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Neuropsychosocial profiles of current and future adolescent alcohol misusers

Nature volume 512, pages 185–189 (2014)Cite this article

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Abstract

A comprehensive account of the causes of alcohol misuse must accommodate individual differences in biology, psychology and environment, and must disentangle cause and effect. Animal models1 can demonstrate the effects of neurotoxic substances; however, they provide limited insight into the psycho-social and higher cognitive factors involved in the initiation of substance use and progression to misuse. One can search for pre-existing risk factors by testing for endophenotypic biomarkers2 in non-using relatives; however, these relatives may have personality or neural resilience factors that protect them from developing dependence3. A longitudinal study has potential to identify predictors of adolescent substance misuse, particularly if it can incorporate a wide range of potential causal factors, both proximal and distal, and their influence on numerous social, psychological and biological mechanisms4. Here we apply machine learning to a wide range of data from a large sample of adolescents (n = 692) to generate models of current and future adolescent alcohol misuse that incorporate brain structure and function, individual personality and cognitive differences, environmental factors (including gestational cigarette and alcohol exposure), life experiences, and candidate genes. These models were accurate and generalized to novel data, and point to life experiences, neurobiological differences and personality as important antecedents of binge drinking. By identifying the vulnerability factors underlying individual differences in alcohol misuse, these models shed light on the aetiology of alcohol misuse and suggest targets for prevention.

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Figure 1: The relationship between group membership and each feature that was present in at least 9 folds of the final model.
Figure 2: Brain regions associated with binge drinking and the relative contribution of each brain metric to the classification.

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  • 13 August 2014

    The affiliations list was updated to include a missing address.

References

  1. Crews, F. T., Braun, C. J., Hoplight, B., Switzer, R. C., III & Knapp, D. J. Binge ethanol consumption causes differential brain damage in young adolescent rats compared with adult rats. Alcohol. Clin. Exp. Res. 24, 1712–1723 (2000)

    CAS  PubMed  Google Scholar 

  2. Ersche, K. D. et al. Abnormal brain structure implicated in stimulant drug addiction. Science 335, 601–604 (2012)

    ADS  CAS  PubMed  Google Scholar 

  3. Volkow, N. D. et al. High levels of dopamine D2 receptors in unaffected members of alcoholic families: possible protective factors. Arch. Gen. Psychiatry 63, 999–1008 (2006)

    CAS  PubMed  Google Scholar 

  4. Cloninger, C. R. Neurogenetic adaptive mechanisms in alcoholism. Science 236, 410–416 (1987)

    ADS  CAS  PubMed  Google Scholar 

  5. Swendsen, J. et al. Use and abuse of alcohol and illicit drugs in US adolescents: results of the National Comorbidity Survey-Adolescent Supplement. Arch. Gen. Psychiatry 69, 390–398 (2012)

    PubMed  PubMed Central  Google Scholar 

  6. Grant, J. D. et al. Adolescent alcohol use is a risk factor for adult alcohol and drug dependence: evidence from a twin design. Psychol. Med. 36, 109–118 (2006)

    PubMed  Google Scholar 

  7. Ioannidis, J. P. Why most published research findings are false. PLoS Med. 2, e124 (2005)

    PubMed  PubMed Central  Google Scholar 

  8. Button, K. S. et al. Power failure: why small sample size undermines the reliability of neuroscience. Nature Rev. Neurosci. 14, 365–376 (2013)

    CAS  Google Scholar 

  9. Whelan, R. & Garavan, H. Prediction inflation in neuroimaging. Biol. Psychiatry 75, 746–748 (2014)

    PubMed  Google Scholar 

  10. Bellazzi, R. & Zupan, B. Predictive data mining in clinical medicine: current issues and guidelines. Int. J. Med. Inform. 77, 81–97 (2008)

    PubMed  Google Scholar 

  11. Ambroise, C. & McLachlan, G. Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Natl Acad. Sci. USA 99, 6562–6566 (2002)

    ADS  CAS  PubMed  MATH  Google Scholar 

  12. Castellanos-Ryan, N., O’Leary-Barrett, M., Sully, L. & Conrod, P. Sensitivity and specificity of a brief personality screening instrument in predicting future substance use, emotional, and behavioral problems: 18-month predictive validity of the Substance Use Risk Profile Scale. Alcohol. Clin. Exp. Res. 37, E281–E290 (2013)

