1. Computational and Systems Biology
  2. Neuroscience

Structural differences in adolescent brains can predict alcohol misuse

  1. Roshan Prakash Rane  Is a corresponding author
  2. Evert Ferdinand de Man
  3. JiHoon Kim
  4. Kai Görgen
  5. Mira Tschorn
  6. Michael A Rapp
  7. Tobias Banaschewski
  8. Arun LW Bokde
  9. Sylvane Desrivieres
  10. Herta Flor
  11. Antoine Grigis
  12. Hugh Garavan
  13. Penny A Gowland
  14. Rüdiger Brühl
  15. Jean-Luc Martinot
  16. Marie-Laure Paillere Martinot
  17. Eric Artiges
  18. Frauke Nees
  19. Dimitri Papadopoulos Orfanos
  20. Herve Lemaitre
  21. Tomas Paus
  22. Luise Poustka
  23. Juliane Fröhner
  24. Lauren Robinson
  25. Michael N Smolka
  26. Jeanne Winterer
  27. Robert Whelan
  28. Gunter Schumann
  29. Henrik Walter
  30. Andreas Heinz
  31. Kerstin Ritter
  32. IMAGEN consortium
  1. Charité – Universitätsmedizin Berlin (corporate member of Freie Universiät at Berlin, Humboldt-Universiät at zu Berlin, and Berlin Institute of Health), Department of Psychiatry and Psychotherapy, Bernstein Center for Computational Neuroscience, Germany
  2. Faculty IV – Electrical Engineering and Computer Science, Technische Universität Berlin, Germany
  3. Department of Education and Psychology, Freie Universität Berlin, Germany
  4. Science of Intelligence, Research Cluster of Excellence, Germany
  5. Social and Preventive Medicine, Department of Sports and Health Sciences, Intra-faculty unit “Cognitive Sciences”, Faculty of Human Science, and Faculty of Health Sciences Brandenburg, Research Area Services Research and e-Health, University of Potsdam, Germany
  6. Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Germany
  7. Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Ireland
  8. Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology Neuroscience SGDP Centre, King’s College London, United Kingdom
  9. Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Germany
  10. Department of Psychology, School of Social Sciences, University of Mannheim, Germany
  11. NeuroSpin, CEA, Université Paris-Saclay, France
  12. Departments of Psychiatry and Psychology, University of Vermont, United States
  13. Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, United Kingdom
  14. Physikalisch-Technische Bundesanstalt, Germany
  15. Institut National de la Santé et de la Recherche Médicale, INSERM U A10 ”Trajectoires développementales en psychiatrie” Universite Paris-Saclay, Ecole Normale Supérieure Paris-Saclay, CNRS, Centre Borelli, France
  16. AP-HP Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, France
  17. Psychiatry Department, EPS Barthélémy Durand, France
  18. PONS Research Group, Dept of Psychiatry and Psychotherapy, Campus Charite Mitte, Humboldt University, Germany
  19. Institut des Maladies Neurodégénératives, UMR 5293, CNRS, CEA, University of Bordeaux, France
  20. Department of Psychiatry, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Canada
  21. Departments of Psychiatry and Psychology, University of Toronto, Canada
  22. Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, Germany
  23. Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Germany
  24. Department of Psychological Medicine, Section for Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
  25. School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
https://doi.org/10.7554/eLife.77545
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Cite this article
  1. Roshan Prakash Rane
  2. Evert Ferdinand de Man
  3. JiHoon Kim
  4. Kai Görgen
  5. Mira Tschorn
  6. Michael A Rapp
  7. Tobias Banaschewski
  8. Arun LW Bokde
  9. Sylvane Desrivieres
  10. Herta Flor
  11. Antoine Grigis
  12. Hugh Garavan
  13. Penny A Gowland
  14. Rüdiger Brühl
  15. Jean-Luc Martinot
  16. Marie-Laure Paillere Martinot
  17. Eric Artiges
  18. Frauke Nees
  19. Dimitri Papadopoulos Orfanos
  20. Herve Lemaitre
  21. Tomas Paus
  22. Luise Poustka
  23. Juliane Fröhner
  24. Lauren Robinson
  25. Michael N Smolka
  26. Jeanne Winterer
  27. Robert Whelan
  28. Gunter Schumann
  29. Henrik Walter
  30. Andreas Heinz
  31. Kerstin Ritter
  32. IMAGEN consortium
(2022)
Structural differences in adolescent brains can predict alcohol misuse
eLife 11:e77545.
https://doi.org/10.7554/eLife.77545
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13 figures, 5 tables and 1 additional file

Figures

Figure 1
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An overview of the analysis performed.

