An In Vivo Three-Dimensional Magnetic Resonance Imaging-Based Averaged Brain Collection of the Neonatal Piglet (Sus scrofa)

Due to the fact that morphology and perinatal growth of the piglet brain is similar to humans, use of the piglet as a translational animal model for neurodevelopmental studies is increasing. Magnetic resonance imaging (MRI) can be a powerful tool to study neurodevelopment in piglets, but many of the MRI resources have been produced for adult humans. Here, we present an average in vivo MRI-based atlas specific for the 4-week-old piglet. In addition, we have developed probabilistic tissue classification maps. These tools can be used with brain mapping software packages (e.g. SPM and FSL) to aid in voxel-based morphometry and image analysis techniques. The atlas enables efficient study of neurodevelopment in a highly tractable translational animal with brain growth and development similar to humans.     

A Digital Atlas of the Dog Brain

There is a long history and a growing interest in the canine as a subject of study in neuroscience research and in translational neurology. In the last few years, anatomical and functional magnetic resonance imaging (MRI) studies of awake and anesthetized dogs have been reported. Such efforts can be enhanced by a population atlas of canine brain anatomy to implement group analyses. Here we present a canine brain atlas derived as the diffeomorphic average of a population of fifteen mesaticephalic dogs. The atlas includes: 1) A brain template derived from in-vivo, T1-weighted imaging at 1 mm isotropic resolution at 3 Tesla (with and without the soft tissues of the head); 2) A co-registered, high-resolution (0.33 mm isotropic) template created from imaging of ex-vivo brains at 7 Tesla; 3) A surface representation of the gray matter/white matter boundary of the high-resolution atlas (including labeling of gyral and sulcal features). The properties of the atlas are considered in relation to historical nomenclature and the evolutionary taxonomy of the Canini tribe. The atlas is available for download (https://cfn.upenn.edu/aguirre/wiki/public:data_plosone_2012_datta).     

A place preference test in the fish Nile tilapia

The Nile tilapia fish (Oreochromis niloticus) has a high potential to be used as a model in neuroscience studies. In the present study, the preference of the Nile tilapia between a gravel-enriched (GEE), a shelter-enriched (SEE) or a non-enriched (NEE) environment was determined, for developing a place preference model. Nile tilapia had an initial preference for GEE, but after 1 day of observation, the fish stabilized their frequency of visits among compartments. Hence, any stimulus motivating tilapia increase in compartment visiting indicates a positively reinforcing effect. This feature is very useful for the development of new behavioural paradigms for fish in tests using environmental discrimination, such as the conditioning place preference test.     

Chronological Changes in MicroRNA Expression in the Developing Human Brain

Objective

MicroRNAs (miRNAs) are endogenously expressed noncoding RNA molecules that are believed to regulate multiple neurobiological processes. Expression studies have revealed distinct temporal expression patterns in the developing rodent and porcine brain, but comprehensive profiling in the developing human brain has not been previously reported.

Methods

We performed microarray and TaqMan-based expression analysis of all annotated mature miRNAs (miRBase 10.0) as well as 373 novel, predicted miRNAs. Expression levels were measured in 48 post-mortem brain tissue samples, representing gestational ages 14–24 weeks, as well as early postnatal and adult time points.

Results

Expression levels of 312 miRNAs changed significantly between at least two of the broad age categories, defined as fetal, young, and adult.

Conclusions

We have constructed a miRNA expression atlas of the developing human brain, and we propose a classification scheme to guide future studies of neurobiological function.     

Automatic Structural Parcellation of Mouse Brain MRI Using Multi-Atlas Label Fusion

Multi-atlas segmentation propagation has evolved quickly in recent years, becoming a state-of-the-art methodology for automatic parcellation of structural images. However, few studies have applied these methods to preclinical research. In this study, we present a fully automatic framework for mouse brain MRI structural parcellation using multi-atlas segmentation propagation. The framework adopts the similarity and truth estimation for propagated segmentations (STEPS) algorithm, which utilises a locally normalised cross correlation similarity metric for atlas selection and an extended simultaneous truth and performance level estimation (STAPLE) framework for multi-label fusion. The segmentation accuracy of the multi-atlas framework was evaluated using publicly available mouse brain atlas databases with pre-segmented manually labelled anatomical structures as the gold standard, and optimised parameters were obtained for the STEPS algorithm in the label fusion to achieve the best segmentation accuracy. We showed that our multi-atlas framework resulted in significantly higher segmentation accuracy compared to single-atlas based segmentation, as well as to the original STAPLE framework.     