    PubMed  Google Scholar 

  13. Dube, S. et al. Adverse childhood experiences and the association with ever using alcohol and initiating alcohol use during adolescence. J. Adolesc. Health 38, 444.e1–444.e10 (2006)

    Google Scholar 

  14. Rietschel, M. & Treutlein, J. The genetics of alcohol dependence. Ann. NY Acad. Sci. 1282, 39–70 (2013)

    ADS  CAS  PubMed  Google Scholar 

  15. Tyndale, R. F. Genetics of alcohol and tobacco use in humans. Ann. Med. 39, 94–121 (2003)

    Google Scholar 

  16. Ersche, K. D. et al. Cognitive dysfunction and anxious-impulsive personality traits are endophenotypes for drug dependence. Am. J. Psychiatry 169, 926–936 (2012)

    PubMed  PubMed Central  Google Scholar 

  17. Nigg, J. et al. Poor response inhibition as a predictor of problem drinking and illicit drug use in adolescents at risk for alcoholism and other substance use disorders. J. Am. Acad. Child Adolesc. Psychiatry 45, 468–475 (2006)

    PubMed  Google Scholar 

  18. Norman, A. L. et al. Neural activation during inhibition predicts initiation of substance use in adolescence. Drug Alcohol Depend. 119, 216–223 (2011)

    PubMed  PubMed Central  Google Scholar 

  19. Schumann, G. et al. The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Mol. Psychiatry 15, 1128–1139 (2010)

    CAS  PubMed  Google Scholar 

  20. Whelan, R. et al. Adolescent impulsivity phenotypes characterized by distinct brain networks. Nature Neurosci. 15, 920–925 (2012)

    CAS  PubMed  Google Scholar 

  21. Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. Roy. Stat. Soc. B 67, 301–320 (2005)

    MathSciNet  MATH  Google Scholar 

  22. Robbins, T. W., Gillan, C., Smith, D., de Wit, S. & Ersche, K. Neurocognitive endophenotypes of impulsivity and compulsivity: towards dimensional psychiatry. Trends Cogn. Sci. 16, 81–91 (2012)

    PubMed  Google Scholar 

  23. Cloninger, C. R., Svrakic, D. M. & Przybeck, T. R. A psychobiological model of temperament and character. Arch. Gen. Psychiatry 50, 975–990 (1993)

    CAS  PubMed  Google Scholar 

  24. Conrod, P. J., Castellanos, N. & Mackie, C. Personality-targeted interventions delay the growth of adolescent drinking and binge drinking. J. Child Psychol. Psychiatry 49, 181–190 (2008)

    PubMed  Google Scholar 

  25. Goldstein, R. Z. & Volkow, N. Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nature Rev. Neurosci. 12, 652–669 (2011)

    CAS  Google Scholar 

  26. Hare, T. A., Camerer, C. F. & Rangel, A. Self-control in decision-making involves modulation of the vmPFC valuation system. Science 324, 646–648 (2009)

    ADS  CAS  PubMed  Google Scholar 

  27. Grant, B. F., Stinson, F. S. & Harford, T. C. Age at onset of alcohol use and DSM-IV alcohol abuse and dependence: a 12-year follow-up. J. Subst. Abuse 13, 493–504 (2001)

    CAS  PubMed  Google Scholar 

  28. Ghahremani, D. G. et al. Effects of the Youth Empowerment Seminar on impulsive behavior in adolescents. J. Adolesc. Health 53, 139–141 (2013)

    PubMed  Google Scholar 

  29. Gottfredson, D. C. & Wilson, D. B. Characteristics of effective school-based substance abuse prevention. Prev. Sci. 4, 27–38 (2003)

    PubMed  Google Scholar 

  30. Knutson, B., Fong, G. W., Adams, C. M., Varner, J. L. & Hommer, D. Dissociation of reward anticipation and outcome with event-related fMRI. Neuroreport 12, 3683–3687 (2001)

    CAS  PubMed  Google Scholar 

  31. Grosbras, M.-H. & Paus, T. Brain networks involved in viewing angry hands or faces. Cereb. Cortex 16, 1087–1096 (2006)

