The structure of cognition in 9 and 10 year-old children and associations with problem behaviors: Findings from the ABCD study’s baseline neurocognitive battery

Abstract

The Adolescent Brain Cognitive Development (ABCD) study is poised to be the largest single-cohort long-term longitudinal study of neurodevelopment and child health in the United States. Baseline data on N = 4521 children aged 9–10 were released for public access on November 2, 2018. In this paper we performed principal component analyses of the neurocognitive assessments administered to the baseline sample. The neurocognitive battery included seven measures from the NIH Toolbox as well as five other tasks. We implemented a Bayesian Probabilistic Principal Components Analysis (BPPCA) model that incorporated nesting of subjects within families and within data collection sites. We extracted varimax-rotated component scores from a three-component model and associated these scores with parent-rated Child Behavior Checklist (CBCL) internalizing, externalizing, and stress reactivity. We found evidence for three broad components that encompass general cognitive ability, executive function, and learning/memory. These were significantly associated with CBCL scores in a differential manner but with small effect sizes. These findings set the stage for longitudinal analysis of neurocognitive and psychopathological data from the ABCD cohort as they age into the period of maximal adolescent risk-taking.

Behavioral and neural signatures of working memory in childhood

“…Positive relationships between working memory, language abilities, and fluid intelligence replicate previous work on the structure of cognition in children and adults (61)(62)(63). As expected, children with stronger working memory abilities (measured with the List Sorting Working Memory Test) also showed better performance on tests of episodic memory (Picture Sequence Memory), short-term memory (Rey Auditory Verbal Learning), and low-and high-load working memory (emotional n-back 0-and 2back conditions, respectively).…”

“…Although Bayesian probabilistic PCA can account for missing data as well as the nesting of participants in families and data collection sites (61), this approach assumes that missing data occur randomly, independent of other sample features (101). This assumption is violated in the current sample, as, for example, children with better working memory function are less likely to be missing other behavioral measures (Spearman correlation between list sorting performance and number of missing performance measures = -0.06, p = 6.39×10 -5 ).…”

Distinct regionalization patterns of cortical morphology are associated with cognitive performance across different domains

“…Due to the unprecendented statistical power in this study, we can infer that total CSA is unlikely, across other studies, to account for any more individual variability in cognitive scores in children of this age. Indeed, this effect is much smaller than the shared variance across cognitive measures that has been attributed to 'g' (~21%) in a similar sample from the ABCD study 33 . Moreover, the relative importance of this measure for predicting individual variability in cognition differed as a function of the type of intelligence measured, which is inconsistent with this being a global effect.…”

Parental and social factors in relation to child psychopathology, behavior, and cognitive function

“…org), including flanker (inhibitory control), dimensional change card sort (cognitive flexibility), list sorting working memory (working memory), picture sequence memory (episodic memory), pattern comparison processing speed (processing speed), picture vocabulary (vocabulary comprehension), and oral reading recognition tasks (reading decoding). ABCD also administered Matrix Reasoning Task from the Wechsler Intelligence Test for Children-V (fluid Reasoning) 33 , Little Man Task (LMT, visual-spatial processing), Rey Auditory Verbal Learning Test (RAVLT, auditory learning, memory, and recognition), and Cash Choice Task (a singleitem delayed gratification measure with dichotomous scoring). Notably, we employed the response accuracy of LMT, the delayed recall accuracy of RAVLT, and the total scaled score of Matrix Reasoning.…”