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Auteur Rutvik SHAH |
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Multi-level predictors of depression symptoms in the Adolescent Brain Cognitive Development (ABCD) study / Tiffany C. HO in Journal of Child Psychology and Psychiatry, 63-12 (December 2022)
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Titre : Multi-level predictors of depression symptoms in the Adolescent Brain Cognitive Development (ABCD) study Type de document : Texte imprimé et/ou numérique Auteurs : Tiffany C. HO, Auteur ; Rutvik SHAH, Auteur ; Jyoti MISHRA, Auteur ; April C. MAY, Auteur ; Susan F. TAPERT, Auteur Article en page(s) : p.1523-1533 Langues : Anglais (eng) Mots-clés : Child Adolescent Humans Depression/psychology Magnetic Resonance Imaging/methods Family Conflict Brain/diagnostic imaging Cognition ABCD Study Adolescence depression functional MRI (fMRI) sleep Index. décimale : PER Périodiques Résumé : BACKGROUND: While identifying risk factors for adolescent depression is critical for early prevention and intervention, most studies have sought to understand the role of isolated factors rather than across a broad set of factors. Here, we sought to examine multi-level factors that maximize the prediction of depression symptoms in US children participating in the Adolescent Brain and Cognitive Development (ABCD) study. METHODS: A total of 7,995 participants from ABCD (version 3.0 release) provided complete data at baseline and 1-year follow-up data. Depression symptoms were measured with the Child Behavior Checklist. Predictive features included child demographic, environmental, and structural and resting-state fMRI variables, parental depression history and demographic characteristics. We used linear (elastic net regression, EN) and non-linear (gradient-boosted trees, GBT) predictive models to identify which set of features maximized prediction of depression symptoms at baseline and, separately, at 1-year follow-up. RESULTS: Both linear and non-linear models achieved comparable results for predicting baseline (EN: MAE=3.757; R(2) =0.156; GBT: MAE=3.761; R(2) =0.147) and 1-year follow-up (EN: MAE=4.255; R(2) =0.103; GBT: MAE=4.262; R(2) =0.089) depression. Parental history of depression, greater family conflict, and shorter child sleep duration were among the top predictors of concurrent and future child depression symptoms across both models. Although resting-state fMRI features were relatively weaker predictors, functional connectivity of the caudate was consistently the strongest neural feature associated with depression symptoms at both timepoints. CONCLUSIONS: Consistent with prior research, parental mental health, family environment, and child sleep quality are important risk factors for youth depression. Functional connectivity of the caudate is a relatively weaker predictor of depression symptoms but may represent a biomarker for depression risk. En ligne : http://dx.doi.org/10.1111/jcpp.13608 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=490
in Journal of Child Psychology and Psychiatry > 63-12 (December 2022) . - p.1523-1533[article] Multi-level predictors of depression symptoms in the Adolescent Brain Cognitive Development (ABCD) study [Texte imprimé et/ou numérique] / Tiffany C. HO, Auteur ; Rutvik SHAH, Auteur ; Jyoti MISHRA, Auteur ; April C. MAY, Auteur ; Susan F. TAPERT, Auteur . - p.1523-1533.
Langues : Anglais (eng)
in Journal of Child Psychology and Psychiatry > 63-12 (December 2022) . - p.1523-1533
Mots-clés : Child Adolescent Humans Depression/psychology Magnetic Resonance Imaging/methods Family Conflict Brain/diagnostic imaging Cognition ABCD Study Adolescence depression functional MRI (fMRI) sleep Index. décimale : PER Périodiques Résumé : BACKGROUND: While identifying risk factors for adolescent depression is critical for early prevention and intervention, most studies have sought to understand the role of isolated factors rather than across a broad set of factors. Here, we sought to examine multi-level factors that maximize the prediction of depression symptoms in US children participating in the Adolescent Brain and Cognitive Development (ABCD) study. METHODS: A total of 7,995 participants from ABCD (version 3.0 release) provided complete data at baseline and 1-year follow-up data. Depression symptoms were measured with the Child Behavior Checklist. Predictive features included child demographic, environmental, and structural and resting-state fMRI variables, parental depression history and demographic characteristics. We used linear (elastic net regression, EN) and non-linear (gradient-boosted trees, GBT) predictive models to identify which set of features maximized prediction of depression symptoms at baseline and, separately, at 1-year follow-up. RESULTS: Both linear and non-linear models achieved comparable results for predicting baseline (EN: MAE=3.757; R(2) =0.156; GBT: MAE=3.761; R(2) =0.147) and 1-year follow-up (EN: MAE=4.255; R(2) =0.103; GBT: MAE=4.262; R(2) =0.089) depression. Parental history of depression, greater family conflict, and shorter child sleep duration were among the top predictors of concurrent and future child depression symptoms across both models. Although resting-state fMRI features were relatively weaker predictors, functional connectivity of the caudate was consistently the strongest neural feature associated with depression symptoms at both timepoints. CONCLUSIONS: Consistent with prior research, parental mental health, family environment, and child sleep quality are important risk factors for youth depression. Functional connectivity of the caudate is a relatively weaker predictor of depression symptoms but may represent a biomarker for depression risk. En ligne : http://dx.doi.org/10.1111/jcpp.13608 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=490