    PubMed  Google Scholar 

  32. Costa, P. T. & McCrae, R. Domains and facets: hierarchical personality assessment using the revised NEO personality inventory. J. Pers. Assess. 64, 21–50 (1995)

    PubMed  Google Scholar 

  33. Woicik, P. A., Stewart, S., Pihl, R. & Conrod, P. The Substance Use Risk Profile Scale: a scale measuring traits linked to reinforcement-specific substance use profiles. Addict. Behav. 34, 1042–1055 (2009)

    PubMed  Google Scholar 

  34. Cloninger, C. R. The Temperament and Character Inventory-Revised (Washington Univ., 1999)

    Google Scholar 

  35. Wechsler, D. Wechsler Intelligence Scale For Children–Fourth Edition (WISC-IV) (The Psychological Corporation, 2003)

    Google Scholar 

  36. Kirby, K. N., Petry, N. M. & Bickel, W. K. Heroin addicts discount delayed rewards at higher rates than non-drug using controls. J. Exp. Psychol. Gen. 128, 78–87 (1999)

    CAS  PubMed  Google Scholar 

  37. Newcomb, M. D., Huba, G. J. & Bentler, P. M. A multidimensional assessment of stressful life events among adolescents: derivation and correlates. J. Health Soc. Behav. 22, 400–415 (1981)

    Google Scholar 

  38. Pausova, Z. et al. Genes, maternal smoking, and the offspring brain and body during adolescence: design of the Saguenay Youth Study. Hum. Brain Mapp. 28, 502–518 (2007)

    PubMed  PubMed Central  Google Scholar 

  39. Petersen, A. C., Crockett, L. & Richards, M. A self-report measure of pubertal status: reliability, validity, and initial norms. J. Youth Adolesc. 17, 117–133 (1988)

    CAS  PubMed  Google Scholar 

  40. Stacey, D. et al. RASGRF2 regulates alcohol-induced reinforcement by influencing mesolimbic dopamine neuron activity and dopamine release. Proc. Natl Acad. Sci. USA 109, 21128–21133 (2012)

    ADS  CAS  PubMed  Google Scholar 

  41. Hibell, B. et al. The 1995 ESPAD report: alcohol and other drug use among students in 26 European countries. (Swedish Council for Information on Alcohol and Other Drugs, 1997)

  42. Johnson, V. E. Revised standards for statistical evidence. Proc. Natl Acad. Sci. USA 110, 19313–19317 (2013)

    ADS  CAS  PubMed  MATH  Google Scholar 

  43. Nuzzo, R. Scientific method: statistical errors. Nature 506, 150–152 (2014)

    ADS  CAS  Google Scholar 

Download references

Acknowledgements

This work received support from the following sources: the European Union-funded FP6 Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) (LSHM-CT- 2007-037286), the FP7 projects IMAGEMEND (602450; IMAging GEnetics for MENtal Disorders) and MATRICS (603016), the Innovative Medicine Initiative Project EU-AIMS (115300-2), a Medical Research Council Programme Grant “Developmental pathways into adolescent substance abuse” (93558), the Swedish funding agency FORMAS, the Medical Research Council and the Wellcome Trust (Behavioural and Clinical Neuroscience Institute, University of Cambridge), the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, the Bundesministerium für Bildung und Forschung (BMBF grants 01GS08152; 01EV0711; eMED SysAlc 01ZX1311A; Forschungsnetz AERIAL), the Deutsche Forschungsgemeinschaft (DFG): Reinhart-Koselleck Award SP 383/5-1 and grants SM 80/7-1, SFB 940/1, FOR 1617), the French MILDT (Mission Interministérielle de Lutte contre la Drogue et la Toxicomanie), the CENIR (Centre de NeuroImagerie de Recherche, Pr. S. Lehéricy) within the ICM institute, the National Institute of Mental Health (MH082116), a National Institutes of Health Center of Biomedical Research Excellence award P20GM103644 from the National Institute of General Medical Sciences and the Tobacco Centers of Regulatory Science award P50DA036114. The authors acknowledge the Vermont Advanced Computing Core which is supported by NASA (NNX 06AC88G), at the University of Vermont for providing high performance computing resources that have contributed to the research results reported within this paper.

