An anatomical atlas provides a detailed map for medical and biological studies of anatomy. These atlases are important for understanding normal anatomy and the development and function of structures, and for determining the etiology of congenital abnormalities. Unfortunately, for biologists, generating such atlases is difficult, especially ones with the informative content and aesthetic quality that characterize human anatomy atlases. Building such atlases requires knowledge of the species being studied and experience with an art form that can faithfully record and present this knowledge, both of which require extensive training in considerably different fields. (For some background on anatomical atlases, see the related sidebar.)

With the latest innovations in data acquisition and computing techniques, atlas building has changed dramatically. We can now create atlases from 3D images of biological specimens, allowing for high-quality, faithful representations. Labeling of structures using fluorescently tagged antibodies, confocal 3D scanning of these labeled structures, volume rendering, segmentation, and surface reconstruction techniques all promise solutions to the problem of building atlases.

However, biology researchers still ask, \"Is there a set of tools we can use or a practical workflow we can follow so that we can easily build models from our biological data?\" To help answer this question, computer scientists have developed many algorithms, tools, and program codes. Unfortunately, most of these researchers have tackled only one aspect of the problem or provided solutions to special cases. So, the general question of how to build anatomical atlases remains unanswered.

For a satisfactory answer, biologists need a practical workflow they can easily adapt for different applications. In addition, reliable tools that can fit into the workflow must be readily available. Finally, examples using the workflow and tools to build anatomical atlases would demonstrate these resources' utility for biological research.

To build a mouse limb atlas for studying the development of the limb musculoskeletal system, University of Utah biologists, artists, and computer scientists have designed a generalized workflow for generating anatomical atlases. We adapted it from a CG artist's workflow of building 3D models for animated films and video games. The tools we used to build the atlas were mostly commercial, industry-standard software packages. Having been developed, tested, and employed for industrial use for decades, CG artists' workflow and tools, with certain adaptations, are the most suitable for making high-quality anatomical atlases, especially under strict budgetary and time limits. Biological researchers have been largely unaware of these resources. By describing our experiences in this project, we hope to show biologists how to use these resources to make anatomically accurate, high-quality, and useful anatomical atlases.

In this paper we present the results from a series of focus groups on the visualization of uncertainty in Equation-Of-State (EOS) models. The initial goal was to identify the most effective ways to present EOS uncertainty to analysts, code developers, and material modelers. Four prototype visualizations were developed to presented EOS surfaces in a three-dimensional, thermodynamic space. Focus group participants, primarily from Sandia National Laboratories, evaluated particular features of the various techniques for different use cases and discussed their individual workflow processes, experiences with other visualization tools, and the impact of uncertainty to their work. Related to our prototypes, we found the 3D presentations to be helpful for seeing a large amount of information at once and for a big-picture view; however, participants also desired relatively simple, two-dimensional graphics for better quantitative understanding, and because these plots are part of the existing visual language for material models. In addition to feedback on the prototypes, several themes and issues emerged that are as compelling as the original goal and will eventually serve as a starting point for further development of visualization and analysis tools. In particular, a distributed workflow centered around material models was identified. Material model stakeholders contribute and extract information at different points in this workflow depending on their role, but encounter various institutional and technical barriers which restrict the flow of information. An effective software tool for this community must be cognizant of this workflow and alleviate the bottlenecks and barriers within it. Uncertainty in EOS models is defined and interpreted differently at the various stages of the workflow. In this context, uncertainty propagation is difficult to reduce to the mathematical problem of estimating the uncertainty of an output from uncertain inputs.

Keywords: netl

Exploring and analyzing the temporal evolution of features in large-scale time-varying datasets is a common problem in many areas of science and engineering. One natural representation of such data is tracking graphs, i.e., constrained graph layouts that use one spatial dimension to indicate time and show the “tracks” of each feature as it evolves, merges or disappears. However, for practical data sets creating the corresponding optimal graph layouts that minimize the number of intersections can take hours to compute with existing techniques. Furthermore, the resulting graphs are often unmanageably large and complex even with an ideal layout. Finally, due to the cost of the layout, changing the feature definition, e.g. by changing an iso-value, or analyzing properly adjusted sub-graphs is infeasible. To address these challenges, this paper presents a new framework that couples hierarchical feature definitions with progressive graph layout algorithms to provide an interactive exploration of dynamically constructed tracking graphs. Our system enables users to change feature definitions on-the-fly and filter features using arbitrary attributes while providing an interactive view of the resulting tracking graphs. Furthermore, the graph display is integrated into a linked view system that provides a traditional 3D view of the current set of features and allows a cross-linked selection to enable a fully flexible spatio-temporal exploration of data. We demonstrate the utility of our approach with several large-scale scientific simulations from combustion science.

