TY - JOUR
T1 - Predicting Depression Onset in Young People Based on Clinical, Cognitive, Environmental, and Neurobiological Data
AU - IMAGEN Consortium
AU - Toenders, Yara J.
AU - Kottaram, Akhil
AU - Dinga, Richard
AU - Davey, Christopher G.
AU - Banaschewski, Tobias
AU - Bokde, Arun L.W.
AU - Quinlan, Erin Burke
AU - Desrivières, Sylvane
AU - Flor, Herta
AU - Grigis, Antoine
AU - Garavan, Hugh
AU - Gowland, Penny
AU - Heinz, Andreas
AU - Brühl, Rüdiger
AU - Martinot, Jean Luc
AU - Paillère Martinot, Marie Laure
AU - Nees, Frauke
AU - Orfanos, Dimitri Papadopoulos
AU - Lemaitre, Herve
AU - Paus, Tomáš
AU - Poustka, Luise
AU - Hohmann, Sarah
AU - Fröhner, Juliane H.
AU - Smolka, Michael N.
AU - Walter, Henrik
AU - Whelan, Robert
AU - Stringaris, Argyris
AU - van Noort, Betteke
AU - Penttilä, Jani
AU - Grimmer, Yvonne
AU - Insensee, Corinna
AU - Becker, Andreas
AU - Schumann, Gunter
AU - Schmaal, Lianne
N1 - Funding Information:
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.
Funding Information:
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]).
Funding Information:
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. TB served in an advisory or consultancy role for Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg GmbH, Shire. He received conference support or speaker's fee by Lilly, Medice, Novartis and Shire. He has been involved in clinical trials conducted by Shire & Viforpharma. He received royalties from Hogrefe, Kohlhammer, CIP Medien, Oxford University Press. The present work is unrelated to the above grants and relationships. LP served in an advisory or consultancy role for Roche and Viforpharm and received speaker's fee from Shire. She received royalties from Hogrefe, Kohlhammer, and Schattauer. The present work is unrelated to the above grants and relationships. All other authors report no biomedical financial interests or potential conflicts of interest.
Publisher Copyright:
© 2021 Society of Biological Psychiatry
PY - 2022/4
Y1 - 2022/4
N2 - 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.
AB - 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.
KW - Adolescents
KW - Depression
KW - Machine learning
KW - Major depressive disorder
KW - Penalized logistic regression
KW - Prediction
UR - http://www.scopus.com/inward/record.url?scp=85127179326&partnerID=8YFLogxK
U2 - 10.1016/j.bpsc.2021.03.005
DO - 10.1016/j.bpsc.2021.03.005
M3 - Article
C2 - 33753312
AN - SCOPUS:85127179326
SN - 2451-9022
VL - 7
SP - 376
EP - 384
JO - Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
JF - Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
IS - 4
ER -