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

Toenders, Yara J.; Kottaram, Akhil; Dinga, Richard; Davey, Christopher G.; Banaschewski, Tobias; Bokde, Arun L.W.; Quinlan, Erin Burke; Desrivi�res, Sylvane; Flor, Herta; Grigis, Antoine; Garavan, Hugh; Gowland, Penny; Heinz, Andreas; Br�hl, R�diger; Martinot, Jean-Luc; Paill�re Martinot, Marie-Laure; Nees, Frauke; Orfanos, Dimitri Papadopoulos; Lemaitre, Herve; Paus, Tom�; Poustka, Luise; Hohmann, Sarah; Fr�hner, Juliane H.; Smolka, Michael N.; Walter, Henrik; Whelan, Robert; Stringaris, Argyris; van Noort, Betteke; Penttil�, Jani; Grimmer, Yvonne; Insensee, Conrinna; Becker, Andreas; Schumann, Gunter; Schmaal, Lianne

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Authors

Yara J. Toenders

Akhil Kottaram

Richard Dinga

Christopher G. Davey

Tobias Banaschewski

Arun L.W. Bokde

Erin Burke Quinlan

Sylvane Desrivi�res

Herta Flor

Antoine Grigis

Hugh Garavan

Andreas Heinz

R�diger Br�hl

Jean-Luc Martinot

Marie-Laure Paill�re Martinot

Frauke Nees

Dimitri Papadopoulos Orfanos

Herve Lemaitre

Tom� Paus

Luise Poustka

Sarah Hohmann

Juliane H. Fr�hner

Michael N. Smolka

Henrik Walter

Robert Whelan

Argyris Stringaris

Betteke van Noort

Jani Penttil�

Yvonne Grimmer

Conrinna Insensee

Andreas Becker

Gunter Schumann

Lianne Schmaal



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 multi-site longitudinal study in adolescents, the IMAGEN study, was used to predict future (subthreshold) major depressive disorder (MDD) onset in healthy adolescents. Based on 2-year and 5-year follow-up data, participants were grouped into: 1) developing an MDD diagnosis or subthreshold MDD and 2) healthy controls. Baseline measurements of 145 variables from different modalities (clinical, cognitive, environmental and structural magnetic resonance imaging [MRI]) at age 14 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 highest to the prediction were validated in an independent hold-out sample (3 independent IMAGEN sites; N=137).

Results
The area under the receiver operating characteristics curve (AUROC) for predicting depression onset ranged between 0.70-0.72 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 AUROC between 0.68-0.72 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, life events, personality traits, brain structure variables.

Citation

Toenders, Y. J., Kottaram, A., Dinga, R., Davey, C. G., Banaschewski, T., Bokde, A. L., …Schmaal, L. (2022). Predicting Depression Onset in Young People Based on Clinical, Cognitive, Environmental, and Neurobiological Data. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 7(4), 376-384. https://doi.org/10.1016/j.bpsc.2021.03.005

Journal Article Type Article
Acceptance Date Mar 9, 2021
Online Publication Date Mar 19, 2021
Publication Date 2022-04
Deposit Date Jun 24, 2021
Publicly Available Date Mar 29, 2024
Journal Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
Print ISSN 2451-9022
Electronic ISSN 2451-9030
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 7
Issue 4
Pages 376-384
DOI https://doi.org/10.1016/j.bpsc.2021.03.005
Keywords Cognitive Neuroscience; Biological Psychiatry; Radiology Nuclear Medicine and imaging; Clinical Neurology
Public URL https://nottingham-repository.worktribe.com/output/5722414
Publisher URL https://www.sciencedirect.com/science/article/abs/pii/S2451902221000823?via%3Dihub

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