Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Neonatal encephalopathy prediction of poor outcome with diffusion-weighted imaging connectome and fixel-based analysis

Abstract

Background

Better biomarkers of eventual outcome are needed for neonatal encephalopathy. To identify the most potent neonatal imaging marker associated with 2-year outcomes, we retrospectively performed diffusion-weighted imaging connectome (DWIC) and fixel-based analysis (FBA) on magnetic resonance imaging (MRI) obtained in the first 4 weeks of life in term neonatal encephalopathy newborns.

Methods

Diffusion tractography was available in 15 out of 24 babies with MRI, five each with normal, abnormal motor outcome, or death. All 15 except one underwent hypothermia as initial treatment. In abnormal motor and death groups, DWIC found 19 white matter pathways with severely disrupted fiber orientation distributions.

Results

Using random forest classification, these disruptions predicted the follow-up outcomes with 89–99% accuracy. These pathways showed reduced integrity in abnormal motor and death vs. normal tone groups (p < 10−6). Using ranked supervised multi-view canonical correlation and depicting just three of the five dimensions of the analysis, the abnormal motor and death were clearly differentiated from each other and the normal tone group.

Conclusions

This study suggests that a machine-learning model for prediction using early DWIC and FBA could be a possible way of developing biomarkers in large MRI datasets having clinical outcomes.

Impact

  • Early connectome and FBA of clinically acquired DWI provide a new noninvasive imaging tool to predict the long-term motor outcomes after birth, based on the severity of white matter injury.

  • Disrupted white matter connectivity as a novel neonatal marker achieves high accuracy of 89–99% to predict 2-year motor outcomes using conventional machine-learning classification.

  • The proposed neonatal marker may allow better prognostication that is important to elucidate neural repair mechanisms and evaluate treatment modalities in neonatal encephalopathy.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Fiber orientation distribution (FOD) function as a potential biomarker of white matter injury.
Fig. 2: Schematic of the DWIC analysis to construct the connectome graph, G = (Ω, S), of an individual infant.
Fig. 3: To the naked eye, three groups of 2-year outcomes can be differentiated by comparing the shapes of FOD functions in the somatosensory motor system.
Fig. 4: Trend for differentiating three groups of 2-year outcomes by comparing five DWIC markers that were measured from neural pathways of interest.
Fig. 5: Using α < 0.05, significant alterations of FOD functions underlying early hypoxic injuries were found in subcortical regions of abnormal motor and death groups, including thalamus, posterior limb of internal capsule, and cerebellar peduncle.
Fig. 6: Prediction of the eventual outcome made easier by ranked SMVCCA showing a figure plotting three of the five fused dimensions, which provided the most significant discrimination for three groups (Fig. 4a)

References

  1. 1.

    Tusor, N. et al. Prediction of neurodevelopmental outcome after hypoxic-ischemic encephalopathy treated with hypothermia by diffusion tensor imaging analyzed using tract-based spatial statistics. Pediatr. Res. 72, 63–69 (2012).

    Article  Google Scholar 

  2. 2.

    Martinez-Biarge, M. et al. Predicting motor outcome and death in term hypoxic-ischemic encephalopathy. Neurology 76, 2055–2061 (2011).

    CAS  Article  Google Scholar 

  3. 3.

    Brown, C. J. et al. Prediction of motor function in very preterm infants using connectome features and local synthetic instances. In International Conference on Medical Image Computing and Computer-Assisted Intervention, (eds Nassir, N., Joachim, H., William, M. W., & Alejandro, F.) 69–76 (Springer, 2015).

  4. 4.

    Jeong, J. W., Sundaram, S., Behen, M. E. & Chugani, H. T. Differentiation of speech delay and global developmental delay in children using DTI tractography-based connectome. Am. J. Neuroradiol. 37, 1170–1177 (2016).

    Article  Google Scholar 

  5. 5.

    Raffelt, D. A. et al. Investigating white matter fibre density and morphology using fixel-based analysis. Neuroimage 144, 58–73 (2017).

    Article  Google Scholar 

  6. 6.

    Tournier, J. D., Calamante, F., Gadian, D. G. & Connelly, A. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage 23, 1176–1185 (2004).

    Article  Google Scholar 

  7. 7.

    Wedeen, V. J. et al. Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers. Neuroimage 41, 1267–1277 (2008).

    CAS  Article  Google Scholar 

  8. 8.

    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).

    Article  Google Scholar 

  9. 9.

