Anatomical Atlas Research Articles

Functional Parcellation of Individual Cerebral Cortex Based on Functional MRI.

The human brain atlas assists us to enhance our scientific understanding of brain structure and function. The typical anatomical atlases are mainly based on brain morphometry which cannot ensure the consistency of structure and function, and are also hard to cover individual functional differences especially in cerebral cortex. Thus, in recent years, functional atlases for individuals have captured great attention, since they are essential not only for identifying the unique functional organization of individual brains, but also to explore individual variations in behaviors. In this study, a novel approach was proposed to accurately parcellate the whole cerebral cortex at the individual level using resting-state functional magnetic resonance image (rs-fMRI). To examine the functional homogeneity in parcellation, a new evaluation criterion, similarity of cluster (SC) coefficient, was proposed. The parcellation results demonstrated the high consistency between two resting-state sessions (Dice >0.72). The most consistent parcellation appeared in the frontal cortex and the least consistent parcellation appeared in the occipital cortex. The functional homogeneity of subregions was high in frontal cortex and insula whereas low in precentral gyrus. According to SC value, the optimal clustering number was about 1600 per hemisphere. Identification accuracy was 100% between two rs-fMRI sessions, and it was also above 0.97 for rest-task and task-task sessions.

The SIGMA rat brain templates and atlases for multimodal MRI data analysis and visualization

Preclinical imaging studies offer a unique access to the rat brain, allowing investigations that go beyond what is possible in human studies. Unfortunately, these techniques still suffer from a lack of dedicated and standardized neuroimaging tools, namely brain templates and descriptive atlases. Here, we present two rat brain MRI templates and their associated gray matter, white matter and cerebrospinal fluid probability maps, generated from ex vivo {mathrm{T}}_2^ ast-weighted images (90 µm isotropic resolution) and in vivo T2-weighted images (150 µm isotropic resolution). In association with these templates, we also provide both anatomical and functional 3D brain atlases, respectively derived from the merging of the Waxholm and Tohoku atlases, and analysis of resting-state functional MRI data. Finally, we propose a complete set of preclinical MRI reference resources, compatible with common neuroimaging software, for the investigation of rat brain structures and functions.

КОНЦЕПТУАЛИЗАЦИЯ НАЦИОНАЛЬНО-КУЛЬТУРНЫХ СТЕРЕОТИПОВ СКВОЗЬ ПРИЗМУ УКРАИНСКОЙ СОМАТИЧЕСКОЙ ФРАЗЕОЛОГИИ

The article examines the conceptualization of national and cultural stereotypes through the prism of the Ukrainian somatic phraseology. The paper provides a comprehensive analysis of cultural semantics of the Ukrainian phraseological units with somatic components and phraseological units, which codify and euphemize a human body, its natural functions. Euphemistic phraseological units are based on taboos in the Ukrainian linguo-culture, explicate natural interrelation of the somatic code with long-established cultural traditions of the Ukrainian people and conceptualize national and cultural stereotypes of perceiving reality. The somatic cultural code in the Ukrainian phraseology is an atlas of human anatomy where the upper, middle and lower parts of a human body are represented unequally.

Novel atlas of fiber directions built from ex-vivo diffusion tensor images of porcine hearts

Cardiac MR image-based predictive models integrating statistical atlases of heart anatomy and fiber orientations can aid in better diagnosis of cardiovascular disease, a major cause of death worldwide. Such atlases have been built from diffusion tensor (DT) images and can be used in anisotropic models for personalized computational electro-mechanical simulations when the fiber directions from DTI are not available. In this paper, we propose a framework for building the first statistical fiber atlas from high-resolution ex-vivo DT images of porcine hearts. A mean geometry that represents the average cardiac morphology of the dataset was first generated via groupwise registration. Then, the associated average cardiac fiber architecture was mapped out by computing the mean of the transformed DT fields of the subjects. To evaluate the stability of the atlas, we performed leave-one-out cross-validation. The resulting tensor statistics indicate that the fiber atlas could accurately describe the fiber architecture of a healthy pig heart.

