Association of Gray Matter and Personality Development With Increased Drunkenness Frequency During Adolescence

Gabriel H Robert; Qiang Luo; Tao Yu; Congying Chu; Alex Ing; Tianye Jia; Dimitri Papadopoulos Orfanos; Erin Burke-Quinlan; Sylvane Desrivières; Barbara Ruggeri; Philip Spechler; Bader Chaarani; Nicole Tay; Tobias Banaschewski; Arun L W Bokde; Uli Bromberg; Herta Flor; Vincent Frouin; Penny Gowland; Andreas Heinz; Bernd Ittermann; Jean-Luc Martinot; Marie-Laure Paillère Martinot; Frauke Nees; Luise Poustka; Michael N Smolka; Nora C Vetter; Henrik Walter; Robert Whelan; Patricia Conrod; Ted Barker; Hugh Garavan; Gunter Schumann

doi:10.1001/jamapsychiatry.2019.4063

. 2019 Dec 18;77(4):409–419. doi: 10.1001/jamapsychiatry.2019.4063

Association of Gray Matter and Personality Development With Increased Drunkenness Frequency During Adolescence

Gabriel H Robert ^1,^2,^3,^4,⁵, Qiang Luo ^6,^7,⁸, Tao Yu ^9,^10,^11,¹², Congying Chu ^1,⁴, Alex Ing ^1,⁴, Tianye Jia ^1,⁴, Dimitri Papadopoulos Orfanos ¹³, Erin Burke-Quinlan ^1,⁴, Sylvane Desrivières ^1,⁴, Barbara Ruggeri ^1,⁴, Philip Spechler ¹⁴, Bader Chaarani ¹⁴, Nicole Tay ^1,⁴, Tobias Banaschewski ¹⁵, Arun L W Bokde ¹⁶, Uli Bromberg ¹³, Herta Flor ^17,¹⁸, Vincent Frouin ¹³, Penny Gowland ¹⁹, Andreas Heinz ²⁰, Bernd Ittermann ²¹, Jean-Luc Martinot ^22,²³, Marie-Laure Paillère Martinot ^24,²⁵, Frauke Nees ¹⁸, Luise Poustka ²⁶, Michael N Smolka ²⁷, Nora C Vetter ²⁷, Henrik Walter ²⁸, Robert Whelan ²⁹, Patricia Conrod ³⁰, Ted Barker ^1,⁴, Hugh Garavan ¹⁴, Gunter Schumann ^1,^4,^6,^31,^32,^✉, for the IMAGEN Consortium

¹Institute of Psychiatry, Psychology and Neuroscience, Centre for Population Neuroscience and Stratified Medicine (PONS), King's College London, London, United Kingdom

²Behavior and Basal Ganglia Unit (EA-4712), University of Rennes 1, Rennes, France

³Pôle Hospitalo-Universitaire de Psychiatrie Adulte, Rennes, France

⁴Social, Genetic, and Developmental Psychiatry Centre, King's College London, London, United Kingdom

⁵U1228, Empenn, Institut National de la Santé et de la Recherche Médicale & Institut National de Recherche en Informatique et Automatique, Paris, France

⁶Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China

⁷State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institute of Brain Science, Fudan University, Shanghai, China

⁸Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China

⁹Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, Shanghai, China

¹⁰Shanghai Center for Women and Children's Health, Shanghai, China

¹¹Shanghai Key Laboratory of Psychotic Disorders, Shanghai Institute of Mental Health, Shanghai Jiao Tong University, Shanghai, China

¹²Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China

¹³Department of Systems Neuroscience, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany

¹⁴Departments of Psychiatry and Psychology, University of Vermont, Burlington

¹⁵Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

¹⁶School of Medicine and Trinity College Institute of Neuroscience, Discipline of Psychiatry, Trinity College Dublin, Dublin, Ireland

¹⁷Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

¹⁸Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany

¹⁹Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom

²⁰Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité, Universitätsmedizin Berlin, Berlin, Germany

²¹Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Berlin, Germany

²²Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 Neuroimaging & Psychiatry, Faculté de Médecine, Université Paris-Sud, Le Kremlin-Bicêtre, Sorbonne Paris Cité, Paris, France

²³Université Paris Descartes, Sorbonne Paris Cité, Paris, France

²⁴Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 Neuroimaging & Psychiatry, Université Paris-Sud, University Paris Descartes-Sorbonne Paris Citél, Paris, France

²⁵Assistance Publique des Hôpitaux de Paris, Department of Adolescent Psychopathology and Medicine, Maison de Solenn, Cochin Hospital, Paris, France

²⁶Department of Child and Adolescent Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria

²⁷Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany

²⁸Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité, Universitätsmedizin Berlin, Research Division of Mind and Brain Berlin, Berlin, Germany

²⁹Department of Psychology, University College Dublin, Belfield, Dublin, Ireland

³⁰Department of Psychiatry and Addictology, Medical Faculty, University of Montreal, Montréal, Québec, Canada

³¹PONS Research Group, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Humboldt University, Berlin, Germany

³²Leibniz Institute for Neurobiology, Magdeburg, Germany

Group Information: The members of the IMAGEN Consortium are listed at the end of the article.

Accepted for Publication: September 24, 2019.

^✉

Corresponding Author: Gunter Schumann, MD, Institute of Psychiatry, Psychology and Neuroscience, Centre for Population Neuroscience and Stratified Medicine (PONS), King’s College London, London SE5 8AF, United Kingdom (gunter.schumann@kcl.ac.uk).

Published Online: December 18, 2019. doi:10.1001/jamapsychiatry.2019.4063

Author Contributions: Drs Robert and Schumann had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Robert and Luo contributed equally to this work and should be considered co-first authors.

Concept and design: Robert, Luo, Flor, Heinz, Nees, Smolka, Conrod, Schumann.

Acquisition, analysis, or interpretation of data: Robert, Luo, Yu, Chu, Ing, Jia, Papadopoulos Orfanos, Burke-Quinlan, Desrivières, Ruggeri, Spechler, Chaarani, Tay, Banaschewski, Bokde, Bromberg, Frouin, Gowland, Ittermann, Martinot, Paillère-Martinot, Nees, Poustka, Vetter, Walter, Whelan, Conrod, Barker, Garavan, Schumann.

Drafting of the manuscript: Robert, Luo, Yu, Chu, Jia, Flor, Frouin, Heinz, Schumann.

Critical revision of the manuscript for important intellectual content: Robert, Luo, Ing, Jia, Papadopoulos Orfanos, Burke Quinlan, Desrivières, Ruggeri, Spechler, Chaarani, Tay, Banaschewski, Bokde, Bromberg, Gowland, Heinz, Ittermann, Martinot, Paillère-Martinot, Nees, Poustka, Smolka, Vetter, Walter, Whelan, Conrod, Barker, Garavan, Schumann.

Statistical analysis: Robert, Luo, Chu, Ing, Jia, Spechler, Chaarani, Tay, Barker, Garavan.

Obtained funding: Luo, Heinz, Ittermann, Martinot, Paillère-Martinot, Smolka, Vetter, Conrod, Schumann.

