Multi‐level predictors of depression symptoms in the Adolescent Brain Cognitive Development (ABCD) study
Bibliographic Data
| ID | 21344767 |
|---|---|
| Authors | Tiffany C Ho (0000-0002-4500-6364, Department of Psychiatry and Behavioral Sciences Weill Institute for Neurosciences University of California, San Francisco San Francisco CA USA, corresponding author), R Shah (0000-0002-7884-3791, University of San Diego), Jyoti Mishra (0000-0001-6612-4557, Department of Psychiatry University of California, San Diego San Diego CA USA), April C May (0000-0001-6110-0193, Department of Psychiatry University of California, San Diego San Diego CA USA), Susan F Tapert (0000-0001-7259-6112, Department of Psychiatry University of California, San Diego San Diego CA USA) |
| Year | 2022 |
| Volume | 63 |
| Issue | 12 |
| Pages | 1523-1533 |
| Publication date | 2022-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Child Psychology and Psychiatry (JOURNAL) |
| Journal identifiers | ISSN: 0021-9630 • E-ISSN: 1469-7610 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/jcpp.13608 |
| PMID | 35307818 |
| OpenAlex | W4221064726 |
| Language | EN |
| References cited | 53 |
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: = 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
Cognition · Depression (economics) · Linear regression · Machine learning · Psychiatry · Clinical Psychology · Digital Mental Health Interventions · Functional Brain Connectivity Studies · Mental Health Research Topics · Psychology
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Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-Sads-PL)
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Major Depression in the National Comorbidity Survey–Adolescent Supplement
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| Highly cited | No |
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