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

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.

Personalized machine learning of depressed mood using wearables.

Depression is a multifaceted illness with large interindividual variability in clinical response to treatment. In the era of digital medicine and precision therapeutics, new personalized treatment approaches are warranted for depression. Here, we use a combination of longitudinal ecological momentary assessments of depression, neurocognitive sampling synchronized with electroencephalography, and lifestyle data from wearables to generate individualized predictions of depressed mood over a 1-month time period. This study, thus, develops a systematic pipeline for N-of-1 personalized modeling of depression using multiple modalities of data. In the models, we integrate seven types of supervised machine learning (ML) approaches for each individual, including ensemble learning and regression-based methods. All models were verified using fourfold nested cross-validation. The best-fit as benchmarked by the lowest mean absolute percentage error, was obtained by a different type of ML model for each individual, demonstrating that there is no one-size-fits-all strategy. The voting regressor, which is a composite strategy across ML models, was best performing on-average across subjects. However, the individually selected best-fit models still showed significantly less error than the voting regressor performance across subjects. For each individual's best-fit personalized model, we further extracted top-feature predictors using Shapley statistics. Shapley values revealed distinct feature determinants of depression over time for each person ranging from co-morbid anxiety, to physical exercise, diet, momentary stress and breathing performance, sleep times, and neurocognition. In future, these personalized features can serve as targets for a personalized ML-guided, multimodal treatment strategy for depression.

Impact of Childhood Trauma on Executive Function in Adolescence-Mediating Functional Brain Networks and Prediction of High-Risk Drinking.

BACKGROUND: Childhood trauma is known to impart risk for several adverse life outcomes. Yet, its impact during adolescent development is not well understood. We aimed to investigate the relationships among childhood trauma, functional brain connectivity, executive dysfunction (ED), and the development of high-risk drinking in adolescence. METHODS: Data from the National Consortium on Alcohol and Neurodevelopment in Adolescence (sample size = 392, 55% female) cohort were used. This included resting-state functional magnetic resonance imaging at baseline, childhood trauma and ED self-reports, and detailed interviews on alcohol and substance use collected at baseline and at 4 annual follow-ups. We used longitudinal regression analyses to confirm the relationship between childhood trauma and ED, identified the mediating functional brain network hubs, and used these linkages to predict future high-risk drinking in adolescence. RESULTS: Childhood trauma severity was significantly related to ED in all years. At baseline, distributed functional connectivity from hub regions in the bilateral dorsal anterior cingulate cortex, right anterior insula, right intraparietal sulcus, and bilateral pre- and postcentral gyri mediated the relationship between childhood trauma and ED. Furthermore, high-risk drinking in follow-up years 1-4 could be predicted with high accuracy from the trauma-affected functional brain networks that mediated ED at baseline, together with age, childhood trauma severity, and extent of ED. DISCUSSION: Functional brain networks, particularly from hub regions important for cognitive and sensorimotor control, explain the relationship between childhood trauma and ED and are important for predicting future high-risk drinking. These findings are relevant for the prognosis of alcohol use disorders.

Clinical Outcomes With the Use of Prophylactic Versus Therapeutic Anticoagulation in Coronavirus Disease 2019.

OBJECTIVES: To determine the impact of anticoagulation on inhospital mortality among coronavirus disease 2019-positive patients with the a priori hypothesis that there would be a lower risk of inhospital mortality with use of preemptive therapeutic over prophylactic dose enoxaparin or heparin. DESIGN SETTING: Retrospective cohort study from April 1, 2020, to April 25, 2020. The date of final follow-up was June 12, 2020 Two large, acute-care hospitals in Western Connecticut. PATIENTS: Five hundred and one inpatients were identified after discharge as 18 years or older and positive for severe acute respiratory syndrome coronavirus 2. The final sample size included 374 patients after applying exclusion criteria. Demographic variables were collected via hospital billing inquiries, whereas the clinical variables were abstracted from patients' medical records. EXPOSURE: Preemptive enoxaparin or heparin at a therapeutic or prophylactic dose. MAIN RESULTS: When comparing treatments through multivariable analysis, risk of inhospital mortality was 2.3 times greater in patients receiving preemptive therapeutic anticoagulation (95% CI = 1.0-4.9; p = 0.04). Additionally, the average treatment effects were higher (ß = 0.11, p = 0.01) in the therapeutic group. CONCLUSIONS: An increase in inhospital mortality was observed among patients on preemptive therapeutic anticoagulation. Thus, in the management of coronavirus disease 2019 and its complications, we recommend further research and cautious use of preemptive therapeutic over prophylactic anticoagulation.