Morphometric features extracted from structural brain imaging are used to predict Adolescent Alcohol Misuse (AAM) developed by the age of 22 using machine learning. To understand the causal relationship between AAM and the brain, three separate analyses are performed by using imaging data collected at three stages of adolescence: age 14, age 19, and age 22.

Figure 2
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Comparing confound correction techniques.

Five input-output settings are compared within each confound correction technique: X→y, X→cs⁢e⁢x, X→cs⁢i⁢t⁢e, cs⁢e⁢x→y, and cs⁢i⁢t⁢e→y. (a) shows the results before any correction is performed, (b) shows the results of performing confound regression, and (c) and (d) show the results from counterbalancing by undersampling the majority class and oversampling the minority class, respectively. Statistical significance is obtained from 1,000 permutation tests and is shown with ** if p<0.01, * if p<0.05, and ‘n.s’ if p≥0.05.

Figure 3 with 1 supplement
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Results of the ML exploration experiments: The ten phenotypes of AAM tested are listed on the y-axis and the four ML models are represented with different color coding as shown in the legend of figure (a).

For a given AAM label and ML model, the point represents the mean balanced accuracy across the 7-fold CV and the bars represent its standard deviation. Figure (a) shows the results when the imaging data from age 22 (FU3) is used, figure (b) shows results for age 19 (FU2) and figure (c) for age 14. Figure (d) shows the results from all three time point analyses in a single plot along with the interval of the balanced accuracy that were non-significant (p≤0.05) when tested with permutation tests.

Figure 3—figure supplement 1
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ML exploration results shown with AUC-ROC metric.
Figure 4 with 1 supplement
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Final results for the three time point analyses on the ‘Binge’ drinking AAM phenotype obtained with the two non-linear ML models, kernel-based support vector machine (SVM-rbf) and gradient boosting (GB).

The figure shows the mean balanced accuracy achieved by each ML model within each analysis while the table lists the combined average scores for each analysis. The ML models are retrained seven times on data e⁢x⁢p⁢l⁢o⁢r⁢e with different random seeds and evaluated on data h⁢o⁢l⁢d⁢o⁢u⁢t to obtain an estimate of the accuracy with a standard deviation. Statistical significance is obtained from 1000 permutation tests and is shown with ** if p<0.01, * if p<0.05, and ‘n.s’ if p≥0.05.

Figure 4—figure supplement 1
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Visualization of the permutation test results.
Figure 5
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Most informative structural features for SVM-rbf model’s predictions on data h⁢o⁢l⁢d⁢o⁢u⁢t.

Most important features are listed and their locations are shown on a template brain for a better intuition for each of the three time point analyses. The features are color coded to also display whether these features have lower-than-average or higher-than-average values when the model predicts alcohol misusers. This figure is only illustrative and an exhaustive list of all informative features with their corresponding SHAP values are given in the Appendix 1—table 3. (Acronyms:: AAM: adolescence alcohol misuse, area: surface area, volume: gray matter volume, thickness: average thickness, thicknessstd: standard deviation of thickness, intensity: mean intensity, meancurv: integrated rectified mean curvature, gauscurv: integrated rectified gaussian curvature, curvind: intrinsic curvature index).

Figure 6
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Analysis repeated with leave-one-site-out cross validations (CV).
Figure 7
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The IMAGEN dataset: (a) Data is collected longitudinally at 4 stages of adolescence - age 14 or baseline (BL), age 16 or follow-up 1 (FU1), age 19 or follow-up 2 (FU2) and, finally age 22 or follow-up 3 (FU3).

The blue bar shows the number of subjects with brain imaging data. (b) The distribution of subjects across sex and the site of recruitment, for the 1182 subjects that were scanned at FU3 (c) The same distribution across sex and site also showing the proportion of subjects that meet the AUDIT ’risky drinkers’ category at FU3.

Figure 8
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A schematic representation of the experimental procedure followed for all 3 time point analyses.

