New insights into the developing rabbit brain using diffusion tensor tractography and generalized q-sampling MRI

Down a Rabbit Hole: Burrowing Behaviour and Larger Home Ranges are Related to Larger Brains in Leporids

Studies on the evolution of brain size variation usually focus on large clades encompassing broad phylogenetic groups. This risks introducing ‘noise’ in the results, often obscuring effects that might be detected in less inclusive clades. Here, we focus on a sample of endocranial volumes (endocasts) of 18 species of rabbits and hares (Lagomorpha: Leporidae), which are a discrete radiation of mammals with a suitably large range of body sizes. Using 60 individuals, we test five popular hypotheses on brain size and olfactory bulb evolution in mammals. We also address the pervasive issue of missing data, using multiple phylogenetic imputations as to conserve the full sample size for all analyses. Our analyses show that home range and burrowing behaviour are the only predictors of leporid brain size variation. Litter size, which is one of the most widely reported constraints on brain size, was unexpectedly not associated with brain size. However, a constraining effect may be masked by a strong association of litter size with temperature seasonality, warranting further study. Lastly, we show that unreasonable estimations of phylogenetic signal (Pagel’s lamba) warrant additional caution when using small sample sizes, such as ours, in comparative studies.

Features of the superficial white matter as biomarkers for the detection of Alzheimer's disease and mild cognitive impairment: A diffusion tensor imaging study

BACKGROUND

With the development of medical imaging and processing tools, accurate diagnosis of diseases has been made possible by intelligent systems. Owing to the remarkable ability of support vector machines (SVMs) for diseases diagnosis, extensive research has been conducted using the SVM algorithm for the classification of Alzheimer's disease (AD) and mild cognitive impairment (MCI).

OBJECTIVES

In this study, we applied an automated method to classify patients with AD and MCI and healthy control (HC) subjects based on the diffusion tensor imaging (DTI) features in the superficial white matter (SWM).

PARTICIPANTS

For this purpose, DTI data were downloaded from the Alzheimer's Disease Neuroimaging Initiative (ADNI). This method employed DTI data from 72 subjects: 24 subjects as HC, 24 subjects with MCI, and 24 subjects with AD.

MEASURE

ments: DTI processing was performed using DSI Studio software and all machine learning analyses were performed using MATLAB software.

RESULTS

The linear kernel of SVM was the best classifier, with an accuracy of 95.8% between the AD and HC groups, followed by the quadratic kernel of SVM with an accuracy of 83.3% between the MCI and HC groups and the Gaussian kernel of SVM with an accuracy of 83.3% between the AD and MCI groups.

CONCLUSIONS

Given the importance of diagnosing AD and MCI as well as the role of superficial white matter in the diagnosis of neurodegenerative diseases, in this study, the features of different DTI methods of the SWM are discussed, which could be a useful tool to assist in the diagnosis of AD and MCI.

Developmental Changes in the Integration of Affective and Cognitive Corticostriatal Pathways are Associated with Reward-Driven Behavior

The relative influence of affective and cognitive processes on behavior is increasingly understood to transform through development, from adolescence into adulthood, but the neuroanatomical mechanisms underlying this change are not well understood. We analyzed diffusion magnetic resonance imaging in 115 10- to 28-year-old participants to identify convergent corticostriatal projections from cortical systems involved in affect and cognitive control and determined the age-related differences in their relative structural integrity. Results indicate that the relative integrity of affective projections, in relation to projections from cognitive control systems, decreases with age and is positively associated with reward-driven task performance. Together, these findings provide new evidence that developmental differences in the integration of corticostriatal networks involved in affect and cognitive control underlie known developmental decreases in the propensity for reward-driven behavior into adulthood.

Differences between generalized Q-sampling imaging and diffusion tensor imaging in visualization of crossing neural fibers in the brain

Purpose

The aim of this study was to discuss the advantages of GQI reconstruction in the imaging of nerve fibers at crossing regions. Compared with DTI, the paper also discussed the advantages of GQI in imaging principles.

Methods

3-T MRI data from five normal participants were reconstructed using GQI and DTI. After adjusting the parameters, we compared the differences in reconstructed nerve fibers at the crossing regions between the two methods. To complete this study, we chose four obvious examples (the optic nerve, the Superior cerebellar peduncles, the intersection of the pyramidal tract, the corpus callosum and the arcuate fibers and the intersection of the supplementary motor area (SMA) and the anterior part of arcuate fasciculus) to illustrate.

Results

By reconstructing nerve fibers in three regions, we can find that crossing-area images of nerve fibers significantly differed between DTI and GQI reconstruction. Although crossing fibers could be clearly and completely visualized after GQI reconstruction, they showed artifacts, incompleteness, deletions, and fractures after DTI reconstruction. After GQI reconstruction, we can find that there were two or more nerve fibers in each voxel. However, only one nerve fiber was present in each voxel after DTI reconstruction.

Conclusion

The imaging of crossing fibers is more complete, consistent, and accurate when they are reconstructed by GQI than when they are reconstructed by DTI.

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.

