Masters Theses

Date of Award


Degree Type


Degree Name

Master of Arts



Major Professor

Aaron Buss

Committee Members

Kalynn Schulz, Greg Reynolds


Cognitive flexibility is a subset of executive function that involves flexibly adapting one’s behavior to meet the demands of a changing environment. In a cognitive task, this often entails shifts of attention between dimensions of a stimulus or flexibly changing response sets. This skill improves greatly throughout middle childhood and is supported by a frontal-parietal neural network. The level of synchrony in activation, or functional connectivity, between frontal and parietal regions has been reported to increase over development even after cognitive flexibility has stabilized. The current study aims to assess changes in functional connectivity across the age range when this ability most rapidly develops. fNIRS was used to measure synchrony in hemodynamic activation of the frontal and parietal cortices in children at age 5, 7 and 9. Functional connectivity was measured at a resting state and while children performed three tasks requiring cognitive flexibility. Task performance and connectivity strength were compared across age groups. Cognitive flexibility improved greatly with age, aligning with previous literature. Evidence was found for refinement of local connectivity within the frontal cortex, such that weaker connections decreased in strength with age and stronger connections increased in strength. Further, connectivity between frontal and parietal regions was greater for 9-year-olds when task demands increased, reflecting greater synchrony of this network with age. Understanding the neural dynamics associated with the development of flexibility promotes a better understanding of the brain-behavior relationship. This line of research can also allow us to make comparisons with atypically developing populations, such as those with Autism, who have impairments in this skill. By understanding how neural architecture develops to support executive function in typical populations, we can better understand how deficits arise from atypical trajectories.

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