A Digital Framework to Build,Visualize and Analyze a Gene Expression Atlas with Cellular Resolution in Zebrafish Early Embryogenesis

A gene expression atlas is an essential resource to quantify and understand the multiscale processes of embryogenesis in time and space. The automated reconstruction of a prototypic 4D atlas for vertebrate early embryos, using multicolor fluorescence in situ hybridization with nuclear counterstain, requires dedicated computational strategies. To this goal, we designed an original methodological framework implemented in a software tool called Match-IT. With only minimal human supervision, our system is able to gather gene expression patterns observed in different analyzed embryos with phenotypic variability and map them onto a series of common 3D templates over time, creating a 4D atlas. This framework was used to construct an atlas composed of 6 gene expression templates from a cohort of zebrafish early embryos spanning 6 developmental stages from 4 to 6.3 hpf (hours post fertilization). They included 53 specimens, 181,415 detected cell nuclei and the segmentation of 98 gene expression patterns observed in 3D for 9 different genes. In addition, an interactive visualization software, Atlas-IT, was developed to inspect, supervise and analyze the atlas. Match-IT and Atlas-IT, including user manuals, representative datasets and video tutorials, are publicly and freely available online. We also propose computational methods and tools for the quantitative assessment of the gene expression templates at the cellular scale, with the identification, visualization and analysis of coexpression patterns, synexpression groups and their dynamics through developmental stages.     

Pronounced genetic differentiation of small,isolated and fragmented tilapia populations inhabiting the Magadi Soda Lake in Kenya

Lake Magadi, an alkaline hypersaline lake in Kenya, is one of the most extreme water bodies known. Although its water temperatures often exceed 40°C, a particular lineage of ‘dwarf’ tilapia, Alcolapia grahami, has evolved remarkable adaptations to survive in this hostile environment. Magadi tilapia exists in small fragmented populations in isolated lagoons within Lake Magadi and its satellite Lake, Little Magadi. In spite of the potential this tilapia holds for understanding evolutionary processes in stressful environments, few genetic studies have focused on this species. We examined the genetic diversity and spatial genetic relationships of Magadi tilapia populations using microsatellite and mitochondrial markers. High levels of genetic variation were found to be supporting the hypothesis that A. grahami populations represent remnants of a much larger fish population that inhabited paleo-lake Orolonga. In contrast to previous studies, we found a well-supported genetic structure of A. grahami consisting of three differentiated genetic clusters (a) Little Magadi, (b) Fish Spring Lagoon and (c) Rest of Magadi. Given the importance of this species to the Magadi ecosystem and its potential evolutionary significance, the three genetic clusters should be considered as separate gene pools and conservation strategies aimed at protecting the species based on these clusters are recommended.     

A Multi-Atlas Based Method for Automated Anatomical Rat Brain MRI Segmentation and Extraction of PET Activity

Introduction

Preclinical in vivo imaging requires precise and reproducible delineation of brain structures. Manual segmentation is time consuming and operator dependent. Automated segmentation as usually performed via single atlas registration fails to account for anatomo-physiological variability. We present, evaluate, and make available a multi-atlas approach for automatically segmenting rat brain MRI and extracting PET activies.

Methods

High-resolution 7T 2DT2 MR images of 12 Sprague-Dawley rat brains were manually segmented into 27-VOI label volumes using detailed protocols. Automated methods were developed with 7/12 atlas datasets, i.e. the MRIs and their associated label volumes. MRIs were registered to a common space, where an MRI template and a maximum probability atlas were created. Three automated methods were tested: 1/registering individual MRIs to the template, and using a single atlas (SA), 2/using the maximum probability atlas (MP), and 3/registering the MRIs from the multi-atlas dataset to an individual MRI, propagating the label volumes and fusing them in individual MRI space (propagation & fusion, PF). Evaluation was performed on the five remaining rats which additionally underwent [¹⁸F]FDG PET. Automated and manual segmentations were compared for morphometric performance (assessed by comparing volume bias and Dice overlap index) and functional performance (evaluated by comparing extracted PET measures).

