Reinforcement-related cognitive processes, such as reward processing, inhibitory control and social–emotional regulation are critical components of externalising and internalising behaviours. It is unclear to what extent the deficit in each of these processes contributes to individual behavioural symptoms, how their neural substrates give rise to distinct behavioural outcomes and whether neural activation profiles across different reinforcement-related processes might differentiate individual behaviours. We created a statistical framework that enabled us to directly compare functional brain activation during reward anticipation, motor inhibition and viewing emotional faces in the European IMAGEN cohort of 2,000 14-year-old adolescents. We observe significant correlations and modulation of reward anticipation and motor inhibition networks in hyperactivity, impulsivity, inattentive behaviour and conduct symptoms, and we describe neural signatures across cognitive tasks that differentiate these behaviours. We thus characterise shared and distinct functional brain activation patterns underling different externalising symptoms and identify neural stratification markers, while accounting for clinically observed comorbidity.
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IMAGEN data are available from a dedicated database: https://imagen2.cea.fr.
Custom code that supports the findings of this study is available from the corresponding author upon request.
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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 Horizon 2020-funded ERC Advanced Grant ‘STRATIFY’ (brain network-based stratification of reinforcement-related disorders; 695313), National Natural Science Foundation of China (81801773 and 81873909), the Shanghai Pujiang Project (18PJ1400900), ERANID (understanding the interplay between cultural, biological and subjective factors in drug use pathways; PR-ST-0416-10004), BRIDGET (JPND brain imaging, cognition, dementia and next generation GEnomics; MR/N027558/1), the Human Brain Project (SGA 2, 785907, and SGA 3, 945539), the FP7 project MATRICS (603016), the Medical Research Council Grant ‘c-VEDA’ (consortium on vulnerability to externalizing disorders and addictions; MR/N000390/1), the National Institute of Health (NIH) (R01DA049238, A decentralized macro and micro gene-by-environment interaction analysis of substance use behavior and its brain biomarkers), the National Institute for Health Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, the Bundesministerium für Bildung und Forschung (grants 01GS08152 and 01EV0711 and Forschungsnetz AERIAL 01EE1406A and 01EE1406B), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-2, SFB 940, TRR 265 and NE 1383/14-1), the Medical Research Foundation and Medical Research Council (grants MR/R00465X/1 and MR/S020306/1), the National Institutes of Health (NIH)-funded ENIGMA (grants 5U54EB020403-05 and 1R56AG058854-01). Further support was provided by grants from: ANR ANR-12-SAMA-0004, AAPG2019 - GeBra), the Eranet Neuron (AF12-NEUR0008-01 - WM2NA and ANR-18-NEUR00002-01 - ADORe), the Fondation de France (00081242), the Fondation pour la Recherche Médicale (DPA20140629802), the Mission Interministérielle de Lutte Contre les Drogues et les Conduites Addictives (MILDECA), the Assistance-Publique – Hôpitaux de Paris and INSERM (interface grant), Paris Sud University IDEX 2012, the Fondation de l’Avenir (grant AP-RM-17-013), the Fédération pour la Recherche sur le Cerveau, the National Institutes of Health, Science Foundation Ireland (16/ERCD/3797) and U.S.A. (Axon, Testosterone and Mental Health during Adolescence; RO1 MH085772-01A1), and by NIH Consortium grant U54 EB020403 (supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence), the 111 Project (B18015), the National Key Research and Development Program of China (2018YFC0910503 and 2018YFC1312900), the NSFC (81930095 and 91630314), The Key Project of Shanghai Science and Technology Innovation Plan (16JC1420402), the Shanghai Municipal Science and Technology Major Project (2018SHZDZX01) and Zhangjiang Lab. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
T.B. served in an advisory or consultancy role for Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg and Shire. He received conference support or speaker’s fees from Lilly, Medice, Novartis and Shire. He has been involved in clinical trials conducted by Shire and Vifor Pharma. He received royalties from Hogrefe, Kohlhammer, CIP Medien and Oxford University Press. The present work is unrelated to the above grants and relationships. G.J.B. has received honoraria from General Electric Healthcare for teaching on scanner programming courses. The other authors declare no competing interests.
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Editor recognition statement: Primary Handling Editor: Marike Schiffer.
Dendrogram trees and static cut at 90% quantile of height of branches for (A) MID, (B) SST and (C) EFT nodes from WVCNA.
Overlapped Functional Brain Regions (Cohen’s D > 0.30) Identified across All Three Tasks.
RCCA results between fMRI and externalising behaviours based on 1000 Permutation with predefined Regulation Parameters: A. The Effect Size (η2) and Confidence Intervals; B. P-values.
RCCA results between fMRI and internalising behaviours based on 1000 Permutation with predefined regulation parameters.
The figure of experimental paradigm was adapted from a previous publication6.
The figure of experimental paradigm was adapted from a previous publication22.
The figure of experimental paradigm was adapted from a previous publication52.
The soft-thresholds were picked as 7 for MID, 8 for EFT and 7 for SST.
Supplementary Tables 2–4 and the full list of consortium members.
a, Functional brain regions identified through WVCNA for the MID task and the following hierarchical clustering results at the height of the top 90%. b, Functional brain regions identified through WVCNA for the SST task and the following hierarchical clustering results at the height of the top 90%. c, Functional brain regions identified through WVCNA for the EFT task and the following hierarchical clustering results at the height of the top 90%
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Jia, T., Ing, A., Quinlan, E.B. et al. Neurobehavioural characterisation and stratification of reinforcement-related behaviour. Nat Hum Behav 4, 544–558 (2020). https://doi.org/10.1038/s41562-020-0846-5