TY - JOUR
T1 - Prediction of brain maturity in infants using machine-learning algorithms
AU - Smyser, Christopher D.
AU - Dosenbach, Nico U.F.
AU - Smyser, Tara A.
AU - Snyder, Abraham Z.
AU - Rogers, Cynthia E.
AU - Inder, Terrie E.
AU - Schlaggar, Bradley L.
AU - Neil, Jeffrey J.
N1 - Funding Information:
This work was supported by the National Institutes of Health [grant numbers K02 NS089852 to C.D.S., UL1 TR000448 to C.D.S, K12 HD076224 to N.U.F.D., K23 NS088590 to N.U.F.D., K23 MH105179 to C.E.R., R01 HD05709801 to T.E.I. and J.J.N., P30 NS048056 to A.Z.S., P30 HD062171 and R01 HD061619 ]; Child Neurology Foundation [to C.D.S. and N.U.F.D.]; Cerebral Palsy International Research Foundation [to C.D.S.]; The Dana Foundation [to C.D.S.]; and the Barnes-Jewish Hospital Foundation . Research reported in this publication was also supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Number U54HD087011 to the Intellectual and Developmental Disabilities Research Center at Washington University. The National Institutes of Health and other funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript and the content is solely the responsibility of the authors.
Publisher Copyright:
© 2016 Elsevier Inc.
PY - 2016/8/1
Y1 - 2016/8/1
N2 - 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 study, we evaluated resting state-functional MRI data from 50 preterm-born infants (born at 23-29 weeks of gestation and without moderate-severe brain injury) scanned at term equivalent postmenstrual age compared with data from 50 term-born control infants studied within the first week of life. Using 214 regions of interest, binary support vector machines distinguished term from preterm infants with 84% accuracy (p < 0.0001). Inter- and intra-hemispheric connections throughout the brain were important for group categorization, indicating that widespread changes in the brain's functional network architecture associated with preterm birth are detectable by term equivalent age. Support vector regression enabled quantitative estimation of birth gestational age in single subjects using only term equivalent resting state-functional MRI data, indicating that the present approach is sensitive to the degree of disruption of brain development associated with preterm birth (using gestational age as a surrogate for the extent of disruption). This suggests that support vector regression may provide a means for predicting neurodevelopmental outcome in individual infants.
AB - 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 study, we evaluated resting state-functional MRI data from 50 preterm-born infants (born at 23-29 weeks of gestation and without moderate-severe brain injury) scanned at term equivalent postmenstrual age compared with data from 50 term-born control infants studied within the first week of life. Using 214 regions of interest, binary support vector machines distinguished term from preterm infants with 84% accuracy (p < 0.0001). Inter- and intra-hemispheric connections throughout the brain were important for group categorization, indicating that widespread changes in the brain's functional network architecture associated with preterm birth are detectable by term equivalent age. Support vector regression enabled quantitative estimation of birth gestational age in single subjects using only term equivalent resting state-functional MRI data, indicating that the present approach is sensitive to the degree of disruption of brain development associated with preterm birth (using gestational age as a surrogate for the extent of disruption). This suggests that support vector regression may provide a means for predicting neurodevelopmental outcome in individual infants.
KW - Developmental neuroimaging
KW - Functional MRI
KW - Infant
KW - Multivariate pattern analysis
KW - Prematurity
UR - http://www.scopus.com/inward/record.url?scp=84969498387&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2016.05.029
DO - 10.1016/j.neuroimage.2016.05.029
M3 - Article
C2 - 27179605
AN - SCOPUS:84969498387
VL - 136
SP - 1
EP - 9
JO - NeuroImage
JF - NeuroImage
SN - 1053-8119
ER -