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Prediction of brain maturity in infants using machine-learning algorithms
Open accessAbstract
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 imaging201–203”
From Imaging structural and functional brain development in early childhood (2018) · cites this paper as [202]
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Cites
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,723
Schölkopf, B., & Smola, A. J. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/4175.001.0001 · Cited by 11,934
Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.015 · Cited by 11,314
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909
Spectral Analysis and its Applications.
Freiberger, W. F., Jenkins, G. M., & Watts, D. G. (1969) · Journal of the American Statistical Association · doi:10.2307/2286100 · Cited by 4,990
Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Williams, C. K. I. (2003) · Journal of the American Statistical Association · doi:10.1198/jasa.2003.s269 · Cited by 4,390
Functional Network Organization of the Human Brain
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., Vogel, A. C., Laumann, T. O., Miezin, F. M., Schlaggar, B. L., et al. (2011) · Neuron · doi:10.1016/j.neuron.2011.09.006 · Cited by 4,124
Self-citationMethods to detect, characterize, and remove motion artifact in resting state fMRI
Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.048 · Cited by 3,748
Self-citationAn overview of mortality and sequelae of preterm birth from infancy to adulthood
Saigal, S., & Doyle, L. W. (2008) · The Lancet · doi:10.1016/s0140-6736(08)60136-1 · Cited by 2,818
Beyond mind-reading: multi-voxel pattern analysis of fMRI data
Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.07.005 · Cited by 2,271
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Items 101–138 of 138
Irzan, H., Hütel, M., O’Reilly, H., Sebastien Ourselin, A., Marlow, N., & Melbourne, A. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103037 · Cited by 5
Identifying mild traumatic brain injury using measures of frequency-specified networks
Salsabilian, S., Bibineyshvili, Y., Margolis, D. J., & Najafizadeh, L. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac954e · Cited by 5
Zhang, W., Wang, L., Wu, X., Li, Y., Zhang, Y., Yin, H. J., Zhang, L., Lui, S., & Gong, Q. (2024) · Neuropsychopharmacology · doi:10.1038/s41386-024-02021-y · Cited by 4
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Damera, S. R., Asis-Cruz, J. D., Cook, K. M., Kapse, K., Spoehr, E., Murnick, J., Basu, S., Andescavage, N., & Limperopoulos, C. (2024) · iScience · doi:10.1016/j.isci.2024.109662 · Cited by 5
Smartphone-Based Screening for Cardiovascular Diseases: A Trend?
Mazzu-Nascimento, T., Evangelista, D. N., Abubakar, O., Roscani, M. G., Aguilar, R. S., Chachá, S. G. F., da Rosa, P. R., & Silva, D. F. (2021) · International Journal of Cardiovascular Sciences · doi:10.36660/ijcs.20210096 · Cited by 6
Ensemble learning with 3D convolutional neural networks for connectome-based predictionPreprint
Khosla, M., Jamison, K. W., Kuceyeski, A. F., & Sabuncu, M. R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.06219 · Cited by 4
Ethical and legal implications of the methodological crisis in neuroimagingPreprint
Kellmeyer, P. (2017) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/2gehg · Cited by 4
Hybrid Thresholding Method in Detection and Extraction of Brain Hemorrhage on the CT-Scan Image
Sumijan, S., Yuhandri, Y., & Boy, W. (2021) · Journal of Computer Scine and Information Technology · doi:10.35134/jcsitech.v7i2.2 · Cited by 3
Boerwinkle, V. L., Sussman, B. L., Agarwal, S., & Scher, M. S. (2022) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/p8bn7 · Cited by 3
Yan, S., Hu, X., Song, X., Yao, K., & Qu, S. (2026) · Acta Mechanica Sinica · doi:10.1007/s10409-025-24940-x · Cited by 2
Li, Z., Fang, H., Fan, W., Wu, J., Cui, J., Li, B., & Wang, C. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae374 · Cited by 2
Specialized Neurological Studies
de Vries, L. S., & Volpe, J. J. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-10513-5.00013-9 · Cited by 2
Event cache: An independent component in working memory
Zhou, H., Wu, J., Li, J., Pan, Z., Lu, J., Shen, M., Wang, T., Hu, Y., & Gao, Z. (2025) · Science Advances · doi:10.1126/sciadv.adt3063 · Cited by 2