Administrative, technical, or material support: Yu, Chu, Papadopoulos Orfanos, Ruggeri, Spechler, Chaarani, Tay, Bokde, Gowland, Heinz, Ittermann, Martinot, Nees, Smolka, Vetter, Walter, Whelan, Garavan, Schumann.

Supervision: Papadopoulos Orfanos, Desrivières, Banaschewski, Bokde, Flor, Heinz, Ittermann, Walter, Whelan, Garavan, Schumann.

Data alignment and integration, QC, pseudonymization: Papadopoulos Orfanos.

Image data analysis: Frouin.

Conflict of Interest Disclosures: Dr Robert reported receiving grants from Fondation Recherche Médicale, Fondation Deniker, and Fondation Avenir during the conduct of the study as well as personal fees from Lundbeck-Otsuka and Janssen outside the submitted work. Dr Luo reported being a visiting fellow at Clare Hall, University of Cambridge, United Kingdom, during the revision of this work. Dr Desrivières reported receiving grants from Medical Research Council during the conduct of the study. Dr Banaschewski reported serving in an advisory or consultancy role for Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg GmbH, and Shire; receiving conference support or speaker fees from Lilly, Medice, Novartis, and Shire; and receiving royalties from Hogrefe, Kohlhammer, CIP Medien, and Oxford University Press. Dr Bokde reported receiving grants from Science Foundation Ireland during the conduct of the study. Dr Poustka reported receiving speaker fees from Shire and author royalties from Hogrefe and Bern. Dr Smolka reported receiving grants from Bundesministerium für Bildung und Forschung and the European Commission during the conduct of the study. Dr Walter reported receiving grants from the European Union during the conduct of the study. No other disclosures were reported.

Funding/Support: This study was supported in part by grant LSHM-CT- 2007-037286 from the European Union–funded FP6 Integrated Project IMAGEN; the European Research Council Advanced Grant STRATIFY 695313 from the Horizon 2020; grant PR-ST-0416-10004 from the European Research Area Network on Illicit Drugs; grant MR/N027558/1 from BRIDGET (Brain Imaging Cognition Dementia and Next Generation Genomics); grant 602450 from the FP7 projects IMAGEMEND and grant 603016 from MATRICS; grant 115300-2 from the Innovative Medicine Initiative Project EU-AIMS; grant MR/N000390/1 from the Medical Research Council Grant c-VEDA, the Swedish Research Council FORMAS, the National Institute for Health Research, Biomedical Research Centre at South London and Maudsley NHS (National Health Service) Foundation Trust, and King’s College London; grants 01GS08152, 01EV0711, eMED SysAlc01ZX1311A, and Forschungsnetz AERIAL from the Bundesministerium für Bildung und Forschung; and grants SM 80/7-1, SM 80/7-2, and SFB 940/1 from the Deutsche Forschungsgemeinschaft. Further support was provided by grants AF12-NEUR0008-01-WM2NA and ANR-12-SAMA-0004 from the ANR; the Fondation de France, the Fondation pour la Recherche Médicale, and the Mission Interministérielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives; grant DPA20140629802 from the Fondation pour la Recherche Médicale; the Fondation de l’Avenir, Paris Sud University IDEX 2012; grant 16/ERCD/3797 from the National Institutes of Health (NIH) Science Foundation Ireland; grant RO1 MH085772-01A1 from the NIH; and grant U54 EB020403 from the NIH Consortium. Dr Robert was funded by the Fondation pour la Recherche Médicale (grant SPE20130326586), the Fondation Pierre Deniker, the Centre Hospitalier Guillaume Régnier, the Scientific Institut Servier, and Janssen & Janssen. Dr Luo was funded by grants 81873909 and 91630314 from the National Natural Science Foundation of China, grant 17ZR1444400 from the Natural Science Foundation of Shanghai, grant 2018YFC0910503 from the National Key Research and Development Program of China, grant 16JC1420402 from the key project of Shanghai Science and Technology Innovation Plan, grant B18015 from the 111 Project, grant 2018SHZDZX01 from the Shanghai Municipal Science and Technology Major Project, and by ZJLab.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Group Information: Lisa Albrecht, MSc, Charité; Mercedes Arroyo, PhD, Cambridge University; Eric Artiges, MD, PhD, INSERM; Semiha Aydin, MSc, Physikalisch-Technische Bundesanstalt; Christine Bach, MSc, Central Institute of Mental Health; Tobias Banaschewski, MD, PhD, Central Institute of Mental Health; Alexis Barbot, PhD, Commissariat à l’Energie Atomique; Gareth Barker, PhD, Institute of Psychiatry, Psychology and Neuroscience; Arun L. W. Bokde, PhD, Trinity College Dublin; Zuleima Bricaud, MSc, INSERM; Uli Bromberg, PhD, University of Hamburg; Ruediger Bruehl, PhD, Physikalisch-Technische Bundesanstalt; Christian Büchel, MD, PhD, University of Hamburg; Anna Cattrell, PhD, Institute of Psychiatry, Psychology and Neuroscience; Patricia Conrod, PhD, Institute of Psychiatry, Psychology and Neuroscience; Katharina Czech, MD, Charité; Jeffrey Dalley, PhD, Cambridge University; Sylvane Desrivières, PhD, Institute of Psychiatry, Psychology and Neuroscience; Tahmine Fadai, PhD, University of Hamburg; Herta Flor, MD, PhD, Central Institute of Mental Health; Vincent Frouin, PhD, Commissariat à l’Energie Atomique; Jürgen Gallinat, MD, PhD, University Medical Center Hamburg-Eppendorf; Hugh Garavan, PhD, Trinity College Dublin; Fanny Gollier Briand, MSc, INSERM; Penny Gowland, PhD, University of Nottingham; Bert Heinrichs, PhD, Deutsches Referenzzentrum für Ethik; Andreas Heinz, MD, PhD, Charité; Thomas Hübner, PhD, Technische Universität Dresden; Albrecht Ihlenfeld, PhD, Physikalisch-Technische Bundesanstalt; Alex Ing, PhD, Institute of Psychiatry, Psychology and Neuroscience; Bernd Ittermann, PhD, Physikalisch-Technische Bundesanstalt; Tianye Jia, PhD, Institute of Psychiatry, Psychology and Neuroscience; Jennifer Jones, MSc, Trinity College Dublin; Eleanor Kennedy, MSc, Institute of Psychiatry, Psychology and Neuroscience; Dirk Lanzerath, PhD, Deutsches Referenzzentrum für Ethik; Mark Lathrop, PhD, McGill University and Genome Quebec Innovation Centre; Claire Lawrence, PhD, University of Nottingham; Hervé Lemaitre, PhD, INSERM; Katharina Lüdemann, PhD, Charité; Christine Macare, PhD, Institute of Psychiatry, Psychology and Neuroscience; Karl Mann, PhD, Central Institute of Mental Health; Adam Mar, PhD, Cambridge University; Jean-Luc Martinot, MD, PhD, INSERM; Eva Mennigen, MD, PhD, Technische Universität Dresden; Fabiana Mesquita de Carvahlo, PhD, Institute of Psychiatry, Psychology and Neuroscience; Kathrin Müller, PhD, Technische Universität Dresden; Frauke Nees, PhD, Central Institute of Mental Health; Charlotte Nymberg, PhD, Institute of Psychiatry, Psychology and Neuroscience; Marie-Laure Paillère, MD, PhD, INSERM; Tomas Paus, PhD, University of Toronto; Zdenka Pausova, MD, University of Toronto; Jean-Baptiste Poline, PhD, University of California, Berkeley and Commissariat à l’Energie Atomique; Luise Poustka, MD, Central Institute of Mental Health; Erin Burke-Quinlan, PhD, Institute of Psychiatry, Psychology and Neuroscience; Jan Reuter, PhD, Charité; Stephan Ripke, PhD, Technische Universität Dresden; Trevor Robbins, PhD, Cambridge University; Gabriel H. Robert, MD, PhD, Institute of Psychiatry, Psychology and Neuroscience; Sarah Rodehacke, PhD, Technische Universität Dresden; Barbara Ruggeri, PhD, Institute of Psychiatry, Psychology and Neuroscience; Dirk Schmidt, PhD, Technische Universität Dresden; Sophia Schneider, PhD, University of Hamburg; Florian Schubert, PhD, Physikalisch-Technische Bundesanstalt; Michael N. Smolka, MD, PhD, Technische Universität Dresden; Wolfgang Sommer, PhD, Central Institute of Mental Health; Rainer Spanagel, PhD, Central Institute of Mental Health; Claudia Speiser, PhD, GABO: Milliarium MbH & Co. KG; Tade Spranger, PhD, Deutsches Referenzzentrum für Ethik/Institut of Science and Ethics; Alicia Stedman, MSc, University of Nottingham; Dai Stephens, PhD, University of Sussex; Nicole Strache, PhD, Charité; Andreas Ströhle, MD, PhD, Charité; Maren Struve, MSc, Central Institute of Mental Health; Naresh Subramaniam, PhD, Cambridge University; Amir Tahmasebi, PhD, University of Toronto; David Theobald, MSc, Cambridge University; Nora C. Vetter, MD, PhD, Technische Universität Dresden; Helene Vulser, MD, PhD, INSERM; Bernadeta Walaszek, PhD, Physikalisch-Technische Bundesanstalt; Robert Whelan, PhD, Trinity College Dublin; Steve Williams, PhD, Institute of Psychiatry, Psychology and Neuroscience; Bing Xu, PhD, Institute of Psychiatry, Psychology and Neuroscience; Juliana Yacubian, PhD, University of Hamburg; and Veronika Ziesch, PhD, Technische Universität Dresden.

