TY - JOUR
T1 - Polyneuro risk scores capture widely distributed connectivity patterns of cognition
AU - Byington, Nora
AU - Grimsrud, Gracie
AU - Mooney, Michael A.
AU - Cordova, Michaela
AU - Doyle, Olivia
AU - Hermosillo, Robert J.M.
AU - Earl, Eric
AU - Houghton, Audrey
AU - Conan, Gregory
AU - Hendrickson, Timothy J.
AU - Ragothaman, Anjanibhargavi
AU - Carrasco, Cristian Morales
AU - Rueter, Amanda
AU - Perrone, Anders
AU - Moore, Lucille A.
AU - Graham, Alice
AU - Nigg, Joel T.
AU - Thompson, Wesley K.
AU - Nelson, Steven M.
AU - Feczko, Eric
AU - Fair, Damien A.
AU - Miranda-Dominguez, Oscar
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2023/4
Y1 - 2023/4
N2 - Resting-state functional connectivity (RSFC) is a powerful tool for characterizing brain changes, but it has yet to reliably predict higher-order cognition. This may be attributed to small effect sizes of such brain-behavior relationships, which can lead to underpowered, variable results when utilizing typical sample sizes (N∼25). Inspired by techniques in genomics, we implement the polyneuro risk score (PNRS) framework - the application of multivariate techniques to RSFC data and validation in an independent sample. Utilizing the Adolescent Brain Cognitive Development® cohort split into two datasets, we explore the framework's ability to reliably capture brain-behavior relationships across 3 cognitive scores – general ability, executive function, learning & memory. The weight and significance of each connection is assessed in the first dataset, and a PNRS is calculated for each participant in the second. Results support the PNRS framework as a suitable methodology to inspect the distribution of connections contributing towards behavior, with explained variance ranging from 1.0 % to 21.4 %. For the outcomes assessed, the framework reveals globally distributed, rather than localized, patterns of predictive connections. Larger samples are likely necessary to systematically identify the specific connections contributing towards complex outcomes. The PNRS framework could be applied translationally to identify neurologically distinct subtypes of neurodevelopmental disorders.
AB - Resting-state functional connectivity (RSFC) is a powerful tool for characterizing brain changes, but it has yet to reliably predict higher-order cognition. This may be attributed to small effect sizes of such brain-behavior relationships, which can lead to underpowered, variable results when utilizing typical sample sizes (N∼25). Inspired by techniques in genomics, we implement the polyneuro risk score (PNRS) framework - the application of multivariate techniques to RSFC data and validation in an independent sample. Utilizing the Adolescent Brain Cognitive Development® cohort split into two datasets, we explore the framework's ability to reliably capture brain-behavior relationships across 3 cognitive scores – general ability, executive function, learning & memory. The weight and significance of each connection is assessed in the first dataset, and a PNRS is calculated for each participant in the second. Results support the PNRS framework as a suitable methodology to inspect the distribution of connections contributing towards behavior, with explained variance ranging from 1.0 % to 21.4 %. For the outcomes assessed, the framework reveals globally distributed, rather than localized, patterns of predictive connections. Larger samples are likely necessary to systematically identify the specific connections contributing towards complex outcomes. The PNRS framework could be applied translationally to identify neurologically distinct subtypes of neurodevelopmental disorders.
KW - BWAS
KW - Big data
KW - MRI
KW - Neuroimaging
KW - PNRS
KW - Reproducibility
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U2 - 10.1016/j.dcn.2023.101231
DO - 10.1016/j.dcn.2023.101231
M3 - Article
C2 - 36934605
AN - SCOPUS:85150250723
SN - 1878-9293
VL - 60
JO - Developmental Cognitive Neuroscience
JF - Developmental Cognitive Neuroscience
M1 - 101231
ER -