Prediction of Motor Function in Very Preterm Infants Using Connectome Features and Local Synthetic Instances
We propose a method to identify preterm infants at highest risk of adverse motor function (identified at 18 months of age) using connectome features from a diffusion tensor image (DTI) acquired shortly after birth. For each full-brain DTI, a connectome is constructed and network features are extracted. After further reducing the dimensionality of the feature vector via PCA, SVM is used to discriminate between normal and abnormal motor scores. We further introduce a novel method to produce realistic synthetic training data in order to reduce the effects of class imbalance. Our method is tested on a dataset of 168 DTIs of 115 very preterm infants, scanned between 27 and 45 weeks post-menstrual age. We show that using our synthesized training data can consistently improve classification accuracy while setting a baseline for this challenging prediction problem. This work presents the first image analysis approach to predicting impairment in motor function in preterm-born infants.
KeywordsPreterm Infant Kernel Density Estimation Training Instance Class Imbalance Positive Instance
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- 3.Ball, G., Pazderova, L., Chew, A., Tusor, N., Merchant, N., Arichi, T., Allsop, J.M., Cowan, F.M., Edwards, A.D., Counsell, S.J.: Thalamocortical connectivity predicts cognition in children born preterm. Cerebral Cortex p. bhu 331 (2015)Google Scholar
- 4.Bayley, N.: Manual for the Bayley Scales of Infant Development, 3rd edn. Harcourt, San Antonio (2006)Google Scholar
- 6.Howson, C.P., Kinney, M.V., Lawn, J.L.: Born too soon: The global action report on preterm birth. World Health Organization, Geneva (2012)Google Scholar
- 12.Miller, S.P., Ferriero, D.M., Leonard, C., Piecuch, R., Glidden, D.V., Partridge, J.C., Perez, M., Mukherjee, P., Vigneron, D.B., Barkovich, A.J.: Early brain injury in premature newborns detected with mri is associated with adverse early neurodevelopmental outcome. The Journal of Pediatrics 147(5), 609–616 (2005)CrossRefGoogle Scholar
- 15.Shi, F., Yap, P.T., Wu, G., Jia, H., Gilmore, J.H., Lin, W., Shen, D.: Infant brain atlases from neonates to 1-and 2-year-olds. PLoS One 6(4), e18746 (2011)Google Scholar
- 16.Wang, R., Benner, T., Sorensen, A.G., Wedeen, V.J.: Diffusion toolkit: a software package for diffusion imaging data processing and tractography. Proc. Intl. Soc. Mag. Reson. Med. 15, 3720 (2007)Google Scholar
- 18.Ziv, E., Tymofiyeva, O., Ferriero, D.M., Barkovich, A.J., Hess, C.P., Xu, D.: A machine learning approach to automated structural network analysis: application to neonatal encephalopathy. PloS One 8(11), e78824 (2013)Google Scholar