Results

Only the SA method showed volume bias. Dice indices were significantly different between methods (PF>MP>SA). PET regional measures were more accurate with multi-atlas methods than with SA method.

Conclusions

Multi-atlas methods outperform SA for automated anatomical brain segmentation and PET measure’s extraction. They perform comparably to manual segmentation for FDG-PET quantification. Multi-atlas methods are suitable for rapid reproducible VOI analyses.     

Stereotaxic atlas of the brain of Octodon degus.

We present a stereotaxic atlas of the brain of the trumpet-tailed rat or degu (Octodon degus), an hystricomorph rodent native to Chile and one which has become increasingly popular as a research animal, among other things because of its use as a model for diabetic cataracts and its tendency to become hyperglycemic. The atlas contains 38 transverse and two sagittal sections of the brain covering pros-, mes-, and rhombencephalon, as well as diagrams of the brain's surface anatomy. It was constructed from brains of young adult male degus but can be used readily in studies of adult females, since there is no apparent sexual dimorphism in the brain size of this rodent. Ninety percent of 40 experimental lesions used to check the accuracy of the atlas were correctly placed. The fore- and midbrain of the degu are generally more compact than corresponding regions of the brain in the laboratory rat (suborder Myomorpha) and the guinea pig (another hystricomorph). The amygdaloid complex extends further forward in the telencephalon. Major mesencephalic nuclei and fiber tracts are more rostral in position. However, superior and inferior colliculi are much longer in degus than rats. The basic organization of the rhombencephalon is similar in degus and rats, although there are clearcut differences in the length or size of some hindbrain nuclei.     

Atlas-Guided Quantification of White Matter Signal Abnormalities on Term-Equivalent Age MRI in Very Preterm Infants: Findings Predict Language and Cognitive Development at Two Years of Age

The developmental significance of the frequently encountered white matter signal abnormality (WMSA) findings on MRI around term-equivalent age (TEA) in very preterm infants, remains in question. The use of conventional qualitative analysis methods is subjective, lacks sufficient reliability for producing accurate and reproducible WMSA diagnosis, and possibly contributes to suboptimal neurodevelopmental outcome prediction. The advantages of quantitative over qualitative diagnostic approaches have been widely acknowledged and demonstrated. The purpose of this study is to objectively and accurately quantify WMSA on TEA T2-weighted MRI in very preterm infants and to assess whether such quantifications predict 2-year language and cognitive developmental outcomes. To this end, we constructed a probabilistic brain atlas, exclusively for very preterm infants to embed tissue distributions (i.e. to encode shapes, locations and geometrical proportion of anatomical structures). Guided with this atlas, we then developed a fully automated method for WMSA detection and quantification using T2-weighted images. Computer simulations and experiments using in vivo very preterm data showed very high detection accuracy. WMSA volume, particularly in the centrum semiovale, on TEA MRI was a significant predictor of standardized language and cognitive scores at 2 years of age. Independent validation of our automated WMSA detection algorithm and school age follow-up are important next steps.     

A Diffusion-Tensor-Based White Matter Atlas for Rhesus Macaques

Atlases of key white matter (WM) structures in humans are widely available, and are very useful for region of interest (ROI)-based analyses of WM properties. There are histology-based atlases of cortical areas in the rhesus macaque, but none currently of specific WM structures. Since ROI-based analysis of WM pathways is also useful in studies using rhesus diffusion tensor imaging (DTI) data, we have here created an atlas based on a publicly available DTI-based template of young rhesus macaques. The atlas was constructed to mimic the structure of an existing human atlas that is widely used, making results translatable between species. Parcellations were carefully hand-drawn on a principle-direction color-coded fractional anisotropy image of the population template. The resulting atlas can be used as a reference to which registration of individual rhesus data can be performed for the purpose of white-matter parcellation. Alternatively, specific ROIs from the atlas may be warped into individual space to be used in ROI-based group analyses. This atlas will be made publicly available so that it may be used as a resource for DTI studies of rhesus macaques.     