In the ML exploration stage, we experiment with four ML models and 10 phenotypes of AAM on 80% of the data (data e⁢x⁢p⁢l⁢o⁢r⁢e) using a sevenfold cross-validation scheme. Once the best ML model, the best phenotype of AAM, and the most appropriate confound-control technique are determined, the generalization test is performed on data i⁢n⁢f⁢e⁢r by using the data h⁢o⁢l⁢d⁢o⁢u⁢t subset as the test data. The result from the generalization test are reported as the final results and the informative brain features are determined at this stage using SHAP (Lundberg and Lee, 2017).

Appendix 1—figure 1
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Visualizing AAM phenotype categorization: A qualitative comparison showing how the ten AAM phenotypes categorize the same subjects into the three alcohol user classes – risky alcohol users, moderate users, and safe or non-users.

Each color-coded vertical line in the diagram represents one subject, out of the total 1182 subjects. It can be observed that the Frequency, Onset, and Amount phenotypes categorize very differently from Binge, showing that they capture different factors of alcohol misuse. All AUDIT-derived phenotypes are similar to each other but are different from the Binge phenotype. Furthermore, sex and site-specific variations can be detected. For instance, more males appear on the ‘risky’ groups compared to females. Similarly, most subjects from Dublin are clustered on the risky side.

Appendix 1—figure 2
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ML exploration results per site: Accuracy of the non-linear models per site in the main experiments.

The sites are ordered from low to high accuracy.

Appendix 1—figure 3
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ML exploration results per site in leave-one-site-out: Accuracy of the non-linear models per site in the leave-one-site-out.

The sites are ordered from low to high accuracy.

Author response image 1
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Author response image 2
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Tables

Table 1
Literature review of studies that look into structural brain differences between adolescent alcohol misusers (AAMs) and control subjects.

The studies are sorted by the year of publication. For each study, the sample size ‘n’, the main analysis technique, and the main structural differences found in AAMs are listed.

Study (year)nAnalysis / methodSructural differences in AAMs
De Bellis et al., 200036Statistically compare (univariate)regional brain volumes between groupsLower hippocampal volume.
Nagel et al., 200531Statistically compare (univariate)regional brain volumes between groupsLower volume only in left hippocampus aftercontrolling for other psychiatric comorbidities.
De Bellis et al., 200542Statistically compare (univariate)regional brain volumes between groupsLower pFC, cerebellum volumes in malesbut AAMs had comorbid mental disorders.
McQueeny et al., 200928Mass-univariate analysis ofskeletonized FA voxels (DTI)Binge drinkers had lower FA in18 white matter areas.
Squeglia et al., 201259Statistically compare (univariate) regional brain volumes between groupsNo effect of binge drinking oncortical thickness and sex-specificdifferences among AAMs in left frontal cortex.
Jacobus et al., 201354Mass-univariate analysis of skeletonized FA voxels (DTI)No effect in AAM-only group, but lowerFA in AAM and comorbid marijuana users.
Luciana et al., 201355Longitudinal mass-univariate analysis of cortical thickness, white matter extent, DTI-extracted FA and MDAccelerated GM thinning in mid frontal gyrus, attenuated WM growth with lower FAin left caudate, thalamus.
Whelan et al., 2014692Exploratory analysis using ML to find best predictors of AAM amongdemographic, psychosocial, genetic, cortical volumes, and fMRI variablesCurrent AAMs have lower GMVs in parts of frontal lobe and higher GMV in right putamen. Future AAMs have lower GMV in right parahippocampal gyrus and higher in left postcentral gyrus.
Squeglia et al., 2015137Exploratory analysis using ML to find best predictors of AAM among demographic, neuropsychological, cortical thickness, and fMRI variablesFuture AAM have thinner GM inprecuneus, lateral occipital, ACC, PCC, and frontal and temporal cortex.
Pfefferbaum et al., 2018483Longitudinal mass-univariate analysisof GMV developmentAccelerated GMV reduction in frontal brain regions.
Jones and Nagel, 2019113Modeling the WM microstructure development (DTI) for each voxelAltered frontostriatal WM microstructureis predictive of future AAM.
Kühn et al., 2019≈1500Growth curve modeling ofGM volumesHigher GMV in caudate nucleus and left cerebellum predicts future AAMs
Seo et al., 2019≈1000ML analysis of cue-related brain region followed by mass-univariate analysis for identifying region importanceCurrent AAMs show reduced GMV inmedial-pFC, oFC, thalamus, bilateral ACC,left amygdala and anterior insular.
Sullivan et al., 2020548Longitudinal mass-univariate (GLM)analysis of cerebellar region volumesCerebellum: accelerated GM decline in 2 sub-regions and accelerated expansion ofWM in one sub-region and CSF.
Robert et al., 2020726Mass-univariate analyses of voxels, followed by analysis of the direction of causality using causal bayesian networksAccelerated GM atrophy in parts of the temporal cortex and left prefrontal cortex.
Filippi et al., 2021671ML analysis for predictors ofresilence towards polysubstance useAdolescents resilient to PSU show larger GMV in the bilateral cingulate gyrus.

