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.

Public Health Relevance

The executive system develops rapidly during youth. However, failures of executive function are a substantial source of morbidity and mortality in adolescence, and are also impacted by a wide variety of mental illnesses. This proposal utilizes novel tools from network science to describe fundamental mechanisms regarding how executive function develops during youth. Such knowledge may be critical for developing more biologically grounded psychiatric diagnosis and targeted early treatment interventions.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21MH106799-02
Application #
9242703
Study Section
Child Psychopathology and Developmental Disabilities Study Section (CPDD)
Program Officer
Friedman-Hill, Stacia
Project Start
2016-03-15
Project End
2018-02-28
Budget Start
2017-03-01
Budget End
2018-02-28
Support Year
2
Fiscal Year
2017
Total Cost
$201,250
Indirect Cost
$76,250
Name
University of Pennsylvania
Department
Biomedical Engineering
Type
Schools of Engineering
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
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Telesford, Qawi K; Lynall, Mary-Ellen; Vettel, Jean et al. (2016) Detection of functional brain network reconfiguration during task-driven cognitive states. Neuroimage 142:198-210
Shanmugan, Sheila; Satterthwaite, Theodore D (2016) Neural Markers of the Development of Executive Function: Relevance for Education. Curr Opin Behav Sci 10:7-13
Khambhati, Ankit N; Davis, Kathryn A; Lucas, Timothy H et al. (2016) Virtual Cortical Resection Reveals Push-Pull Network Control Preceding Seizure Evolution. Neuron 91:1170-82