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Probabilistic atlases are widely used in the neuroscience community as a tool for providing a standard space for comparison of subjects and as tissue priors used to enhance the intensity-based classification of brain MRI. Most efforts so far have focused on static brain atlases either for adult or pediatric cohorts. In contrast to the adult brain the rapid growth of the neonatal brain requires an age-specific spatial probabilistic atlas to provide suitable anatomical and structural information. In this paper we describe a 4D probabilistic atlas that allows dynamic generation of prior tissue probability maps for any chosen stage of neonatal brain development between 29 and 44 gestational weeks. The atlas is created from the segmentations of 142 neonatal subjects at different ages using a kernel-based regression method and provides prior tissue probability maps for six structures - cortex, white matter, subcortical grey matter, brainstem, cerebellum and cerebro-spinal fluid. The atlas is publicly available at www.brain-development.org., (Copyright © 2010 Elsevier Inc. All rights reserved.)

We present methods for the quantitative analysis of brain growth based on the registration of longitudinal MR image data with the use of Jacobian determinant maps to characterise neuroanatomical changes. The individual anatomies, growth maps and tissue classes are also spatially normalised in an 'average space' and aggregated to provide atlases for the population at each timepoint. The average space representation is obtained using the average intersubject transformation within each timepoint. In an exemplar study, this approach is used to assess brain development in 25 infants between 1 and 2 years, and we show consistency in growth estimates between registration and segmentation approaches.

Segmentation and reconstruction of cortical surfaces from magnetic resonance (MR) images are more challenging for developing neonates than adults. This is mainly due to the dynamic changes in the contrast between gray matter (GM) and white matter (WM) in both T1- and T2-weighted images (T1w and T2w) during brain maturation. In particular in neonatal T2w images WM typically has higher signal intensity than GM. This causes mislabeled voxels during cortical segmentation, especially in the cortical regions of the brain and in particular at the interface between GM and cerebrospinal fluid (CSF). We propose an automatic segmentation algorithm detecting these mislabeled voxels and correcting errors caused by partial volume effects. Our results show that the proposed algorithm corrects errors in the segmentation of both GM and WM compared to the classic expectation maximization (EM) scheme. Quantitative validation against manual segmentation demonstrates good performance (the mean Dice value: 0.758+/-0.037 for GM and 0.794+/-0.078 for WM). The inner, central and outer cortical surfaces are then reconstructed using implicit surface evolution. A landmark study is performed to verify the accuracy of the reconstructed cortex (the mean surface reconstruction error: 0.73 mm for inner surface and 0.63 mm for the outer). Both segmentation and reconstruction have been tested on 25 neonates with the gestational ages ranging from approximately 27 to 45 weeks. This preliminary analysis confirms previous findings that cortical surface area and curvature increase with age, and that surface area scales to cerebral volume according to a power law, while cortical thickness is not related to age or brain growth.

Infants born preterm have a high incidence of neurodevelopmental impairment in later childhood, often associated with poorly defined cerebral white matter abnormalities. Diffusion tensor imaging quantifies the diffusion of water within tissues and can assess microstructural abnormalities in the developing preterm brain. Tract-based spatial statistics (TBSS) is an automated observer-independent method of aligning fractional anisotropy (FA) images from multiple subjects to allow groupwise comparisons of diffusion tensor imaging data. We applied TBSS to test the hypothesis that preterm infants have reduced fractional anisotropy in specific regions of white matter compared to term-born controls. We studied 26 preterm infants with no evidence of focal lesions on conventional magnetic resonance imaging (MRI) at term equivalent age and 6 healthy term-born control infants. We found that the centrum semiovale, frontal white matter and the genu of the corpus callosum showed significantly lower FA in the preterm group. Infants born at less than or equal to 28 weeks gestational age (n=11) displayed additional reductions in FA in the external capsule, the posterior aspect of the posterior limb of the internal capsule and the isthmus and middle portion of the body of the corpus callosum. This study demonstrates that TBSS provides an observer-independent method of identifying white matter abnormalities in the preterm brain at term equivalent age in the absence of focal lesions.

Our aim was to investigate the feasibility of studying white matter tracts and connections between the thalamus and the cortex in 2-year-old infants who were born preterm by probabilistic magnetic resonance (MR) tractography. Using this approach, we were able to visualize and quantify connectivity distributions in a number of white matter tracts, including the corticospinal tracts, optic radiations, fibers of the genu and splenium of the corpus callosum, superior longitudinal fasciculus and inferior fronto-occipital fasciculus, and to map the distribution within thalamus of fibers connecting to specific cortical regions. In eleven infants with no MR evidence of focal cerebral lesions and appropriate neurodevelopment as shown by general quotient (GQ) scores above 100, we mapped cortical connections to the thalamus that appeared similar to those reported in adults. However, in a proof-of-principle experiment, we examined one further child with marked white matter abnormalities and found that the volume and pattern of thalamo-cortical connections were severely disrupted. This technique promises to be a useful tool for assessing connectivity in the developing brain and in infants with lesions.

Preterm birth is associated with a high incidence of neurodevelopmental deficits. Magnetic resonance imaging (MRI) has proved to be a valuable tool for monitoring development in the preterm brain. We used a dedicated time-of-flight (TOF) magnetic resonance angiography (MRA) protocol at 3 Tesla (3T) optimized to assess morphological characteristics of the neonatal cerebral vessels associated with preterm birth in a sample of 37 infants. We found statistically significant decreased tortuosity in all proximal segments of the cerebral vasculature (anterior, middle and posterior cerebral arteries) in the preterm infants imaged at term equivalent age compared to the term born infants, with no differences in vessel diameter between the two groups. This distinct phenotype of decreased tortuosity was shown to persist until 18 months of age in longitudinal MRA studies in infants born preterm, suggesting that this is not a delay in maturation. Biparietal head diameter measurements were significantly smaller in the preterm at term infants and were inversely correlated with middle cerebral artery tortuosity measurements in both the term born and the preterm at term infants. To our knowledge, this is the first systematic MRA study on the effect of preterm delivery on neonatal cerebral vasculature. Our intention is to build on the findings of this study by combining the data with other measurements of brain growth and vascular haemodynamics to understand more about the interdependence of vessel and brain development and their relationship to prematurity.