Insights into brain development and disease from neurogenetic analyses in Drosophila melanogaster

Groundbreaking work by Obaid Siddiqi has contributed to the powerful genetic toolkit that is now available for studying the nervous system of Drosophila. Studies carried out in this powerful neurogenetic model system during the last decade now provide insight into the molecular mechanisms that operate in neural stem cells during normal brain development and during abnormal brain tumorigenesis. These studies also provide strong support for the notion that conserved molecular genetic programs act in brain development and disease in insects and mammals including humans.     

Fully Automated Whole-Head Segmentation with Improved Smoothness and Continuity,with Theory Reviewed

Individualized current-flow models are needed for precise targeting of brain structures using transcranial electrical or magnetic stimulation (TES/TMS). The same is true for current-source reconstruction in electroencephalography and magnetoencephalography (EEG/MEG). The first step in generating such models is to obtain an accurate segmentation of individual head anatomy, including not only brain but also cerebrospinal fluid (CSF), skull and soft tissues, with a field of view (FOV) that covers the whole head. Currently available automated segmentation tools only provide results for brain tissues, have a limited FOV, and do not guarantee continuity and smoothness of tissues, which is crucially important for accurate current-flow estimates. Here we present a tool that addresses these needs. It is based on a rigorous Bayesian inference framework that combines image intensity model, anatomical prior (atlas) and morphological constraints using Markov random fields (MRF). The method is evaluated on 20 simulated and 8 real head volumes acquired with magnetic resonance imaging (MRI) at 1 mm3 resolution. We find improved surface smoothness and continuity as compared to the segmentation algorithms currently implemented in Statistical Parametric Mapping (SPM). With this tool, accurate and morphologically correct modeling of the whole-head anatomy for individual subjects may now be feasible on a routine basis. Code and data are fully integrated into SPM software tool and are made publicly available. In addition, a review on the MRI segmentation using atlas and the MRF over the last 20 years is also provided, with the general mathematical framework clearly derived.     

Use of Brain MRI Atlases to Determine Boundaries of Age-Related Pathology: The Importance of Statistical Method

IntroductionNeurodegenerative disease diagnoses may be supported by the comparison of an individual patient’s brain magnetic resonance image (MRI) with a voxel-based atlas of normal brain MRI. Most current brain MRI atlases are of young to middle-aged adults and parametric, e.g., mean ±standard deviation (SD); these atlases require data to be Gaussian. Brain MRI data, e.g., grey matter (GM) proportion images, from normal older subjects are apparently not Gaussian. We created a nonparametric and a parametric atlas of the normal limits of GM proportions in older subjects and compared their classifications of GM proportions in Alzheimer’s disease (AD) patients.MethodsUsing publicly available brain MRI from 138 normal subjects and 138 subjects diagnosed with AD (all 55–90 years), we created: a mean ±SD atlas to estimate parametrically the percentile ranks and limits of normal ageing GM; and, separately, a nonparametric, rank order-based GM atlas from the same normal ageing subjects. GM images from AD patients were then classified with respect to each atlas to determine the effect statistical distributions had on classifications of proportions of GM in AD patients.ResultsThe parametric atlas often defined the lower normal limit of the proportion of GM to be negative (which does not make sense physiologically as the lowest possible proportion is zero). Because of this, for approximately half of the AD subjects, 25–45% of voxels were classified as normal when compared to the parametric atlas; but were classified as abnormal when compared to the nonparametric atlas. These voxels were mainly concentrated in the frontal and occipital lobes.DiscussionTo our knowledge, we have presented the first nonparametric brain MRI atlas. In conditions where there is increasing variability in brain structure, such as in old age, nonparametric brain MRI atlases may represent the limits of normal brain structure more accurately than parametric approaches. Therefore, we conclude that the statistical method used for construction of brain MRI atlases should be selected taking into account the population and aim under study. Parametric methods are generally robust for defining central tendencies, e.g., means, of brain structure. Nonparametric methods are advisable when studying the limits of brain structure in ageing and neurodegenerative disease.