Additional Contributions: Mitul Mehta, PhD, Center for Neuroimaging Science, Institute of Psychiatry, Psychology and Neuroscience, provided help with neuroimaging analyses. Audrey Lavenu, PhD, Florian Naudet, MD, PhD, and Florian Lemaître, PharmD, PhD, Department of Pharmacology, Rennes Hospital, University of Rennes 1, provided advice on statistics. Jujiao Kang, BSc, Shanghai Center for Mathematical Science, Fudan University, provided the calculations for the polygenic risk scores. These individuals received no compensation for their contributions.

^✉

Corresponding author.

PMCID: PMC6990803 PMID: 31851304

This cohort study assesses the directionality of the association between gray matter development and frequency of alcohol intoxication in European adolescents by performing causal bayesian network, temporality, and exposure-response curve analyses.

Key Points

Question

What is the directionality of the association between the increased frequency of drunkenness and gray matter development during adolescence?

Findings

In this cohort study of 726 adolescents enrolled in the IMAGEN European cohort, the 3 complementary approaches used (causal bayesian networks, temporality analyses, and exploration of exposure-response curves) suggested that accelerated gray matter atrophy in the frontal and posterior temporal cortices was associated with an increased risk for drunkenness.

Meaning

Findings from this study suggest that the neurotoxicity interpretation of the drinking-related acceleration of gray matter atrophy should be applied with caution.

Abstract

Importance

Alcohol abuse correlates with gray matter development in adolescents, but the directionality of this association remains unknown.

Objective

To investigate the directionality of the association between gray matter development and increase in frequency of drunkenness among adolescents.

Design, Setting, and Participants

This cohort study analyzed participants of IMAGEN, a multicenter brain imaging study of healthy adolescents in 8 European sites in Germany (Mannheim, Dresden, Berlin, and Hamburg), the United Kingdom (London and Nottingham), Ireland (Dublin), and France (Paris). Data from the second follow-up used in the present study were acquired from January 1, 2013, to December 31, 2016, and these data were analyzed from January 1, 2016, to March 31, 2018. Analyses were controlled for sex, site, socioeconomic status, family history of alcohol dependency, puberty score, negative life events, personality, cognition, and polygenic risk scores. Personality and frequency of drunkenness were assessed at age 14 years (baseline), 16 years (first follow-up), and 19 years (second follow-up). Structural brain imaging scans were acquired at baseline and second follow-up time points.

Main Outcomes and Measures

Increases in drunkenness frequency were measured by latent growth modeling, a voxelwise hierarchical linear model was used to observe gray matter volume, and tensor-based morphometry was used for gray matter development. The hypotheses were formulated before the data analyses.

Results

A total of 726 adolescents (mean [SD] age at baseline, 14.4 [0.38] years; 418 [58%] female) were included. The increase in drunkenness frequency was associated with accelerated gray matter atrophy in the left posterior temporal cortex (peak: t_1,710 = –5.8; familywise error (FWE)–corrected P = 7.2 × 10⁻⁵; cluster: 6297 voxels; P = 2.7 × 10⁻⁵), right posterior temporal cortex (cluster: 2070 voxels; FWE-corrected P = .01), and left prefrontal cortex (peak: t_1,710 = –5.2; FWE-corrected P = 2 × 10⁻³; cluster: 10 624 voxels; P = 1.9 × 10⁻⁷). According to causal bayesian network analyses, 73% of the networks showed directionality from gray matter development to drunkenness increase as confirmed by accelerated gray matter atrophy in late bingers compared with sober controls (n = 20 vs 60; β = 1.25; 95% CI, −2.15 to −0.46; t_1,70 = 0.3; P = .004), the association of drunkenness increase with gray matter volume at age 14 years (β = 0.23; 95% CI, 0.01-0.46; t_1,584 = 2; P = .04), the association between gray matter atrophy and alcohol drinking units (β = −0.0033; 95% CI, −6 × 10⁻³ to −5 × 10⁻⁴; t_1,509 = −2.4; P = .02) and drunkenness frequency at age 23 years (β = −0.16; 95% CI, −0.28 to −0.03; t_1,533 = −2.5; P = .01), and the linear exposure-response curve stratified by gray matter atrophy and not by increase in frequency of drunkenness.

Conclusions and Relevance

This study found that gray matter development and impulsivity were associated with increased frequency of drunkenness by sex. These results suggest that neurotoxicity-related gray matter atrophy should be interpreted with caution.

