Preterm birth alters the development of cortical microstructure and morphology at term-equivalent age

Abstract

Introduction The dynamic nature and complexity of the cellular events that take place during the last trimester of pregnancy make the developing cortex particularly vulnerable to perturbations. Abrupt interruption to normal gestation can lead to significant deviations to many of these processes, resulting in atypical trajectory of cortical maturation in preterm birth survivors. Methods We sought to first map typical cortical micro- and macrostructure development using invivo MRI in a large sample of healthy term-born infants scanned after birth ( n = 259). Then we offer a comprehensive characterization of the cortical consequences of preterm birth in 76 preterm infants scanned at term-equivalent age (37–44 weeks postmenstrual age). We describe the group-average atypicality, the heterogeneity across individual preterm infants, and relate individual deviations from normative development to age at birth and neurodevelopment at 18 months. Results In the term-born neonatal brain, we observed heterogeneous and regionally specific associations between age at scan and measures of cortical morphology and microstructure, including rapid surface expansion, greater cortical thickness, lower cortical anisotropy and higher neurite orientation dispersion. By term-equivalent age, preterm infants had on average increased cortical tissue water content and reduced neurite density index in the posterior parts of the cortex, and greater cortical thickness anteriorly compared to term-born infants. While individual preterm infants were more likely to show extreme deviations (over 3.1 standard deviations) from normative cortical maturation compared to term-born infants, these extreme deviations were highly variable and showed very little spatial overlap between individuals. Measures of regional cortical development were associated with age at birth, but not with neurodevelopment at 18 months. Conclusion We showed that preterm birth alters cortical micro- and macrostructural maturation near the time of full-term birth. Deviations from normative development were highly variable between individual preterm infants.

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“…However, this inductive bias towards locality also has drawbacks, since it induces a limited receptive field that impairs the modelling of long-range spatial dependencies between distant parts of an image. This prevents CNNs from efficiently modelling processes that are diffuse in space and/or time; something that is known to be true for a wide range of biomedical applications, including multiorgan segmentation [42], cognitive and neurodevelopmental modelling [10], [43] and estimation of the spatio-temporal dynamics of the heart [3], [44].…”

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“…Surface deep learning architectures offer important opportunity for cortical applications since human brain morphology and functional topography varies considerably across individuals, in ways that violate the assumptions of traditional image registration [7], and thereby limit the sensitivity of populationbased comparisons. Since cortical surface representations encode more biologically meaningful geodesic distances between cortical areas [6], [8], [9], [31], they are able to support more precise comparison of features of cortical micro and cyto-architecture [7], [10], shape [22] and function [7], [22]. This is important since changes to cortical organisation are implicated in numerous neurological [16], [17], psychiatric [18], [19] and developmental disorders [20], [21].…”

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“…Studying surfaces and manifolds is critical for many applications in computer graphics [34,38,41], protein interaction [19,39] and biomedical analysis including cardiac [37,55], brain [14,18,21,22,44,45] imaging, with applications in biophysical and shape modelling [8,20,54], and mapping of cortical organisation [14,22,53]. While the shapes of meshes may vary greatly, ultimately, all problems may be reduced to analysis of functions over tessellated, deformable meshes.…”