Objective: Evidence from prospective studies of high-risk infants suggests that early symptoms of autism usually emerge late in the first or early in the second year of life after a period of relatively typical development. The authors prospectively examined white matter fiber tract organization from 6 to 24 months in high-risk infants who developed autism spectrum disorders (ASDs) by 24 months.

Method: The participants were 92 highrisk infant siblings from an ongoing imaging study of autism. All participants had diffusion tensor imaging at 6 months and behavioral assessments at 24 months; a majority contributed additional imaging data at 12 and/or 24 months. At 24 months, 28 infants met criteria for ASDs and 64 infants did not. Microstructural properties of white matter fiber tracts reported to be associated with ASDs or related behaviors were characterized by fractional anisotropy and radial and axial diffusivity.

Results: The fractional anisotropy trajectories for 12 of 15 fiber tracts differed significantly between the infants who developed ASDs and those who did not. Development for most fiber tracts in the infants with ASDs was characterized by higher fractional anisotropy values at 6 months followed by slower change over time relative to infants without ASDs. Thus, by 24 months of age, those with ASDs had lower values.

Conclusions: These results suggest that aberrant development of white matter pathways may precede the manifestation of autistic symptoms in the first year of life. Longitudinal data are critical to characterizing the dynamic age-related brain and behavior changes underlying this neurodevelopmental disorder.

In this issue of CG&A, researchers share their R&D findings and results on applying visual analytics (VA) to extreme-scale data. Having surveyed these articles and other R&D in this field, we've identified what we consider the top challenges of extreme-scale VA. To cater to the magazine's diverse readership, our discussion evaluates challenges in all areas of the field, including algorithms, hardware, software, engineering, and social issues.

Keywords: scidac, Large-scale systems, Visual analytics, computer graphics, extreme-scale visual analytics, high-performance computing, interaction, large-data visualization, user interfaces, visual analytics

Extreme-scale visual analytics (VA) is about applying VA to extreme-scale data. The articles in this special issue examine advances related to extreme-scale VA problems, their analytical and computational challenges, and their real-world applications.

The large volume of data associated with social networks hinders the unaided user from interpreting network content in real time. This problem is compounded by the fact that there are limited tools available for enabling robust visual social network exploration. We present a network activity visualization using a novel aggregation glyph called the clyph. The clyph intuitively combines spatial, temporal, and quantity data about multiple network events. We also present several case studies where major network events were easily identified using clyphs, establishing them as a powerful aid for network users and owners.

Direct volume rendering has been an active area of research for over two decades. Transfer function design remains a difficult task since current methods, such as traditional 1D and 2D transfer functions are not always effective for all datasets. Various 1D or 2D transfer function spaces have been proposed to improve classification exploiting different aspects, such as using the gradient magnitude for boundary location and statistical, occlusion, or size metrics. In this paper, we present a novel transfer function method which can provide more specificity for data classification by combining different transfer function spaces. In this work, a 2D transfer function can be combined with 1D transfer functions which improve the classification. Specifically, we use the traditional 2D scalar/gradient magnitude, 2D statistical, and 2D occlusion spectrum transfer functions and combine these with occlusion and/or size-based transfer functions to provide better specificity. We demonstrate the usefulness of the new method by comparing to the following previous techniques: 2D gradient magnitude, 2D occlusion spectrum, 2D statistical transfer functions and 2D size based transfer functions.

Keywords: transfer function, volume rendering, classification, user interface, nih, scidac, kaust

We propose an automatic approach for segmenting the left atrium from MRI images. In particular, the thoracic aorta is detected and used as a salient feature to find a seed region that lies inside the left atrium. A hybrid energy that combines robust statistics and localized region intensity information is employed to evolve active contours from the seed region to capture the whole left atrium. The experimental results demonstrate the accuracy and robustness of our approach.

Understanding how users perceive 3D geometric models can provide a basis for creating more effective tools for visualization in applications such as CAD or 3D medical imaging. This dissertation examines how orientation indicators affect users' accuracy in perceiving the shape of a 3D object shown as multiple views. Multiple views force users to infer the orientation of an object and recognize corresponding features between distinct vantage points. These are difficult tasks, and not all users are able to carry them out accurately. A cognitive experimental paradigm is used to evaluate the effectiveness of four types of orientation indicators on a person's ability to compare views of objects presented in different orientations. The orientation indicators implemented were colocated, non-colocated, static, and dynamic. The study accounts for additional factors including task, object complexity, axis of rotation, and users' individual differences in spatial abilities. Results show that a colocated orientation indicator helps users the most in comparing multiple views, and that the effect is correlated with a person's spatial ability. Besides the main finding, this dissertation helps demonstrate the application of a particular experimental paradigm and analysis as well as the importance of considering individual differences when designing interface aids.