Nikolay Ivanovich Pirogov (1810-1881): Anatomical research to develop surgery.

The 19th century Russian surgeon Nikolay Ivanovich Pirogov believed passionately in the importance of anatomy for surgeons. His interest in anatomy began as a medical student in Moscow. After graduating in 1828 Pirogov entered the postgraduate German‐Baltic University of Dorpat (now Tartu in the Republic of Estonia) where he studied anatomy and surgery. After completing his study, he remained to research the consequences of ligation of the aorta in a series of animal experiments, which formed the core of his doctoral thesis. He wanted to determine the feasibility of aortic ligation as a treatment for patients with an aneurysm of the aorta or iliac artery. He discovered that success was only likely when the aorta was ligated between the two mesenteric arteries and the ligature gradually tightened, an approach surgically difficult in humans. Pirogov then spent 2 years at the Charité Hospital in Berlin before returning to Russia. In 1841, he was appointed Professor of Applied Anatomy and Surgery at the Imperial Medico‐Surgical Academy in Saint Petersburg. He instituted the teaching of microscopy and histology to the medical curriculum and in 1846 formed the Institute for Applied Anatomy within the academy, where in addition to teaching medical students future teachers of anatomy in Russia were trained. Pirogov published extensively on anatomy, including several anatomical atlases, the most notable his three‐dimensional atlas of topographical anatomy published in four volumes between 1852 and 1859. Today Pirogov's contributions to anatomy are remembered in a number of anatomical structures named after him. Clin. Anat., 33:714–730, 2020. © 2019 Wiley Periodicals, Inc.

Enhanced and unified anatomical labeling for a common mouse brain atlas

Anatomical atlases in standard coordinates are necessary for the interpretation and integration of research findings in a common spatial context. However, the two most-used mouse brain atlases, the Franklin-Paxinos (FP) and the common coordinate framework (CCF) from the Allen Institute for Brain Science, have accumulated inconsistencies in anatomical delineations and nomenclature, creating confusion among neuroscientists. To overcome these issues, we adopt here the FP labels into the CCF to merge the labels in the single atlas framework. We use cell type-specific transgenic mice and an MRI atlas to adjust and further segment our labels. Moreover, detailed segmentations are added to the dorsal striatum using cortico-striatal connectivity data. Lastly, we digitize our anatomical labels based on the Allen ontology, create a web-interface for visualization, and provide tools for comprehensive comparisons between the CCF and FP labels. Our open-source labels signify a key step towards a unified mouse brain atlas.

A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum

An important aspect of network-based analysis is robust node definition. This issue is critical for functional brain network analyses, as poor node choice can lead to spurious findings and misleading inferences about functional brain organization. Two sets of functional brain nodes from our group are well represented in the literature: (1) 264 volumetric regions of interest (ROIs) reported in Power et al., 2011, and (2) 333 cortical surface parcels reported in Gordon et al., 2016. However, subcortical and cerebellar structures are either incompletely captured or missing from these ROI sets. Therefore, properties of functional network organization involving the subcortex and cerebellum may be underappreciated thus far. Here, we apply a winner-take-all partitioning method to resting-state fMRI data to generate novel functionally-constrained ROIs in the thalamus, basal ganglia, amygdala, hippocampus, and cerebellum. We validate these ROIs in three datasets using several criteria, including agreement with existing literature and anatomical atlases. Further, we demonstrate that combining these ROIs with established cortical ROIs recapitulates and extends previously described functional network organization. This new set of ROIs is made publicly available for general use, including a full list of MNI coordinates and functional network labels.

Affordable Custom Three-Dimensional Anatomy Atlases.

