Prediction of brain maturity in infants using machine-learning algorithms

Christopher D Smyser; Nico U F Dosenbach; Tara A Smyser; Abraham Z Snyder; Cynthia E Rogers; Terrie E Inder; Bradley L Schlaggar; Jeffrey J Neil

doi:10.1016/j.neuroimage.2016.05.029

Prediction of brain maturity in infants using machine-learning algorithms

Neuroimage. 2016 Aug 1:136:1-9. doi: 10.1016/j.neuroimage.2016.05.029. Epub 2016 May 11.

Authors

Christopher D Smyser¹, Nico U F Dosenbach², Tara A Smyser³, Abraham Z Snyder⁴, Cynthia E Rogers⁵, Terrie E Inder⁶, Bradley L Schlaggar⁷, Jeffrey J Neil⁸

Affiliations

¹ 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.
² Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA. Electronic address: dosenbachn@neuro.wustl.edu.
³ Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA. Electronic address: smysert@psychiatry.wustl.edu.
⁴ 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.
⁵ Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA. Electronic address: rogersc@psychiatry.wustl.edu.
⁶ Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA. Electronic address: tinder@partners.org.
⁷ 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.
⁸ Department of Neurology, Boston Children's Hospital, 333 Longwood Avenue, Boston, MA 02115, USA. Electronic address: jeffrey.neil@childrens.harvard.edu.

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.

Keywords: Developmental neuroimaging; Functional MRI; Infant; Multivariate pattern analysis; Prematurity.

MeSH terms

Algorithms*
Brain / anatomy & histology
Brain / growth & development*
Brain Mapping / methods
Female
Gestational Age*
Humans
Image Enhancement / methods
Image Interpretation, Computer-Assisted / methods*
Infant, Newborn
Infant, Premature / growth & development*
Magnetic Resonance Imaging / methods*
Male
Pattern Recognition, Automated / methods
Prognosis
Regression Analysis
Reproducibility of Results
Sensitivity and Specificity
Support Vector Machine*

Abstract

MeSH terms

Grants and funding