Autism is a complex neurological condition characterized by childhood onset of dysfunction in multiple cognitive domains including socio-emotional function, speech and language, and processing of internally versus externally directed stimuli. Although gross brain anatomic differences in autism are well established, recent studies investigating regional differences in brain structure and function have yielded divergent and seemingly contradictory results. How regional abnormalities relate to the autistic phenotype remains unclear. We hypothesized that autism exhibits distinct perturbations in network-level brain architecture, and that cognitive dysfunction may be reflected by abnormal network structure. Network-level anatomic abnormalities in autism have not been previously described. We used structural covariance MRI to investigate network-level differences in gray matter structure within two large-scale networks strongly implicated in autism, the salience network and the default mode network, in autistic subjects and age-, gender-, and IQ-matched controls. We report specific perturbations in brain network architecture in the salience and default-mode networks consistent with clinical manifestations of autism. Extent and distribution of the salience network, involved in social-emotional regulation of environmental stimuli, is restricted in autism. In contrast, posterior elements of the default mode network have increased spatial distribution, suggesting a 'posteriorization' of this network. These findings are consistent with a network-based model of autism, and suggest a unifying interpretation of previous work. Moreover, we provide evidence of specific abnormalities in brain network architecture underlying autism that are quantifiable using standard clinical MRI.

Atrial fibrillation (AF) is the most common sustained arrhythmia encountered in adult cardiology.1,2 Several studies have demonstrated that AF is associated with electrical, contractile, and structural remodeling (SRM) in the left atrium (LA) that contributes to the persistence and sustainability of the arrhythmia.3-7 It has also been shown that the end result of this remodeling process is loss of atrial myocytes and increased collagen content and hence fibrosis of the LA wall.5 Delayed enhancement MRI (DE-MRI) using gadolinium contrast has been demonstrated to localize and quantify the degree of SRM or fibrosis associated with AF in the LA.8

DE-MRI has also been shown to be useful in localizing and quantifying scar formation in the LA following radiofrequency ablation (RFA).9,10 The pulmonary vein (PV) antral region can be visualized to assess circumferential PV scarring that results from RFA lesions/ablation. In addition, the amount of scarring to the LA after catheter ablation can be quantified as a proportion of the total left atrial volume.

Rhythm control of AF using catheter ablation has yielded varying results in different patient populations.11 Identifying the ideal candidate for catheter ablation remains a significant challenge. In addition, a number of different approaches to catheter ablation have been reported and most experts agree that 1 ablation strategy does not fit allAF patients.11-15 Therefore, selecting the proper strategy for a particular patient is also an important determinant of procedure success.

We used DE-MRI to quantify both the degree of SRM/fibrosis pre-ablation and scar formation post ablation. Our aim was to identify predictors of successful ablation in a group of patients stratified according to pre-ablation fibrosis. This would help select the most appropriate ablation strategy for the individual AF ablation candidate.

We present a sensitivity analysis of the optimization of the probe placement in radiofrequency (RF) ablation which takes the uncertainty associated with bio-physical tissue properties (electrical and thermal conductivity) into account. Our forward simulation of RF ablation is based upon a system of partial differential equations (PDEs) that describe the electric potential of the probe and the steady state of the induced heat. The probe placement is optimized by minimizing a temperature-based objective function such that the volume of destroyed tumor tissue is maximized. The resulting optimality system is solved with a multi-level gradient descent approach. By evaluating the corresponding optimality system for certain realizations of tissue parameters (i.e. at certain, well-chosen points in the stochastic space) the sensitivity of the system can be analyzed with respect to variations in the tissue parameters. For the interpolation in the stochastic space we use a stochastic finite element approach with piecewise multilinear ansatz functions on adaptively refined, hierarchical grids. We underscore the significance of the approach by applying the optimization to CT data obtained from a real RF ablation case.

Keywords: netl, stochastic sensitivity analysis, stochastic partial dierential equations, stochastic nite element method, adaptive sparse grid, heat transfer, multiscale modeling, representation of uncertainty

Purpose: A connectome is a comprehensive description of synaptic connectivity for a neural domain. Our goal was to produce a connectome data set for the inner plexiform layer of the mammalian retina. This paper describes our first retinal connectome, validates the method, and provides key initial findings.