Morais, F. L., da Silva, R. C. L., Silva, A. B., Simão, E. P., de Mello, M. E. F., Canejo, S. P. D. S., Mendes, K. M., Neto, W. B., Silva, J. R. F. D., Barros, M. H. L. F. D. S., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3582503 · Cited by 2
Manikandan, A., Shriram, S., Sarathchandran, C., Sambandam, P., & Rohith, N. D. (2021) · International Journal of Current Research and Review · doi:10.31782/ijcrr.2021.sp208 · Cited by 2
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Self-citationWang, F., Yin, Y., Yang, Y., Liang, T. B., Huang, T., He, C., Hu, J., Zhang, J., Yang, Y., Xing, Q., et al. (2021) · Annals of Translational Medicine · doi:10.21037/atm-21-574 · Cited by 2
Automated cot-side tracking of functional brain age in preterm infantsPreprint
Stevenson, N. J., Oberdorfer, L., Tataranno, M. L., Breakspear, M., Colditz, P. B., de Vries, L. S., Benders, M. J. N. L., Klebermass-Schrehof, K., Vanhatalo, S., & Roberts, J. A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/848218 · Cited by 2
Bonneau, L., Flamant, C., Esvan, M., Roué, J., Favrais, G., Gascoin-Lachambre, G., Cabon, S., Porée, F., Carrault, G., & Pladys, P. (2026) · Acta Paediatrica · doi:10.1111/apa.70462 · Cited by 1
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Aman, N., & Islam, M. R. (2021) · Algorithms for intelligent systems · doi:10.1007/978-981-16-0586-4_42 · Cited by 1
Rajkumar, E., Geetha, V., & Sekar, G. S. (2021) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs51430.2021.9441775 · Cited by 1
Imaging early brain structural and functional development
Ouyang, M., Smyser, C. D., Neil, J. J., & Huang, H. (2021) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-12-816633-8.00018-1 · Cited by 1
Self-citationTang, Z., Liu, Z., Li, R., Cui, X., Li, H., Dong, E., & Tian, J. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2254200 · Cited by 1
Vijayalakshmi, S., Durgadevi, P., Gayathri, S. P., & Shariff, A. S. M. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-2211-4_44 · Cited by 1
Brain age prediction and deviations from normative trajectories in the neonatal connectomePreprint
Sun, H., Mehta, S., Khaitova, M., Cheng, B., Hao, X., Spann, M. N., & Scheinost, D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.23.590811 · Cited by 1
Multi-Class Brain Age Discrimination Using Machine Learning Algorithm
Li, H.-C., Chang-yu, C., Chou, C.-H., Hsu, C., Chang, M., Chiu, Y.-S., & Chai, J. (2019) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc48188.2019.8949317
Statistical Perspectives and Machine Learning Algorithms
Ajaypradeep, N., & Sasikala, R. (2020) · Advances in early childhood and K-12 education · doi:10.4018/978-1-7998-7053-1.ch016
Risk of abnormal outcomes based on basic and advanced MRI measurements
Ahtam, B., Solti, M., & Grant, P. E. (2021) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-12-816633-8.00020-x
Ubiquitous Technologies for Emotion Recognition
Baños, O., Castro, L. A., & Villalonga, C. (2021) · MDPI eBooks · doi:10.3390/books978-3-0365-1801-5
Ethical and legal implications of the methodological crisis in neuroimagingPreprint
Kellmeyer, P. (2017) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/2gehg_v1
Zhao, J., Xu, Y., Cui, Z., Li, H., Sun, L., Liang, X., Han, M., Zeng, Z., Li, Q., Zhao, T., et al. (2026) · Nature Communications · doi:10.1101/2025.11.18.689151 · Cited by 1
Predicting Brain Age in Functional Neuroimages using Node Centrality Measures
Taimouri, M., & Ravindra, V. (2025) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3765612.3767213
Aperiodic parameters of the fMRI power spectrum associate with preterm birth and neonatal age
Suuronen, I., Luotonen, S., Railo, H., Airola, A., Bano, W., Merisaari, H., Pulli, E. P., Wigley, I. L. C. M., Vartiainen, E., Hashempour, N., et al. (2026) · Communications Biology · doi:10.1038/s42003-025-09488-5 · Cited by 3
Infant Brain Abnormality Classification Using Transfer Learning and Deep CNN Models from MRI
Yashasvi, B. N., & Kavya, N. P. (2026) · SN Computer Science · doi:10.1007/s42979-026-04797-w
Etiology and Advanced Detection Techniques for Fetal Brain Abnormalities: A Comprehensive Study
Gaikwad, N., & Vijayalakshmi, S. (2025) · International Conference on Research in Intelligent Computing in Engineering (RICE) · doi:10.1109/rice67503.2025.11439996
Attenni, M., Vinci, G. V., & Scattoni, M. L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.24.754103
Metadata
- 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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