Neonatal Morphometric Similarity Networks Predict Atypical Brain Development Associated with Preterm Birth

  • Paola GaldiEmail author
  • Manuel Blesa
  • Gemma Sullivan
  • Gillian J. Lamb
  • David Q. Stoye
  • Alan J. Quigley
  • Michael J. Thrippleton
  • Mark E. Bastin
  • James P. Boardman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11083)


Morphometric similarity networks (MSNs) have been recently proposed as a novel, robust, and biologically plausible approach to generate structural connectomes from neuroimaging data. In this work, we apply this method to multi-centre neonatal data (postmenstrual age range: 37–45 weeks) to predict brain dysmaturation in preterm infants. To achieve this goal, we combined different imaging sequences (diffusion and structural MRI) to extract a set of metrics from cortical and subcortical brain regions (e.g. regional volumes, diffusion tensor metrics, neurite orientation dispersion and density imaging features) which were used to construct a similarity network. A regression model was then trained to predict postmenstrual age at the time of scanning from inter-regional connections. Finally, to quantify brain maturation, the Relative Brain Network Maturation Index (RBNMI) was computed as the difference between predicted and actual age. The model predicted chronological age with a mean absolute error of 0.88 (±0.63) weeks, and it consistently predicted preterm infants to have a lower RBNMI than term infants. We conclude that MSNs derived from multimodal imaging predict chronological brain development accurately, and provide a data-driven approach for defining cerebral dysmaturation associated with preterm birth.


Morphometric similarity networks Preterm brain Brain developmental delay Multi-modal MRI 



We are grateful to the families who consented to take part in the study. This work was supported by Theirworld ( and was undertaken in the MRC Centre for Reproductive Health, which is funded by MRC Centre Grant (MRC G1002033). MJT was supported by NHS Lothian Research and Development Office. Participants were scanned in the University of Edinburgh Imaging Research MRI Facility at the Royal Infirmary of Edinburgh which was established with funding from The Wellcome Trust, Dunhill Medical Trust, Edinburgh and Lothians Research Foundation, Theirworld, The Muir Maxwell Trust and many other sources; we thank the University’s Imaging Research staff for providing the infant scanning. Part of the results were obtained using data made available from the Developing Human Connectome Project (King’s College London - Imperial College London - Oxford University dHCP Consortium) funded by the European Research Council under the European Union’s Seventh Framework Programme (FP/2007–2013)/ERC Grant Agreement no. 319456.

Supplementary material

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Supplementary material 1 (pdf 127 KB)


