Recruiting the ABCD sample: Design considerations and procedures

Garavan, Hugh; Bartsch, Hauke; Conway, Kevin P.; DeCastro, Alex; Goldstein, Rita Z.; Heeringa, Steven G.; Jernigan, Terry L.; Potter, Alexandra; Thompson, Wesley K.; Zahs, Daniel A.

doi:10.1016/j.dcn.2018.04.004

Cited by 861 publications

(884 citation statements)

References 28 publications

(31 reference statements)

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“…The ABCD Study is a longitudinal study across 21 data acquisition sites in the U.S. following 11,875 children starting at 9 and 10 years old. Detailed study designs and recruitment procedures, imaging acquisitions, and preprocessing pipelines are described in Garavan et al, (2018), B.J. Casey et al, (2018) and Hagler et al, (2018) respectively.…”

Section: Empirical Datamentioning

confidence: 99%

The Bayesian polyvertex score (PVS-B): a whole-brain phenotypic prediction framework for neuroimaging studies

Zhao

Palmer

Thompson

et al. 2019

Preprint

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The traditional brain mapping approach has greatly advanced our understanding of the localized effect of the brain on behavior. However, the statistically significant brain regions identified by the standard mass univariate models only explain minimal variance in behavior despite increased sample sizes and statistical power, highlighting the nonsparseness of the explanatory signal in the brain. We introduced the Bayesian polyvertex score (PVS-B), a wholebrain prediction framework that aggregates the effect sizes across all vertices to predict individual variability in behavior. The PVS-B estimates the posterior mean effect size at each vertex with the summary statistics from the brain mapping approach and the correlation structure of the imaging phenotype. Empirical data showed that the PVS-B was able to double the variance explained of the total composite cognition score by an nBack fMRI contrast when compared to prediction models based on the mass univariate parameter estimates as well as models based on p-value thresholds. A fivefold improvement in variance explained by the PVS-B was observed using the stop signal fMRI contrast to predict individual variability in the stop signal reaction time. We believe that the PVS-B can shed light on the multivariate investigation of brain-behavioral associations and will empower small scale neuroimaging studies with more reliable and accurate effect size estimates. Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). The ABCD Study is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood.

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Section: Empirical Datamentioning

confidence: 99%

The Bayesian polyvertex score (PVS-B): a whole-brain phenotypic prediction framework for neuroimaging studies

Zhao

Palmer

Thompson

et al. 2019

Preprint

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“…This paper analysed the full baseline sample from release 2.0.1 (NDAR DOI: 10.15154/1504041). The study used specific recruitment strategies to create a populationbased, demographically diverse sample, however it is not necessarily representative of the U.S. national population (Compton, Dowling, & Garavan, 2019;Garavan et al, 2018). Due to the inclusion of a wide range of individuals across different races, ethnicities and socioeconomic backgrounds, it is important to control for effects of self-declared race and ethnicity, household income and parental education when using this dataset due to potential confounding associations between these demographic variables and our effects of interest.…”

Section: Samplementioning

confidence: 99%

Determining the association between regionalisation of cortical morphology and cognition in 10,145 children

Zhao

Loughnan

et al. 2019

Preprint

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During development individuals undergo protracted changes in cortical morphology, which coincide with changes in cognition. A number of studies have measured the association between brain structure and cognition in children, however these frequently rely on mass univariate statistics or ROI-based comparisons and do not always measure cortical morphology relative to global brain measures. After controlling for global brain measures, we can see a residual regionalisation pattern indicating the size or thickness of different regions relative to the total cortical surface area or mean thickness. Individual variability in this regionalisation may be important for predicting between subject variability in cognitive performance. Here we sought to determine whether the relative configuration of cortical architecture across the whole cortex was associated with cognition using a novel multivariate omnibus statistical test (MOSTest). This method is optimally powered to detect associations that are widely distributed across the cortex. We then used the Polyvertex score (PVS) to quantify the magnitude of the association between vertex-wise cortical morphology and cognitive performance. We have determined that the relative pattern of cortical architecture, after removing the variance associated with global brain measures, predicted unique variance associated with cognition across different modalities.

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“…longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years The recruitment strategy has been described in detail previously 31 . Children were recruited from 22 study sites and ABCD is following children at 21 study sites across the United States.…”

Section: Resultsmentioning

confidence: 99%

Economic, social, and physiological resilience predict brain structure and cognitive performance in 9 - 10-year-old children

Gonzalez

Palmer²,

Uban³

et al. 2019

Preprint

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While children with economic disadvantage are at risk for poorer outcomes in cognitive and brain development, less understood is the contribution of other factors in the broader socioeconomic context that may more closely index the underlying mechanisms influencing risk and resilience. We examined brain structure and cognitive test performance in association with economic disadvantage and 22 measures in the broader socioeconomic context among n = 8,158 demographically diverse 9-10-year-old children from the ABCD Study. Total cortical surface area and total cognition scores increased as a function of income-to-needs, with the steepest differences most apparent among children below and near poverty relative to their wealthier peers. We found three latent factors encompassing distinct relationships among our proximal measures, including social, economic, and physiological well-being, each associated with brain structure and cognitive performance independently of economic advantage.Our findings will inform future studies of risk and resilience in developmental outcomes for children with economic disadvantage.

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Recruiting the ABCD sample: Design considerations and procedures

Cited by 861 publications

References 28 publications

The Bayesian polyvertex score (PVS-B): a whole-brain phenotypic prediction framework for neuroimaging studies

The Bayesian polyvertex score (PVS-B): a whole-brain phenotypic prediction framework for neuroimaging studies

Determining the association between regionalisation of cortical morphology and cognition in 10,145 children

Economic, social, and physiological resilience predict brain structure and cognitive performance in 9 - 10-year-old children

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