Intrinsic motivation is essential for academic success and cognitive growth, but limited work has examined the neuroanatomical underpinnings of intrinsic motivation from a network perspective, particularly in early childhood. Using graph theoretical analysis, this study investigated global and local properties of structural connectivity networks in relation to intrinsic motivation within a vulnerable group of children at early school age. Fifty-three 7 year-old children born extremely preterm (<28 weeks' gestational age)/extremely low birth weight (<1000 g) underwent T1 and diffusion weighted imaging. Structural connectivity networks were generated using 162 cortical and subcortical nodes, and edges were created using constrained spherical deconvolution-based tractography. Global and node-specific network measures were analyzed in association with self-reported aspects of intrinsic motivation for school learning (Mastery, Challenge and Curiosity) using linear regression. Results indicated that increased information transfer across the network was associated with greater Mastery, while increased clustering and small-world topology related to greater Challenge. Increased efficiency and connection strength of the striatum in particular, related to greater intrinsic motivation. These findings suggest that both integrated and segregated network communication support aspects of intrinsic motivation in childhood, and shed new light on structural network properties important for intrinsic motivation orientations in extremely preterm children at early school age.
Keywords: Diffusion weighted imaging; Graph theory; Intrinsic motivation; Preterm birth.