Histological Datasets Research Articles

Multimodal vessel mapping for precise large area alignment of functional optical imaging data to neuroanatomical preparations in marmosets

Imaging technologies, such as intrinsic optical imaging (IOI), functional magnetic resonance imaging (fMRI) or multiphoton microscopy provide excellent opportunities to study the relationship between functional signals recorded from a cortical area and the underlying anatomical structure. This, in turn, requires accurate alignment of the recorded functional imaging data with histological datasets from the imaged tissue obtained after the functional experiment. This alignment is complicated by distortions of the tissue which naturally occur during histological treatment, and is particularly difficult to achieve over large cortical areas, such as primate visual areas. We present here a method that uses IOI vessel maps revealed in the time course of the intrinsic signal, in combination with vascular casts and vascular lumen labeling techniques together with a pseudo three dimensional (p3D) reconstruction of the tissue architecture in order to facilitate alignment of IOI data with posthoc histological datasets. We demonstrate that by such a multimodal vessel mapping approach, we are able to constitute a hook in anatomical-functional data alignment that enables the accurate assignment of functional signals over large cortical regions. As an example, we present precise alignments of IOI responses showing orientation selectivity of primate V1 with anatomical sections stained for cytochrome-oxidase-reactivity.

Reptilian spermatogenesis: A histological and ultrastructural perspective.

Until recently, the histology and ultrastructural events of spermatogenesis in reptiles were relatively unknown. Most of the available morphological information focuses on specific stages of spermatogenesis, spermiogenesis, and/or of the mature spermatozoa. No study to date has provided complete ultrastructural information on the early events of spermatogenesis, proliferation and meiosis in class Reptilia. Furthermore, no comprehensive data set exists that describes the ultrastructure of the entire ontogenic progression of germ cells through the phases of reptilian spermatogenesis (mitosis, meiosis and spermiogenesis). The purpose of this review is to provide an ultrastructural and histological atlas of spermatogenesis in reptiles. The morphological details provided here are the first of their kind and can hopefully provide histological information on spermatogenesis that can be compared to that already known for anamniotes (fish and amphibians), birds and mammals. The data supplied in this review will provide a basic model that can be utilized for the study of sperm development in other reptiles. The use of such an atlas will hopefully stimulate more interest in collecting histological and ultrastructural data sets on spermatogenesis that may play important roles in future nontraditional phylogenetic analyses and histopathological studies in reptiles.

Can we improve the negative predictive value of faecal calprotectin for the diagnosis of IBS in primary care?

IntroductionRecent data has established faecal calprotectin (FC) as a highly sensitive and specific test for detecting inflammatory bowel disease (IBD) in patients referred to secondary care.1 Computer modelling has shown...

