Prediction of brain maturity in infants using machine-learning algorithms

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...

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Vydané v:NeuroImage (Orlando, Fla.) Ročník 136; s. 1 - 9
Hlavní autori: Smyser, Christopher D., Dosenbach, Nico U.F., Smyser, Tara A., Snyder, Abraham Z., Rogers, Cynthia E., Inder, Terrie E., Schlaggar, Bradley L., Neil, Jeffrey J.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Elsevier Inc 01.08.2016
Elsevier Limited
Predmet:
Age
Algorithms
Alzheimer's disease
Brain - anatomy & histology
Brain - growth & development
Brain Mapping - methods
Brain research
Classification
Developmental neuroimaging
Female
Functional MRI
Gestational Age
Hospitals
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Infant
Infant, Newborn
Infant, Premature - growth & development
Magnetic Resonance Imaging - methods
Male
Medical imaging
Methods
Multivariate pattern analysis
Pattern Recognition, Automated - methods
Pediatrics
Prematurity
Prognosis
Regression Analysis
Reproducibility of Results
Sensitivity and Specificity
Studies
Support Vector Machine
Multivariate pattern analysis
Developmental neuroimaging
PMA
rs-fMRI
NICU
MNI
Infant
SVM
LOOCV
RSN
ROI
SVR
Prematurity
GA
BOLD
Functional MRI
MVPA
EPI
ISSN:1053-8119, 1095-9572
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Shrnutí: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. •Multivariate pattern analysis was applied to neonatal functional MRI data.•Support vector machines accurately distinguished term- from preterm-born infants.•Differences in inter- and intrahemispheric functional connectivity were widespread.•Support vector regression estimated birth gestational age of individual infants.•Results suggest potential for presymptomatic prediction of developmental outcomes.
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SourceType-Scholarly Journals-1
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ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2016.05.029