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      Adolescent binge drinking disrupts normal trajectories of brain functional organization and personality maturation

      research-article
      a , b , z , 1 , a , b , z , 1 , b , c , z , * , d , e , e , f , g , h , i , i , j , k , l , m , n , o , p , q , r , g , j , l , s , g , t , t , o , u , v , e , a , b , w , x , y , z , IMAGEN Consortium 2
      NeuroImage : Clinical
      Elsevier
      Adolescent, Binge drinking, Resting state, Personality, Genome, Co-development, rsfMRI, resting-state fMRI, rsFC, resting-state functional connectivity, SNP, single nucleotide polymorphism, ESPAD, European School Survey Project on Alcohol and Drugs, Cantab, Cambridge Neuropsychological Test Automated Battery, NEO-PI-R, Revised NEO Personality Inventory, SURPS, Substance Use Risk Profile Scale, FDR, false discovery rate, SVM, support-vector machine, iFC, increased rsFC, dFC, decreased rsFC, rSNP, risk SNP, pSNP, protective SNP, LOO, leave-one-out, NRI, net reclassification improvement, ROC, receiver operational characteristic, ANOVA, Analysis of Variance, AUC, area under curve

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          Abstract

          Adolescent binge drinking has been associated with higher risks for the development of many health problems throughout the lifespan. Adolescents undergo multiple changes that involve the co-development processes of brain, personality and behavior; therefore, certain behavior, such as alcohol consumption, can have disruptive effects on both brain development and personality maturation. However, these effects remain unclear due to the scarcity of longitudinal studies. In the current study, we used multivariate approaches to explore discriminative features in brain functional architecture, personality traits, and genetic variants in 19-year-old individuals ( n = 212). Taking advantage of a longitudinal design, we selected features that were more drastically altered in drinkers with an earlier onset of binge drinking. With the selected features, we trained a hierarchical model of support vector machines using a training sample ( n = 139). Using an independent sample ( n = 73), we tested the model and achieved a classification accuracy of 71.2%. We demonstrated longitudinally that after the onset of binge drinking the developmental trajectory of improvement in impulsivity slowed down. This study identified the disrupting effects of adolescent binge drinking on the developmental trajectories of both brain and personality.

          Highlights

          • History of binge drinking could be identified by multivariate features at age 19.

          • Adolescent binge drinking disrupted frontal connectivity maturation in the brain.

          • Impulsivity improvement slowed down after the onset of adolescent binge drinking.

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          Most cited references27

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          A whole brain fMRI atlas generated via spatially constrained spectral clustering.

          Connectivity analyses and computational modeling of human brain function from fMRI data frequently require the specification of regions of interests (ROIs). Several analyses have relied on atlases derived from anatomical or cyto-architectonic boundaries to specify these ROIs, yet the suitability of atlases for resting state functional connectivity (FC) studies has yet to be established. This article introduces a data-driven method for generating an ROI atlas by parcellating whole brain resting-state fMRI data into spatially coherent regions of homogeneous FC. Several clustering statistics are used to compare methodological trade-offs as well as determine an adequate number of clusters. Additionally, we evaluate the suitability of the parcellation atlas against four ROI atlases (Talairach and Tournoux, Harvard-Oxford, Eickoff-Zilles, and Automatic Anatomical Labeling) and a random parcellation approach. The evaluated anatomical atlases exhibit poor ROI homogeneity and do not accurately reproduce FC patterns present at the voxel scale. In general, the proposed functional and random parcellations perform equivalently for most of the metrics evaluated. ROI size and hence the number of ROIs in a parcellation had the greatest impact on their suitability for FC analysis. With 200 or fewer ROIs, the resulting parcellations consist of ROIs with anatomic homology, and thus offer increased interpretability. Parcellation results containing higher numbers of ROIs (600 or 1,000) most accurately represent FC patterns present at the voxel scale and are preferable when interpretability can be sacrificed for accuracy. The resulting atlases and clustering software have been made publicly available at: http://www.nitrc.org/projects/cluster_roi/. Copyright © 2011 Wiley Periodicals, Inc.
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            Mental health of college students and their non-college-attending peers: results from the National Epidemiologic Study on Alcohol and Related Conditions.

