2016
DOI: 10.1016/j.neuroimage.2015.08.055
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Machine-learning to characterise neonatal functional connectivity in the preterm brain

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 u… Show more

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Cited by 108 publications
(99 citation statements)
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“…18). In the first null model, group membership was randomly reshuffled between PD-nonMCI and PD-MCI, and the SVM procedure was repeated, in a total of 1,000 iterations.…”
Section: Resultsmentioning
confidence: 99%
“…18). In the first null model, group membership was randomly reshuffled between PD-nonMCI and PD-MCI, and the SVM procedure was repeated, in a total of 1,000 iterations.…”
Section: Resultsmentioning
confidence: 99%
“…Early studies find only modest relationships between structural and functional biomarkers and cognition and behaviour. Machine-learning based prediction frameworks are beginning to be applied to infant imaging 201203 and are needed to better predict developmental outcomes from early imaging parameters, in context of genetic, environmental and birth-related variables. It is unlikely that a single or even a few imaging parameters will have sufficient power for accurate prediction.…”
Section: Discussionmentioning
confidence: 99%
“…Motor cortex responses to a passive motor balloon task become more bilateral from 31 weeks (top row, C) to term age (41 weeks, bottom row, C) (Arichi et al., ). Finally the repertoire of resting state networks seen in adults is clearly evident in the neonatal brain at term‐equivalent age (Ball et al., ), (D), but the pattern of connectivity between the networks is different in the preterm‐born brain, allowing discrimination [Colour figure can be viewed at wileyonlinelibrary.com]…”
Section: Assessing Tissue Functionmentioning
confidence: 99%
“…The application of machine learning approaches to neonatal data is still relatively novel. It has been used to better understand alterations associated with prematurity at birth, showing that term and preterm babies can be classified blindly based on their patterns of functional connectivity (Ball et al., ; see Figure D). Wee et al.…”
Section: From Making Observations To Explaining Behaviourmentioning
confidence: 99%