  1. Acronyms::: GM:grey matter; WM:white matter; CSF-cerebrospinal fluid; GMV:grey matter volume; pFC:prefrontal Cortex; oFC:orbitofrontal cortex; ACC:anterior cingulate cortex; PCC:posterior cingulate cortex; GLM:generalized linear models; ML:machine learning; DTI:Diffusion Tensor Imaging; FA:Fractional Anisotropy; MD:mean diffusivity.

Table 2
10 phenotypes of Adolescent Alcohol Misuse (AAM) are derived and compared in this analysis.

A description of each phenotype is provided here along with the link to the IMAGEN questionnaires ID used to generate the phenotype.

No.PhenotypeDescriptionQuestionnaire
1FrequencyNumber of occasions drinking alcohol in last 12 monthsESPAD 8b.
2AmountNumber of alcohol drinks consumed on atypical drinking occasionESPAD prev31,AUDIT q2.
3OnsetHad one or more binge-drinking experiences by the age of 14ESPAD 29d
4BingeTotal drunk episodes from binge-drinking in lifetime (by age 22)ESPAD 19a,AUDIT q3.
5Binge-growthLongitudinal trajectory of binge-drinking experiences had per yearGrowth curveof ESPAD 19b.
6AUDITAUDIT screening test performed at the year of scanAUDIT-total (q1-10).
7AUDIT-quickOnly the first 3 questions of AUDIT screening testAUDIT-freq (q1-3).
8AUDIT-growthLongitudinal changes in the AUDIT score measured over the yearsGrowth curve ofAUDIT-total.
9Combined-seoA combined risky-drinking phenotype from Seo et al., 2019 generated using amount, frequency, and binge-drinking dataESPAD 8b, 17b, 19b,and TLFB alcohol2
10Combined-oursA combined risky-drinking phenotype developed by clusteringamount, frequency, and binge-drinking trajectoryAUDIT q1, q2,ESPAD 19a, growthcurve of ESPAD 19b.
Appendix 1—table 1
Hyperparameters: Each machine learning (ML) model has a set of hyperparameters that are tuned using an inner 5-fold cross-validation during the ML exploration stage.

For both C and, γ higher values lead to overfitting and lower values can lead to underfitting. For gradient boosting, the maximum depth of the trees is set at, 5 the maximum numbers of estimators at, 100 and the subsampling of input features is disabled as counterbalancing is used. The remaining parameters are set at the default values as defined in the scikit-learn python package.

Modelhyperparametervalues tested
Logistic regressionC: Inverse of L2 regularization strength1000, 100, 1.0, 0.001
Linear support vector machineC: Inverse of L2 regularization strength1000, 100, 1.0, 0.001
Kernel-basedsupport vector machineC: Inverse of L2 regularization strengthγ: kernel coefficient of RBF kernel1000, 100, 1.0, 0.001’auto’, ’scale’
Gradient boostinglearning_rate0.05, 0.25
Appendix 1—table 2
AAM phenotype categorization: The table explains how the ten AAM phenotypes are derived from the respective IMAGEN questionnaire.

It lists the total values in that question and what range of values are used to categorize the subjects into safe users, moderate users and heavy users, respectively. For reference, the sample sizes (n) obtained at FU3 by using these value ranges are also shown in the brackets.

