Identifying reproducible individual differences in childhood functional brain networks: An ABCD study

Abstract

The 21-site Adolescent Brain Cognitive Development (ABCD) study provides an unparalleled opportunity to characterize functional brain development via resting-state functional connectivity (RSFC) and to quantify relationships between RSFC and behavior. This multi-site data set includes potentially confounding sources of variance, such as differences between data collection sites and/or scanner manufacturers, in addition to those inherent to RSFC (e.g., head motion). The ABCD project provides a framework for characterizing and reproducing RSFC and RSFC-behavior associations, while quantifying the extent to which sources of variability bias RSFC estimates. We quantified RSFC and functional network architecture in 2,188 9–10-year old children from the ABCD study, segregated into demographically-matched discovery (N =1,166) and replication datasets (N = 1,022). We found RSFC and network architecture to be highly reproducible across children. We did not observe strong effects of site; however, scanner manufacturer effects were large, reproducible, and followed a “short-to-long” association with distance between regions. Accounting for potential confounding variables, we replicated that RSFC between several higher-order networks was related to general cognition. In sum, we provide a framework for how to characterize RSFC-behavior relationships in a rigorous and reproducible manner using the ABCD dataset and other large multi-site projects.

Functional connectome fingerprinting accuracy in youths and adults is similar when examined on the same day and 1.5‐years apart

“…Furthermore, because identification accuracy in both youth and adults was lower across a longer period of time and similar edges contributed to same day and 1.5-year identification accuracy, our results suggest that this reduction is not solely due to known developmental changes. Significant cognitive development occurs through adolescence (Larsen & Luna, 2018;Luna et al, 2015;Steinberg, 2005), in the context of evidence for stability at the group level in network properties Jalbrzikowski, Murty, et al, 2019;Marek et al, 2015;Marek et al, 2019). The stability in identification accuracy across development further supports that implication that network properties contain individualized foundational properties that define uniqueness.…”

Towards Reproducible Brain-Wide Association Studies

“…To further test the reproducibility of univariate brain-phenotype associations, we determined the out-of-sample replication of brain-wide associations using established discovery (N=1,964) and replication (N=1,964) ABCD sets, matched across a broad range of demographic factors 29 (Extended data Fig. S9).…”

“…For network level analyses, correlations were averaged across previously defined canonical functional networks 59 . Inter-individual difference connectome-wide spatial components, which are not bound by network boundaries 29,33 , were computed by performing principal component analysis (PCA) on a matrix composed of all ROI x ROI pairs (edges) from each participant.…”

Shared and unique brain network features predict cognition, personality and mental health in childhood

“…Whole-brain connectome-wide neurodevelopmental studies have found associations between resting-state functional network organization and behavioral traits (Satterthwaite et al 2015, Karcher et al 2019, Marek et al 2019, Pornpattananangkul et al 2019 . However, clinical decisions are made at the individual level (Milham et al 2017, Bzdok andMeyer-Lindenberg 2018) .…”