title: 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 study, we evaluated resting state-functional MRI data from 50 preterm-born infants (born at 23-29weeks 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.
authors.affiliation: Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Department of Pediatrics, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA. Electronic address: smyserc@neuro.wustl.edu.
authors.forename: Christopher D
authors.initials: CD
authors.lastname: Smyser
authors.affiliation: Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA. Electronic address: dosenbachn@neuro.wustl.edu.
authors.forename: Nico U F
authors.initials: NU
authors.lastname: Dosenbach
authors.affiliation: Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA. Electronic address: smysert@psychiatry.wustl.edu.
authors.forename: Tara A
authors.initials: TA
authors.lastname: Smyser
authors.affiliation: Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA. Electronic address: avi@npg.wustl.edu.
authors.forename: Abraham Z
authors.initials: AZ
authors.lastname: Snyder
authors.affiliation: Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA. Electronic address: rogersc@psychiatry.wustl.edu.
authors.forename: Cynthia E
authors.initials: CE
authors.lastname: Rogers
authors.affiliation: Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA. Electronic address: tinder@partners.org.
authors.forename: Terrie E
authors.initials: TE
authors.lastname: Inder
authors.affiliation: Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Department of Pediatrics, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Department of Neurobiology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA. Electronic address: schlaggarb@neuro.wustl.edu.
authors.forename: Bradley L
authors.initials: BL
authors.lastname: Schlaggar
authors.affiliation: Department of Neurology, Boston Children's Hospital, 333 Longwood Avenue, Boston, MA 02115, USA. Electronic address: jeffrey.neil@childrens.harvard.edu.
authors.forename: Jeffrey J
authors.initials: JJ
authors.lastname: Neil
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology / Q000254:growth & development*; D001931:Brain Mapping / Q000379:methods; D005260:Female; D005865:Gestational Age; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007231:Infant, Newborn; D007234:Infant, Premature / Q000254:growth & development*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods; D011379:Prognosis; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: Developmental neuroimaging; Functional MRI; Infant; Multivariate pattern analysis; Prematurity