Multi-level predictors of depression symptoms in the Adolescent Brain Cognitive Development (ABCD) study

Abstract

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; R² = 0.156; GBT: MAE = 3.761; R² = 0.147) and 1-year follow-up (EN: MAE = 4.255; R² = 0.103; GBT: MAE = 4.262; R² = 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.

Keywords: ABCD Study; Adolescence; depression; functional MRI (fMRI); sleep.

Figure 1.

Visualization of the machine learning approach used to identify features contributing to the prediction of concurrent depression symptoms.

Figure 2.. Shapley values of the top 10 features in the elastic net (A) and gradient boosted trees (B) models predicting baseline depression symptoms.

The summary plots indicate the relationship between the value of a feature and the impact on the prediction, thus combining feature importance with feature effects. Each point on the summary plot is a Shapley value for a feature and an instance. The position on the y-axis is determined by the feature and on the x-axis by the Shapley value. The color represents the value of the feature from low (blue) to high (pink). The features are ordered according to their importance. See Figure S2 for a summary of the magnitude of Shapley values per feature in each model. FC=functional connectivity; Hx=history; RTN=retrosplenial temporal network; Rx=prescription medication

Figure 3.. Shapley values of the top 10 features in the elastic net (A) and gradient boosted trees (B) models predicting 1-year follow-up depression symptoms.

The summary plots indicate the relationship between the value of a feature and the impact on the prediction, thus combining feature importance with feature effects. Each point on the summary plot is a Shapley value for a feature and an instance. The position on the y-axis is determined by the feature and on the x-axis by the Shapley value. The color represents the value of the feature from low (blue) to high (pink). The features are ordered according to their importance. See Figure S3 for a summary of the magnitude of Shapley values per feature in each model. BMI=body mass index; CON=cingulo-opercular network; CPN=cingulo-parietal network; FC=functional connectivity; Hx=history; RTN=retrosplenial temporal network; Rx=prescription medication