The connectome of the rat central nervous system in neuroVIISAS

Event Abstract Back to Event The connectome of the rat central nervous system in neuroVIISAS Oliver Schmitt1*, Peter Eipert1, Andreas Wree1 and Klaus-Peter Schmitz2 1 University of Rostock, Dep. of Anatomy, Germany 2 University of Rostock, Dep. of Biomedical Engeneering, Germany Background. Most tracing studies have been performed in the rat central nervous system. However, an integrative approach to analyze and compare these tracing data on all levels of the rat central nervous system is not available. The generic platform neuroVIISAS allows to integrate neuroontological, atlas, visualization, connectivity analysis and population based simulations techniques for all types of nervous systems. Material. In an ongoing metastudy 830 tracing publications have been evaluated with regard to ipsi-, contralateral connections, projection densities, projection directions and neurochemical features. 56000 conenctions of the rat central nervous system are available in neuroVIISAS. The connectivity data are related to 8400 neuroanatomic regions that are organized hierarchically. The topographic relations between regions are defined by ontological relations like e.g. „part_of“. The regions are mapped to a high resolution affine linear and elastic registered histological dataset with an isotropic resolution of 5 x 5 x 5 µm³ and to the atlas dataset of the Paxinos and Watson stereotactic rat brain atlas (2007). Methods. neuroVIISAS has been developed in JAVA (Java Development Kit (JDK)) and is running on 32 and 64 Bit Windows and Linux systems. The Visualization Toolkit (VTK) is used for 3D-visualization of brain region surfaces within neuroVIISAS. VTK consists of a C++ class library that is accessed via a JAVAwrapper. Further packages are InfoNode Docking Windows (v1.5), which is a JAVA Swing based docking windows framework and the icon library Fugue Icons (v2.0). Furthermore, Python, PyNEST and NEST are necessary to generate simulation scripts under neuroVIISAS and for performing the simulation in a Linux environment. Result. The connectome of the rat nervous system has features of a scale-free Watts-Strogatz network. The average cluster coefficient is 0.112 and the avergae path length is 4.844. The average valency is 8.975 and the centrality is 0.081. Motif-analysis of 3 node directed motifs showed significant frequencies for divergent, convergent and reciprocal motifs. The lateral hypothalamic area possesses largest in- and outdegrees. We will present results of local connectivity analysis, e.g., cluster coefficient, eigenvector centrality, Shapley values, modularity analysis and communicability features of the connectome components. Based on these parameters the extrapyramidal system has been selected and visualized for an examplary simulation based on real neuroanatomic connectivity (Fig. 1). Outlook. neuroVIISAS integrates basic components of neuroinformatic techniques like neuroontological data structures, attributes, hierarchies, mapping/atlasing, multimodal data integration (histology, atlas, MRI datasets), connectivity analysis, multidimensional visualization, simulation definition/control/analysis/visualization in a generic platform in combination with the largest connectivity database. Currently tools are under development that allows to match tractographic data with connectivity data to compute precise spatial pathways. Multiscale techniques for visualization, connectivity filtering and simulation analysis are of particular interest to understand effects of scaling within simulations. Finally, multicompartment models of neurons for more specific local circuit definitions are under development in order to simulate complex functions like motor control by extrapyramidal and cerebellar subsystems. Figure 1 Keywords: computational neuroscience Conference: 4th INCF Congress of Neuroinformatics, Boston, United States, 4 Sep - 6 Sep, 2011. Presentation Type: Poster Presentation Topic: Computational neuroscience Citation: Schmitt O, Eipert P, Wree A and Schmitz K (2011). The connectome of the rat central nervous system in neuroVIISAS. Front. Neuroinform. Conference Abstract: 4th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2011.08.00025 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 17 Oct 2011; Published Online: 19 Oct 2011. * Correspondence: Dr. Oliver Schmitt, University of Rostock, Dep. of Anatomy, Rostock, Germany, schmitt@med.uni-rostock.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Oliver Schmitt Peter Eipert Andreas Wree Klaus-Peter Schmitz Google Oliver Schmitt Peter Eipert Andreas Wree Klaus-Peter Schmitz Google Scholar Oliver Schmitt Peter Eipert Andreas Wree Klaus-Peter Schmitz PubMed Oliver Schmitt Peter Eipert Andreas Wree Klaus-Peter Schmitz Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Learning task-optimal registration cost functions for localizing cytoarchitecture and function in the cerebral cortex.

Image registration is typically formulated as an optimization problem with multiple tunable, manually set parameters. We present a principled framework for learning thousands of parameters of registration cost functions, such as a spatially-varying tradeoff between the image dissimilarity and regularization terms. Our approach belongs to the classic machine learning framework of model selection by optimization of cross-validation error. This second layer of optimization of cross-validation error over and above registration selects parameters in the registration cost function that result in good registration as measured by the performance of the specific application in a training data set. Much research effort has been devoted to developing generic registration algorithms, which are then specialized to particular imaging modalities, particular imaging targets and particular postregistration analyses. Our framework allows for a systematic adaptation of generic registration cost functions to specific applications by learning the "free" parameters in the cost functions. Here, we consider the application of localizing underlying cytoarchitecture and functional regions in the cerebral cortex by alignment of cortical folding. Most previous work assumes that perfectly registering the macro-anatomy also perfectly aligns the underlying cortical function even though macro-anatomy does not completely predict brain function. In contrast, we learn 1) optimal weights on different cortical folds or 2) optimal cortical folding template in the generic weighted sum of squared differences dissimilarity measure for the localization task. We demonstrate state-of-the-art localization results in both histological and functional magnetic resonance imaging data sets.

