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Peer-reviewed journal article

Variability in cognitive task performance in early adolescence is associated with stronger between-network anticorrelation and future attention problems

Sarah E. ChangiD, Agatha LenartowicziD, Gerhard HellemanniD, Lucina Q. UddiniD, Carrie E. BeardeniD

Abstract

BackgroundIntra-individual variability (IIV) during cognitive task performance is a key behavioral index of attention and consistent marker of ADHD. In adults, lower IIV has been associated with anticorrelation between the default mode network (DMN) and dorsal attention network (DAN) - thought to underlie effective allocation of attention. However, whether these behavioral and neural markers of attention are (i) associated with each other and (ii) can predict future attention-related deficits has not been examined in a developmental, population-based cohort. MethodsWe examined relationships at the baseline visit between IIV on three cognitive tasks, DMN-DAN anticorrelation, and parent-reported attention problems using the Adolescent Brain and Cognitive Development Study (n=11,878 participants, aged 9-10, female=47.8%). We also investigated whether behavioral and neural markers of attention at baseline predicted attention problems, 1, 2, and 3 years later. ResultsAt baseline, greater DMN-DAN anticorrelation was associated with lower IIV across all three cognitive tasks (unstandardized {beta}: 0.22-0.25). Older age at baseline was associated with stronger DMN-DAN anticorrelation and lower IIV ({beta}: -0.005--0.0004). Weaker DMN-DAN anticorrelation and IIV were cross-sectionally associated with attention problems ({beta}: 1.41-7.63). Longitudinally, lower IIV at baseline was associated with less severe attention problems, 1-3 years later, after accounting for baseline attention problems ({beta}: 0.288-0.77). ConclusionsThe results suggest that IIV in early adolescence is associated with worsening attention problems in a representative cohort of US youth. Attention deficits in early adolescence may be important for understanding and predicting future cognitive and clinical outcomes.

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