Evolution of the Linked Architecture of Network Control and Executive Function in Adolescence

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 DESCRIPTION (provided by applicant): The rapid development of executive function is a hallmark of adolescence, and requires the integrated recruitment of large-scale neural circuitry spanning multiple brain regions. Here we propose to develop and apply methods from network control theory to brain imaging data in order to allow us to understand how executive function develops in youth. Recent advances in control and dynamical systems theory have provided quantitative diagnostics of network controllability, which collectively define how external input t network nodes (in this case brain areas) can move the entire system (in this case cognitive function). Further, these methods allow quantitative estimation of the costs of disparate control structures. While these techniques have not previously been applied to brain imaging data, they provide an intuitive mechanism for executive function, and therefore have the potential to be putative biomarkers of executive capability. In this proposal, we capitalize upon existing diffusion imaging and working-memory task fMRI data acquired in a large sample of youth ages 8-22 imaged as part of the Philadelphia Neurodevelopmental Cohort (PNC). In Aim 1, we will describe the network control structure of the brain's structural connectome by (i) identifying driver nodes of network control and mapping their relationship to known executive networks and (ii) quantifying test-retest reliability of network control diagnostics. In Aim 2, we will chart th evolution of network control in adolescence by characterizing the development of network control diagnostics using cross-sectional (n=968) and longitudinal data (n=350) PNC data. In Aim 3, we will determine how network control is associated with executive function by examining whether individuals with higher levels of network control demonstrate better executive functioning, and investigating if baseline controllability predicts subsequent longitudinal improvement of executive function. In Aim 4, we will provide a publically available toolbox for measurement of network controllability as a resource to the neuroimaging community. This multi-disciplinary research has the potential to yield high-impact discoveries, and therefore represents a good fit for the R21 mechanism. Risks associated with the innovation in this project are tempered by the strength of our team (expertise in network science, developmental neuroimaging, and neuropsychiatry), the multiple levels of hypotheses to be tested, convergent preliminary data, and our intimate familiarity with the PNC dataset.