In this paper, we present methodology and tools for creating custom three-dimensional anatomical atlases for medical education based on web technologies and open-source software. Traditional medical anatomy education has relied on printed anatomical atlases, in-person lectures, and anatomical dissection. This traditional approach underserves populations in need due to high cost, lack of availability of materials, and a limited of trained educators. Open and free online anatomical atlases developed by an international community of experts help remedy these shortcomings, more fully addressing the sustainable development goals of quality education and good health at a global scale. We have developed a web application, the Atlas Assembly Tool, that assists medical professionals in assembling atlases for open dissemination throughout the world. Our tool aims to complement existing open source medical projects such as 3D Slicer and the Open Anatomy Browser to provide a pipeline from original medical image data to web-browsable atlases suitable for medical education. Our development is an integral part of the European INTERREG MACbioIDi project, promoting sustainable development through technology-based medical education and training in close collaboration with partners in Europe, Africa and the United States.

3D Printed Anatomical Model of a Rat for Medical Imaging

Abstract For medical research, approximately 115 million animals are needed every year. Rodents are used to test possible applications and procedures for the diagnosis of anatomical and physiological diseases. However, working with living animals increases the complexity of an experiment. Accurate experimental planning is essential in order to fulfill the 3R rules (replace, reduce and refine). Especially in tracer-based imaging modalities, such as magnetic particle imaging (MPI), where only nanoparticles give a positive contrast, the anatomical structure of the rodent is not visible without co-registration with another imaging modality. This leads to problems in the experimental planning, as parameters, such as field of view, rodent position and tracer concentration, have to be determined without visual feedback. In this work, a 3D CAD rat model is presented, which can be used to improve the experiment planning and thus reduce the number of animals required. It was determined using an anatomy atlas and 3D printed with stereolithography. The resulting model contains the most important organs and vessels as hollow cavities. By filling these with appropriate tracer materials, the phantom can be used in different imaging modalities such as MPI, magnetic resonance imaging (MRI) or computed tomography (CT). In a first MPI measurement, the phantom was filled with superparamagnetic nanoparticles. Finally, a successful visualization of all organs and vessels of the phantom was possible. This enables the planning of the experiment and the optimization of experimental parameters for a region of interest, where certain organs in a living animal are localized.

Cortico-basal white matter alterations occurring in Parkinson's disease.

Magnetic resonance imaging studies typically use standard anatomical atlases for identification and analyses of (patho-)physiological effects on specific brain areas; these atlases often fail to incorporate neuroanatomical alterations that may occur with both age and disease. The present study utilizes Parkinson’s disease and age-specific anatomical atlases of the subthalamic nucleus for diffusion tractography, assessing tracts that run between the subthalamic nucleus and a-priori defined cortical areas known to be affected by Parkinson’s disease. The results show that the strength of white matter fiber tracts appear to remain structurally unaffected by disease. Contrary to that, Fractional Anisotropy values were shown to decrease in Parkinson’s disease patients for connections between the subthalamic nucleus and the pars opercularis of the inferior frontal gyrus, anterior cingulate cortex, the dorsolateral prefrontal cortex and the pre-supplementary motor, collectively involved in preparatory motor control, decision making and task monitoring. While the biological underpinnings of fractional anisotropy alterations remain elusive, they may nonetheless be used as an index of Parkinson’s disease. Moreover, we find that failing to account for structural changes occurring in the subthalamic nucleus with age and disease reduce the accuracy and influence the results of tractography, highlighting the importance of using appropriate atlases for tractography.

Multiscale spatial gradient features for 18F-FDG PET image-guided diagnosis of Alzheimer’s disease