PhenotypeIMAGEN questionnaireTotalrangeSafe users range (n)Moderate misusersrange (n)Heavy misusers range (n)
FrequencyESPAD 8b0-60-4 (397)5 (270)6 (372)
AmountAUDIT q20-40 (413)1 (403)2-4 (219)
OnsetESPAD 29d11-2116-21 (531)14-15 (288)11-14 (216)
BingeESPAD 19a0-60-3 (299)4-5 (336)6 (400)
Binge-growthGrowth curveof ESPAD 19b0-90-2 (379)3-5 (420)6-9 (236)
AUDITAUDIT-total0-400-4 (443)5-7 (274)8-40 (318)
AUDIT-quickAUDIT-freq0-120-3 (402)4-5 (359)6-12 (274)
AUDIT-growthGrowth curveof AUDIT-total0-60,3 (377)4 (404)2,5,6 (254)
Combined-seoESPAD 8b, 17b, 19b,and TLFB alcohol20-20 (345)1 (404)2 (286)
Combined-oursAUDIT q1, q2,ESPAD 19a, growthcurve of ESPAD 19b0-30 (429)1 (403)2 (203)
Appendix 1—table 3
Most informative sMRI features: An exhaustive list of the ‘most informative’ features in all three time point analyses provided along with their obtained SHAP values across seven repetitions.

SHAP values that didn’t surpass the threshold are shown in italic. (Acronyms: area: surface area, volume: gray matter volume, thickness: average thickness, thicknessstd: standard deviation of thickness, intensity: mean intensity, meancurv: integrated rectified mean curvature, gauscurv: integrated rectified gaussian curvature, curvind: intrinsic curvature index, foldind: folding index;)

