Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas.

Joint registration of a stack of 2D histological sections to recover 3D structure ("3D histology reconstruction") finds application in areas such as atlas building and validation of in vivo imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as "banana effect" (straightening of curved structures) and "z-shift" (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast differences and the strong nonlinear distortion of the tissue, including artefacts such as folds and tears. In this paper, we present a probabilistic model of spatial deformation that yields reconstructions for multiple histological stains that that are jointly smooth, robust to outliers, and follow the reference shape. The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. We consider two likelihood models: Gaussian (ℓ2 norm, which can be minimised in closed form) and Laplacian (ℓ1 norm, minimised with linear programming). Results on synthetic deformations on multiple MR modalities, show that our method can accurately and robustly register multiple contrasts even in the presence of outliers. The framework is used for accurate 3D reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas, showing its benefits on real data with severe distortions. Moreover, we also provide the registration of the reconstructed volume to MNI space, bridging the gaps between two of the most widely used atlases in histology and MRI. The 3D reconstructed volumes and atlas registration can be downloaded from https://openneuro.org/datasets/ds003590. The code is freely available at https://github.com/acasamitjana/3dhirest.

Neurocognitive sequelae of antenatal corticosteroids in a late preterm rabbit model.

BACKGROUND: Late preterm birth is associated with short-term respiratory and adaptive problems. Although antenatal corticosteroids seem to reduce the respiratory burden, this may come at the cost of adverse neuropsychological outcomes later in life. This impact has not been investigated. OBJECTIVE: Herein, we investigate what the short- and long-term neurodevelopmental effects of a single course of betamethasone in simulated late preterm birth. STUDY DESIGN: Time-mated pregnant does received 0.1 mg/kg betamethasone (n=8) or 1 mL saline intramuscular (n=6) at the postconceptional ages of 28 and 29 days. The antenatal corticosteroid dose and scheme were based on previous studies and were comparable with routine clinical use. Cesarean delivery was done on postconceptional age 30 days (term=31 days), and new-born rabbits were foster-cared for 28 days and were thereafter cared for in group housing. Neonatal lung function testing and short-term neurobehavioral testing was done. Open field, spontaneous alternation, and novel object recognition tests were subsequently performed at 4 and 8 weeks of age. On postnatal day 1 and at 8 weeks, a subgroup was euthanized and transcardially perfuse fixated. Ex vivo high-resolution Magnetic Resonance Imaging was used to calculate the Diffusion Tensor Imaging-derived fractional anisotropy and mean diffusivity. Fixated brains underwent processing and were serial sectioned, and a set of 3 coronal sections underwent anti-NeuN, Ki67, and terminal deoxynucleotidyl transferase dUTP nick end labeling staining. RESULTS: Antenatal corticosteroid exposure was associated with improved neonatal lung function, yet resulted in a long-term growth deficit that coincided with a persistent neurobehavioral deficit. We demonstrated lower neonatal motor scores; a persistent anxious behavior in the open field test with more displacements, running, and self-grooming episodes; persistent lower alternation scores in the T-Maze test; and lower discriminatory indexes in the novel object recognition. On neuropathological assessment, antenatal corticosteroid exposure was observed to result in a persistent lower neuron density and fewer Ki67+ cells, particularly in the hippocampus and the corpus callosum. This coincided with lower diffusion tensor imaging-derived fractional anisotropy scores in the same key regions. CONCLUSION: Clinical equivalent antenatal corticosteroid exposure in this late preterm rabbit model resulted in improved neonatal lung function. However, it compromised neonatal and long-term neurocognition.

Publisher Correction: Early neuropathological and neurobehavioral consequences of preterm birth in a rabbit model.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Early neuropathological and neurobehavioral consequences of preterm birth in a rabbit model.

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.

A magnetic resonance multi-atlas for the neonatal rabbit brain.

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.