Preterm birth is the most significant problem in contemporary obstetrics accounting for 5–18% of worldwide deliveries. Encephalopathy of prematurity encompasses the multifaceted diffuse brain injury resulting from preterm birth. Current animal models exploring the underlying pathophysiology of encephalopathy of prematurity employ significant insults to generate gross central nervous system abnormalities. To date the exclusive effect of prematurity was only studied in a non-human primate model. Therefore, we aimed to develop a representative encephalopathy of prematurity small animal model only dependent on preterm birth. Time mated New-Zealand white rabbit does were either delivered on 28 (pre-term) or 31 (term) postconceptional days by caesarean section. Neonatal rabbits underwent neurobehavioral evaluation on 32 days post conception and then were transcardially perfuse fixed. Neuropathological assessments for neuron and oligodendrocyte quantification, astrogliosis, apoptosis and cellular proliferation were performed. Lastly, ex-vivo high-resolution Magnetic Resonance Imaging was used to calculate T1 volumetric and Diffusion Tensor Imaging derived fractional anisotropy and mean diffusivity. Preterm birth was associated with a motoric (posture instability, abnormal gait and decreased locomotion) and partial sensory (less pain responsiveness and failing righting reflex) deficits that coincided with global lower neuron densities, less oligodendrocyte precursors, increased apoptosis and less proliferation. These region-specific histological changes corresponded with Magnetic Resonance Diffusion Tensor Imaging differences. The most significant differences were seen in the hippocampus, caudate nucleus and thalamus of the preterm rabbits. In conclusion this model of preterm birth, in the absence of any other contributory events, resulted in measurable neurobehavioral deficits with associated brain structural and Magnetic Resonance Diffusion Tensor Imaging findings.

The rabbit model has become increasingly popular in neurodevelopmental studies as it is best suited to bridge the gap in translational research between small and large animals. In the context of preclinical studies, high-resolution magnetic resonance imaging (MRI) is often the best modality to investigate structural and functional variability of the brain, both in vivo and ex vivo. In most of the MRI-based studies, an important requirement to analyze the acquisitions is an accurate parcellation of the considered anatomical structures. Manual segmentation is time-consuming and typically poorly reproducible, while state-of-the-art automated segmentation algorithms rely on available atlases. In this work we introduce the first digital neonatal rabbit brain atlas consisting of 12 multi-modal acquisitions, parcellated into 89 areas according to a hierarchical taxonomy. Delineations were performed iteratively, alternating between segmentation propagation, label fusion and manual refinements, with the aim of controlling the quality while minimizing the bias introduced by the chosen sequence. Reliability and accuracy were assessed with cross-validation and intra- and inter-operator test-retests. Multi-atlas, versioned controlled segmentations repository and supplementary materials download links are available from the software repository documentation at https://github.com/gift-surg/SPOT-A-NeonatalRabbit.

Recovering the 3D structure of a stack of histological sections (3D histology reconstruction) requires a linearly aligned reference volume in order to minimize z-shift and "banana effect". Reconstruction can then be achieved by computing 2D registrations between each section and its corresponding resampled slice in the volume. However, these registrations are often inaccurate due to their inter-modality nature and to the strongly nonlinear deformations introduced by histological processing. Here we introduce a probabilistic model of spatial deformations to efficiently refine these registrations, without the need to revisit the imaging data. Our method takes as input a set of nonlinear registrations between pairs of 2D images (within or across modalities), and uses Bayesian inference to estimate the most likely spanning tree of latent transformations that generated the measured deformations. Results on synthetic and real data show that our algorithm can effectively 3D reconstruct the histology while being robust to z-shift and banana effect. An implementation of the approach, which is compatible with a wide array of existing registration methods, is available at JEI's website: www.jeiglesias.com.

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Johannes van der Merwe, Lennart van der Veeken, Sebastiano Ferraris, Jaan Toelen and Jan Deprest were omitted. This has now been corrected in the HTML and PDF versions of the Article.

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