Prediction of brain maturity in infants using machine-learning algorithms
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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.
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Machine-learning based prediction frameworks are beginning to be applied to infant imaging.
“Machine-learning based prediction frameworks are beginning to be applied to infant imaging201–203”
From Imaging structural and functional brain development in early childhood (2018) · cites this paper as [202]
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- Authors
- Smyser, C. D., Dosenbach, N. U. F., Smyser, T. A., Snyder, A. Z., Rogers, C. E., Inder, T. E., Schlaggar, B. L., & Neil, J. J.
- Affiliations
- Washington University in St. Louis (US)
- Brigham and Women's Hospital (US)
- Boston Children's Hospital (US)
- Corresponding author
- Smyser, Christopher D.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-08
- Volume
- 136
- Pages
- 1-9
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC4914443
- OpenAlex
- W2363249082
- Cited by
- 140
- References
- 66
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 15 · 2023: 9 · 2022: 17 · 2021: 18 · 2020: 15 · 2019: 17 · 2018: 20 · 2017: 15 · 2016: 1
- FWCI
- 11.13
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (K02 NS089852, UL1 TR000448, K12 HD076224, K23 NS088590, K23 MH105179, R01 HD05709801, P30 NS048056, P30 HD062171, R01 HD061619)
- Child Neurology Foundation
- Cerebral Palsy International Research Foundation
- The Dana Foundation
- Barnes-Jewish Hospital Foundation
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Infant Development and Preterm Care
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Neonatal and fetal brain pathology
- Keywords
- preterm birth, resting-state functional connectivity, term-equivalent age, gestational age estimation, support vector regression, support vector machine, multivariate pattern analysis, neurodevelopmental outcome prediction, interhemispheric connectivity, preterm infants
- MeSH terms
- Algorithms, Brain, Brain Mapping, Female, Gestational Age, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Infant, Newborn, Infant, Premature, Magnetic Resonance Imaging, Male, Pattern Recognition, Automated, Prognosis, Regression Analysis, Sensitivity and Specificity, Reproducibility of Results, Support Vector Machine
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