Development of Cortical Morphology Evaluated with Longitudinal MR Brain Images of Preterm Infants

Abstract

Introduction: The cerebral cortex develops rapidly in the last trimester of pregnancy. In preterm infants, brain development is very vulnerable because of their often complicated extra-uterine conditions. The aim of this study was to quantitatively describe cortical development in a cohort of 85 preterm infants with and without brain injury imaged at 30 and 40 weeks postmenstrual age (PMA).

Methods: In the acquired T2-weighted MR images, unmyelinated white matter (UWM), cortical grey matter (CoGM), and cerebrospinal fluid in the extracerebral space (CSF) were automatically segmented. Based on these segmentations, cortical descriptors evaluating volume, surface area, thickness, gyrification index, and global mean curvature were computed at both time points, for the whole brain, as well as for the frontal, temporal, parietal, and occipital lobes separately. Additionally, visual scoring of brain abnormality was performed using a conventional scoring system at 40 weeks PMA.

Results: The evaluated descriptors showed larger change in the occipital lobes than in the other lobes. Moreover, the cortical descriptors showed an association with the abnormality scores: gyrification index and global mean curvature decreased, whereas, interestingly, median cortical thickness increased with increasing abnormality score. This was more pronounced at 40 weeks PMA than at 30 weeks PMA, suggesting that the period between 30 and 40 weeks PMA might provide a window of opportunity for intervention to prevent delay in cortical development.

Fig 1. Automatic segmentation of CoGM at 30 (top) and 40 weeks PMA (bottom) for the same patient, shown in four slices of the T2-weighted images.

Note that, because of the limited resolution, CSF inside the sulci was not always visible, which resulted in local overestimation of cortical thickness.

Fig 2. Automatic parcellation of the images acquired at 30 weeks (top) and 40 weeks (bottom) in frontal (red and orange), temporal (yellow and green), parietal (blue and purple), and occipital (pink and brown) lobes.

The images were scaled separately and therefore do not show change in size of the brain.

Fig 3. Local mean curvature of the inner cortical surface as obtained by automatic segmentation on the images acquired at 30 weeks PMA (top) and at 40 weeks PMA (bottom) for one patient.

Red indicates positive curvature, blue indicates negative curvature, and yellow indicates zero curvature. The images were scaled separately and therefore do not show change in size of the brain.

Fig 4. UWM volume (top left), CoGM volume (top right), inner cortical surface area (middle left), median cortical thickness (middle right), gyrification index (bottom left), global mean curvature (bottom right), for the images acquired at 30 and 40 weeks PMA, shown versus PMA at the time of scanning.

Spearman’s rank correlation coefficients (ρ) and the corresponding p-values are shown for 30 (left) and 40 weeks PMA (right) separately.

Fig 5. Regional evaluation (in terms of standard boxplots) for, from top to bottom: UWM volume, inner cortical surface area, median cortical thickness, gyrification index, and global mean curvature.

The columns show, from left to right: the results for the images acquired at 30 weeks, the results for the images acquired at 40 weeks, and the ratio between the results for the images acquired at 40 and 30 weeks. Note that, for the descriptors acquired at 30 and 40 weeks PMA, UWM volume and inner cortical surface area were dependent of the size of the defined regions of the parcellation, while median cortical thickness, gyrification index, and global mean curvature were independent of size. In every frame the results are shown for the right (R) and left (L) hemispheres, the right (FR) and left (FL) frontal lobes, the right (TR) and left (TL) temporal lobes, the right (PR) and left (PL) parietal lobes, and the right (OR) and left (OL) occipital lobes.

Fig 6. Average regional increase factors for both hemispheres visualised on the inner cortical surface of one randomly chosen patient.

The highest increase factor is shown in red and the lowest increase factor is shown in yellow; the range was set separately per descriptor. Note that this figure provides a visualisation of the data in the last column of Fig 5.

Fig 7. Global cortical morphology descriptors computed from the images acquired at 30 (left) and 40 weeks PMA (right), as a function of total brain (left column) and CoGM abnormality score (right column).

From top to bottom: CoGM volume, median cortical thickness, inner cortical surface area, gyrification index, global mean curvature. The four abnormality classes are (i) normal, (ii) mild abnormality, (iii) moderate abnormality, (iv) severe abnormality. Note that there is only one subject in the severe class for the total brain abnormality score. In the statistical evaluation the moderate and severe classes were combined for both the total brain abnormality scoring and the CoGM abnormality scoring. Significant differences with the normal class are indicated with an asterisk (*).

Fig 8. CoGM volume, median cortical thickness, gyrification index, and global mean curvature computed from the images acquired at 40 weeks PMA versus the width of the interhemispheric fissure, measured between the crowns of the superior frontal gyri.

Spearman’s rank correlation coefficients (ρ) and the corresponding p-values are shown at the bottom right. No significant correlation was found for inner cortical surface area.