Investigating Brain Age Deviation in Preterm Infants: A Deep Learning Approach

  • Susmita SahaEmail author
  • Alex Pagnozzi
  • Joanne George
  • Paul B. Colditz
  • Roslyn Boyd
  • Stephen Rose
  • Jurgen Fripp
  • Kerstin Pannek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11076)


This study examined postmenstrual age (PMA) estimation (in weeks) from brain diffusion MRI of very preterm born infants (born <31weeks gestational age), with an objective to investigate how differences in estimated brain age and PMA were associated with the risk of Cerebral Palsy disorders (CP). Infants were scanned up to 2 times, between 29 and 46 weeks (w) PMA. We applied a deep learning 2D convolutional neural network (CNN) regression model to estimate PMA from local image patches extracted from the diffusion MRI dataset. These were combined to form a global prediction for each MRI scan. We found that CNN can reliably estimate PMA (Pearson’s r = 0.6, p < 0.05) from MRIs before 36 weeks of age (‘Early’ scans). These results revealed that the local fractional anisotropy (FA) measures of these very early scans preserved age specific information. Most interestingly, infants who were later diagnosed with CP were more likely to have an estimated younger brain age from ‘Early’ scans, the estimated age deviations were significantly different (Regression coefficient: −2.16, p < 0.05, corrected for actual age) compared to those infants who were not diagnosed with CP.


Preterm CNN Cerebral Palsy Postmenstrual age Deep learning 


  1. 1.
    George, J., et al.: PPREMO: a prospective cohort study of preterm infant brain structure and function to predict neurodevelopmental outcome. BMC Pediatr. 15(1), 123 (2015)CrossRefGoogle Scholar
  2. 2.
    McIntyre, S., Morgan, C., Walker, K., Novak, I.: Cerebral Palsy-don’t delay. Dev. Disabil. Res. Rev. 17(2), 114–129 (2011)CrossRefGoogle Scholar
  3. 3.
    George, J., et al.: Relationship between very early brain structure and neuromotor, neurological and neurobehavioral function in infants born <31 weeks gestational age. Early Hum. Dev. 117, 74–82 (2018)CrossRefGoogle Scholar
  4. 4.
    Zhang, J.: Multivariate analysis and machine learning in Cerebral Palsy research. Front. Neurol. 8, 715 (2017)CrossRefGoogle Scholar
  5. 5.
    Dittrich, E., et al.: A spatio-temporal latent atlas for semi-supervised learning of fetal brain segmentations and morphological age estimation. Med. Image Anal. 18(1), 9–21 (2014)CrossRefGoogle Scholar
  6. 6.
    Cole, J., et al.: Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. NeuroImage 163, 115–124 (2017)CrossRefGoogle Scholar
  7. 7.
    Huang, T., Chen, H., Fujimoto, R.: Age estimation from brain MRI images using deep learning. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, Australia. IEEE (2017)Google Scholar
  8. 8.
    George, J., et al.: Validation of an MRI brain injury and growth scoring system in very preterm infants scanned at 29- to 35-week postmenstrual age. Am. J. Neuroradiol. 38(7), 1435–1442 (2017)CrossRefGoogle Scholar
  9. 9.
    Jensen, A., Holmer, B.: White matter damage in 4,725 term-born infants is determined by head circumference at birth: the missing link. Obstet. Gynecol. Int. 2018, 1–12 (2018)CrossRefGoogle Scholar
  10. 10.
    Kuban, K., et al.: Developmental correlates of head circumference at birth and two years in a cohort of extremely low gestational age newborns. J. Pediatr. 155(3), 344–349.e3 (2009)CrossRefGoogle Scholar
  11. 11.
    Babcock, M., et al.: Injury to the preterm brain and cerebral palsy: clinical aspects, molecular mechanisms, unanswered questions, and future research directions. J. Child Neurol. 24(9), 1064–1084 (2009)CrossRefGoogle Scholar

Copyright information

© Crown 2018

Authors and Affiliations

  1. 1.Australian E-Health Research CentreCSIROBrisbaneAustralia
  2. 2.Centre for Clinical Research, Faculty of MedicineThe University of QueenslandBrisbaneAustralia
  3. 3.Queensland Cerebral Palsy and Rehabilitation Research Centre, Centre for Children’s Health Research, Faculty of MedicineThe University of QueenslandBrisbaneAustralia

Personalised recommendations