Background and Objective:18F-FluoroDeoxyGlucose Positron Emission Tomography (18F-FDG PET) is one of the imaging biomarkers to diagnose Alzheimer’s Disease (AD). In 18F-FDG PET images, the changes of voxels’ intensities reflect the differences of glucose rates, therefore voxel intensity is usually used as a feature to distinguish AD from Normal Control (NC), or at earlier stage to distinguish between progressive and stable Mild Cognitive Impairment (pMCI and sMCI). In this paper, 18F-FDG PET images are characterized in an alternative way—the spatial gradient, which is motivated by the observation that the changes of 18F-FDG rates also cause gradient changes.Methods: We improve Histogram of Oriented Gradient (HOG) descriptor to quantify spatial gradients, thereby achieving the goal of diagnosing AD. First, the spatial gradient of 18F-FDG PET image is computed, and then each subject is segmented into different regions by using an anatomical atlas. Second, two types of improved HOG features are extracted from each region, namely Small Scale HOG and Large Scale HOG, then some relevant regions are selected based on a classifier fed with spatial gradient features. Last, an ensemble classification framework is designed to make a decision, which considers the performance of both individual and concatenated selected regions.Results: the evaluation is done on ADNI dataset. The proposed method outperforms other state-of-the-art 18F-FDG PET-based algorithms for AD vs. NC with an accuracy, a sensitivity and a specificity values of 93.65%, 91.22% and 96.25%, respectively. For the case of pMCI vs. sMCI, the three metrics are 75.38%, 74.84% and 77.11%, which is significantly better than most existing methods. Besides, promising results are also achieved for multiple classifications under 18F-FDG PET modality.Conclusions:18F-FDG PET images can be characterized by spatial gradient features for diagnosing AD and its early stage, and the proposed ensemble framework can enhance the classification performance.

Mapping the human subcortical auditory system using histology, postmortem MRI and in vivo MRI at 7T.

Studying the human subcortical auditory system non-invasively is challenging due to its small, densely packed structures deep within the brain. Additionally, the elaborate three-dimensional (3-D) structure of the system can be difficult to understand based on currently available 2-D schematics and animal models. Wfe addressed these issues using a combination of histological data, post mortem magnetic resonance imaging (MRI), and in vivo MRI at 7 Tesla. We created anatomical atlases based on state-of-the-art human histology (BigBrain) and postmortem MRI (50 µm). We measured functional MRI (fMRI) responses to natural sounds and demonstrate that the functional localization of subcortical structures is reliable within individual participants who were scanned in two different experiments. Further, a group functional atlas derived from the functional data locates these structures with a median distance below 2 mm. Using diffusion MRI tractography, we revealed structural connectivity maps of the human subcortical auditory pathway both in vivo (1050 µm isotropic resolution) and post mortem (200 µm isotropic resolution). This work captures current MRI capabilities for investigating the human subcortical auditory system, describes challenges that remain, and contributes novel, openly available data, atlases, and tools for researching the human auditory system.

Joint Denervation: Anatomic Atlas of Surgical Technique, A. LeeDellonSpringer; 1st ed. 2019 edition (March 4, 2019), 277 pages, Price $134.62

Joint Denervation: Anatomic Atlas of Surgical Technique, A. LeeDellonSpringer; 1st ed. 2019 edition (March 4, 2019), 277 pages, Price $134.62

Beltrami-net: domain-independent deep D-bar learning for absolute imaging with electrical impedance tomography (a-EIT)

Objective: To develop, and demonstrate the feasibility of, a novel image reconstruction method for absolute electrical impedance tomography (a-EIT) that pairs deep learning techniques with real-time robust D-bar methods and examine the influence of prior information on the reconstruction. Approach: A D-bar method is paired with a trained convolutional neural network (CNN) as a post-processing step. Training data is simulated for the network using no knowledge of the boundary shape by using an associated nonphysical Beltrami equation rather than simulating the traditional current and voltage data specific to a given domain. This allows the training data to be boundary shape independent. The method is tested on experimental data from two EIT systems (ACT4 and KIT4) with separate training sets of varying prior information. Main results: Post-processing the D-bar images with a CNN produces significant improvements in image quality measured by structural SIMilarity indices (SSIMs) as well as relative and image errors. Significance: This work demonstrates that more general networks can be trained without being specific about boundary shape, a key challenge in EIT image reconstruction. The work is promising for future studies involving databases of anatomical atlases.

A Data Structure for Real-Time Aggregation Queries of Big Brain Networks.