Author information

Authors and Affiliations

  1. Department of Psychiatry, University of Vermont, Burlington, 05401, Vermont, USA

    Robert Whelan, Nick Ortiz & Hugh Garavan

  2. Department of Psychology, University College Dublin, Dublin 4, Ireland,

    Robert Whelan

  3. Department of Radiology, University of Vermont, Burlington, 05401, Vermont, USA

    Richard Watts

  4. Vermont Center for Children, Youth, and Families, University of Vermont, Burlington, 05401, Vermont, USA

    Catherine A. Orr

  5. Department of Pediatrics, University of Vermont, Burlington, 05401, Vermont, USA

    Robert R. Althoff

  6. Department of Psychology, University of Vermont, Burlington, 05401, Vermont, USA

    Robert R. Althoff & Hugh Garavan

  7. Institut National de la Santé et de la Recherche Médicale, INSERM CEA Unit 1000 “Imaging & Psychiatry”, University Paris Sud, 91400 Orsay, France,

    Eric Artiges & Jean-Luc Martinot

  8. Department of Psychiatry, Orsay Hospital, 4 place du General Leclerc, 91400 Orsay, France,

    Eric Artiges

  9. Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany,

    Tobias Banaschewski, Herta Flor, Mira Fauth-Bühler, Karl Mann, Frauke Nees & Marcella Rietschel

  10. Institute of Psychiatry, King’s College London, London SE5 8AF, UK,

    Gareth J. Barker, Fabiana M. Carvalho, Patricia J. Conrod & Gunter Schumann

  11. Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland,

    Arun L. W. Bokde & Hugh Garavan

  12. Department of Systems Neuroscience, Universitätsklinikum Hamburg Eppendorf, 20246 Hamburg, Germany,

    Christian Büchel & Juergen Gallinat

  13. Department of Psychology, Stanford University, Stanford, 94305, California, USA

    Christian Büchel

  14. Department of Psychiatry, Université de Montreal, CHU Ste Justine Hospital, Montreal H3T 1C5, Canada,

    Patricia J. Conrod

  15. Department of Addictive Behaviour and Addiction Medicine, Heidelberg University, 68159 Mannheim, Germany,

    Mira Fauth-Bühler

  16. 14 CEA, DSV, I2BM, Neurospin bat 145, 91191 Gif-Sur-Yvette, France,

    Vincent Frouin

  17. Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité–Universitätsmedizin Berlin 10117, Germany,

    Juergen Gallinat, Andreas Heinz, Marie-Laure Paillère-Martinot & Andreas Ströhle

  18. Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, 01062 Dresden, Germany,

    Gabriela Gan & Michael N. Smolka

  19. School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK,

    Penny Gowland

  20. Physikalisch-Technische Bundesanstalt (PTB), 10587 Berlin, Germany,

    Bernd Ittermann

  21. School of Psychology, University of Nottingham, Nottingham NG7 2RD, UK,

    Claire Lawrence

  22. AP-HP Department of Adolescent Psychopathology and Medicine, Maison de Solenn, University Paris Descartes, 75006 Paris, France,

    Jean-Luc Martinot & Marie-Laure Paillère-Martinot

  23. Neuroscience Graduate Program, University of Vermont, Burlington, 05401, Vermont, USA

    Nick Ortiz

  24. Rotman Research Institute, University of Toronto, Toronto, Ontario M5R 0A3, Canada,

    Tomas Paus

  25. Montreal Neurological Institute, McGill University, H3A 2B4, Canada,

    Tomas Paus

  26. The Hospital for Sick Children, University of Toronto, Toronto, Ontario M5G 0A4, Canada,

    Zdenka Pausova

  27. Behavioural and Clinical Neuroscience Institute and Department of Psychology, University of Cambridge, Cambridge CB2 1TN, UK

    Trevor W. Robbins

  28. MRC Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, London WC2R 2LS, UK,

    Gunter Schumann

  29. Campus Charité Mitte, Charité–Universitätsmedizin, Berlin 10117, Germany.,

    Lisa Albrecht, Katharina Czech, Nikolay Ivanov, Katharina Lüdemann, Michael Rapp, Alexander Romanowski & Nicole Strache

