Journal article

Machine-learning to characterise neonatal functional connectivity in the preterm brain

G Ball, P Aljabar, T Arichi, N Tusor, D Cox, N Merchant, P Nongena, JV Hajnal, AD Edwards, SJ Counsell

NeuroImage | ACADEMIC PRESS INC ELSEVIER SCIENCE | Published : 2016

Abstract

Brain development is adversely affected by preterm birth. Magnetic resonance image analysis has revealed a complex fusion of structural alterations across all tissue compartments that are apparent by term-equivalent age, persistent into adolescence and adulthood, and associated with wide-ranging neurodevelopment disorders. Although functional MRI has revealed the relatively advanced organisational state of the neonatal brain, the full extent and nature of functional disruptions following preterm birth remain unclear. In this study, we apply machine-learning methods to compare whole-brain functional connectivity in preterm infants at term-equivalent age and healthy term-born neonates in order..

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Grants

Awarded by National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme


Awarded by Medical Research Council


Awarded by National Institute for Health Research


Funding Acknowledgements

This work was supported by the Medical Research Council (UK), the NIHR comprehensive BRC award to Guy's & St Thomas' NHS Foundation Trust in partnership with King's College London and King's College Hospital NHS Foundation Trust. This work summarises independent research funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (grant reference number RP-PG-0707-10154). The views expressed are those of the authors and are not necessarily those of the NHS, the NIHR or the Department of Health.