Demographic and mental health assessments in the adolescent brain and cognitive development study: Updates and age-related trajectories

Deanna, M; Albaugh, Matthew D.; Baskin‐Sommers, Arielle; Bryant, Brittany E.; Clark, Duncan B.; Dick, Anthony Steven; Feczko, Eric; Foxe, John J.; Gee, Dylan G.; Giedd, Jay N.; Glantz, Meyer D.; Hudziak, James J.; Karcher, Nicole R.; LeBlanc, Kimberly H.; Maddox, Melanie; McGlade, Erin; Mulford, Carrie F.; Nagel, Bonnie J.; Neigh, Gretchen N.; Palmer, Clare E.; Potter, Alexandra; Sher, Kenneth J.; Tapert, Susan F.; Thompson, Wesley K.; Xie, Laili

doi:10.1016/j.dcn.2021.101031

Cited by 55 publications

(44 citation statements)

References 92 publications

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“…The initial goal of the ABCD Study was to examine risk and resilience factors associated with the development of substance use disorders (i.e., cannabis), but the aims of the project have since expanded and now will inform population-level inferences about the biopsychosocial correlates of mental and physical health in the second decade of life ( Barch et al, 2021 ). As with any longitudinal study, understanding the purpose of the study, how study data were collected, and any historical cohort effects on data collection is essential.…”

Section: Features Of the Abcd Studymentioning

confidence: 99%

“…When considering starting a project using ABCD data, it is important to first gain an understanding of what questions can be answered from the study. This step will involve reviewing the study’s overall design, methods for data collection, measures included at each wave ( Barch et al, 2018 , Casey et al, 2018 , Hagler et al, 2019 ) and what, if any, changes are expected to be made in future waves of the study (for an example, see Barch et al, 2021 ). Reviewing preregistered studies that plan to use ABCD data ( https://osf.io/search/ ) and existing studies of ABCD data found on the ABCD Study website ( https://abcdstudy.org/publications/ ), can aid in conceptualizing what types of questions can be answered using ABCD data.…”

Section: Think Before You Do: Necessary Steps and Considerations When...mentioning

confidence: 99%

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A practical guide for researchers and reviewers using the ABCD Study and other large longitudinal datasets

Saragosa-Harris

Chaku

MacSweeney

et al. 2022

Developmental Cognitive Neuroscience

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Section: Features Of the Abcd Studymentioning

confidence: 99%

Section: Think Before You Do: Necessary Steps and Considerations When...mentioning

confidence: 99%

A practical guide for researchers and reviewers using the ABCD Study and other large longitudinal datasets

Saragosa-Harris

Chaku

MacSweeney

et al. 2022

Developmental Cognitive Neuroscience

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“…Our measures of child mental health outcomes are comprehensive and consistent with the DSM-5. 33,35 However, interpretation of our findings is also subjective to several limitations. First, all mental health measures were based on self-report.…”

Section: Strengths and Limitationsmentioning

confidence: 82%

“…34 The rationale for using a parent/caregiver-report instrument for comprehensive mental health assessment has been discussed previously by the ABCD investigators. 33,[35][36][37] We used parent-and child-reported current and past suicidal ideation and suicide attempts from the Kiddie Schedule for Affective Disorders and Schizophrenia-Present and Lifetime Version using the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) criteria (K-SADS-PL DSM-5). 38,39 Any endorsement of the 18 suicidality-related items by either parent or child was included.…”

Section: Child Mental Health Outcomesmentioning

confidence: 99%

Social Determinants of Health and Suicidal Behaviors among Children: U.S. Longitudinal Adolescent Brain Cognitive Development (ABCD) Study