Introduction

Alcohol intoxication (ie, drunkenness) is frequent among adolescents and conveys greater risk for alcohol abuse.¹ Although alcohol addiction has been associated with brain atrophy,² heavy drinking in adolescents is also associated with reduced volume and thickness of frontal and temporal gray matter.³ Longitudinal structural brain studies found greater frontocortical and temporal cortex thinning in adolescents who did not drink alcohol at baseline but transitioned into alcohol abuse during follow-up^4,5 compared with adolescents who drank no or low amounts of alcohol. However, this difference was absent when the groups were matched for age and when adolescents with no or low drinking were compared with those who transitioned into moderate drinking.⁶

During adolescence, the development of reward processing has been suggested to precede the development of cognitive control,^7,8 thus promoting risky decision-making, including alcohol abuse.⁹ Moreover, the regions that are most sensitive to alcohol-related atrophy are also involved in brain networks engaged in response inhibition,¹⁰ decision-making,^11,12 and alcohol-triggered emotions.¹³ Atrophy in anterior cingulate and in superior frontal and middle temporal gyri is a factor in alcohol abuse.^14,15,16,17 Together, these observations suggest a role for brain developmental mechanisms in the onset of alcohol abuse.

Suggestions by previous studies that heavy drinking was associated with brain damage in adolescents were based on nonsignificant group difference at baseline and significant group difference after the onset of drinking.^3,4,5,6 This exclusive reliance on time precedence ignores the dynamic nature of brain development that begins even before birth¹⁸ and might be associated with alcohol-related developmental trajectories that are established before the onset of drinking. Instead, causality may be inferred from observational studies using corroborative evidence, including but not restricted to temporality.¹⁹ Furthermore, studies did not report behavioral changes, such as in personality during adolescence, which are known to be factors in alcohol abuse.²⁰

Thus, the directionality of the association between brain development and frequency of drunkenness remains unknown to date. Specifically, is alcohol abuse associated with changes in brain structure in adolescents and young adults, or is there a trajectory of brain development that is a contributing factor in behavior, which may put certain adolescents at greater risk of alcohol abuse?

In this cohort study, we adopted 3 complementary approaches to investigate the directionality of the association between gray matter development and the increase in drunkenness frequency. The first approach was causal bayesian network (CBN), which belongs to probabilistic reasoning and provides graphic representations of network conditional dependencies.²¹ Causal bayesian network addresses the questions of directionality, uncertainty, and complexity in a set of random, interrelated variables²² and is used in various fields.^23,24,25 Reliable application of CBN requires a multidimensional assessment of interrelated features that possibly mediate the association between the brain and frequency of drunkenness, including sociodemographic status, genetics, cognition, behaviors, and personality. The second approach was temporality analyses in 3 different samples of alcohol consumers. The third approach was exploration of the exposure-response curves. The full procedure is detailed in the eMethods and eAppendix in the Supplement. The analyses workflow and participant flowchart are shown in eFigures 1 and 2 in the Supplement.

Methods

Participants

The present cohort study analyzed participants enrolled in IMAGEN, a prospective, multicenter brain imaging study.²⁶ Healthy adolescents were recruited at age 14 years from schools around 8 sites in Germany (Mannheim, Dresden, Berlin, and Hamburg), the United Kingdom (London and Nottingham), Ireland (Dublin), and France (Paris). Data from the second follow-up used in the present study were acquired from January 1, 2013, to December 31, 2016, and were analyzed from January 1, 2016, to March 31, 2018. Exclusion criteria are detailed in the eMethods in the Supplement. Participants’ alcohol, cannabis, and tobacco consumption and personality features were assessed at ages 14 years (baseline), 16 years (first follow-up), 19 years (second follow-up), and 23 years (third follow-up), thereby reducing the confounding factor of age. Structural brain imaging and cognitive measures were acquired at baseline and second follow-up. Written informed consent was obtained from all participants. This study was approved by the institutional ethics committee of King’s College London, University of Nottingham, Trinity College Dublin, University of Heidelberg, Technische Universität Dresden, Commissariat à l'Energie Atomique et aux Energies Alternatives, and University Medical Center at the University of Hamburg in accordance with the Declaration of Helsinki.²⁷ The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

Main Outcomes and Measures

Frequency of drunkenness was measured with the following question on the European School Survey Project on Alcohol and Other Drugs: How many times did you get drunk in the last 12 months (intoxicated from drinking alcoholic beverages, for example staggering when walking, not being able to speak properly, throwing up, or not remembering what happened)? The mean value of each response category was used for analyses (eg, a value of 3 on the European School Survey Project on Alcohol and Other Drugs referred to 3 to 5 episodes of drunkenness, yielding a score of 4). The increase in drunkenness frequency estimated by latent growth modeling was quantitative and normally distributed.

After the quality control procedure, 1938 (969 × 2) scans were preprocessed using the SPM-12b longitudinal pairwise tool (Functional Imaging Laboratory Methods Group). The midpoint within-participant templates were segmented with the VBM-8 toolbox (Christian Gaser, University of Jena) to avoid using adult tissue probability maps.²⁸ A between-participant template was generated with the diffeomorphic anatomical registration through exponentiated lie (DARTEL) algebra.²⁹ SPM-12b and SPM8-5236 (VBM8) were run on MATLAB, version 7.14.0 (The MathWorks Inc).

Confounding factors (socioeconomic status, puberty score, negative life events, and family history of alcohol dependency) were controlled for (eFigure 1 and eMethods in the Supplement). A polygenic risk score (PRS) for alcohol consumption³⁰ was required to meet the CBN assumptions to reduce the risk of identifying a spurious direct link. Cognition (working memory, decision-making, and response inhibition) and behavior (delay discounting, passive avoidance learning, and personality) variables are detailed in the eMethods in the Supplement. Alcohol drinking units (at age 23 years) were acquired using the timeline follow-back method. Body mass index was not controlled for (eMethods in the Supplement). Missing values were imputed using multiple imputation.³¹

Statistical Analysis

Associations between quantitative variables were tested using hierarchical linear models, a 1-level random intercept for site and sex with lmerTest, version 3.1-0 (Per Bruun Brockhoff). All P values were Bonferroni corrected (eTable 1 in the Supplement). Two-sided P < .05 was considered statistically significant.

Increases in drunkenness frequency and in personality changes were estimated with latent growth modeling. Mplus (Muthén & Muthén) provided the slope (ie, the increase) and the intercept (ie, drunkenness frequency at age 14 years, given the frequency at each time point) for each participant. Missing values were estimated using maximum likelihood from all of the data available³² under the missing-completely-at-random assumption (Little MCAR test: χ²_1,290 = 286; P = .60).