  30. University of Cambridge, Cambridge CB2 1TN, UK.,

    Mercedes Arroyo & Jeffrey Dalley

  31. Physikalisch-Technische Bundesanstalt (PTB), 10587 Berlin, Germany.,

    Semiha Aydin, Ruediger Bruehl, Albrecht Ihlenfeld, Florian Schubert & Bernadeta Walaszek

  32. Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany.,

    Christine Bach, Luise Poustka, Christine Schmäl, Wolfgang Sommer, Rainer Spanagel, Sabina Steiner & Maren Struve

  33. Commissariat à l'Energie Atomique, 14 CEA, DSV, I2BM, Neurospin bat 145, 91191 Gif-Sur-Yvette, France.,

    Alexis Barbot & Jean-Baptiste Poline

  34. Institut National de la Santé et de la Recherche Médicale, INSERM CEA Unit 1000 “Imaging & Psychiatry”, University Paris Sud, 91400 Orsay, France.,

    Zuleima Bricaud, Fanny Gollier Briand, Hervé Lemaitre, Ruben Miranda, Jani Pentilla & Helene Vulser

  35. Universitätsklinikum Hamburg Eppendorf, 20246 Hamburg, Germany.,

    Uli Bromberg, Tahmine Fadai, Sophia Schneider & Juliana Yacubian

  36. Institute of Psychiatry, King’s College London, London SE5 8AF, UK.,

    Anna Cattrell, Sylvane Desrivieres, Tianye Jia, Agnes Kepa, Lourdes Martinez-Medina, Charlotte Nymberg, Barbara Ruggeri, David Stacey, Lauren Topper, Helen Werts, Steve Williams & C. Peng Wong

  37. GABO:milliarium mbH & Co. KG 80333 Munich, Germany.,

    Birgit Fuchs

  38. University of Nottingham, Nottingham NG7 2RD, UK.,

    Kay Head & Nadja Heym

  39. Deutsches Referenzzentrum für Ethik, D 53113 Bonn, Germany.,

    Bert Heinrichs & Dirk Lanzerath

  40. Technische Universität Dresden, 01062 Dresden, Germany.,

    Thomas Hübner, Kathrin Müller, Stephan Ripke, Sarah Rodehacke, Dirk Schmidt & Veronika Ziesch.

  41. Delosis, Twickenham, Middlesex TW1 4AE, UK.,

    James Ireland & John Rogers

  42. Trinity College Dublin, Dublin 2, Ireland.,

    Jennifer Jones

  43. Centre National de Génotypage, 91057 Evry Cedex, France.,

    Mark Lathrop

  44. PERTIMM, 92600 Asnières-Sur-Seine, France.,

    Xavier Mignon

  45. Tembit Software GmbH, 13507 Berlin, Germany.,

    Markus Schroeder

  46. University of Sussex, Brighton BN1 9RH, UK.,

    Dai Stephens

  47. University of Toronto, Toronto, Ontario M5G 0A4, Canada.,

    Amir Tahmasebi

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  1. Robert Whelan
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  2. Richard Watts
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  3. Catherine A. Orr
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  4. Robert R. Althoff
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  5. Eric Artiges
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  6. Tobias Banaschewski
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  9. Christian Büchel
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  11. Patricia J. Conrod
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  12. Herta Flor
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  13. Mira Fauth-Bühler
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  14. Vincent Frouin
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  15. Juergen Gallinat
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  16. Gabriela Gan
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  17. Penny Gowland
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  18. Andreas Heinz
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  19. Bernd Ittermann
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  20. Claire Lawrence
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  21. Karl Mann
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  22. Jean-Luc Martinot
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  23. Frauke Nees
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  24. Nick Ortiz
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  25. Marie-Laure Paillère-Martinot
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  26. Tomas Paus
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  28. Marcella Rietschel
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  29. Trevor W. Robbins
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  30. Michael N. Smolka
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  31. Andreas Ströhle
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  32. Gunter Schumann
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  33. Hugh Garavan
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Consortia

