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Predicting depression onset in young people based on clinical, cognitive, environmental, and neurobiological data: archival report

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 ris...

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Main Authors: Toenders, Yara J. (Author) , Kottaram, Akhil (Author) , Dinga, Richard (Author) , Davey, Christopher G. (Author) , Banaschewski, Tobias (Author) , Bokde, Arun L. W. (Author) , Quinlan, Erin Burke (Author) , Desrivières, Sylvane (Author) , Flor, Herta (Author) , Grigis, Antoine (Author) , Garavan, Hugh (Author) , Gowland, Penny (Author) , Heinz, Andreas (Author) , Brühl, Rüdiger (Author) , Martinot, Jean-Luc (Author) , Paillère Martinot, Marie-Laure (Author) , Nees, Frauke (Author) , Orfanos, Dimitri Papadopoulos (Author) , Lemaitre, Herve (Author) , Paus, Tomáš (Author) , Poustka, Luise (Author) , Hohmann, Sarah (Author) , Fröhner, Juliane H. (Author) , Smolka, Michael N. (Author) , Walter, Henrik (Author) , Whelan, Robert (Author) , Stringaris, Argyris (Author) , van Noort, Betteke (Author) , Penttilä, Jani (Author) , Grimmer, Yvonne (Author) , Insensee, Corinna (Author) , Becker, Andreas (Author) , Schumann, Gunter (Author) , Schmaal, Lianne (Author)
Format: Article (Journal)
Language:English
Published: April 2022
In: Biological psychiatry. Cognitive neuroscience and neuroimaging
Year: 2022, Volume: 7, Issue: 4, Pages: 376-384
ISSN:2451-9030
DOI:10.1016/j.bpsc.2021.03.005
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.bpsc.2021.03.005
Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S2451902221000823
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Author Notes:Yara J. Toenders, Akhil Kottaram, Richard Dinga, Christopher G. Davey, Tobias Banaschewski, Arun L.W. Bokde, Erin Burke Quinlan, Sylvane Desrivières, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Frauke Nees, Dimitri Papadopoulos Orfanos, Herve Lemaitre, Tomáš Paus, Luise Poustka, Sarah Hohmann, Juliane H. Fröhner, Michael N. Smolka, Henrik Walter, Robert Whelan, Argyris Stringaris, Betteke van Noort, Jani Penttilä, Yvonne Grimmer, Corinna Insensee, Andreas Becker, Gunter Schumann, IMAGEN Consortium, and Lianne Schmaal

MARC

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520 |a 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. 
650 4 |a Adolescents 
650 4 |a Depression 
650 4 |a Machine learning 
650 4 |a Major depressive disorder 
650 4 |a Penalized logistic regression 
650 4 |a Prediction 
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