Computation of a finite element-conformal tetrahedral mesh approximation for simulated soft tissue deformation using a deformable surface model

In this article, we present a new method for the generation of surface meshes of biological soft tissue. The method is based on the deformable surface model technique and is extended to histological data sets. It relies on an iterative adjustment towards polygonal segments describing the histological structures of the soft tissue. The generated surface meshes allow for the construction of volumetric meshes through a standard constrained Delaunay approach and, thus, for the application in finite element methods. The geometric properties of volumetric meshes have an immediate influence on the numerical conditioning and, therewith, on the stability of the finite element method and the convergence of iterative solvers. In this article, the influence of the surface meshes on the quality of the volumetric meshes is analysed in terms of the spectral condition number of the stiffness matrices, which are assembled within Newton's method. The non-linear material behavior of biological soft tissue is modeled by the Mooney-Rivlin material law. The subject is motivated by the requirements of virtual surgery.

Probabilistic tractography of the optic radiations—An automated method and anatomical validation

Accurately tracing the optic radiations in living humans has important implications for studying the relationship between tract structure or integrity and visual function, in health and disease. Probabilistic tractography is an established method for tracing white matter tracts in humans. Prior studies have used this method to trace the optic radiations, but operator-dependent factors, particularly variability in seed voxel placement and choice of connectivity threshold to select between tract and non-tract voxels, remain potential causes of significant variability. Methods using prior information to modify tract images risk introducing error by underestimating individual variability, particularly in subjects with abnormal anatomy. Finally, existing methods lack thorough validation against a histological standard, causing difficulty in evaluating individual methods, and quantitatively comparing methods.Here we describe a method for producing binary optic radiation images using an existing, well-validated tractography method. All stages are automated, including mask image generation, and thresholds are objectively selected by comparing tract images with existing probabilistic histological data in stereotaxic space. Data from two subject groups are presented; the first used to derive analysis parameters, and the second to test these parameters in an independent sample. Validation utilised a novel variant of receiver operating characteristic analysis, providing both justification for this method and a metric by which tractography methods might be compared generally. The resulting tracts match the histological data well; images generated in individuals matched the histological group data about as well as did images derived in individuals from that histological data set, with a low false positive rate.

Relation between non-uniform hemodynamics and sites of altered permeability and lesion growth at the rabbit aorto-celiac junction

Using the rabbit’s aorto-celiac junction as a representative atherosclerotic model, the hemodynamics of a bifurcating blood vessel are numerically simulated and three hemodynamic parameters are compared. The wall shear stress (WSS), the oscillatory shear index (OSI), and the spatial wall shear stress gradient (WSSG) are considered in this study. Locally enhanced wall permeabilities and intimal macrophages are generally considered to be involved in atherogenesis, and here the primary concern is with the hemodynamic influence on these early stages of the disease process. In comparing the segmental averages of the indicator functions and previously published intimal white blood cell densities, only the WSSG shows a statistically significant correlation. All three indicators have selective strengths in determining sites of early lesion growth around the aorto-celiac flow divider. At the proximal end of the flow divider on the lateral side of the orifice, there are elevated values of the OSI as well as WSSG and low WSS values. Regions of elevated wall permeabilities compare with the regions of elevated WSSG along the lateral and distal portions of the flow divider. Largely dependent upon the present input pulse with reverse flow, the OSI indicates relatively high values throughout the flow domain, however, it is important when utilized in conjunction with low WSS regions. This study presents a rationale for further quantitative correlative studies in the rabbit model based on additional histological data sets.

Fast inversion of matrices arising in image processing

In recent years, new nonlinear partial differential equation (PDE) based approaches have become popular for solving image processing problems. Although the outcome of these methods is often very promising, their actual realization is in general computationally intensive. Therefore, accurate and efficient schemes are needed. The aim of this paper is twofold. First, we will show that the three dimensional alignment problem of a histological data set of the human brain may be phrased in terms of a nonlinear PDE. Second, we will devise a fast direct solution technique for the associated structured large systems of linear equations. In addition, the potential of the derived method is demonstrated on real-life data.