the IMAGEN Consortium

  • Lisa Albrecht
  • , Mercedes Arroyo
  • , Semiha Aydin
  • , Christine Bach
  • , Alexis Barbot
  • , Zuleima Bricaud
  • , Uli Bromberg
  • , Ruediger Bruehl
  • , Anna Cattrell
  • , Katharina Czech
  • , Jeffrey Dalley
  • , Sylvane Desrivieres
  • , Tahmine Fadai
  • , Birgit Fuchs
  • , Fanny Gollier Briand
  • , Kay Head
  • , Bert Heinrichs
  • , Nadja Heym
  • , Thomas Hübner
  • , Albrecht Ihlenfeld
  • , James Ireland
  • , Nikolay Ivanov
  • , Tianye Jia
  • , Jennifer Jones
  • , Agnes Kepa
  • , Dirk Lanzerath
  • , Mark Lathrop
  • , Hervé Lemaitre
  • , Katharina Lüdemann
  • , Lourdes Martinez-Medina
  • , Xavier Mignon
  • , Ruben Miranda
  • , Kathrin Müller
  • , Charlotte Nymberg
  • , Jani Pentilla
  • , Jean-Baptiste Poline
  • , Luise Poustka
  • , Michael Rapp
  • , Stephan Ripke
  • , Sarah Rodehacke
  • , John Rogers
  • , Alexander Romanowski
  • , Barbara Ruggeri
  • , Christine Schmäl
  • , Dirk Schmidt
  • , Sophia Schneider
  • , Markus Schroeder
  • , Florian Schubert
  • , Wolfgang Sommer
  • , Rainer Spanagel
  • , David Stacey
  • , Sabina Steiner
  • , Dai Stephens
  • , Nicole Strache
  • , Maren Struve
  • , Amir Tahmasebi
  • , Lauren Topper
  • , Helene Vulser
  • , Bernadeta Walaszek
  • , Helen Werts
  • , Steve Williams
  • , C. Peng Wong
  • , Juliana Yacubian
  •  & Veronika Ziesch.

Contributions

T.B., G.J.B., A.L.W.B., C.B., F.M.C., P.J.C., H.F., M.F.-B., J.G., H.G., P.G., A.H., B.I., K.M., J.-L.M., F.N., T.P., M.R., C.L., Z.P., M.-L.P.-M., M.N.S., A.S., M.R. and T.W.R. acquired the data. R. Whelan., H.G., C.A.O. and N.O. analysed the behavioural data. G.G. calculated the family history data. R. Whelan, R. Watts and E.A. carried out neuroimaging data processing and analysis. R.R.A., V.F. and G.S. carried out genotyping and genetic analysis. R. Whelan and H.G. prepared the manuscript. C.A.O, P.J.C., J.G., T.P., T.W.R. and G.S. edited the manuscript.

Corresponding authors

Correspondence to Robert Whelan or Hugh Garavan.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 A schematic of the analysis protocol.

A schematic of the analysis protocol showing the inner cross-validation loop (to optimize the imaging parameters), the middle cross-validation loop (to optimize the elastic net parameters) and the outer loop (to quantify the generalizability). An external validation was also performed to quantify generalizability to a slightly different phenotype. The percentage of the sample used in each step is also displayed. AUC, area under the receiver-operating characteristic curve.

Extended Data Figure 2 Receiver-operating characteristics (ROC), precision-recall (PR) curves.

a, ROC of age-14 binge drinking classification, with age-14 nicotine included (Analysis 1). b, PR of age-14 binge drinking classification, with age-14 nicotine included (Analysis 1). c, ROC of age-14 binge drinking external generalization, with age-14 nicotine included (Analysis 6). AUC = 0.68, 95% CI = 0.59–0.76). At the optimum point in the AUC curve, 93% of binge drinkers and 34% of non-binge drinkers were correctly classified, significantly better than chance (P = 0.035), given a base rate of 24% non-binge drinkers. d, PR of age-14 binge drinking external generalization, with age-14 nicotine included (Analysis 6). At the maximum F-score value, this corresponds to a precision rate of 47% and a recall rate of 54%. e, ROC of age-14 binge drinking classification, with age-14 nicotine excluded (Analysis 2). f, PR of age-14 binge drinking classification, with age-14 nicotine excluded (Analysis 2). g, ROC of age-14 binge drinking external generalization, with age-14 nicotine excluded (Analysis 7). AUC = 0.80, 95% CI 0.73–0.85. At the optimum point in the AUC curve, 95% of binge drinkers and 34% of non-binge drinkers were correctly classified, significantly better than chance (P = 0.016), given a base rate of 24% non-binge drinkers. h, PR of age-14 binge drinking external generalization, with age-14 nicotine excluded (Analysis 7). At the maximum F-score value, this corresponds to a precision rate of 56% and a recall rate of 57%. i, ROC of age-16 binge drinking classification (Analysis 8). j, PR of age-16 binge drinking classification (Analysis 8). k, ROC of age-14 binge drinking external generalization (Analysis 11). l, PR of 14-year-old binge drinking external generalization (Analysis 11). AUC, area under the curve. CI, confidence interval.

