Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study

Jianzhong Chen, Angela Tam, Valeria Kebets, Csaba Orban, Leon Qi Rong Ooi, Christopher L. Asplund, Scott Marek, Nico U. F. Dosenbach, Simon B. Eickhoff, Danilo Bzdok, Avram J. Holmes and B. T. Thomas Yeo ()
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Jianzhong Chen: National University of Singapore
Angela Tam: National University of Singapore
Valeria Kebets: National University of Singapore
Csaba Orban: National University of Singapore
Leon Qi Rong Ooi: National University of Singapore
Christopher L. Asplund: National University of Singapore
Scott Marek: Washington University School of Medicine
Nico U. F. Dosenbach: Washington University School of Medicine
Simon B. Eickhoff: Heinrich-Heine University Düsseldorf
Danilo Bzdok: McGill University
Avram J. Holmes: Yale University, Departments of Psychology and Psychiatry
B. T. Thomas Yeo: National University of Singapore

Nature Communications, 2022, vol. 13, issue 1, 1-17

Abstract: Abstract How individual differences in brain network organization track behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, most studies focus on single behavioral traits, thus not capturing broader relationships across behaviors. In a large sample of 1858 typically developing children from the Adolescent Brain Cognitive Development (ABCD) study, we show that predictive network features are distinct across the domains of cognitive performance, personality scores and mental health assessments. On the other hand, traits within each behavioral domain are predicted by similar network features. Predictive network features and models generalize to other behavioral measures within the same behavioral domain. Although tasks are known to modulate the functional connectome, predictive network features are similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood.

Date: 2022
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DOI: 10.1038/s41467-022-29766-8

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