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Neuropsychosocial profiles of current and future adolescent alcohol misusers

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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.

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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.

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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.

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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.

Source data

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). ce, 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).

Source data

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.

Source data

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.

Source data

Extended Data Table 1 A list of all features that were used
Extended Data Table 2 Subject characteristics and percentage of imputed data for each group
Extended Data Table 3 Beta weights for classification of age-14 binge drinking and prediction of age-16 binge drinking

Supplementary information

Supplementary information

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Supplementary Data

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Supplementary Data

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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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