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Predicting development of adolescent drinking behaviour from whole brain structure at 14 years of age

  1. Simone Kühn  Is a corresponding author
  2. Anna Mascharek
  3. Tobias Banaschewski
  4. Arun Bodke
  5. Uli Bromberg
  6. Christian Büchel
  7. Erin Burke Quinlan
  8. Sylvane Desrivieres
  9. Herta Flor
  10. Antoine Grigis
  11. Hugh Garavan
  12. Penny A Gowland
  13. Andreas Heinz
  14. Bernd Ittermann
  15. Jean-Luc Martinot
  16. Frauke Nees
  17. Dimitri Papadopoulos Orfanos
  18. Tomas Paus
  19. Luise Poustka
  20. Sabina Millenet
  21. Juliane H Fröhner
  22. Michael N Smolka
  23. Henrik Walter
  24. Robert Whelan
  25. Gunter Schumann
  26. Ulman Lindenberger
  27. Jürgen Gallinat
  28. IMAGEN Consortium
  1. University Medical Center Hamburg-Eppendorf, Germany
  2. Max Planck Institute for Human Development, Germany
  3. Heidelberg University, Germany
  4. Trinity College Dublin, Ireland
  5. University Medical Centre Hamburg-Eppendorf, Germany
  6. King’s College London, United Kingdom
  7. University of Mannheim, Germany
  8. Université Paris-Saclay, France
  9. University of Vermont, United States
  10. University of Nottingham, United Kingdom
  11. Charité – Universitätsmedizin Berlin, Germany
  12. Physikalisch-Technische Bundesanstalt (PTB), Germany
  13. Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University ParisSud, University Paris Descartes, France
  14. Holland Bloorview Kids Rehabilitation Hospital, Canada
  15. University of Toronto, Canada
  16. University Medical Centre Göttingen, Germany
  17. Technische Universität Dresden, Germany
Research Article
Cite this article as: eLife 2019;8:e44056 doi: 10.7554/eLife.44056
4 figures, 2 tables and 1 additional file

Figures

Preparation of AUDIT Sum-Scores for two-part latent growth mixture model.

Upper row: data from original scale (Sum Score), zeros are shown in black and indicate non-drinking individuals. Middle row: Transformation of data into consumer and non-consumer without fine-grading of alcohol use scores. Bottom row: Alcohol use score (AUDIT Sum-Score) for individuals who drink at all. Note that to enhance readability of the figure, sum-scales (upper and bottom row) are truncated at a score of 20.

https://doi.org/10.7554/eLife.44056.003
Two-part latent growth mixture model.

c = continuous, d = discrete, BL = baseline, FU = follow up, I = intercept, S = slope, TBV = total brain volume, MRI site was not a single indicator as depicted for reasons of simplicity, but consisted of 9–1 separate indicators dummy coding the different scanners used.

https://doi.org/10.7554/eLife.44056.006
Brain regions showing a significant regression path from brain voxel to the latent slope of alcohol use score increase over time.

The higher the grey matter volume the larger the slope increase.

https://doi.org/10.7554/eLife.44056.007
Author response image 1

Tables

Table 1
Severity of alcohol use at three measurement occasions according to AUDIT.*
https://doi.org/10.7554/eLife.44056.004
Baseline
n** = 1794 (100%)
Follow-up 1
n = 1439 (100%)
Follow-up 2
n = 1284 (100%)
No use at all855 (47.7%)255 (17.7%)95 (7.4%)
Unproblematic use872 (48.7%)961 (66.7%)823 (64.1%)
Medium level of alcohol problems64 (3.6%)218 (15.3%)329 (10.7%)
High level of alcohol problems2 (0.1%)4 (0.3%)28 (2.2%)
Indicating dependence1 (0.1%)1 (0.1%)9 (0.9%)
  1. *Note: Categorization is based on the interpretation guideline of the World Health Organization: Cut-offs scores are: 0–7 = unproblematic use, 8–15: simple advice focused on the reduction of hazardous drinking warranted, 16–19: brief counseling and continued monitoring warranted, above 20: further diagnostic for alcohol dependence strongly warranted.

    **Note: 20 individuals had missing data, in total adding up to 1814.

Table 2
Estimated parameters in probability of use vs. non-use and alcohol use score with nuisance variables on the clinical data (not yet including brain data)
https://doi.org/10.7554/eLife.44056.005
InterceptSlope
EstimateSEEstimateSE
Part 1: Prevalence of alcohol drinking (use vs. non-use)=discrete part of the model
Mean0.568**0.0110.188**0.006
Variance0.090**0.0090.024**0.004
Part 2: Alcohol use score of AUDIT = continuous part of the model
Mean0.693**0.0370.4980.642
Variance0.618**0.0870.218**0.046
Regression onto Part two slope
Sex−0.183**0.046
Age−0.0000.000
TBV0.000*0.000
Site_London0.410*0.163
Site_Nottingham0.368*0.161
Site_Dublin0.517*0.167
Site_Berlin0.0910.170
Site_Hamburg0.1220.162
Site_Mannheim0.0380.163
Site_Paris0.0790.163
Site_Dresden−0.0440.163
Covariances
Covariance between intercept and slope in Part 1−0.033**0.005
Covariance between intercept and slope in Part 2−0.0780.050
Covariance between the intercepts of Part 1 and Part 20.124**0.012
  1. *p < 0.05 , **p<0.001, SE = standard error, TBV = total brain volume

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