  1. 1.
    Batalle, D., Edwards, A.D., O’Muircheartaigh, J.: Annual research review: not just a small adult brain: understanding later neurodevelopment through imaging the neonatal brain. J. Child Psychol. Psychiatr. 59(4), 350–371 (2018)CrossRefGoogle Scholar
  2. 2.
    Telford, E.J., Cox, S.R., Fletcher-Watson, S., Anblagan, D., Sparrow, S., et al.: A latent measure explains substantial variance in white matter microstructure across the newborn human brain. Brain Struct. Funct. 222(9), 4023–4033 (2017)CrossRefGoogle Scholar
  3. 3.
    Batalle, D., Hughes, E.J., Zhang, H., Tournier, J.D., Tusor, N., et al.: Early development of structural networks and the impact of prematurity on brain connectivity. NeuroImage 149, 379–392 (2017)CrossRefGoogle Scholar
  4. 4.
    Batalle, D., O’Muircheartaigh, J., Makropoulos, A., Kelly, C.J., Dimitrova, R., et al.: Different patterns of cortical maturation before and after 38 weeks gestational age demonstrated by diffusion MRI in vivo. NeuroImage (2018). Scholar
  5. 5.
    Counsell, S.J., Ball, G., Edwards, A.D.: New imaging approaches to evaluate newborn brain injury and their role in predicting developmental disorders. Cur. Opin. Neurol. 27(2), 168–175 (2014)CrossRefGoogle Scholar
  6. 6.
    Van Den Heuvel, M.P., Kersbergen, K.J., De Reus, M.A., Keunen, K., et al.: The neonatal connectome during preterm brain development. Cereb. Cortex 25(9), 3000–3013 (2015)CrossRefGoogle Scholar
  7. 7.
    Brown, C.J., et al.: Prediction of brain network age and factors of delayed maturation in very preterm infants. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 84–91. Springer, Cham (2017). Scholar
  8. 8.
    Alexander-Bloch, A., Giedd, J.N., Bullmore, E.: Imaging structural co-variance between human brain regions. Nat. Rev. Neurosci. 14(5), 322–336 (2013)CrossRefGoogle Scholar
  9. 9.
    Li, W., et al.: Construction of individual morphological brain networks with multiple morphometric features. Front. Neuroanat. 11, 34 (2017)CrossRefGoogle Scholar
  10. 10.
    Mahjoub, I., Mahjoub, M.A., Rekik, I.: Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states. Sci. Rep. 8(1), 4103 (2018)CrossRefGoogle Scholar
  11. 11.
    Shi, F., Yap, P.T., Gao, W., Lin, W., Gilmore, J.H., Shen, D.: Altered structural connectivity in neonates at genetic risk for schizophrenia: a combined study using morphological and white matter networks. NeuroImage 62(3), 1622–1633 (2012)CrossRefGoogle Scholar
  12. 12.
    Ball, G., Aljabar, P., Nongena, P., Kennea, N., Gonzalez-Cinca, N., et al.: Multimodal image analysis of clinical influences on preterm brain development. Ann. Neurol. 82(2), 233–246 (2017)CrossRefGoogle Scholar
  13. 13.
    Seidlitz, J., Váša, F., Shinn, M., Romero-Garcia, R., Whitaker, K.J.: Morphometric similarity networks detect microscale cortical organization and predict inter-individual cognitive variation. Neuron 97(1), 231–247.e7 (2018)CrossRefGoogle Scholar
  14. 14.
    Makropoulos, A., Robinson, E.C., Schuh, A., Wright, R., Fitzgibbon, S., et al.: The developing human connectome project: a minimal processing pipeline for neonatal cortical surface reconstruction. NeuroImage 173, 88–112 (2018)CrossRefGoogle Scholar
  15. 15.
    Makropoulos, A., Aljabar, P., Wright, R., Hüning, B., Merchant, N., et al.: Regional growth and atlasing of the developing human brain. NeuroImage 125, 456–478 (2016)CrossRefGoogle Scholar
  16. 16.
    Veraart, J., Novikov, D.S., Christiaens, D., Ades-aron, B., Sijbers, J., Fieremans, E.: Denoising of diffusion MRI using random matrix theory. NeuroImage 142, 394–406 (2016)CrossRefGoogle Scholar
  17. 17.
    Andersson, J.L., Skare, S., Ashburner, J.: How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage 20(2), 870–888 (2003)CrossRefGoogle Scholar
  18. 18.
    Andersson, J.L., Sotiropoulos, S.N.: An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage 125, 1063–1078 (2016)CrossRefGoogle Scholar
  19. 19.
    Andersson, J.L., Graham, M.S., Zsoldos, E., Sotiropoulos, S.N.: Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images. NeuroImage 141, 556–572 (2016)CrossRefGoogle Scholar
  20. 20.
    Andersson, J.L., Graham, M.S., Drobnjak, I., Zhang, H., Filippini, N., Bastiani, M.: Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement. NeuroImage 152, 450–466 (2017)CrossRefGoogle Scholar
  21. 21.
    Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)CrossRefGoogle Scholar
  22. 22.
    Bastiani, M., Andersson, J., Cordero-Grande, L., Murgasova, M., Hutter, J., et al.: Automated processing pipeline for neonatal diffusion MRI in the developing human connectome project. NeuroImage (2018). Scholar
  23. 23.
    Greve, D.N., Fischl, B.: Accurate and robust brain image alignment using boundary-based registration. NeuroImage 48(1), 63–72 (2009)CrossRefGoogle Scholar
  24. 24.
    Zhang, H., Schneider, T., Wheeler-Kingshott, C.A., Alexander, D.C.: NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61(4), 1000–1016 (2012)CrossRefGoogle Scholar
  25. 25.
    Fortin, J.P., Parker, D., Tunç, B., Watanabe, T., Elliott, M.A., et al.: Harmonization of multi-site diffusion tensor imaging data. NeuroImage 161, 149–170 (2017)CrossRefGoogle Scholar
  26. 26.
    Boardman, J.P., Counsell, S.J., Rueckert, D., Kapellou, O., Bhatia, K.K., et al.: Abnormal deep grey matter development following preterm birth detected using deformation-based morphometry. NeuroImage 32(1), 70–78 (2006)CrossRefGoogle Scholar
  27. 27.
    Ball, G., Boardman, J.P., Aljabar, P., Pandit, A., Arichi, T., et al.: The influence of preterm birth on the developing thalamocortical connectome. Cortex 49(6), 1711–1721 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Paola Galdi
    • 1
    Email author
  • Manuel Blesa
    • 1
  • Gemma Sullivan
    • 1
  • Gillian J. Lamb
    • 1
  • David Q. Stoye
    • 1
  • Alan J. Quigley
    • 2
  • Michael J. Thrippleton
    • 3
  • Mark E. Bastin
    • 3
  • James P. Boardman
    • 1
    • 3
  1. 1.MRC Centre for Reproductive HealthUniversity of EdinburghEdinburghUK
  2. 2.Department of RadiologyRoyal Hospital for Sick ChildrenEdinburghUK
  3. 3.Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK

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