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Developing a Framework for Studying Brain Networks in Neonatal Hypoxic-Ischemic Encephalopathy

  • Finn LennartssonEmail author
  • Angela Darekar
  • Koushik Maharatna
  • Daniel Konn
  • David Allen
  • J-Donald Tournier
  • John Broulidakis
  • Brigitte Vollmer
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 894)

Abstract

Newborns with hypoxic-ischemic encephalopathy (HIE) are at high risk of brain injury, with subsequent developmental problems including severe neuromotor, cognitive and behavioral impairment. Neural correlates of cognitive and behavioral impairment in neonatal HIE, in particular in infants who survive without severe neuromotor impairment, are poorly understood. It is reasonable to hypothesize that in HIE both structural and functional brain networks are altered, and that this might be the neural correlate of impaired cognitive and/or behavioral impairment in HIE.

Here, an analysis pipeline to study the structural and functional brain networks from neonatal MRI in newborns with HIE is presented. The structural connectivity is generated from dense whole-brain tractograms derived from diffusion-weighted MR fibre tractography. This investigation of functional connectivity focuses on the emerging resting state networks (RSNs), which are sensitive to injuries from hypoxic-ischemic insults to the newborn brain. In conjunction with the structural connectivity, alterations to the structuro-functional connectivity of the RSNs can be studied. Preliminary results from a proof-of-concept study in a small cohort of newborns with HIE are promising. The obstacles encountered and improvements to the pipeline are discussed. The framework can be further extended for joint analysis with EEG functional-connectivity.

Keywords

Hypoxic-ischemic encephalopathy Connectivity Diffusion MRI Resting-state functional MRI Networks Human brain 

