Efficiency of structural connectivity networks relates to intrinsic motivation in children born extremely preterm

Leona Pascoe; Deanne Thompson; Megan Spencer-Smith; Richard Beare; Chris Adamson; Katherine J. Lee; Claire Kelly; Nellie Georgiou-Karistianis; Chiara Nosarti; Elisha Josev; Gehan Roberts; Lex W. Doyle; Marc L. Seal; Peter J. Anderson

doi:10.1007/s11682-018-9918-9

Efficiency of structural connectivity networks relates to intrinsic motivation in children born extremely preterm

Leona Pascoe^*, Deanne Thompson, Megan Spencer-Smith, Richard Beare, Chris Adamson, Katherine J. Lee, Claire Kelly, Nellie Georgiou-Karistianis, Chiara Nosarti, Elisha Josev, Gehan Roberts, Lex W. Doyle, Marc L. Seal, Peter J. Anderson

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	1-14
Number of pages	14
Journal	Brain Imaging and Behavior
DOIs	https://doi.org/10.1007/s11682-018-9918-9
Publication status	E-pub ahead of print - 30 Jun 2018

Keywords

Diffusion weighted imaging
Graph theory
Intrinsic motivation
Preterm birth

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/s11682-018-9918-9

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Pascoe, L., Thompson, D., Spencer-Smith, M., Beare, R., Adamson, C., Lee, K. J., Kelly, C., Georgiou-Karistianis, N., Nosarti, C., Josev, E., Roberts, G., Doyle, L. W., Seal, M. L., & Anderson, P. J. (2018). Efficiency of structural connectivity networks relates to intrinsic motivation in children born extremely preterm. Brain Imaging and Behavior, 1-14. Advance online publication. https://doi.org/10.1007/s11682-018-9918-9

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abstract = "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{\textquoteright} 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",

author = "Leona Pascoe and Deanne Thompson and Megan Spencer-Smith and Richard Beare and Chris Adamson and Lee, {Katherine J.} and Claire Kelly and Nellie Georgiou-Karistianis and Chiara Nosarti and Elisha Josev and Gehan Roberts and Doyle, {Lex W.} and Seal, {Marc L.} and Anderson, {Peter J.}",

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Pascoe, L, Thompson, D, Spencer-Smith, M, Beare, R, Adamson, C, Lee, KJ, Kelly, C, Georgiou-Karistianis, N, Nosarti, C, Josev, E, Roberts, G, Doyle, LW, Seal, ML & Anderson, PJ 2018, 'Efficiency of structural connectivity networks relates to intrinsic motivation in children born extremely preterm', Brain Imaging and Behavior, pp. 1-14. https://doi.org/10.1007/s11682-018-9918-9

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T1 - Efficiency of structural connectivity networks relates to intrinsic motivation in children born extremely preterm

AU - Pascoe, Leona

AU - Thompson, Deanne

AU - Spencer-Smith, Megan

AU - Beare, Richard

AU - Adamson, Chris

AU - Lee, Katherine J.

AU - Kelly, Claire

AU - Georgiou-Karistianis, Nellie

AU - Nosarti, Chiara

AU - Josev, Elisha

AU - Roberts, Gehan

AU - Doyle, Lex W.

AU - Seal, Marc L.

AU - Anderson, Peter J.

PY - 2018/6/30

Y1 - 2018/6/30

N2 - 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.

AB - 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.

KW - Diffusion weighted imaging

KW - Graph theory

KW - Intrinsic motivation

KW - Preterm birth

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JO - Brain Imaging and Behavior

JF - Brain Imaging and Behavior

ER -

Efficiency of structural connectivity networks relates to intrinsic motivation in children born extremely preterm

Abstract

Keywords

UN SDGs

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