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| Verfasst von: | Toenders, Yara J. [VerfasserIn]  |
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| | Kottaram, Akhil [VerfasserIn] |
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| | Dinga, Richard [VerfasserIn] |
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| | Davey, Christopher G. [VerfasserIn] |
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| | Banaschewski, Tobias [VerfasserIn] |
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| | Bokde, Arun L. W. [VerfasserIn] |
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| | Quinlan, Erin Burke [VerfasserIn] |
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| | Desrivières, Sylvane [VerfasserIn] |
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| | Flor, Herta [VerfasserIn] |
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| | Grigis, Antoine [VerfasserIn] |
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| | Garavan, Hugh [VerfasserIn] |
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| | Gowland, Penny [VerfasserIn] |
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| | Heinz, Andreas [VerfasserIn] |
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| | Brühl, Rüdiger [VerfasserIn] |
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| | Martinot, Jean-Luc [VerfasserIn] |
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| | Paillère Martinot, Marie-Laure [VerfasserIn] |
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| | Nees, Frauke [VerfasserIn] |
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| | Orfanos, Dimitri Papadopoulos [VerfasserIn] |
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| | Lemaitre, Herve [VerfasserIn] |
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| | Paus, Tomáš [VerfasserIn] |
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| | Poustka, Luise [VerfasserIn] |
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| | Hohmann, Sarah [VerfasserIn] |
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| | Fröhner, Juliane H. [VerfasserIn] |
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| | Smolka, Michael N. [VerfasserIn] |
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| | Walter, Henrik [VerfasserIn] |
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| | Whelan, Robert [VerfasserIn] |
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| | Stringaris, Argyris [VerfasserIn] |
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| | van Noort, Betteke [VerfasserIn] |
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| | Penttilä, Jani [VerfasserIn] |
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| | Grimmer, Yvonne [VerfasserIn] |
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| | Insensee, Corinna [VerfasserIn] |
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| | Becker, Andreas [VerfasserIn] |
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| | Schumann, Gunter [VerfasserIn] |
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| | Schmaal, Lianne [VerfasserIn] |
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| Titel: | Predicting depression onset in young people based on clinical, cognitive, environmental, and neurobiological data |
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| Titelzusatz: | archival report |
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| Verf.angabe: | 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 |
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| E-Jahr: | 2022 |
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| Jahr: | April 2022 |
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| Umfang: | 9 S. |
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| Fussnoten: | Online verfügbar: 19 March 2021, Artikelversion: 5 April 2022 ; Gesehen am 26.03.2024 |
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| Titel Quelle: | Enthalten in: Biological psychiatry. Cognitive neuroscience and neuroimaging |
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| Ort Quelle: | Amsterdam [u.a.] : Elsevier Inc., 2016 |
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| Jahr Quelle: | 2022 |
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| Band/Heft Quelle: | 7(2022), 4 vom: Apr., Seite 376-384 |
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| ISSN Quelle: | 2451-9030 |
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| 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. |
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| DOI: | doi:10.1016/j.bpsc.2021.03.005 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext: https://doi.org/10.1016/j.bpsc.2021.03.005 |
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| | Volltext: https://www.sciencedirect.com/science/article/pii/S2451902221000823 |
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| | DOI: https://doi.org/10.1016/j.bpsc.2021.03.005 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Adolescents |
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| | Depression |
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| | Machine learning |
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| | Major depressive disorder |
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| | Penalized logistic regression |
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| | Prediction |
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| K10plus-PPN: | 1884332498 |
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| Verknüpfungen: | → Zeitschrift |
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Predicting depression onset in young people based on clinical, cognitive, environmental, and neurobiological data / Toenders, Yara J. [VerfasserIn]; April 2022 (Online-Ressource)
