The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The ABCD Study is a collaborative effort, including a Coordinating Center, 21 data acquisition sites across the United States, and a Data Analysis and Informatics Center (DAIC). The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data will provide a resource of unprecedented scale and depth for studying typical and atypical development. Here, we describe the baseline neuroimaging processing and subject-level analysis methods used by the ABCD DAIC in the centralized processing and extraction of neuroanatomical and functional imaging phenotypes. Neuroimaging processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI. Highlights: An overview of the MRI processing pipeline for the ABCD Study A discussion on the challenges of large, multisite population studies A methodological reference for users of publicly shared data from the ABCD Study

Undertreatment of depression is common among children and adolescents, but evidence of the impact of undertreatment of depression on risk of suicide is limited due to the low base rate of suicide in the population and lack of sufficient data sources. We developed a microsimulation model that uses evidence from multiple sources to study the impact of different durations of antidepressant treatment on suicide risk in a synthesized sample that is nationally representative of children and adolescents with major depressive disorder. Compared with receiving no treatment, suicide rate and risk of suicide attempt both decreased with increasing duration of antidepressant treatment (for 12 weeks, suicide rate ratios = 0.78 (95% credible interval (CrI): 0.58, 1.15), 36 weeks, 0.65 (95% CrI: 0.44, 0.90), and 52 weeks, 0.63 (95% CrI: 0.45, 0.72); for suicide attempt: 12 weeks, suicide risk ratios = 0.68 (95% CrI: 0.62, 0.69), 36 weeks, 0.56 (95% CrI: 0.52, 0.57), and 52 weeks, 0.55 (95% CrI: 0.51, 0.56). The suicide rate and risk of suicide attempt were lower in children than in adolescents. Males had a lower risk of suicide attempt but higher suicide rate than females. The findings from the microsimulation model show that completion of 12–36 weeks of antidepressant treatment may reduce suicide attempt and suicide among children and adolescents with major depressive disorder.

Background: Antipsychotic medication (APM) can cause weight gain, insulin resistance, dyslipidemias, and an increased risk of developing type-2 diabetes and cardiovascular disease among youth. The study sought to increase water consumption, reduce sugar-sweetened beverage (SSB) intake, and prevent unhealthy weight gain via a healthy lifestyle intervention among youth newly started on a second-generation APM for psychiatric treatment. Methods: This randomized controlled trial enrolled 148 Medicaid-insured youth (ages 8–17) recently starting APM. The treatment group received both a biweekly home-delivery of bottled water and parental phone support from a family navigator. In-home visits conducted at baseline, three months, and six months assessed the participants’ height/weight and dietary intake. All participants received basic healthy lifestyle education emphasizing increased water intake and decreased SSB consumption. Longitudinal linear mixed models were conducted to examine between-group and within-group changes in BMI z-scores, and water/SSB intake. Results: No significant between-group differences in BMI z-score were found at three (p = 0.908) and six months (p = 0.919). However, the within-group increase in BMI z-score in the control group was significant from baseline to three months (p = 0.029). A between-group comparison found the treatment group significantly increased their water intake at three (p = 0.006) and six months (p = 0.002). No between-group differences were identified at three and six months for the reduction in SSB, although the treatment group did demonstrate a decrease from baseline to three months (p = 0.004). Conclusions: Neither group experienced unhealthy increases (>0.5%) in BMI z-score over the six months. Providing a safe/free water supply showed a superior improvement in water consumption in the treatment group, and an initial decrease in SSB. Further studies are needed to identify feasible, effective, and sustainable lifestyle interventions tailored to this at-risk population.

We discuss MET, a learning-based algorithm proposed for perceiving a patient's level of engagement during telehealth sessions. We leverage latent vectors corresponding to Affective and Cognitive features frequently used in psychology literature to understand a person's level of engagement in a semi-supervised GAN-based framework. We showcase the efficacy of this method from the perspective of mental health and more specifically how this can be leveraged for a better understanding of patient engagement during telemental health sessions. To further the development of similar technologies that can be useful for telehealth, we also plan to release a dataset MEDICA containing 1299 video clips, each 3 seconds long and show experiments on the same. Our framework reports a 40% improvement in RMSE (Root Mean Squared Error) over state-of-the-art methods for engagement estimation. In our real-world tests, we also observed positive correlations between the working alliance inventory scores reported by psychotherapists. This indicates the potential of the proposed model to present patient engagement estimations that aligns well with the engagement measures used by psychotherapists.