Causal bayesian networks, following the Bayes theorem, modeled the posterior conditional probability of a consequence after observation of the distribution of the probability of new previous evidences in an iterative process. This approach is suited to modeling the directionality between variables acquired at the same time and to providing probabilistic dependencies in a directed acyclic graph.²¹ In addition, given a set of variables, CBN can be estimated in a data-driven approach. Each network corresponds to a goodness of fit to the observed data score (bayesian gaussian equivalent). The procedure of “hill climbing” adds, deletes, and reverses arcs in the current directed acyclic graph at a time until the bayesian gaussian equivalent no longer improves.³³^(p19-20)

We reported only the edges replicated in more than 90% of the 10 000 bootstrapped CBNs,³³ and their directionality was the dominant direction (>50% of the bootstrapped CBNs^24,33). Increases in drunkenness frequency between ages 14 and 19 years, drunkenness frequency at age 14 years, gray matter development between ages 14 and 19 years (first principal component), gray matter volume at age 14 years (first principal component of the same clusters), and PRS were considered. All CBN analyses used bnlearn.³³

We considered individuals with minimal experiences with alcohol, tobacco, and cannabis use until they were 16 years of age (117 had a maximum of 2 occasions of drinking alcohol in their lifetime¹⁶ from the initial sample of 726 participants). We compared gray matter development (first principal component) among the late drinkers (ie, for 20 participants, episodes of drunkenness occurred during mainly the last month before the scans at 19 years of age; for 60 participants in the sober control group, 0 lifetime drunkenness episodes occurred).

We tested whether gray matter volume among the 3 clusters (first principal component) at age 14 years was associated with increased drunkenness frequency after age 14 to 19 years in a selected subsample of participants without any episode of drunkenness the year before age 14 years (n = 604).

We tested whether gray matter atrophy (first principal component) was associated with frequency of drunkenness (n = 594) and alcohol drinking units at age 23 years (n = 532). We used the increase in binge drinking (ie, 5 drinks in a row) to control for previous alcohol intoxication.

We stratified effect sizes according to site by sex. We explored the exposure-response curves¹⁹ (n = 726) by ranking the strata according to increasing gray matter atrophy and increasing drunkenness frequency.

Results

In total, 2216 healthy adolescents were recruited into the IMAGEN cohort. The present study included 726 (33%) of these participants with good-quality data (Table 1; eFigure 2 in the Supplement). Among the 726 participants, the mean (SD) age at baseline was 14.4 (0.38) years, 418 (58%) were female, and all were white. One hundred and two individuals (14%) had at least 1 drunkenness episode.

Table 1. Variables Description Within the Sample^a .

Variable	Baseline	Follow-up 1	Follow-up 2
Age, y	14.4 (0.38) [12.9 to 15.7]	16.5 (0.56) [15 to 18.8]	18.8 (0.6) [17.1 to 21.1]
Sex, No. (%)
Female	418 (58)	ND	ND
Male	308 (42)	ND	ND
Site proportion, No. (%)
London	120 (17)	ND	ND
Nottingham	138 (19)	ND	ND
Dublin	48 (7)	ND	ND
Berlin	72 (10)	ND	ND
Hamburg	82 (11)	ND	ND
Mannheim	92 (13)	ND	ND
Paris	66 (9)	ND	ND
Dresden	108 (15)	ND	ND
ESPAD
Frequency of drunkenness	0.5 (1.8) [0 to 29.5]	4.2 (8.7) [0 to 41]	9.7 (12.5) [0 to 41]
Tobacco	0.16 (1.4) [0 to 21]	0.8 (2.7) [0 to 21]	1.6 (4) [0 to 21]
Cannabis	0.26 (2.4) [0 to 41]	2.6 (8.3) [0 to 41]	5.1 (11.7) [0 to 41]
LEQ
Negative life events	6.4 (2.8) [0 to 16]	5.9 (2.7) [0 to 17]	3.6 (2.2) [0 to 14]
SURPS
Impulsivity	2.4 (0.4) [1.4 to 4]	2.2 (0.4) [1 to 3.4]	2.1 (0.4) [1 to 3.4]
Sensation	2.6 (0.5) [1 to 4]	2.7 (0.5) [1.2 to 4]	2.7 (0.5) [1 to 4]
Anxiety sensitivity	2.3 (0.4) [1 to 3.8]	2.3 (0.5) [1 to 4]	2.4 (0.5) [1 to 4]
Negative thinking	1.9 (0.4) [1 to 3.4]	1.8 (0.4) [1 to 3.6]	1.9 (0.4) [1 to 4]
NEO PI
Neuroticism	23.13 (7.5) [4 to 45]	22.57 (5.9) [1 to 44]	20.8 (8.2) [1 to 47]
Extraversion	29.7 (5.9) [10 to 45]	29.2 (5.9) [10 to 45]	29.4 (5.9) [11 to 45]
Openness	26.7 (5.7) [11 to 45]	27.7 (5.9) [8 to 48]	28.9 (6.4) [12 to 45]
Agreeableness	29.6 (5.1) [6 to 44]	30.2 (5.3) [11 to 45]	32.3 (5.5) [9 to 46]
Conscientiousness	27.9 (6.6) [8 to 48]	28.6 (7) [9 to 47]	30.2 (7.2) [4 to 48]
CGT
Deliberation time, ms	2245.55 (7194.52) [736.51 to 181 363.1]	ND	1626.21 (699.30) [734.5 to 12 682.45]
Risk taking	0.52 (0.14) [0.05 to 0.89]	ND	0.52 (0.12) [0.13 to 0.86]
Delay aversion	0.23 (0.14) [−0.7 to 0.77]	ND	0.20 (0.15) [−0.13 to 0.83]
Quality of decision-making	0.94 (0.08) [0.45 to 1]	ND	0.96 (0.06) [0.55 to 1]
Overall bet	0.48 (0.13) [0.05 to 0.83]	ND	0.48 (0.11) [0.14 to 0.83]
Risk adjustment	1.65 (0.96) [−0.6 to 4.6]	ND	1.98 (0.95) [−0.3 to 4.78]
Pattern recognition memory, No. of correct trials	95.3 (7.6) [41.7 to 100]	ND	95.9 (7.1) [54 to 100]
Rapid visual processing	0.9 (0.05) [0.7 to 1]	ND	0.93 (0.04) [0 to 1]
Spatial working memory
Between errors	18.6 (13) [0 to 63]	ND	11.1 (12.2) [0 to 74]
Strategy	31 (5.4) [18 to 43]	ND	27.7 (6.2) [18 to 44]
Affective go or no-go mean correct latency, ms
Negative	490 (111.8) [222 to 888]	ND	513.3 (89.5) [215 to 964]
Positive	473.5 (107.8) [196.9 to 828.9]	ND	497.5 (87.8) [239 to 903]
Affective go or no-go total omissions, No.
Negative	11.4 (7.9) [0 to 36]	ND	6.2 (5.5) [0 to 36]
Positive	13.3 (7.3) [0 to 36]	ND	8 (5.4) [0 to 36]
Delay discounting κ value	0.023 (0.03) [0 to 0.25]	ND	0.024 (0.03) [0 to 0.24]

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Abbreviations: CGT, Cambridge Guessing Task (modified version of the Cambridge Gambling Task; CGT variables detailed in the eMethods in the Supplement); ESPAD, European School Survey Project on Alcohol and Other Drugs; LEQ, Life Events Questionnaire; ND, no data acquired at the corresponding time point; NEO PI, Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness Personality Inventory; SURPS, Substance Use Risk Profile Scale (this scale measured sensation seeking, impulsivity, anxiety sensitivity, and negative thinking).

^{^a}

All values given as mean (SD) [range] unless otherwise indicated.

