Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study. Chen, J., Tam, A., Kebets, V., Orban, C., Ooi, L. Q. R., Asplund, C. L., Marek, S., Dosenbach, N. U. F., Eickhoff, S. B., Bzdok, D., Holmes, A. J., & Yeo, B. T. T. Nature Communications, 13(1):2217, April, 2022. Number: 1 Publisher: Nature Publishing Group![Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{chen_shared_2022,
title = {Shared and unique brain network features predict cognitive, personality, and mental health scores in the {ABCD} study},
volume = {13},
copyright = {2022 The Author(s)},
issn = {2041-1723},
url = {https://www.nature.com/articles/s41467-022-29766-8},
doi = {10.1038/s41467-022-29766-8},
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.},
language = {en},
number = {1},
urldate = {2022-05-02},
journal = {Nature Communications},
author = {Chen, Jianzhong and Tam, Angela and Kebets, Valeria and Orban, Csaba and Ooi, Leon Qi Rong and Asplund, Christopher L. and Marek, Scott and Dosenbach, Nico U. F. and Eickhoff, Simon B. and Bzdok, Danilo and Holmes, Avram J. and Yeo, B. T. Thomas},
month = apr,
year = {2022},
note = {Number: 1
Publisher: Nature Publishing Group},
keywords = {Cognitive neuroscience, Computational neuroscience},
pages = {2217},
}
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