Differences in the functional brain architecture of sustained attention and working memory in youth and adults

Abstract

Sustained attention (SA) and working memory (WM) are critical processes, but the brain networks supporting these abilities in development are unknown. We characterized the functional brain architecture of SA and WM in 9- to 11-year-old children and adults. First, we found that adult network predictors of SA generalized to predict individual differences and fluctuations in SA in youth. A WM model predicted WM performance both across and within children—and captured individual differences in later recognition memory—but underperformed in youth relative to adults. We next characterized functional connections differentially related to SA and WM in youth compared to adults. Results revealed 2 network configurations: a dominant architecture predicting performance in both age groups and a secondary architecture, more prominent for WM than SA, predicting performance in each age group differently. Thus, functional connectivity (FC) predicts SA and WM in youth, with networks predicting WM performance differing more between youths and adults than those predicting SA.

Neighborhood air pollution is negatively associated with neurocognitive maturation in early adolescence

“…The n-back task (see Methods) includes 0-back task blocks, in which participants are asked to indicate via button press whether every image in a series matches a target image, and 2-back blocks, in which participants indicate whether every image matches the image seen two trials before (26 sec per block; 8 blocks per run; two approximately 5-min runs per participant at each time-point). The nback task is thought to assess cognitive processes including sustained attention, working memory, and cognitive control [Barch et al, 2013;Kardan et al, 2022], and is reliably related to other measures of childhood cognitive performance (e.g., the NIH-Toolbox; Rosenberg et al, 2020) and attentional problems (e.g., the Child Behavior Checklist attention subscale; Kardan et al, 2022). We found that n-back accuracy significantly improved from baseline to Y2 (mean Δ Acc = +7.3%, t = 45.80, p < .001).…”

“…To capture the development of brain networks supporting cognitive performance, we quantified the strength of an existing predictive brain network, the cognitive composite Connectome-based Predictive Model (ccCPM). This "publication-preregistered" network model was defined in Kardan et al (2022) and shared at https://nda.nih.gov/study.html?id=1849. The strength of expression of ccCPM was measured using each participant's fMRI connectivity during the n-back task at both baseline and Y2 (n = 1,159 with sufficient fMRI data at both timepoints; see Methods for fMRI exclusion criteria).…”

“…The ccCPM includes two sets of functional brain connections (Figure 2) that predict higher or lower performance on the out-of-scanner NIH Toolbox Cognitive Battery (Weintraub et al, 2013; see Methods). The ccCPM was previously trained in ABCD Study baseline fMRI connectivity data using leave-one-site-out cross-validation (Kardan et al, 2022). Here, the ccCPM brain expression score was calculated from baseline and Y2 data iteratively for participants from the left-out sites to estimate the neurodevelopmental change in network predictors of general cognitive performance between the two ages.…”

“…Connectome-based predictive modeling (CPM) is an approach that identifies functional connectivity networks (here connectivity is correlated fMRI activity time-courses of pairs of brain regions) that are related to behavior and measures the strength of these networks in previously unseen individuals to make predictions about their behavior [Shen et al, 2017]. Since adolescence is a critical period for the development of attention and working memory and the brain networks supporting them [Spear, 2013;Keller et al, 2022;Kardan et al, 2022], it is possible that environmental influences such as exposure to PM2.5 and NO2 may influence this protracted [Keller et al, 2022] neurobehavioral development.…”

“…The current study seeks to bridge these literatures by assessing the relationship between neighborhood air pollution and developmental change in attention and working memory performance, as well as the brain networks that support general cognitive performance in youth, from pre-to early adolescence. We utilized neighborhood socioeconomic and air pollution variables in conjunction with behavioral and neuroimaging measures of attention and memory from the baseline time-point (ages 9-10) and the two-year follow-up time-point (Y2; Kardan et al, 2022), from baseline to Y2 for each participant using their fMRI connectivity at each time-point. To ensure independent associations with air pollution are assessed, we included other family and environmental factors that may predict individual differences in neurocognition [Viner et al, 2012;Vargas et al, 2020;Sripada et al, 2021] as covariates in our models.…”