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Polyneuro risk scores capture widely distributed connectivity patterns of cognition

2023; Elsevier BV; Volume: 60; Linguagem: Inglês

10.1016/j.dcn.2023.101231

1878-9307

Nora Byington, Gracie Grimsrud, Michael A. Mooney, Michaela Cordova, Olivia Doyle, Robert Hermosillo, Eric Earl, Audrey Houghton, Gregory Conan, Timothy Hendrickson, Anjanibhargavi Ragothaman, Cristian Morales Carrasco, Amanda Rueter, Anders Perrone, Lucille A. Moore, Alice M. Graham, Joel T. Nigg, Wesley K. Thompson, Scott M. Nelson, Eric Feczko, Damien A. Fair, Óscar Miranda-Domínguez,

Attention Deficit Hyperactivity Disorder

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.