Understanding the patterns of communication between brain regions, and how they develop across childhood, is critical for understanding the development of the neural mechanisms that implement complex cognitive operations such as reasoning. Functional connectivity, or correlations in patterns of brain activation (measured via fMRI), is thought to reflect this inter-regional communication. But communication between brain regions ultimately depends on structural connectivity: the white matter tracts that, either directly or indirectly, connect them. This proposal is aimed at resolving two open questions: 1) What are the dynamic relationships between structural and functional connectivity, for the key networks known to be involved in reasoning and other higher cognitive processes, as these develop together across childhood?, and 2) How do these dynamic relationships affect developmental improvements in reasoning ability? The answers that we obtain will provide both fundamental insight into the development of brain connectivity and mechanistic insight into the development of reasoning ability. This knowledge could impact future research both on educational and training programs designed to teach reasoning ability, and on interventions or medical programs designed to correct problems in reasoning.

To address these questions, we will combine fMRI, DTI, and behavioral data from three longitudinal developmental datasets, collected at UC Berkeley, UC Davis, and Vanderbilt University. By combining datasets, we are able to examine longitudinal data from 400 children and young adults between the ages of 6 and 22. Our primary measures of interest include a) intrinsic functional connectivity between specific regions of interest; b) structural connectivity, measured via fractional anisotropy along tracts that connect these regions; and c) reasoning ability, measured via standard cognitive tests. Our main analyses will focus on connectivity within and between two brain networks, the fronto-parietal network and cingulo-opercular network, which are most closely associated with reasoning and other higher cognitive functions. Mixed-model regression analyses will be employed to examine concurrent relationships among structural and functional connectivity in these networks and reasoning ability. Multivariate latent difference score models will be employed to examine lead-lag relationships. The outcome of this research will be a model of interacting structural and functional connectivity development across childhood, and of how the co-development of these two indicators of neural communication relates to developmental improvements in reasoning ability.

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University of California Davis
United States
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