Recent advances in neuro-imaging allowed big brain-initiatives and consortia to create vast resources of brain data that can be mined by researchers for their individual projects. Exploring the relationship between genes, brain circuitry, and behavior is one of the key elements of neuroscience research. This requires fusion of spatial connectivity data at varying scales, such as whole brain correlated gene expression, structural and functional connectivity. With ever-increasing resolution, these tend to exceed the past state-of-the art in size and complexity by several orders of magnitude. Since current analytical workflows in neuroscience involve time-consuming manual data-aggregation, incorporating efficient techniques for handling big connectivity data is a necessity. We propose a novel data structure enabling the interactive exploration of heterogeneous neurobiological connectivity data with billions of edges. Based on this data structure we realized Aggregation Queries, i.e. the aggregated connectivity from, to or between brain areas allows experts to compare the multimodal networks residing at different scales, or levels of hierarchically organized anatomical atlases. Executed on-demand on volumetric gene expression and connectivity data, they allow an interactive dissection of networks in real-time and based on their spatial context. The data structure is optimized in order to be accessible directly from the hard disk, since connectivity of large-scale networks typically exceeds the memory size of current consumer level PCs. This allows experts to embed and explore their own experimental data in the framework of public data resources without the need for their own large-scale infrastructure. Our data structure outperforms state-of-the-art graph engines in retrieving connectivity of arbitrary user defined local brain areas. We demonstrate the feasibility of our approach by analyzing fear-related functional neuroanatomy in mice. Further, we show its versatility by comparing multimodal brain networks linked to autism. Importantly, we achieve cross-species congruence in retrieving human psychiatric traits networks, which facilitates the selection of neural substrates to be further studied in mouse models.

Angiographic Atlas of the Visceral Vascular Anatomy in Translational Rat Models.

To characterize angiographic and cross-sectional imaging anatomy of the rat visceral vasculature in 2 translational models. Animal studies were conducted in accordance with institutional guidelines and approval of the Institutional Animal Care and Use Committees. Retrospective review of digital subtraction arteriography was performed in 65 Wistar and 50 Sprague-Dawley male rats through a left common carotid artery or right common femoral artery approach. MR imaging of the abdomen was performed on the rats to correlate imaging modalities. Aortography was performed in 3 locations, including cranial to the celiac artery, cranial to the renal arteries, and cranial to the caudal (inferior) mesenteric artery, enabling characterization of the visceral branch arteries in all 65 Wistar rats. Selective arteriography of first-, second-, and third-order branch vessels of the aorta was performed allowing characterization of normal and variant anatomy. Dedicated selective arteriography was performed of the celiac artery in 65 Wistar and 10 Sprague-Dawley rats, of the common hepatic artery in 65 Wistar and 50 Sprague-Dawley rats, and of the cranial mesenteric artery in 43 Wistar rats. MR imaging enabled correlation with the lobar and portal venous anatomy. Analysis of arteriography and MR imaging in these rat models will provide translational researchers with anatomic details needed to develop new endovascular protocols for small animal research in interventional radiology.

BrainTrawler: A visual analytics framework for iterative exploration of heterogeneous big brain data

In recent years, big brain-initiatives and consortia have created vast resources of publicly available brain data that can be used by neuroscientists for their own research experiments. This includes microscale connectivity data—brain-network graphs with billions of edges—and vast spatial gene expression resources—the representation of tens of thousands genes in brain space. Their joint analysis for higher order relations in structural or functional neuroanatomy would enable the genetic dissection of brain networks on a genome-wide scale. Current experimental workflows involve only time-consuming manual aggregation and extensive graph theoretical analysis of data from different sources, which rarely provide spatial context to operate continuously on different scales.In this paper, we propose BrainTrawler, a task-driven, web-based framework that incorporates visual analytics methods to explore heterogeneous neurobiological data. It facilitates spatial indexing to query large-scale voxel-level connectivity data and gene expression collections in real-time. Relating data to the hierarchical structure of common anatomical atlases enables the retrieval on different anatomical levels. Together with intuitive network visualization, iterative visual queries, and quantitative information this allows the genetic dissection of multimodal networks on local/global scales in a spatial context.We demonstrate the relevance of our approach for neuroscience by exploring social-behavior and memory/learning related functional neuroanatomy in mice.

