Predicting Depression Onset in Young People Based on Clinical, Cognitive, Environmental, and Neurobiological Data

IMAGEN Consortium

doi:10.1016/j.bpsc.2021.03.005

Predicting Depression Onset in Young People Based on Clinical, Cognitive, Environmental, and Neurobiological Data

IMAGEN Consortium

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	376-384
Number of pages	9
Journal	Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
Volume	7
Issue number	4
DOIs	https://doi.org/10.1016/j.bpsc.2021.03.005
Publication status	Published - Apr 2022

Keywords

Adolescents
Depression
Machine learning
Major depressive disorder
Penalized logistic regression
Prediction

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10.1016/j.bpsc.2021.03.005

Cite this

@article{3e526c2023944bd79c602b009fb71674,

title = "Predicting Depression Onset in Young People Based on Clinical, Cognitive, Environmental, and Neurobiological Data",

abstract = "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.",

keywords = "Adolescents, Depression, Machine learning, Major depressive disorder, Penalized logistic regression, Prediction",

author = "{IMAGEN Consortium} and Toenders, {Yara J.} and Akhil Kottaram and Richard Dinga and Davey, {Christopher G.} and Tobias Banaschewski and Bokde, {Arun L.W.} and Quinlan, {Erin Burke} and Sylvane Desrivi{\`e}res and Herta Flor and Antoine Grigis and Hugh Garavan and Penny Gowland and Andreas Heinz and R{\"u}diger Br{\"u}hl and Martinot, {Jean Luc} and {Paill{\`e}re Martinot}, {Marie Laure} and Frauke Nees and Orfanos, {Dimitri Papadopoulos} and Herve Lemaitre and Tom{\'a}{\v s} Paus and Luise Poustka and Sarah Hohmann and Fr{\"o}hner, {Juliane H.} and Smolka, {Michael N.} and Henrik Walter and Robert Whelan and Argyris Stringaris and {van Noort}, Betteke and Jani Penttil{\"a} and Yvonne Grimmer and Corinna Insensee and Andreas Becker and Gunter Schumann and Lianne Schmaal",

note = "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{\textquoteright}s College London, the Bundesministerium f{\"u}r 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 M{\'e}dicale (DPA20140629802), the Mission Interminist{\'e}rielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-H{\^o}pitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012, the Fondation de l{\textquoteright}Avenir (grant AP-RM-17-013), the F{\'e}d{\'e}ration 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 f{\"u}r 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 M{\'e}dicale (DPA20140629802), the Mission Interminist{\'e}rielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-H{\^o}pitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012, the Fondation de l'Avenir (grant AP-RM-17-013), the F{\'e}d{\'e}ration 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: {\textcopyright} 2021 Society of Biological Psychiatry",

year = "2022",

month = apr,

doi = "10.1016/j.bpsc.2021.03.005",

language = "English",

volume = "7",

pages = "376--384",

journal = "Biological Psychiatry: Cognitive Neuroscience and Neuroimaging",

issn = "2451-9022",

publisher = "Elsevier Inc.",

number = "4",

}

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 -

Predicting Depression Onset in Young People Based on Clinical, Cognitive, Environmental, and Neurobiological Data

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