Featureavg.avg.SHAP value
ModalityRegionSideNameTypeFeature valueSHAP valuerun1run2run3run4run5run6run7
FU3 (no. features = 21, threshold≥ 0.008743)
DTISplenium of the corpus callosum-0.873530.0144330.0141270.0136670.0151370.0155690.0168920.0144020.0112
T1wCorticalRightLateral occipital cortexThickness-0.6774260.0133780.0128730.0113530.0125390.0143820.0142060.0173040.011
T1wSubcorticalCerebrospinal fluidIntensity0.79030.0132440.0150390.0139020.0143820.0142250.0146670.0092840.0112
T1wCorticalLeftCaudal anterior cingulate cortexFoldind-0.613880.0127210.0113920.0121860.0149310.0145880.0114120.0172840.0073
T1wSubcorticalBrain-StemIntensity-0.594370.0126370.0117840.0103040.0130490.0146570.0175690.0100690.011
T1wSubcorticalRightAmygdalaVolume0.6641470.0125640.0178040.011480.0151370.0120490.0133530.0083240.0098
T1wCorticalRightParahippocampal gyrusArea0.7707220.0125420.0123730.0101370.014490.0157450.0100490.0152650.0097
T1wCorticalRightCuneus cortexThickness-0.6344560.0123730.0142750.0121960.0134610.011980.0100490.0126860.012
T1wSubcorticalRightHippocampusIntensity0.6233550.01220.0154610.0077250.0109410.0127940.0132550.0097650.0155
T1wSubcorticalLeftHippocampusIntensity0.6633950.0119090.0144220.0100490.0113140.0118430.0110980.0120980.0125
T1wSubcorticalLeftChoroid-plexusVolume-0.7616210.0118840.0100590.0093920.0153530.0129220.0136670.0100490.0117
T1wCorticalRightRostral anterior cingulate cortexThickness0.6367690.0117940.0118530.0091080.0154120.0148630.0137750.0094510.0081
T1wSubcorticalAnterior corpus callosumIntensity-0.6127970.011370.0093330.0103730.0128430.0064410.0143140.0174510.0088
T1wCorticalLeftPericalcarine cortexMeancurv-0.7242560.0113640.0126570.0111570.0145390.0104220.013010.0156370.0021
T1wCorticalRightSuperior parietal cortexThickness-0.6305120.0113210.010510.0106080.0112160.0126470.0132450.0138330.0072
T1wCorticalRightParahippocampal gyrusMeancurv-0.7917550.0109130.0116860.0133820.0074710.0117350.0103730.0108430.0109
DTIRightRetrolenticular part of the internal capsule-0.7124370.0107930.0098430.0096670.0127750.0119610.0101370.0103430.0108
T1wCorticalLeftLateral orbitofrontal cortexMeancurv0.6962620.0107610.0119510.010980.0116270.0041670.0111570.015480.01
T1wCorticalLeftRostral anterior cingulate cortexThickness-0.7460050.0106440.0098240.012490.010490.0105880.0122350.0124710.0064
T1wCorticalRightSupramarginal gyrusThickness-0.8089230.0101110.0096270.0055290.0093330.0092450.0124510.0131860.0114
DTILeftHippocampal component of the cingulum-0.7277280.0094510.0109510.009010.0093920.009020.0111670.0073730.0092
FU2 (no. features = 32, threshold≥ 0.009865)
T1wCorticalRightCaudal anterior cingulate cortexCurvind1.5917560.0191670.0189510.0188820.0183240.0183140.0192350.0213040.0192
T1wCorticalLeftCaudal anterior cingulate cortexThicknessstd-0.7460350.017010.018490.0133530.0224410.0170980.0152650.0178040.0146
T1wCorticalLeftCuneus cortexCurvind0.4625890.0161390.0176570.0153530.0152840.0147550.0161180.0181860.0156
T1wCorticalRightPars traingularisThicknessstd-0.817190.0159970.0117550.0133530.0185390.0236470.0220290.0078430.0148
T1wCorticalLeftPericalcarineCurvind0.010160.0159520.0137550.0170590.0126670.0173820.0188630.0159120.016
T1wCorticalRightInferior temporal gyrusThicknessstd-0.8541320.0157460.0146960.0136960.0239220.0153730.0191080.0169510.0065
T1wSubcorticalAnterior corpus callosumIntensity-0.6421570.0153960.0182550.0151370.0151180.0123140.0161370.0175590.0133
T1wCorticalRightCuneus cortexThickness-0.725790.0149550.0101670.0154710.0189510.0177060.0153430.0131180.0139
T1wCorticalLeftPars opercularisVolume-0.6284460.0146970.0163530.0190780.0184310.0129410.018510.0115880.006
DTILeftCorticospinal tract0.7757360.0143360.0138920.0143140.0152650.0159120.0143040.0155490.0111
T1wSubcorticalWhite matterIntensity-0.7547120.014210.0157650.0152550.0101180.0159510.0148530.0159410.0116
T1wCorticalRightFrontal poleThickness0.6196910.0141480.0176470.0132550.0136080.0172750.0126960.0163330.0082
T1wCorticalLeftPars opercularisArea-0.5826270.0141410.0181570.015980.0147450.0132350.0174120.0114710.008
T1wCorticalLeftFrontal poleCurvind0.7327180.0140780.0154120.0164220.0178820.0157250.0104120.0101960.0125