Xiao

Mann

Hou

et al. 2022

Preprint

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ImportanceSocial determinants of health (SDoH) have well-characterized associations with child mental health outcomes. Their complex, multilevel influences on child mental health are less well understood.ObjectiveTo identify patterns across multiple domains of SDoH and estimate their associations with child mental health and suicidality outcomes over time.DesignPanel study of 11 810 children aged 9 to 10 years who were enrolled in the Adolescent Brain Cognitive Development (ABCD) study and followed from September 1, 2016, and April 24, 2021.SettingNationally-representative, population-based study across 21 sites in the U.S.ParticipantsABCD participants and their participating parents/guardians.Exposures84 structural SDoH factors at baseline across 9 domains (discrimination, crime and drug use, education, health and environment, family type and disability, housing and transportation, minority status and language, socioeconomic status, and urbanization). We used unsupervised machine learning to identify patterns of clustering underlying the SDoH data.Main Outcomes and MeasuresChild mental health was measured with the parent-report Child Behavior Checklist. Suicidal ideation and suicide attempts were measured with child- and parent-report computerized versions of the Kiddie Schedule for Affective Disorders and Schizophrenia.ResultsOf 10 504 children included at baseline (median [SD] age, 9.9 [0.6] years), 5510 [52.5%] were boys and 4994 [47.5%] were girls; 229 (2.2%) were Asian, 1468 (14.0%) were Black, 2128 (20.3%) were Hispanic, 5565 (53.0%) were White, and 1108 (10.5%) were multiracial. Four SDoH patterns were identified: affluence (SDoH Pattern I); structural racism and discrimination (SDoH Pattern II); socioeconomic deprivation (SDoH Pattern III); and high crime, low education, and populated (SDoH Pattern IV) areas. Children with High Socioeconomic Deprivation at baseline (SDoH Pattern III) reported higher rates of externalizing (β, 1.43, 95% CI, 0.83, 2.02), internalizing (β, 0.75, 95% CI, 0.14, 1.37), and total (β, 1.16, 95% CI, 0.50, 1.81) problems, but these trajectories decreased over time. Children with High Structural Racism and Discrimination at baseline were the only group showing increasing trajectories of suicide attempts (OR, 1.42, 95% CI, [1.04, 1.93]) and depression (β, 0.19, 95% CI, 0.08, 0.29) over time. In contrast, living in affluent communities (SDoH Pattern I) was associated with lower rates of all internalizing and externalizing problems at baseline, but increasing trajectories of depression (β, 0.17, 95% CI, 0.09, 0.25), anxiety (β, 0.10, 95% CI, 0.02, 0.18), and withdrawal (β, 0.09, 95% CI, 0.01, 0.17) over time.Conclusions and RelevanceMultiple domains of SDoH are associated with child mental health outcomes in cross section and over time. Targeted structural interventions may improve mental health outcomes and reduce suicide attempts among children.Key PointsQuestionWhat are the social determinants of mental health, suicidal ideation, and suicidal behavior among children in the U.S.?FindingsIn this cohort of 10 504 children, we used machine learning to identify four patterns of social determinants of health (SDoH). At baseline, socioeconomic deprivation was associated with internalizing and externalizing problems. Over follow-up, structural racism and discrimination were associated with suicide attempts.MeaningMultiple dimensions of structural interventions targeting different SDoH are needed to improve child mental health outcomes.

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“…Participants’ psychological resilience at the three-year follow-up was quantified by regressing out lifetime history of adverse experiences from the general psychopathology risk index (100, 101) multiplied by −1. Positive and negative residuals indicate greater and poorer resilience, respectively, relative to the sample mean (i.e., better-vs. worse-than-expected psychological functioning given experienced adversity).…”

Section: Methodsmentioning

confidence: 99%

Psychological Resilience in Adolescence as a function of Genetic Risk for Major Depressive Disorder and Alzheimer’s Disease

Petrican

Fornito

2022

Preprint

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Major Depressive Disorder (MDD) and Alzheimer's Disease (AD) are two pathologies linked to prior stress exposure and altered neurodevelopmental trajectories. As a putative antecedent to AD, MDD could be key to understanding the neurobiological changes that precede the clinical onset of AD by decades. To test this hypothesis, we used longitudinal data from the Adolescent Brain and Cognitive Development study (Ntotal = 980, 470 females) and investigated overlapping connectomic, transcriptomic, and chemoarchitectural correlates of adjustment to stressors (i.e., resilience) among adolescents at genetic risk for AD and MDD, respectively. The potential for perinatal adversity to directly and/or indirectly, via accelerated biological ageing, foster resilience (i.e., "inoculation" effects) was also probed. We identified two distinguishable neurodevelopmental profiles predictive of resilience among MDD-vulnerable adolescents. One profile, expressed among the fastest developing youth, overlapped with areas of greater dopamine receptor density and reflected the maturational refinement of the inhibitory control architecture. The second profile distinguished resilient MDD-prone youth from psychologically vulnerable adolescents genetically predisposed towards AD. This profile, associated with elevated GABA, relative to glutamate, receptor density, captured the longitudinal refinement and increasing context specificity of incentive-related brain activations. Its transcriptomic signature implied that poorer resilience among AD-prone youth may be associated with greater expression of MDD-relevant genes. Our findings are compatible with the proposed role of MDD as a precursor to AD and underscore the pivotal contribution of incentive processing to this relationship. They further speak to the key neuromodulatory role of DA-gonadal hormone interactions in fostering resilience in adolescence.

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Demographic and mental health assessments in the adolescent brain and cognitive development study: Updates and age-related trajectories

Cited by 55 publications

References 92 publications

A practical guide for researchers and reviewers using the ABCD Study and other large longitudinal datasets

A practical guide for researchers and reviewers using the ABCD Study and other large longitudinal datasets

Social Determinants of Health and Suicidal Behaviors among Children: U.S. Longitudinal Adolescent Brain Cognitive Development (ABCD) Study

Psychological Resilience in Adolescence as a function of Genetic Risk for Major Depressive Disorder and Alzheimer’s Disease

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