            Although young adulthood is often characterized by rapid intellectual and social development, college-aged individuals are also commonly exposed to circumstances that place them at risk for psychiatric disorders. To assess the 12-month prevalence of psychiatric disorders, sociodemographic correlates, and rates of treatment among individuals attending college and their non-college-attending peers in the United States. Face-to-face interviews were conducted in the 2001-2002 National Epidemiologic Survey on Alcohol and Related Conditions (N = 43,093). Analyses were done for the subsample of college-aged individuals, defined as those aged 19 to 25 years who were both attending (n = 2188) and not attending (n = 2904) college in the previous year. Sociodemographic correlates and prevalence of 12-month DSM-IV psychiatric disorders, substance use, and treatment seeking among college-attending individuals and their non-college-attending peers. Almost half of college-aged individuals had a psychiatric disorder in the past year. The overall rate of psychiatric disorders was not different between college-attending individuals and their non-college-attending peers. The unadjusted risk of alcohol use disorders was significantly greater for college students than for their non-college-attending peers (odds ratio = 1.25; 95% confidence interval, 1.04-1.50), although not after adjusting for background sociodemographic characteristics (adjusted odds ratio = 1.19; 95% confidence interval, 0.98-1.44). College students were significantly less likely (unadjusted and adjusted) to have a diagnosis of drug use disorder or nicotine dependence or to have used tobacco than their non-college-attending peers. Bipolar disorder was less common in individuals attending college. College students were significantly less likely to receive past-year treatment for alcohol or drug use disorders than their non-college-attending peers. Psychiatric disorders, particularly alcohol use disorders, are common in the college-aged population. Although treatment rates varied across disorders, overall fewer than 25% of individuals with a mental disorder sought treatment in the year prior to the survey. These findings underscore the importance of treatment and prevention interventions among college-aged individuals.
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              Age differences in sensation seeking and impulsivity as indexed by behavior and self-report: evidence for a dual systems model.

              It has been hypothesized that sensation seeking and impulsivity, which are often conflated, in fact develop along different timetables and have different neural underpinnings, and that the difference in their timetables helps account for heightened risk taking during adolescence. In order to test these propositions, the authors examined age differences in sensation seeking and impulsivity in a socioeconomically and ethnically diverse sample of 935 individuals between the ages of 10 and 30, using self-report and behavioral measures of each construct. Consistent with the authors' predictions, age differences in sensation seeking, which are linked to pubertal maturation, follow a curvilinear pattern, with sensation seeking increasing between 10 and 15 and declining or remaining stable thereafter. In contrast, age differences in impulsivity, which are unrelated to puberty, follow a linear pattern, with impulsivity declining steadily from age 10 on. Heightened vulnerability to risk taking in middle adolescence may be due to the combination of relatively higher inclinations to seek excitement and relatively immature capacities for self-control that are typical of this period of development.
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                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                31 March 2019
                2019
                31 March 2019
                : 22
                : 101804
                Affiliations
                [a ]School of Mathematical Sciences, Fudan University, Shanghai 200433, PR China
                [b ]Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, PR China
                [c ]Department of Psychology and the Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge CB2 3EB, UK
                [d ]EA 4712 “Behavior and Basal Ganglia”, Rennes University 1, Rennes, France
                [e ]Centre for Population Neuroscience and Stratified Medicine (PONS), Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry Centre, King's College London, London SE5 8AF, United Kingdom
                [f ]School of Computer Science and Technology, Xidian University, Xi'an 710071, Shannxi, PR China
                [g ]Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
                [h ]Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Ireland
                [i ]University Medical Centre Hamburg-Eppendorf, House W34, 3.OG, Martinistr. 52, 20246, Hamburg, Germany
                [j ]Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
                [k ]Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
                [l ]NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
                [m ]Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, VT, USA
                [n ]Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
                [o ]Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
                [p ]Physikalisch-Technische Bundesanstalt (PTB), Braunschweig, Abbestr. 2 - 12, Berlin, Germany
                [q ]Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Descartes - Sorbonne Paris Cité; and Maison de Solenn, Paris, France
                [r ]Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Descartes; Sorbonne Université; and AP-HP, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
                [s ]Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075 Göttingen, Germany
                [t ]Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
                [u ]School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
                [v ]Developmental and Behavioral Pediatric Department & Child Primary Care Department, MOE-Shanghai Key Lab for Children's Environmental Health, Xinhua Hospital Affiliated To Shang Jiaotong University School of Medicine, Shanghai, PR China
                [w ]Department of Computer Science, University of Warwick, Coventry, UK
                [x ]Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, PR China
                [y ]Shanghai Center for Mathematical Sciences, Shanghai, PR China
                [z ]Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, PR China
                Author notes
                [* ]Correspondence author at: Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, PR China. qluo@ 123456fudan.edu.cn
                [1]

                These authors contributed equally to this work.

                [2]

                IMAGEN consortium ( www.imagen-europe.com) authors and affiliations are listed in the Supplementary Materials.

                Article
                S2213-1582(19)30154-8 101804
                10.1016/j.nicl.2019.101804
                6451196
                30991616
                562d311d-343e-4462-88ed-f99810fad6bb
                © 2019 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 27 November 2018
                : 8 March 2019
                : 29 March 2019
                Categories
                Regular Article

                adolescent,binge drinking,resting state,personality,genome,co-development,rsfmri, resting-state fmri,rsfc, resting-state functional connectivity,snp, single nucleotide polymorphism,espad, european school survey project on alcohol and drugs,cantab, cambridge neuropsychological test automated battery,neo-pi-r, revised neo personality inventory,surps, substance use risk profile scale,fdr, false discovery rate,svm, support-vector machine,ifc, increased rsfc,dfc, decreased rsfc,rsnp, risk snp,psnp, protective snp,loo, leave-one-out,nri, net reclassification improvement,roc, receiver operational characteristic,anova, analysis of variance,auc, area under curve

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