T1wSubcorticalLeftCerebellum cortexIntensity0.6102530.013990.0123040.016520.0162750.0143920.0142350.012510.0117
T1wCorticalRightPrecentral gyrusGauscurv0.2990080.0135560.0141270.0114410.0107250.0133040.0162060.014510.0146
T1wCorticalLeftRostral anterior cingulate cortexThickness-0.9160010.0132680.0103240.0138820.013480.013510.012490.0158920.0133
T1wCorticalLeftCaudal anterior cingulate cortexMeancurv-0.7043520.0132460.0109310.0081470.0207550.0151370.0141960.0118430.0117
T1wSubcorticalBrain-StemIntensity-0.7365920.0132460.01050.0153140.0144710.0141270.0150590.0130.0103
T1wCorticalLeftFusiform gyrusThicknessstd0.7582970.0131780.0129220.0097350.0168530.010.0154710.0130490.0142
T1wCorticalLeftLingual gyrusThicknessstd0.7253560.0130940.0118040.0174610.0068040.0141570.016020.012990.0124
T1wCorticalLeftPars opercularsisMeancurv-0.75110.0130240.0106670.015520.0142350.0133140.0182450.0130980.0061
T1wCorticalLeftInferior temporal gyrusThicknessstd0.731710.0130180.0105290.0101670.0149610.0176270.0113630.0149610.0115
T1wCorticalRightBanks of the superior temporal sulcusMeancurv-0.7668090.0126850.0143140.0118630.0149510.0124610.01450.0133630.0073
T1wSubcorticalRightAccumbens areaIntensity0.6409730.012630.0111080.0123920.0132550.0149710.0161470.0111370.0094
T1wCorticalRightInferior parietal cortexArea0.8440750.0124170.0153240.0086470.0111960.014010.0117840.0129510.013
T1wCorticalLeftPericalcarine cortexThickness-0.7382640.0122660.0106470.0111670.0149510.016480.0116180.0119220.0091
T1wCorticalRightPars opercularisArea-0.5622110.0121390.0108240.0129410.0139020.0133040.0152160.0100290.0088
T1wSubcorticalLeftCerebellum white matterIntensity0.7471480.0120450.0123630.010520.0175980.0150780.0122840.0119020.0046
T1wCorticalLeftSuperior parietal cortexThicknessstd0.7258890.0120030.0115980.0117940.0118430.0160290.0106370.0093630.0128
T1wCorticalRightPostcentral gyrusCurvind0.844780.0113990.0085690.0143530.0123530.0129710.0103430.0103920.0108
T1wSubcorticalLeftInferior lateral ventricleVolume-0.6028080.0109170.0149120.0098820.010490.0110590.010510.0094410.0101
BL (no. features = 46, threshold≥ 0.00993)
T1wSubcorticalRightPallidumVolume0.7757210.0232440.0208920.0198040.0186470.0223330.0251860.0303430.0255
T1wCorticalLeftTemporal poleVolume0.7772930.0214410.0191670.0186960.0196370.020520.024520.0237160.0238
T1wSubcorticalRightCerebellum cortexVolume0.8304380.0203280.0231570.0231180.0189310.0172650.0196080.0220490.0182
T1wSubcorticalAnterior corpus callosumIntensity-0.7118440.018650.0208730.0213530.0121270.0196370.0233730.0129220.0203
T1wCorticalLeftRostral middle frontal gyrusThicknessstd-0.7723790.0185570.0200490.0161470.0134020.020510.0190880.0190490.0217
T1wCorticalRightParahippocampal gyrusArea0.823870.0181410.0237250.0125390.0144310.0217750.0169610.0176180.0199
T1wCorticalRightInferior parietal cortexVolume0.7472010.0180850.0183730.0185490.0126860.0207650.018510.0199710.0177
T1wCorticalLeftLateral occipital cortexThickness-0.7332240.0175170.0176270.0144410.0143730.0257940.0185290.0186670.0132
T1wCorticalRightBanks of the superior temporal sulcusMeancurv-0.71060.0167070.0199310.0195590.0151960.0131270.014980.0147060.0195
T1wCorticalRightParahippocampal gyrusVolume0.8823480.0165980.0205880.0133240.0117060.0203040.0153630.0177650.0171
T1wCorticalLeftPericalcarine cortexThickness-0.6932170.0157630.0156570.0102350.0135690.0191760.0160490.0220690.0136
DTIPosterior corona radiata-0.72440.0156930.0204410.0153240.0117750.0186670.0158730.0157840.012
T1wCorticalRightSuperior parietal cortexThicknessstd0.7514690.0154260.0183330.0170880.0131760.0201960.0098140.0160980.0133
DTIRightPosterior corona radiata-0.6973440.0154060.0204610.0164310.0113330.0129020.0198040.0122450.0147
T1wCorticalLeftParacentral lobuleArea-0.6958950.0149940.0127650.0132940.0138240.0127750.011990.0232840.017
T1wCorticalLeftPars orbitalisArea0.7564530.0149440.0158920.0134410.0113630.0141670.0142450.0169220.0186
T1wCorticalLeftSuperior parietal cortexThicknessstd0.6811010.0149080.0161960.015980.0097840.0124310.0127750.0227160.0145
T1wCorticalRightCuneus cortexVolume-0.7530210.0148050.0152840.0146080.0102550.0120390.0164020.0173920.0177