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Extended Data Figure 3 Brain images showing regions that classify binge drinkers at age 14.

The bar charts show the contribution of each brain metric to the shown clusters. The bar is the average beta weight for each brain metric (normalized to sum to 1 and averaged over the ten outer folds). a, b, Binge drinkers had reduced activity levels in the left putamen and left hippocampus when anticipating a reward (a) and reduced activity in the right hippocampus when rewards were received (b). c–e, Binge drinkers had greater activity in the right precentral and left postcentral gyri (c) when failing to inhibit a response and had greater activity in left and right precuneus (d) when they were successful in inhibiting. When processing angry faces, binge drinkers showed reduced right temporal pole and right cuneus activity (e). f, Binge drinkers had reduced grey matter volume in bilateral ventromedial prefrontal cortex, right inferior and left middle frontal gyri, but increased volume in the right putamen.

Extended Data Figure 4 Classification accuracy for each individual domain and the effects of removing each domain on the classification accuracy.

The y-axis represents the area under the receiver-operating characteristic curve and the error bars represent the 95% confidence intervals (calculated via 10,000 bootstraps). a, The classification accuracy of age-14 binge drinking for each domain separately (Analysis 3). b, the effects of removing each domain on the classification accuracy of age-14 binge drinking (nicotine included in the model; Analysis 4). c, the effects of removing each domain on the classification accuracy of age-14 binge drinking (nicotine excluded from the model; Analysis 5). d, The classification accuracy of age-16 binge drinking for each domain separately (Analysis 9). e, the effects of removing each domain on the classification accuracy of age-16 binge drinking (Analysis 10).

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Extended Data Figure 5 Correlations among the features classifying age-14 binge drinking.

Significant correlations among the selected features (Analysis 2) are displayed (Spearman non-parametric test; P < 0.05). The colour bar denotes the correlation coefficient. GMV, grey matter volume; WMV, white matter volume; SWM, spatial working memory; AGN, affective go/no go; hx, history.

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Extended Data Figure 6 The brain images show regions that predict binge drinking at age 16 from data collected at age 14.

The bar charts show the contribution of each brain metric to the prediction accuracy of the shown clusters, which were derived from the training data. a, b, Future binge drinkers had reduced activation during reward anticipation in occipito-temporal and posterior cingulate regions (a) and for reward outcomes had reduced activity in the left temporal pole but increased activity in bilateral superior frontal gyrus (b). c, When failing to inhibit a motor response, future binge drinkers showed greater activity in the right middle, medial and precentral gyri and in the left postcentral and middle frontal gyri. d, e, Future binge drinkers showed reduced activity in the left middle frontal gyrus when processing angry faces (d) and also had reduced grey matter volume in the right parahippocampal gyrus but increased grey matter volumes in the left postcentral gyrus (e).

Extended Data Figure 7 Correlations among the features predicting age-16 binge drinking.

Significant correlations among the selected features are displayed (Spearman non-parametric test; P < 0.05). The colour bar denotes the correlation coefficient. GMV, grey matter volume; WMV, white matter volume; SWM, spatial working memory; AGN, affective go/no go; hx, history.

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Extended Data Table 1 A list of all features that were used
Full size table
Extended Data Table 2 Subject characteristics and percentage of imputed data for each group
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Extended Data Table 3 Beta weights for classification of age-14 binge drinking and prediction of age-16 binge drinking
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Whelan, R., Watts, R., Orr, C. et al. Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature 512, 185–189 (2014). https://doi.org/10.1038/nature13402

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