Association With Site, Sex, Impulsivity, and Accelerated Gray Matter Atrophy

Drunkenness significantly increased over time (estimated SE = 8.1; P < .001) (eFigure 3 in the Supplement). Significant differences were found between sites (analysis of variance F_7,718 = 12.4; P = 4.8 × 10⁻¹⁵), with higher increases in drunkenness frequency in England and Ireland (London, Nottingham, and Dublin) compared with the continental sites. The mean increase in drunkenness frequency was greater in male compared with female participants (0.52 vs 0.34; 95% CI, 12%-24%; t_1,724 = 5.9; P = 3.6 × 10⁻⁹) (Figure 1A). Among possible personality,²⁰ cognition,³⁴ and behavioral³⁵ confounding factors, only the increases in openness (β = 0.10; 95% CI, 0.05-0.16; t_1,690 = 4; P = 2.4 × 10⁻³) and in impulsivity (β = 1.06; 95% CI, 0.57-1.9; t_1,707 = 3.6; Bonferroni-corrected [25 models] P = 6.7 × 10⁻³) were associated with an increase in drunkenness frequency (Figure 1B and C; eTable 2 in the Supplement). Polygenic risk score was not associated with an increase in drunkenness frequency (β = 3209; 95% CI, −2769 to 9187; t_1,699 = 1; P = .30).

Figure 1. — Error bars are the SEM from the sample at each time point.

We found significant atrophy in the inferior frontal and temporal cortices independent of drunkenness (n = 907) (Figure 2A and B; eTables 7, 8, and 9 in the Supplement). An increase in drunkenness frequency was associated with accelerated gray matter atrophy in the left posterior temporal cortex (peak: t_1,710 = –5.8; familywise error (FWE)–corrected P = 7.2 × 10⁻⁵; cluster: 6297 voxels; P = 2.7 × 10⁻⁵), in the right posterior temporal cortex (cluster: 2070 voxels; FWE-corrected P = .01), and in the left prefrontal cortex (peak: t_1,710 = –5.2; FWE-corrected P = 2 × 10⁻³; cluster: 10 624 voxels; P = 1.9 × 10⁻⁷), extending to the left anterior insula and the anterior cingulate (Figure 2C and Table 2). These 3 clusters were also observed using a voxelwise hierarchical linear model (eFigure 4 and eTable 3 in the Supplement) and were confirmed with the increase of binge drinking (5 drinks in a row) and the first principal component of the 3 clusters (β = −0.76; 95% CI = −0.97 to −0.55; t_1,701 = −7.1; P = 3.5 × 10⁻¹²) (eResults and eFigure 4 in the Supplement).

Table 2. Clusters and Corresponding Features Associating Gray Matter Development and Increase in Frequency of Drunkenness (N = 726 Participants).

Cluster	Cluster Size (Voxels)	AAL Structures	Peak Location MNI Coordinates	P Value for Cluster Level (FWE Corrected)^a^,^b	t Value	Brodmann Area	P Value for Peak Level (FWE Corrected)^a
Left prefrontal	10 624	Lateral frontal gyrus (L)	−42, 34, −12	1.9 × 10⁻⁷	5.2	L BA 47	2 × 10⁻³
		Middle frontal gyrus (L)	−44, 36, 0		4.96	L BA 45	5 × 10⁻³
		Inferior frontal gyrus (L)	−10, 34, −15		4.5	L BA 11	4 × 10⁻²
Left temporal	6297	Inferior temporal gyrus (L) + middle temporal gyrus (L)	−62, −21, −24	2.7 × 10⁻⁵	5.85	BA 20 + BA 21	7.2 × 10⁻⁵
Left temporal	6297	Fusiform gyrus (L)	−52, −56, 0	2.7 × 10⁻⁵	5.26	BA 37	1 × 10⁻³
Right temporal	2070	Middle temporal gyrus (R)	68, −26, −22	1 × 10⁻²	NA	No voxels survived the FWE correction	NA

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Abbreviations: AAL, automatic anatomic label; BA, Brodmann area; FWE, familywise error; L, left; MNI, Montreal Neurological Institute; NA, not applicable; R, right.

^{^a}

All P values were FWE corrected.

^{^b}

P value at the peak level set at P = .001 uncorrected.

Directionality Analyses

Bootstrapping revealed that 100% of the CBNs showed an association between gray matter atrophy and an increase in drunkenness frequency, of which 73% showed a direction from gray matter development to increased drunkenness frequency (Figure 3A). We found increased gray matter atrophy (between ages 14 and 19 years) in the late bingers compared with sober controls (n = 20 vs 60; β = 1.25; 95% CI, −2.15 to −0.46; t_1,70 = 0.3; P = .004). Gray matter volume at age 14 years among nondrinkers was associated with a future increase in drunkenness frequency between ages 14 and 19 years (n = 604; β = 0.23; 95% CI, 0.01-0.46; t_1,584 = 2; P = .04). Conversely, drunkenness frequency at age 14 years was not associated with gray matter development between ages 14 and 19 years in the whole sample (n = 726; β = 0.03; 95% CI, –0.09 to 0.14; t_1,700 = 0.4; P = .60) or in the sample of alcohol drinkers at age 14 years (n = 122; β = −1 × 10⁻³; 95% CI, −0.22 to 0.22; t_1,106 = –0.01; P > .99). Gray matter development was negatively associated with frequency of drunkenness (n = 594; β = –0.16; 95% CI, –0.28 to –0.03; t_1,533 = –2.5; P = .01) and alcohol drinking units at age 23 years (n = 532; β = −0.0033; 95% CI, −6 × 10⁻³ to −5 × 10⁻⁴; t_1,509 = −2.4; P = .02).

Figure 3. — Bayesian gaussian equivalent scores were −4610.7 (A), −4570.6 (B), −6407.7 (C), −3685.6 (D), and −2738.7 (E). The confounding factors (sex, site, puberty development score, negative life events, family history of alcoholism, and socioeconomic status) were modeled by regressing out their corresponding variance from each variable of interest (ie, node). Minus (–) or plus (+) sign indicates either negative or positive associations between the nodes; D indicates direction or proportion of networks (10 000 bootstraps) showing a direction from one node to another; GM, gray matter; PRS, polygenic risk score; and S, strength or the proportion of networks (10 000 bootstraps) with a statistically significant association.

Ranking the strata according to gray matter atrophy revealed a linear exposure-relation curve, whereas using the frequency of drunkenness to rank the groups did not (eFigures 5 and 6 in the Supplement). Individuals with the fastest gray matter atrophy (female participants from London) had a greater increase in drunkenness frequency compared with individuals with the slowest atrophy (male participants from Paris) (β = 0.28; 95% CI, 0.11-0.45; t_1,98 = 3.3; P = .001). Stratifying the sample by site and sex confirmed greater effect sizes in female participants and in Dresden (continent) (eTables 4 and 5 and eFigures 7 and 8 in the Supplement).

