Prediction of brain maturity in infants using machine-learning algorithms.

ABSTRACT: Recent resting-state functional MRI investigations have demonstrated that much of the large-scale functional network architecture supporting motor, sensory and cognitive functions in older pediatric and adult populations is present in term- and prematurely-born infants. Application of new analytical approaches can help translate the improved understanding of early functional connectivity provided through these studies into predictive models of neurodevelopmental outcome. One approach to achieving this goal is multivariate pattern analysis, a machine-learning, pattern classification approach well-suited for high-dimensional neuroimaging data. It has previously been adapted to predict brain maturity in children and adolescents using structural and resting state-functional MRI data. In this s

SUBMITTER: Smyser CD 

PROVIDER: S-EPMC4914443 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature