The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Cited by
Sorted by citations
Imaging structural and functional brain development in early childhood
Gilmore, J. H., Knickmeyer, R. C., & Gao, W. (2018) · Nature Reviews Neuroscience · doi:10.1038/nrn.2018.1 · Cited by 1,013
Rudolph, M. D., Graham, A. M., Feczko, E., Miranda-Dominguez, O., Rasmussen, J. M., Nardos, R., Entringer, S., Wadhwa, P. D., Buss, C., & Fair, D. A. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0128-y · Cited by 374
What the radiologist should know about artificial intelligence – an ESR white paper
European Society of Radiology (2019) · Insights into Imaging · doi:10.1186/s13244-019-0738-2 · Cited by 346
Machine learning algorithms for social media analysis: A survey
T.K., B., Annavarapu, C. S. R., & Bablani, A. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2021.100395 · Cited by 276
Artificial intelligence for precision medicine in neurodevelopmental disorders
Uddin, M. J., Wang, Y., & Woodbury‐Smith, M. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0191-0 · Cited by 261
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