Impulsivity and Gray Matter Development as Independent Sex-Specific Pathways

We found no significant association between gray matter atrophy and increase in impulsivity (β = 0.7; 95% CI, −1.3 to 2.8; t_1,707 = 0.7; P = .50). However, impulsivity at age 14 years correlated strongly with drunkenness frequency at age 14 years (β = 0.6; 95% CI, 0.3-0.9; t_1,712 = 4.1; P = 3.9 × 10⁻⁵) and increase in drunkenness frequency between ages 14 and 19 years (β = 0.3; 95% CI, 0.2-0.5; t_1,708 = 5; P = 6.7 × 10⁻⁷), particularly among male participants (β = 0.6; 95% CI, 0.4-0.9; t_1,293 = 5.4; P = 1 × 10⁻⁷) but not female participants (β = 0.1; 95% CI, −0.03 to 0.3; t_1,405 = 1.5; P = .10). An increase in openness between ages 14 and 19 years was not associated with gray matter atrophy (β = −0.1; 95% CI, –0.3 to 0.06; t_1,695 = –1.3; P = .20). Openness at age 14 years was not associated with drunkenness at this age (β = −0.005; 95% CI, –0.02 to 0.01; t_1,613 = –0.7; P = .40) or with an increase in drunkenness between ages 14 and 19 years (β = 0.008; 95% CI, 0.001-0.01; t_1,709 = 2.4; Bonferroni-corrected P = .07).

Post hoc CBN analyses tested for the directionality of the association between impulsivity and the increase in drunkenness frequency and PRS (5 variables). Impulsivity at age 14 years and increase in drunkenness frequency were associated with 92% of the networks, suggesting that impulsive behavior that was already established at age 14 years was associated with increased drunkenness frequency. Impulsivity and frequency of drunkenness at age 14 years were associated in 91% of the networks, but only 50% of the networks found directionality from impulsivity to drunkenness frequency at this age (Figure 3B).

A third CBN analysis evaluated whether the 2 pathways (ie, related to gray matter or impulsivity) were independent from each other (7 variables). We found stable directionality from gray matter development (76%) predominantly among female participants (93%) and from impulsivity at age 14 years to increase in drunkenness (94%) predominantly among male participants (99%) (Figure 3C-E). Constraining the directionality from the increase of drunkenness toward gray matter development yielded worse model fit indices (eTable 6 in the Supplement). These results remained when the imputed PRS missing values were used (eFigure 9 in the Supplement).

Discussion

Using complementary approaches to determine probable directionality, we found that the accelerated gray matter atrophy in frontal and temporal regions was associated with increased frequency of alcohol intoxication in adolescents. Although this brain development pathway was found in both sexes, it was more prominent in female participants. In male participants, we found a second and independent pathway in which increased impulsivity was associated with increased drunkenness frequency.

The finding that accelerated frontal atrophy was associated with frequency of drunkenness corroborates previous findings.^4,6 However, Pfefferbaum et al⁶ did not find accelerated temporal atrophy, possibly owing to the cortex parcellation, which might have calculated a different pattern of temporal cortex development.³⁵ Moreover, age had a limited confounding effect in our sample (eResults in the Supplement).

Temporal atrophy was greater during ages 14 to 19 years, whereas the age range of 12 to 21 years⁶ might have influenced temporal atrophy variance. For example, Squeglia et al³⁴ found alcohol abuse–related accelerated atrophy in the temporal cortex using a similar approach as in the present study but with a younger sample at baseline. Results of the present study are consistent with those of a recent meta-analysis in substance dependency, which identified the shared pattern of gray matter atrophy (within the bilateral middle temporal gyrus, the left fusiform gyrus, and the right medial orbitofrontal cortex) across various substance use, suggesting that atrophy may underlie substance dependency in general rather than specific neurotoxicity.³⁸ Prenatal exposure to alcohol has been suggested as a factor in gray matter development.^39,40 However, we could not find any significant association between amount of alcohol intake during pregnancy and gray matter development (eResults in the Supplement).

Two important conditions for the successful application of CBN are controlling for potential confounding factors and verifying the results using long-term data.^22,33 Unmeasured factors might confound the association between gray matter development and increase in drunkenness frequency. In the present study, we integrated various confounding factors, including demographic, behavioral, and genetic factors relevant to alcohol use. Thus, the comprehensive and multidomain assessment of possible confounding factors of increased drunkenness¹⁶ meets the assumptions for using CBN. Meanwhile, the longitudinal design of the study supports gray matter atrophy preceding the onset of drunkenness episodes in different temporal patterns of alcohol use. First, baseline gray matter volume was significantly associated with future frequency of drunkenness, corroborating previous results that gray matter development was associated with alcohol abuse.^{14,15,16,17,41} Second, late drinkers had accelerated atrophy. Although accelerated atrophy among late drinkers may not be induced by drunkenness within the last month before the assessment, we cannot formally rule out this possibility. Third, the cerebral pattern related to adolescent drunkenness is also associated with alcohol intake and drunkenness frequency at adulthood. This association remains significant, accounting for an increase in binge drinking that may indicate adult alcohol use⁴² and arguing for time precedence from gray matter development during adolescence to adult alcohol intake.

Plotting the effect sizes according to sex-by-site groups ranked by higher rate of gray matter atrophy revealed a linear trend of exposure-response curve, and the group with the fastest rate of gray matter atrophy had a greater increase in drunkenness frequency compared with the group with the slowest rate of gray matter atrophy. Conversely, ranking the groups according to drunkenness frequency did not provide the typical curve, suggesting that alcohol is toxic.^{43,44,45,46,47}

Limitations

This study has some limitations. Causality can be proven only in randomized clinical trials, which are not feasible for ethical reasons. Therefore, compelling evidence from large, longitudinal, and well-characterized observational studies are the best evidence available according to the Hill criteria for inferring causality.¹⁹ The possible limitations of this approach are the unmeasured confounders that obscure true causality despite controlling for numerous intervening variables. Short of conducting a randomized clinical trial, we cannot rule out the possibility of the simultaneous occurrence of gray matter atrophy and increase in alcohol intoxication without any causation. For example, recent genetic epidemiologic investigations suggested that the presumed protective effect of moderate alcohol intake on stroke might be noncausal.⁴⁸ Moreover, PRS score and the increase in drunkenness were not significantly associated, suggesting that the genetic contribution of drunkenness frequency during adolescence was not fully controlled for.

Although the temporal analyses were performed on 3 different patterns of alcohol consumption with the limited confounding factor of previous alcohol intake, the current design prevented the unambiguous determination that gray matter development occurred before the increase in drunkenness frequency. We believe that cohorts with multiple time points and individuals at high risk for alcohol dependency are needed to increase the proportion of heavy drinkers in future studies. Some CBNs can have equivalent classes, but the increase in drunkenness frequency is part of a V structure network, which renders their identification more robust (eAppendix in the Supplement).

We used voxel-based morphometry to obtain gray matter volume and tensor-based morphometry to obtain gray matter development. Although widely used, these frameworks provided different cerebral features, and strong conclusions require replication.

Conclusions

This study, which included a large, long-term, and well-characterized cohort of healthy adolescents in Europe, found that gray matter development and impulsivity were associated with increased frequency of drunkenness by sex. These findings add to the evidence suggesting a cerebral predisposition to alcohol abuse. We believe the results of this study call for a more cautious interpretation of neurotoxicity-related gray matter atrophy.