T1wCorticalRightPericalcarine cortexThickness-0.5991150.0147420.0164410.0142450.0131080.0135880.0162550.0161080.0135
T1wCorticalLeftRostral anterior cingulate cortexCurvind0.8611640.0143570.0195780.0172060.01250.0131270.0067250.0115390.0198
T1wCorticalRightInferior parietal cortexArea0.7783270.0141510.0163530.0165590.0078430.0159610.0151760.0130780.0141
T1wCorticalRightCuneus cortexThickness-0.6501550.0140620.014010.0127550.0147060.0127550.0134510.0180590.0127
DTIRightRetrolenticular part of internal capsule-0.7158540.0139920.0117650.0178330.007510.0143330.0164220.0137250.0164
T1wCorticalRightInferior parietal cortexThicknessstd0.7103870.0138280.0146370.0137060.0141180.0124020.0088140.0173240.0158
T1wSubcorticalAnterior corpus callosumVolume0.8282490.0138240.0146670.0149510.0141470.0165880.0122250.0106370.0135
T1wCorticalLeftMedial orbitofrontal cortexThicknessstd0.753550.0138150.0184610.0130590.0116080.0097060.019990.0136270.0103
T1wSubcorticalLeftCerebellum cortexVolume0.8158260.0136960.0145590.0122450.0150690.0082450.015510.0176860.0126
T1wCorticalRightPars opercularisThickness-0.7521970.013690.0150.0157840.0057450.013990.0180290.0131270.0142
DTIRightAnterior limb of internal capsule0.7767130.0136620.0109710.0133430.0136470.0118530.0151960.0135590.0171
T1wCorticalRightTransveretemporal cortexThickness0.6932020.0136540.0129410.0143430.0086760.0187840.0143140.011520.015
T1wCorticalRightIsthmus cingulate cortexThicknessstd0.6543390.0136530.0146670.0129310.0119610.0104610.0196180.0132750.0127
T1wCorticalRightMedial orbitofrontal cortexMeancurv0.8670910.0135380.0124120.0102450.0087750.0182550.0120780.016520.0165
DTILeftPosterior corona radiata-0.697220.0131320.0153140.0116960.0100880.0171370.011020.0173430.0093
DTILeftCorticospinal tract0.8239270.0130630.0171080.0144410.0112160.0133730.0131860.0127650.0094
T1wCorticalLeftLateral orbitofrontal cortexMeancurv0.7714920.0127770.0107750.0150590.0067450.0183730.0141760.012020.0123
T1wSubcorticalBrain-StemIntensity-0.7916780.0126190.0158820.0115390.0109510.0104610.0118140.0116670.016
DTISplenium of corpus callosum-0.8172570.0125450.0116270.0104410.0131760.0140290.0131960.0151470.0102
T1wCorticalLeftMedial orbitofrontal cortexArea-0.7680570.0125410.0145490.0122250.0113530.0110390.0110590.0117060.0159
T1wCorticalLeftParacentral lobuleVolume-0.728050.0125170.0100780.0104020.0114310.0121760.0091960.0203530.014
T1wSubcorticalLeftInferior lateral ventricleVolume-0.5412170.012380.0132450.0103240.0138630.0136760.0101860.0096760.0157
T1wCorticalRightPericalcarine cortexVolume-0.628290.0123290.0111760.0147840.0100780.0104510.0110290.0157940.013
T1wCorticalLeftIsthmus cingulate cortexVolume0.879030.0121580.0107940.0113530.0111860.0117250.0094120.0137160.0169
T1wCorticalLeftTemporal poleThicknessstd-0.7800850.0119930.0110490.0117350.0121180.0155980.0076080.0126180.0132
T1wCorticalRightIsthmus cingulate cortexMeancurv0.8596620.0117210.0120390.0150290.011520.0126570.0102840.0104510.0101
DTIRetrorenticular part of internal capsule-0.7459340.0109330.0107650.0106370.004010.0133430.0118330.0140290.0119
DTILeftInferior fronto-occipital fasciculus0.9141310.0107210.0107940.0130690.0057350.0127450.0103630.0111080.0112

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  1. Roshan Prakash Rane
  2. Evert Ferdinand de Man
  3. JiHoon Kim
  4. Kai Görgen
  5. Mira Tschorn
  6. Michael A Rapp
  7. Tobias Banaschewski
  8. Arun LW Bokde
  9. Sylvane Desrivieres
  10. Herta Flor
  11. Antoine Grigis
  12. Hugh Garavan
  13. Penny A Gowland
  14. Rüdiger Brühl
  15. Jean-Luc Martinot
  16. Marie-Laure Paillere Martinot
  17. Eric Artiges
  18. Frauke Nees
  19. Dimitri Papadopoulos Orfanos
  20. Herve Lemaitre
  21. Tomas Paus
  22. Luise Poustka
  23. Juliane Fröhner
  24. Lauren Robinson
  25. Michael N Smolka
  26. Jeanne Winterer
  27. Robert Whelan
  28. Gunter Schumann
  29. Henrik Walter
  30. Andreas Heinz
  31. Kerstin Ritter
  32. IMAGEN consortium
(2022)
Structural differences in adolescent brains can predict alcohol misuse
eLife 11:e77545.
https://doi.org/10.7554/eLife.77545