    Andersson, J. L. R., Skare, S. & Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage 20, 870–888 (2003).

    Article  Google Scholar 

  10. 10.

    Alexander, D. C., Barker, G. J. & Arridge, S. R. Detection and modeling of non-Gaussian apparent diffusion coefficient profiles in human brain data. Magn. Res. Med. 48, 331–340 (2002).

    CAS  Article  Google Scholar 

  11. 11.

    Assemlal, H. E., Tschumperlé, D., Brun, L. & Siddiqi, K. Recent advances in diffusion MRI modeling: angular and radial reconstruction. Med. Image Anal. 15, 369–396 (2011).

    Article  Google Scholar 

  12. 12.

    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).

    Article  Google Scholar 

  13. 13.

    Smith, R. E., Tournier, J. D., Calamante, F. & Connelly, A. SIFT2: enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. Neuroimage 119, 338–351 (2015).

    Article  Google Scholar 

  14. 14.

    Aydogan, D. B. & Shi, Y. Probabilistic tractography for topographically organized connectomes. In International Conference on Medical Image Computing and Computer-Assisted Intervention, (eds Sebastien, O., Leo, J., Mert, R. S., Gozde, U. & William, W.) 201–209 (Springer, 2016).

  15. 15.

    Shi, F. et al. Infant brain atlases from neonates to 1- and 2-year-olds. PLoS ONE 6, e18746 (2011).

    CAS  Article  Google Scholar 

  16. 16.

    Zbynĕk, Š. Rectangular confidence regions for the means of multivariate normal distributions. J. Am. Stat. Assoc. 62, 626–633 (1967).

    Google Scholar 

  17. 17.

    Basser, P. J. & Pierpaoli, C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J. Magn. Reson. 111, 209–219 (1996).

    CAS  Article  Google Scholar 

  18. 18.

    Wheeler-Kingshott, C. A. & Cercignani, M. About “axial” and “radial” diffusivities. Magn. Reson. Med. 61, 1255–1260 (2009).

    Article  Google Scholar 

  19. 19.

    Egorova, N. et al. Pervasive white matter fiber degeneration in ischemic stroke. Stroke 51, 1507–13. (2020).

    Article  Google Scholar 

  20. 20.

    Kelly, C. E. et al. Long-term development of white matter fibre density and morphology up to 13 years after preterm birth: a fixel-based analysis. Neuroimage 220, 117068 (2020).

    Article  Google Scholar 

  21. 21.

    Pannek, K. et al. Fixel-based analysis reveals alterations is brain microstructure and macrostructure of preterm-born infants at term equivalent age. Neuroimage Clin. 18, 51–59 (2018).

    Article  Google Scholar 

  22. 22.

    Pecheva, D. et al. Fixel-based analysis of the preterm brain: disentangling bundle-specific white matter microstructural and macrostructural changes in relation to clinical risk factors. Neuroimage Clin. 23, 101820 (2019).

    Article  Google Scholar 

  23. 23.

    Raffelt, D. A. et al. Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres. Neuroimage 117, 40–55 (2015).

    Article  Google Scholar 

  24. 24.

    Golugula, A. et al. Supervised regularized canonical correlation analysis: integrating histologic and proteomic measurements for predicting biochemical recurrence following prostate surgery. BMC Bioinform. 12, 483 (2011).

    Article  Google Scholar 

  25. 25.

    Lee, G. et al. Supervised multi-view canonical correlation analysis (sMVCCA): integrating histologic and proteomic features for predicting recurrent prostate cancer. IEEE Trans. Med. Imaging 34, 284–297 (2015).

    Article  Google Scholar 

  26. 26.

    Dudink, J., Kerr, J. L., Paterson, K. & Counsell, S. J. Connecting the developing preterm brain. Early Hum. Dev. 84, 777–782 (2008).

    Article  Google Scholar 

  27. 27.

    Gao, W. et al. Temporal and spatial development of axonal maturation and myelination of white matter in the developing brain. Am. J. Neuroradiol. 30, 290–296 (2009).

    CAS  Article  Google Scholar 

  28. 28.

    Gao, W. et al. Evidence on the emergence of the brain’s default network from 2-week-old to 2-year-old healthy pediatric subjects. Proc. Natl Acad. Sci. USA 106, 6790–6795 (2009).

    CAS  Article  Google Scholar 

  29. 29.

