%0 Journal Article %T Predicting Depression Onset in Young People Based on Clinical, Cognitive, Environmental, and Neurobiological Data %+ University of Melbourne %+ Radboud University [Nijmegen] %+ Heidelberg University %+ Trinity College Dublin %+ King‘s College London %+ University of Mannheim %+ Service NEUROSPIN (NEUROSPIN) %+ University of Vermont [Burlington] %+ University of Nottingham, UK (UON) %+ Charité - UniversitätsMedizin = Charité - University Hospital [Berlin] %+ Physikalisch-Technische Bundesanstalt [Braunschweig] (PTB) %+ Trajectoires Développementales en Psychiatrie [Paris] %+ CB - Centre Borelli - UMR 9010 (CB) %+ CHU Pitié-Salpêtrière [AP-HP] %+ Kiel University %+ Institut des Maladies Neurodégénératives [Bordeaux] (IMN) %+ University of Toronto %+ University Medical Center Göttingen (UMG) %+ Technische Universität Dresden = Dresden University of Technology (TU Dresden) %+ National Institute of Mental Health (NIMH) %+ Leibniz Institute for Neurobiology [Magdeburg] (LIN) %+ Fudan University [Shanghai] %A Toenders, Yara %A Kottaram, Akhil %A Dinga, Richard %A Davey, Christopher %A Banaschewski, Tobias %A Bokde, Arun L.W. %A Quinlan, Erin Burke %A Desrivières, Sylvane %A Flor, Herta %A Grigis, Antoine %A Garavan, Hugh %A Gowland, Penny %A Heinz, Andreas %A Brühl, Rüdiger %A Martinot, Jean-Luc %A Paillère Martinot, Marie-Laure %A Nees, Frauke %A Orfanos, Dimitri Papadopoulos %A Lemaitre, Herve %A Paus, Tomáš %A Poustka, Luise %A Hohmann, Sarah %A Fröhner, Juliane %A Smolka, Michael %A Walter, Henrik %A Whelan, Robert %A Stringaris, Argyris %A van Noort, Betteke %A Penttilä, Jani %A Grimmer, Yvonne %A Insensee, Corinna %A Becker, Andreas %A Schumann, Gunter %A Schmaal, Lianne %Z This work was supported by the MQ Brighter Futures Award (MQBFC/2 [to LS]), the National Institute of Mental Health of the National Institutes of Health (Award Number R01MH117601 [to LS]), a National Health and Medical Research Council (NHMRC) Career Development Fellowship (1140764 [to LS]), the Dame Kate Campbell Fellowship from the Faculty of Medicine, Dentistry and Health Sciences at The University of Melbourne, and an NHMRC Career Development Award (141738 [to CGD]).This work received support from the following sources: the European Union–funded FP6 Integrated Project IMAGEN (Reinforcement-related behavior 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), Human Brain Project (HBP SGA 2, 785907, and HBP SGA 3, 945539), the Medical Research Council Grant “c-VEDA” (Consortium on Vulnerability to Externalizing Disorders and Addictions) (MR/N000390/1), the National Institutes 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 (BMBF Grant Nos. 01GS08152; 01EV0711; Forschungsnetz AERIAL 01EE1406A, 01EE1406B; Forschungsnetz IMAC-Mind 01GL1745B), the Deutsche Forschungsgemeinschaft (DFG Grant Nos. SM 80/7-2, SFB 940, TRR 265, NE 1383/14-1), the Medical Research Foundation and Medical Research Council (Grant Nos. MR/R00465X/1 and MR/S020306/1), and the NIH-funded ENIGMA (Grant Nos. 5U54EB020403-05 and 1R56AG058854-01). Further support was provided by grants from the 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), USA (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. %< avec comité de lecture %J Biological Psychiatry: Cognitive Neuroscience and Neuroimaging %I Elsevier %V 7 %N 4 %P 376-384 %8 2022-04 %D 2022 %R 10.1016/j.bpsc.2021.03.005 %M 33753312 %K Adolescents %K Depression %K Machine learning %K Major depressive disorder %K Penalized logistic regression %K Prediction %Z Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC] %Z Life Sciences [q-bio]/Human health and pathologyJournal articles %X Background: Adolescent onset of depression is associated with long-lasting negative consequences. Identifying adolescents at risk for developing depression would enable the monitoring of risk factors and the development of early intervention strategies. Using machine learning to combine several risk factors from multiple modalities might allow prediction of depression onset at the individual level.Methods: A subsample of a multisite longitudinal study in adolescents, the IMAGEN study, was used to predict future (subthreshold) major depressive disorder onset in healthy adolescents. Based on 2-year and 5-year follow-up data, participants were grouped into the following: 1) those developing a diagnosis of major depressive disorder or subthreshold major depressive disorder and 2) healthy control subjects. Baseline measurements of 145 variables from different modalities (clinical, cognitive, environmental, and structural magnetic resonance imaging) at age 14 years were used as input to penalized logistic regression (with different levels of penalization) to predict depression onset in a training dataset (n = 407). The features contributing the highest to the prediction were validated in an independent hold-out sample (three independent IMAGEN sites; n = 137).Results: The area under the receiver operating characteristic curve for predicting depression onset ranged between 0.70 and 0.72 in the training dataset. Baseline severity of depressive symptoms, female sex, neuroticism, stressful life events, and surface area of the supramarginal gyrus contributed most to the predictive model and predicted onset of depression, with an area under the receiver operating characteristic curve between 0.68 and 0.72 in the independent validation sample.Conclusions: This study showed that depression onset in adolescents can be predicted based on a combination multimodal data of clinical characteristics, life events, personality traits, and brain structure variables. %G English %Z Project IMAGEN %Z Human Brain Project %L hal-03670307 %U https://u-paris.hal.science/hal-03670307 %~ CEA %~ SSA %~ CNRS %~ ENS-CACHAN %~ APHP %~ CEA-UPSAY %~ UNIV-PARIS-SACLAY %~ JOLIOT %~ CEA-DRF %~ NEUROSPIN %~ SORBONNE-UNIVERSITE %~ SORBONNE-UNIV %~ SU-MEDECINE %~ SU-MED %~ UNIV-PARIS %~ UNIVERSITE-PARIS %~ UP-SCIENCES %~ UNIVERSITE-PARIS-SACLAY %~ SU-TI %~ ANR %~ CGB %~ ENS-PARIS-SACLAY %~ ENS-PSACLAY %~ CB_UMR9010 %~ GS-ENGINEERING %~ GS-MATHEMATIQUES %~ GS-LIFE-SCIENCES-HEALTH %~ ALLIANCE-SU %~ FRM