Brain anatomy of the 4-day-old European rabbit

The European rabbit (Oryctolagus cuniculus) is a widely used model in fundamental, medical and veterinary neurosciences. Besides investigations in adults, rabbit pups are relevant to study perinatal neurodevelopment and early behaviour. To date, the rabbit is also the only species in which a pheromone - the mammary pheromone (MP) - emitted by lactating females and active on neonatal adaptation has been described. The MP is crucial since it contributes directly to nipple localisation and oral seizing in neonates, i.e. to their sucking success. It may also be one of the non-photic cues arising from the mother, which stimulates synchronisation of the circadian system during pre-visual developmental stages. Finally, the MP promotes neonatal odour associative and appetitive conditioning in a remarkably rapid and efficient way. For these different reasons, the rabbit offers a currently unique opportunity to determine pheromonal-induced brain processing supporting adaptation early in life. Therefore, it is of interest to create a reference work of the newborn rabbit pup brain, which may constitute a tool for future multi-disciplinary and multi-approach research in this model, and allow comparisons related to the neuroethological basis of social and feeding behaviour among newborns of various species. Here, in line with existing experimental studies, and based on original observations, we propose a functional anatomical description of brain sections in 4-day-old rabbits with a particular focus on seven brain regions which appear important for neonatal perception of sensory signals emitted by the mother, circadian adaptation to the short and single daily nursing of the mother in the nest, and expression of specific motor actions involved in nipple localisation and milk intake. These brain regions involve olfactory circuits, limbic-related areas important in reward, motivation, learning and memory formation, homeostatic areas engaged in food anticipation, and regions implicated in circadian rhythm and arousal, as well as in motricity.

Characterizing longitudinal changes in rabbit brains infected with Angiostrongylus Cantonensis based on diffusion anisotropy

Angiostrongylus cantonensis has become a global source of infection in recent years, and the differential diagnosis and timely follow-up are crucial in the management of the infection. Magnetic resonance imaging (MRI) has been suggested as a non-invasive technique in characterizing and localizing lesions during the parasitic infections in the brain. Non-invasive diffusion tensor imaging (DTI) can be used to distinguish microscopic cerebral structures but cannot resolve the more complicated neural structure. Several methods have been proposed to overcome this limitation. One such method, generalized q-sampling imaging (GQI), can be applied to a variety of datasets, including the single shell, multi-shell or grid sampling schemes, which are believed to resolve complicated crossing fibers. This study aimed to characterize angiostrongyliasis in the rabbit brain over a 6-week period using anatomical and diffusion MRI, including DTI and GQI. Our anatomical T2WI and R2 mapping results showed that the ventricle size of the rabbit brain increased after A. cantonensis larvae infection, and the DTI and GQI indices both showed pathological changes in the corpus callosum, hippocampus and cortex over a 6-week infection period. These results were consistent with our histopathological findings. Our results demonstrated that the diagnosis of larvae infection using anatomical and diffusion MRI is possible and that follow-up characterization is informative in revealing the effects of angiostrongyliasis in various brain areas. These support the use of anatomical and diffusion MRI was helpful for diagnosis of eosinophilic meningitis caused by A. cantonensis infection. This non-invasive MRI platform could be used to improve the management of eosinophilic meningitis or eosinophilic meningoencephalitis in humans.

Quantitative Evaluation of Rabbit Brain Injury after Cerebral Hemisphere Radiation Exposure Using Generalized q-Sampling Imaging

Radiation therapy is widely used for the treatment of brain tumors and may result in cellular, vascular and axonal injury and further behavioral deficits. The non-invasive longitudinal imaging assessment of brain injury caused by radiation therapy is important for determining patient prognoses. Several rodent studies have been performed using magnetic resonance imaging (MRI), but further studies in rabbits and large mammals with advanced magnetic resonance (MR) techniques are needed. Previously, we used diffusion tensor imaging (DTI) to evaluate radiation-induced rabbit brain injury. However, DTI is unable to resolve the complicated neural structure changes that are frequently observed during brain injury after radiation exposure. Generalized q-sampling imaging (GQI) is a more accurate and sophisticated diffusion MR approach that can extract additional information about the altered diffusion environments. Therefore, herein, a longitudinal study was performed that used GQI indices, including generalized fractional anisotropy (GFA), quantitative anisotropy (QA), and the isotropic value (ISO) of the orientation distribution function and DTI indices, including fractional anisotropy (FA) and mean diffusivity (MD) over a period of approximately half a year to observe long-term, radiation-induced changes in the different brain compartments of a rabbit model after a hemi-brain single dose (30 Gy) radiation exposure. We revealed that in the external capsule, the GFA right to left (R/L) ratio showed similar trends as the FA R/L ratio, but no clear trends in the remaining three brain compartments. Both the QA and ISO R/L ratios showed similar trends in the all four different compartments during the acute to early delayed post-irradiation phase, which could be explained and reflected the histopathological changes of the complicated dynamic interactions among astrogliosis, demyelination and vasogenic edema. We suggest that GQI is a promising non-invasive technique and as compared with DTI, it has better potential ability in detecting and monitoring the pathophysiological cascades in acute to early delayed radiation-induced brain injury by using clinical MR scanners.