Modelling brain development to detect white matter injury in term and preterm born neonates

O'Muircheartaigh J, Robinson EC, Pietsch M, Wolfers T, Aljabar P, Grande LC, Teixeira RPAG, Bozek J, Schuh A, Makropoulos A, Batalle D, Hutter J, Vecchiato K, Steinweg JK, Fitzgibbon S, Hughes E, Price AN, Marquand A, Reuckert D, Rutherford M, Hajnal JV, Counsell SJ, Edwards AD (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Brain Oxford University Press

Book Volume: 143

Pages Range: 467-479

Journal Issue: 2

DOI: 10.1093/brain/awz412

Abstract

Premature birth occurs during a period of rapid brain growth. In this context, interpreting clinical neuroimaging can be complicated by the typical changes in brain contrast, size and gyrification occurring in the background to any pathology. To model and describe this evolving background in brain shape and contrast, we used a Bayesian regression technique, Gaussian process regression, adapted to multiple correlated outputs. Using MRI, we simultaneously estimated brain tissue intensity on T1- and T2weighted scans as well as local tissue shape in a large cohort of 408 neonates scanned cross-sectionally across the perinatal period. The resulting model provided a continuous estimate of brain shape and intensity, appropriate to age at scan, degree of prematurity and sex. Next, we investigated the clinical utility of this model to detect focal white matter injury. In individual neonates, we calculated deviations of a neonate’s observed MRI from that predicted by the model to detect punctate white matter lesions with very good accuracy (area under the curve 4 0.95). To investigate longitudinal consistency of the model, we calculated model deviations in 46 neonates who were scanned on a second occasion. These infants’ voxelwise deviations from the model could be used to identify them from the other 408 images in 83% (T2-weighted) and 76% (T1-weighted) of cases, indicating an anatomical fingerprint. Our approach provides accurate estimates of non-linear changes in brain tissue intensity and shape with clear potential for radiological use.

Authors with CRIS profile

Jana Hutter

Involved external institutions

King’s College London GB United Kingdom (GB) University of Zagreb / Sveučilište u Zagrebu HR Croatia (HR) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) University of Oxford GB United Kingdom (GB) Radboud University Nijmegen NL Netherlands (NL)

How to cite

APA:

O'Muircheartaigh, J., Robinson, E.C., Pietsch, M., Wolfers, T., Aljabar, P., Grande, L.C.,... Edwards, A.D. (2020). Modelling brain development to detect white matter injury in term and preterm born neonates. Brain, 143(2), 467-479. https://doi.org/10.1093/brain/awz412

MLA:

O'Muircheartaigh, Jonathan, et al. "Modelling brain development to detect white matter injury in term and preterm born neonates." Brain 143.2 (2020): 467-479.

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