References

  1. 1.
    van Schie, P.E.M., Schijns, J., Becher, J.G., Barkhof, F., van Weissenbruch, M.M., Vermeulen, R.J.: Long-term motor and behavioral outcome after perinatal hypoxic-ischemic encephalopathy. Eur. J. Paediatr. Neurol. 19, 354–359 (2015)CrossRefGoogle Scholar
  2. 2.
    Jacobs, S.E., et al.: Cooling for newborns with hypoxic ischaemic encephalopathy. In: Cochrane Database of Systematic Reviews. Wiley, Hoboken (2013)Google Scholar
  3. 3.
    Schreglmann, M., Grund, A., Vollmer, B., Johnson, M.: Systematic review: long-term cognitive and behavioural outcome of neonatal hypoxic-ischaemic encephalopathy in children without CP. Acta Paediatr. (2018, under review)Google Scholar
  4. 4.
    Domnick, N.-K., Gretenkord, S., De Feo, V., Sedlacik, J., Brockmann, M.D., Hanganu-Opatz, I.L.: Neonatal hypoxia–ischemia impairs juvenile recognition memory by disrupting the maturation of prefrontal–hippocampal networks. Exp. Neurol. 273, 202–214 (2015)CrossRefGoogle Scholar
  5. 5.
    Batalle, D., et al.: Altered resting-state whole-brain functional networks of neonates with intrauterine growth restriction. Cortex 77, 119–131 (2016)CrossRefGoogle Scholar
  6. 6.
    Batalle, D., et al.: Altered small-world topology of structural brain networks in infants with intrauterine growth restriction and its association with later neurodevelopmental outcome. NeuroImage 60, 1352–1366 (2012)CrossRefGoogle Scholar
  7. 7.
    Tymofiyeva, O., et al.: Towards the “baby connectome”: mapping the structural connectivity of the newborn brain. PLoS ONE 7, e31029 (2012)CrossRefGoogle Scholar
  8. 8.
    Hagmann, P., Grant, P.E., Fair, D.A.: MR connectomics: a conceptual framework for studying the developing brain. Front. Syst. Neurosci. 6, 43 (2012)CrossRefGoogle Scholar
  9. 9.
    Dennis, E.L., Thompson, P.M.: Reprint of: Mapping connectivity in the developing brain. Int. J. Dev. Neurosci. 32, 41–57 (2014)CrossRefGoogle Scholar
  10. 10.
    Smith, S.M.: Fast robust automated brain extraction. Hum. Brain Mapp. 17, 143–155 (2002)CrossRefGoogle Scholar
  11. 11.
    Jenkinson, M., Bannister, P., Brady, M., Smith, S.: Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17, 825–841 (2002)CrossRefGoogle Scholar
  12. 12.
    Išgum, I., et al.: Evaluation of automatic neonatal brain segmentation algorithms: the NeoBrainS12 challenge. Med. Image Anal. 20, 135–151 (2015)CrossRefGoogle Scholar
  13. 13.
    Devi, C.N., Chandrasekharan, A., Sundararaman, V.K., Alex, Z.C.: Neonatal brain MRI segmentation: a review. Comput. Biol. Med. 64, 163–178 (2015)CrossRefGoogle Scholar
  14. 14.
    Makropoulos, A., et al.: Automatic whole brain MRI segmentation of the developing neonatal brain. IEEE Trans. Med. Imaging 33, 1818–1831 (2014)CrossRefGoogle Scholar
  15. 15.
    Gousias, I.S., et al.: Magnetic resonance imaging of the newborn brain: automatic segmentation of brain images into 50 anatomical regions. PLoS ONE 8, e59990 (2013)CrossRefGoogle Scholar
  16. 16.
    Smith, R.E., Tournier, J.-D., Calamante, F., Connelly, A.: Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. NeuroImage 62, 1924–1938 (2012)CrossRefGoogle Scholar
  17. 17.
    Tournier, J.-D., Calamante, F., Connelly, A.: Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35, 1459–1472 (2007)CrossRefGoogle Scholar
  18. 18.
    Raffelt, D., et al.: Apparent fibre density: a novel measure for the analysis of diffusion-weighted magnetic resonance images. NeuroImage 59, 3976–3994 (2012)CrossRefGoogle Scholar
  19. 19.
    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
  20. 20.
    Andersson, J.L.R., 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
  21. 21.
    Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29, 1310–1320 (2010)CrossRefGoogle Scholar
  22. 22.
    Tournier, J.-D., Calamante, F., Connelly, A.: Determination of the appropriate b value and number of gradient directions for high-angular-resolution diffusion-weighted imaging. NMR Biomed. 26, 1775–1786 (2013)CrossRefGoogle Scholar
  23. 23.
    Mongerson, C.R.L., Jennings, R.W., Borsook, D., Becerra, L., Bajic, D.: Resting-state functional connectivity in the infant brain: methods, pitfalls, and potentiality. Front. Pediatr. 5, 159 (2017)CrossRefGoogle Scholar
  24. 24.
    Smyser, C.D., Neil, J.J.: Use of resting-state functional MRI to study brain development and injury in neonates. Semin. Perinatol. 39, 130–140 (2015)CrossRefGoogle Scholar
  25. 25.
    Beckmann, C.F., Smith, S.M.: Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans. Med. Imaging 23, 137–152 (2004)CrossRefGoogle Scholar
  26. 26.
    Gao, W., Lin, W., Grewen, K., Gilmore, J.H.: Functional connectivity of the infant human brain: plastic and modifiable. Neurosci. Rev. J. Bringing Neurobiol. Neurol. Psychiatry. 23, 169–184 (2016)Google Scholar
  27. 27.
    Smith, R.E., Tournier, J.-D., Calamante, F., Connelly, A.: SIFT: spherical-deconvolution informed filtering of tractograms. NeuroImage 67, 298–312 (2013)CrossRefGoogle Scholar
  28. 28.
    Salimi-Khorshidi, G., Douaud, G., Beckmann, C.F., Glasser, M.F., Griffanti, L., Smith, S.M.: Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers. NeuroImage 90, 449–468 (2014)CrossRefGoogle Scholar
  29. 29.
    Blesa, M., et al.: Parcellation of the healthy neonatal brain into 107 regions using atlas propagation through intermediate time points in childhood. Front Neurosci. 10, 220 (2016)CrossRefGoogle Scholar
  30. 30.
    Jeurissen, B., Tournier, J.-D., Dhollander, T., Connelly, A., Sijbers, J.: Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage 103, 411–426 (2014)CrossRefGoogle Scholar
  31. 31.
    Tymofiyeva, O., Ziv, E., Barkovich, A.J., Hess, C.P., Xu, D.: Brain without anatomy: construction and comparison of fully network-driven structural MRI connectomes. PLoS ONE 9, e96196 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Finn Lennartsson
    • 1
    • 2
    • 3
    Email author
  • Angela Darekar
    • 4
  • Koushik Maharatna
    • 5
  • Daniel Konn
    • 6
  • David Allen
    • 6
  • J-Donald Tournier
    • 7
    • 8
  • John Broulidakis
    • 1
  • Brigitte Vollmer
    • 1
    • 2
  1. 1.Clinical Neurosciences, Clinical and Experimental Sciences, Faculty of MedicineUniversity of SouthamptonSouthamptonUK
  2. 2.Paediatric NeurologySouthampton Children’s HospitalSouthamptonUK
  3. 3.Department of Clinical Sciences Lund, Diagnostic RadiologyLund UniversityLundSweden
  4. 4.Department of Medical PhysicsUniversity Hospital Southampton NHS Foundation TrustSouthamptonUK
  5. 5.Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUK
  6. 6.Clinical NeurophysiologyUniversity Hospital Southampton NHS Foundation TrustSouthamptonUK
  7. 7.Department of Biomedical Engineering, School of Bioengineering and Imaging SciencesKing’s College LondonLondonUK
  8. 8.Centre for the Developing Brain, School of Bioengineering and Imaging SciencesKing’s College LondonLondonUK

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