactivation) in the default-mode network (DMN). The neural mechanisms underlying this switch between activation and deactivation of large-scale brain networks remain completely unknown. Here, we use functional magnetic resonance imaging (fMRI) to investigate the mechanisms underlying switching of brain networks in three different experiments. We first examined this switching process in an auditory event segmentation task. We observed significant activation of the CEN and deactivation of the DMN, along with activation of a third network comprising the right fronto-insular cortex (rFIC) and anterior cingulate cortex (ACC), when participants perceived salient auditory event boundaries. Using chronometric techniques and Granger causality analysis, we show that the rFIC-ACC network, and the rFIC, in particular, plays a critical and causal role in switching between the CEN and the DMN. We replicated this causal connectivity pattern in two additional experiments: (i) a visual attention "oddball" task and (ii) a task-free resting state. These results indicate that the rFIC is likely to play a major role in switching between distinct brain networks across task paradigms and stimulus modalities. Our findings have important implications for a unified view of network mechanisms underlying both exogenous and endogenous cognitive control.
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              Dopamine neuron systems in the brain: an update.

              The basic organization of the catecholamine-containing neuronal systems and their axonal projections in the brain was initially worked out using classical histofluorescence techniques during the 1960s and 1970s. The introduction of more versatile immunohistochemical methods, along with a range of highly sensitive tract-tracing techniques, has provided a progressively more detailed picture, making the dopamine system one of the best known, and most completely mapped, neurotransmitter systems in the brain. The purpose of the present review is to summarize our current knowledge of the diversity and neurochemical features of the nine dopamine-containing neuronal cell groups in the mammalian brain, their distinctive cellular properties, and their ability to regulate their dopaminergic transmitter machinery in response to altered functional demands and aging.

                Author and article information

                BMC Psychiatry
                BMC Psychiatry
                BMC Psychiatry
                BioMed Central (London )
                10 April 2020
                10 April 2020
                : 20
                [1 ]GRID grid.412901.f, ISNI 0000 0004 1770 1022, Mental Health Center, , West China Hospital of Sichuan University, ; Chengdu, 610041 China
                [2 ]Detoxification and Narcotics Control Department of Sichuan Province, Chengdu, 610041 China
                [3 ]Sichuan provincial Compulsory Drug Addiction Treatment Agency for Males, Ziyang, 641400 China
                [4 ]Sichuan provincial Compulsory Drug Addiction Treatment Agency for Females, Deyang, 618007 China
                [5 ]Hospital of Sichuan provincial Compulsory Drug Addiction Treatment Agency for Females, Deyang, 618007 China
                © The Author(s) 2020

                Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                Funded by: National Key Research and Development Program of China
                Award ID: 2017YFC1310401
                Award Recipient :
                Funded by: Science and Technology Department of Sichuan Province
                Award ID: 2017HH0059
                Award Recipient :
                Research Article
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                © The Author(s) 2020


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