    Brown, C. J. et al. Structural network analysis of brain development in young preterm neonates. Neuroimage 101, 667–680 (2014).

    Article  Google Scholar 

  30. 30.

    Kawahara, J. et al. BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment. Neuroimage 146, 1038–49. (2017).

    Article  Google Scholar 

  31. 31.

    Tymofiyeva, O. et al. Towards the “baby connectome”: mapping the structural connectivity of the newborn brain. PLoS ONE 7, e31029 (2012).

    CAS  Article  Google Scholar 

  32. 32.

    Merhar, S. L. et al. Neonatal functional and structural connectivity are associated with cerebral palsy at two years of age. Am. J. Perinatol. 37, 137–145 (2020).

    Article  Google Scholar 

  33. 33.

    Shankaran, S. et al. Neonatal magnetic resonance imaging pattern of brain injury as a biomarker of childhood outcomes following a trial of hypothermia for neonatal hypoxic-ischemic encephalopathy. J. Pediatr. 167, 987–993 (2015).

    Article  Google Scholar 

  34. 34.

    Pannek, K. et al. Brain microstructure and morphology of very preterm-born infants at term equivalent age: associations with motor and cognitive outcomes at 1 and 2 years. Neuroimage 221, 117163 (2020).

    Article  Google Scholar 

  35. 35.

    Button, K. S. et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14, 365–376 (2013).

    CAS  Article  Google Scholar 

  36. 36.

    Forstmeier, W., Wagenmakers, E. J. & Parker, T. H. Detecting and avoiding likely false-positive findings - a practical guide. Biol. Rev. Camb. Philos. Soc. 92, 1941–1968 (2017).

    Article  Google Scholar 

  37. 37.

    Thomas, C. et al. Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited. Proc. Natl Acad. Sci. USA 111, 16574–16579 (2014).

    CAS  Article  Google Scholar 

  38. 38.

    Reveley, C. et al. Superficial white matter fiber systems impede detection of long-range cortical connections in diffusion MR tractography. Proc. Natl Acad. Sci. USA 112, E2820–E2828 (2015).

    CAS  Article  Google Scholar 

  39. 39.

    Genc, S. et al. Impact of b-value on estimates of apparent fibre density. Hum. Brain Mapp. 41, 2583–2595 (2020).

    Article  Google Scholar 

  40. 40.

    Wee, C. Y. et al. Neonatal neural networks predict children behavioral profiles later in life. Hum. Brain Mapp. 38, 1362–1373 (2017).

    Article  Google Scholar 

  41. 41.

    Girault, J. B. et al. White matter connectomes at birth accurately predict cognitive abilities at age 2. Neuroimage 192, 145–155 (2019).

    Article  Google Scholar 

  42. 42.

    Ouyang, M. et al. Diffusion-MRI-based regional cortical microstructure at birth for predicting neurodevelopmental outcomes of 2-year-olds. Elife 9, e58116 (2020).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank the families who took part in the study, our colleagues in the Neonatal Intensive Care Unit, and the Developmental Assessment Clinic at Children’s Hospital of Michigan. S.T. and J.-W.J. have been funded through NS114972 and NS117146 (S.T., P.I.) from NINDS, NIH.

Author information

Affiliations

Authors

Contributions

J.-W.J.—study conception and design, data acquisition, data analysis and interpretation, statistical analysis, drafting manuscript and revision, final approval of the manuscript. M.-H.L.—data analysis and interpretation, statistical analysis, final approval of the manuscript. N.F.—drafting manuscript and revision, final approval of the manuscript. S.D.—drafting manuscript and revision, final approval of the manuscript. S.M.—drafting manuscript and revision, final approval of the manuscript. S.A.—manuscript revision, final approval of the manuscript. R.B.C.—manuscript revision, final approval of the manuscript. S.T.—study conception and design, data analysis and interpretation, drafting manuscript and revision, final approval of the manuscript.

Corresponding author

Correspondence to Jeong-Won Jeong.

Ethics declarations

Competing interests

The authors declare no competing interests.

Statement of consent

The present study was approved by the University’s Institutional Review Board, and a waiver of written informed consent was obtained to perform the analysis of existing data in the clinical archive.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jeong, JW., Lee, MH., Fernandes, N. et al. Neonatal encephalopathy prediction of poor outcome with diffusion-weighted imaging connectome and fixel-based analysis. Pediatr Res (2021). https://doi.org/10.1038/s41390-021-01550-2

Download citation

Search

Quick links