A Visualisation of 3D Lung Anatomy with Augmented Reality as Interactive Medical Learning

Breathing is the process of breathing air or the movement of oxygenated (O2) external air into the body or lungs and exhaling air containing carbon dioxide (CO2) as residual oxidation out of the body. There are several organs that act as the respiratory system organs such as the nose, oesophagus, pharynx, larynx, trachea, bronchus, and lungs. Augmented reality can be utilized as a learning medium for the recognition of anatomy of the respiratory system organs presented in 3D with animation. In this research, to build the object to be used, researchers use blender software for object modelling process. To be able to display 3D objects, researchers apply target image techniques, namely by using a marker obtained from the book Sobotta Human Atlas Anatomy is adapted to the needs of preclinical medical students. When AR Camera captures the marker, the camera detects the pattern contained in the marker, and then matches the data stored in the database. When the pattern is found and there is conformity to that stored in the database, the application will display the 3D objects of the organ of the respiratory system with Augmented Reality technology. The optimal distance of marker detection is 15 - 35 cm, with a 30° – 90° inclination angles.

The anatomical representation of the human body: From epistemological examples deriving from medical history to morphometric imaging performed with the laser scanner technique

The anatomical illustration of the human body is a topic rich in epistemological elements in the course of medical history. Since ancient times concerns about the real correspondence of the scientific and/or artistic representation of human anatomy with the original one arose. First of all, a static two-dimensional representation, the one typical of drawings, was not able to get the depth and complexity of dynamic three-dimensional anatomical morphology. In addition, the epistemic issue that a post-mortem illustration could not somehow correspond to living structures was present even in the past. For a long time the anatomical representation of the human body has been attracting the interest of medical doctors, artists, scholars and philosophers as a fact-finding technique of dissection of corpses preparatory to curative surgical practice in the living body. With regard to that, in the Western world the sixteenth century is often seen as the golden age of normal and macroscopic human anatomy. Major steps in the evolution of the anatomical discipline are the switch from the “normal” to the “pathological” area during the seventeenth century and the transition from the macroscopic to the microscopic level in the eighteenth century; that is true also from an illustrative and iconographic point of view. The tradition of setting up three-dimensional models for the study of the human body dates back to the eighteenth century too. Today’s research techniques in the field of anatomical images are so advanced that they allow the full conformity of human representation, the continuous availability of preserved images, the complete multi-dimensionality of the rendering and the complete dynamism of the whole view. In this context, laser scanner could be the ideal tool to create a new Atlas of Human Anatomy composed of models which are rotatable, observable from every perspective, absolutely faithful to reality, analysable as in a real dissection and carefully measurable.

Spatial registration of serial microscopic brain images to three-dimensional reference atlases with the QuickNII tool.

Modern high throughput brain wide profiling techniques for cells and their morphology, connectivity, and other properties, make the use of reference atlases with 3D coordinate frameworks essential. However, anatomical location of observations made in microscopic sectional images from rodent brains is typically determined by comparison with 2D anatomical reference atlases. A major challenge in this regard is that microscopic sections often are cut with orientations deviating from the standard planes used in the reference atlases, resulting in inaccuracies and a need for tedious correction steps. Overall, efficient tools for registration of large series of section images to reference atlases are currently not widely available. Here we present QuickNII, a stand-alone software tool for semi-automated affine spatial registration of sectional image data to a 3D reference atlas coordinate framework. A key feature in the tool is the capability to generate user defined cut planes through the reference atlas, matching the orientation of the cut plane of the sectional image data. The reference atlas is transformed to match anatomical landmarks in the corresponding experimental images. In this way, the spatial relationship between experimental image and atlas is defined, without introducing distortions in the original experimental images. Following anchoring of a limited number of sections containing key landmarks, transformations are propagated across the entire series of sectional images to reduce the amount of manual steps required. By having coordinates assigned to the experimental images, further analysis of the distribution of features extracted from the images is greatly facilitated.