Supplement.

eMethods. Material and Methods

eResults

eAppendix. Causal Bayesian Networks: Simulated Data

eFigure 1. Analyses Workflow

eFigure 2. Participants’ Flowchart

eFigure 3. Drunkenness Episodes at Each Time Points

eFigure 4. Results from Voxel-Wise Analyses

eFigure 5. Forest Plot of Effect Size: Increasing Frequency of Drunkenness (and 95 Confidence Interval) of the Association Between the Increased Frequency of Drunkenness and Gray Matter Development (1st PC of the 3 clusters, TIV Diff regressed out), Stratified by Site (8 sites) and Gender

eFigure 6. Forest Plot of Effect Size: Increasing Rate of Gray Matter Atrophy (and 95 Confidence Interval) of the Association Between the Increased Frequency of Drunkenness and Gray Matter Development (1st PC of the 3 clusters, TIV Diff regressed out), Stratified by Site (8 sites) and Gender

eFigure 7. Association Between the Gray Matter Development in the 3 Identified Clusters (1st principal component) and the Increase of Drunkenness By Site

eFigure 8. Association Between the Gray Matter Development in the 3 Identified Clusters (1st principal component) and the Increase of Drunkenness By Gender

eFigure 9. Causal Bayesian Network Using Imputed PRS (n = 726)

eTable 1. Multiple Testing Correction

eTable 2. T and P values (Uncorrected) of Association Between Behavioral Variables and Drunkenness at Each Time Points Using Multilevel Modeling

eTable 3. Peak Location (MNI Space) and Cluster Size for Each Cluster According to the General Linear Model and the Hierarchical Linear Model

eTable 4. Estimates, 95 IC and P values Across Sites of the Association Between the Gray Matter Development in the 3 identified Clusters (1st Principal Component) and the Increase of Drunkenness

eTable 5. Estimates, 95 IC and P values Across Gender of the Association Between the Gray Matter Development in the 3 identified Clusters (1st Principal Component) and the Increase of Drunkenness

eTable 6. Bayesian Gaussian Equivalent (BGe) Scores for Each Model (n = 660) and Their Respective Modifications With the Arrow “From” Gray Matter Development “to” Increase of Drunkenness Reversed

eTable 7. 116 Automatic Anatomic Atlases (AAL) Regions of Interest Mean Value of Gray Matter Development Estimated by the Pairwise Longitudinal Processing of SPM12b

eTable 8. 116 Automatic Anatomic Labeling (AAL) Regions of Interest Mean Value of Gray Matter Development in Girls Only, Estimated by the Pairwise Longitudinal Processing of SPM12b

eTable 9. 116 Automatic Anatomic Labeling (AAL) Regions of Interest Mean Value of Gray Matter Development in Boys Only, Estimated by the Pairwise Longitudinal Processing of SPM12b

Click here for additional data file.^{(1.7MB, pdf)}

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48.Millwood IY, Walters RG, Mei XW, et al. ; China Kadoorie Biobank Collaborative Group . Conventional and genetic evidence on alcohol and vascular disease aetiology: a prospective study of 500 000 men and women in China. Lancet. 2019;393(10183):1831-1842. doi: 10.1016/S0140-6736(18)31772-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement.

eMethods. Material and Methods

eResults

eAppendix. Causal Bayesian Networks: Simulated Data

eFigure 1. Analyses Workflow

eFigure 2. Participants’ Flowchart

eFigure 3. Drunkenness Episodes at Each Time Points

eFigure 4. Results from Voxel-Wise Analyses

eFigure 7. Association Between the Gray Matter Development in the 3 Identified Clusters (1st principal component) and the Increase of Drunkenness By Site

eFigure 8. Association Between the Gray Matter Development in the 3 Identified Clusters (1st principal component) and the Increase of Drunkenness By Gender

eFigure 9. Causal Bayesian Network Using Imputed PRS (n = 726)

eTable 1. Multiple Testing Correction

eTable 2. T and P values (Uncorrected) of Association Between Behavioral Variables and Drunkenness at Each Time Points Using Multilevel Modeling

eTable 3. Peak Location (MNI Space) and Cluster Size for Each Cluster According to the General Linear Model and the Hierarchical Linear Model

eTable 4. Estimates, 95 IC and P values Across Sites of the Association Between the Gray Matter Development in the 3 identified Clusters (1st Principal Component) and the Increase of Drunkenness

eTable 5. Estimates, 95 IC and P values Across Gender of the Association Between the Gray Matter Development in the 3 identified Clusters (1st Principal Component) and the Increase of Drunkenness

eTable 7. 116 Automatic Anatomic Atlases (AAL) Regions of Interest Mean Value of Gray Matter Development Estimated by the Pairwise Longitudinal Processing of SPM12b

eTable 8. 116 Automatic Anatomic Labeling (AAL) Regions of Interest Mean Value of Gray Matter Development in Girls Only, Estimated by the Pairwise Longitudinal Processing of SPM12b

eTable 9. 116 Automatic Anatomic Labeling (AAL) Regions of Interest Mean Value of Gray Matter Development in Boys Only, Estimated by the Pairwise Longitudinal Processing of SPM12b

Click here for additional data file.^{(1.7MB, pdf)}

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PERMALINK

Association of Gray Matter and Personality Development With Increased Drunkenness Frequency During Adolescence

Gabriel H Robert, MD, PhD

Qiang Luo, PhD

Tao Yu, MD, PhD

Congying Chu, PhD

Alex Ing, PhD

Tianye Jia, PhD

Dimitri Papadopoulos Orfanos, PhD

Erin Burke-Quinlan, PhD

Sylvane Desrivières, PhD

Barbara Ruggeri, PhD

Philip Spechler, MA

Bader Chaarani, PhD

Nicole Tay, MA

Tobias Banaschewski, MD, PhD

Arun L W Bokde, PhD

Uli Bromberg, PhD

Herta Flor, PhD

Vincent Frouin, PhD

Penny Gowland, PhD

Andreas Heinz, MD, PhD

Bernd Ittermann, PhD

Jean-Luc Martinot, MD, PhD

Marie-Laure Paillère Martinot, MD, PhD

Frauke Nees, PhD

Luise Poustka, MD

Michael N Smolka, MD

Nora C Vetter, PhD

Henrik Walter, MD, PhD

Robert Whelan, PhD

Patricia Conrod, PhD

Ted Barker, PhD

Hugh Garavan, PhD

Gunter Schumann, MD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Participants

Main Outcomes and Measures

Statistical Analysis

Results

Table 1. Variables Description Within the Samplea .

Association With Site, Sex, Impulsivity, and Accelerated Gray Matter Atrophy

Figure 1. Increase in Drunkenness Among 726 Participants.

Figure 2. Gray Matter Development Among Participants.

Table 2. Clusters and Corresponding Features Associating Gray Matter Development and Increase in Frequency of Drunkenness (N = 726 Participants).

Directionality Analyses

Figure 3. Causal Bayesian Networks .

Impulsivity and Gray Matter Development as Independent Sex-Specific Pathways

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Table 1. Variables Description Within the Sample^a .