Detailed semiautomated MRI based morphometry of the neonatal brain: Preliminary results

In the neonate, regional growth trajectories provide information about the coordinated development of cerebral substructures and help identify regional vulnerability by identifying times of faster growth. Segmentation of magnetic resonance images (MRI) has provided detailed information for the myelinated brain but few reports of regional neonatal brain growth exist. We report the method and preliminary results of detailed semiautomated segmentation of 12 normative neonatal brains (gestational age 31.1–42.6 weeks at time of MRI) using volumetric T1-weighted images. Accuracy was confirmed by expert review of every segmented image. In 5 brains, repeat segmentation resulted in intraclass correlation coefficients >0.9 (except for the right amygdala) and an average percent voxel overlap of 90.0%. Artifacts or image quality limited the number of regions segmented in 9/12 data sets and 1/12 was excluded from volumetric analysis due to ventriculomegaly. Brains were segmented into cerebral exterior (N = 8), cerebral lobes (N = 5), lateral ventricles (N = 8), cerebral cortex (N = 6), white matter (N = 6), corpus callosum (N = 7), deep central gray (N = 8), hippocampi (N = 8), amygdalae (N = 8), cerebellar hemispheres (N = 8), vermis (N = 8), midbrain (N = 8), pons (N = 8) and medulla (N = 8). Linear growth (P < 0.05) was identified in all regions except the cerebral white matter, medulla and ventricles. Striking differences in regional growth rates were noted. These preliminary results are consistent with the heterochronous nature of cerebral development and provide initial estimates of regional brain growth and therefore regional vulnerability in the perinatal time period.