Methods: We acquired and assembled a 16.5 terabyte connectome data set RC1 for the rabbit retina at .2 nm resolution using automated transmission electron microscope imaging, automated mosaicking, and automated volume registration. RC1 represents a column of tissue 0.25 mm in diameter, spanning the inner nuclear, inner plexiform, and ganglion cell layers. To enhance ultrastructural tracing, we included molecular markers for 4-aminobutyrate (GABA), glutamate, glycine, taurine, glutamine, and the in vivo activity marker, 1-amino-4-guanidobutane. This enabled us to distinguish GABAergic and glycinergic amacrine cells; to identify ON bipolar cells coupled to glycinergic cells; and to discriminate different kinds of bipolar, amacrine, and ganglion cells based on their molecular signatures and activity. The data set was explored and annotated with Viking, our multiuser navigation tool. Annotations were exported to additional applications to render cells, visualize network graphs, and query the database.

Results: Exploration of RC1 showed that the 2 nm resolution readily recapitulated well known connections and revealed several new features of retinal organization: (1) The well known AII amacrine cell pathway displayed more complexity than previously reported, with no less than 17 distinct signaling modes, including ribbon synapse inputs from OFF bipolar cells, wide-field ON cone bipolar cells and rod bipolar cells, and extensive input from cone-pathway amacrine cells. (2) The axons of most cone bipolar cells formed a distinct signal integration compartment, with ON cone bipolar cell axonal synapses targeting diverse cell types. Both ON and OFF bipolar cells receive axonal veto synapses. (3) Chains of conventional synapses were very common, with intercalated glycinergic-GABAergic chains and very long chains associated with starburst amacrine cells. Glycinergic amacrine cells clearly play a major role in ON-OFF crossover inhibition. (4) Molecular and excitation mapping clearly segregates ultrastructurally defined bipolar cell groups into different response clusters. (5) Finally, low-resolution electron or optical imaging cannot reliably map synaptic connections by process geometry, as adjacency without synaptic contact is abundant in the retina. Only direct visualization of synapses and gap junctions suffices.

Conclusions: Connectome assembly and analysis using conventional transmission electron microscopy is now practical for network discovery. Our surveys of volume RC1 demonstrate that previously studied systems such as the AII amacrine cell network involve more network motifs than previously known. The AII network, primarily considered a scotopic pathway, clearly derives ribbon synapse input from photopic ON and OFF cone bipolar cell networks and extensive photopic GABAergic amacrine cell inputs. Further, bipolar cells show extensive inputs and outputs along their axons, similar to multistratified nonmammalian bipolar cells. Physiologic evidence of significant ON-OFF channel crossover is strongly supported by our anatomic data, showing alternating glycine-to-GABA paths. Long chains of amacrine cell networks likely arise from homocellular GABAergic synapses between starburst amacrine cells. Deeper analysis of RC1 offers the opportunity for more complete descriptions of specific networks.

Keywords: neuroscience, retina, vision, blindness, visus, crcns

Effectively evaluating visualization techniques is a difficult task often assessed through feedback from user studies and expert evaluations. This work presents an alternative approach to visualization evaluation in which brain activity is passively recorded using electroencephalography (EEG). These measurements are used to compare different visualization techniques in terms of the burden they place on a viewer's cognitive resources. In this paper, EEG signals and response times are recorded while users interpret different representations of data distributions. This information is processed to provide insight into the cognitive load imposed on the viewer. This paper describes the design of the user study performed, the extraction of cognitive load measures from EEG data, and how those measures are used to quantitatively evaluate the effectiveness of visualizations.

Most visualizations produced in academia or industry have a specific niche audience that is well versed in either the often complicated visualization methods or the scientific domain of the data. Sometimes it is useful to produce visualizations that can communicate results to a broad audience that will not have the domain specific knowledge often needed to understand the results. In this work, we present our experiences in disseminating the results of two studies to national audience. The resulting visualizations and press releases allowed the studies’ researchers to educate a national, if not global, audience.

Group differences in resting state functional magnetic resonance imaging connectivity between individuals with autism and typically developing controls have been widely replicated for a small number of discrete brain regions, yet the whole-brain distribution of connectivity abnormalities in autism is not well characterized. It is also unclear whether functional connectivity is sufficiently robust to be used as a diagnostic or prognostic metric in individual patients with autism. We obtained pairwise functional connectivity measurements from a lattice of 7266 regions of interest covering the entire grey matter (26.4 million connections) in a well-characterized set of 40 male adolescents and young adults with autism and 40 age-, sex- and IQ-matched typically developing subjects. A single resting state blood oxygen level-dependent scan of 8 min was used for the classification in each subject. A leave-one-out classifier successfully distinguished autism from control subjects with 83% sensitivity and 75\% specificity for a total accuracy of 79\% (P = 1.1 x 10^-7). In subjects less than 20 years of age, the classifier performed at 89\% accuracy (P = 5.4 x 10^-7). In a replication dataset consisting of 21 individuals from six families with both affected and unaffected siblings, the classifier performed at 71\% accuracy (91\% accuracy for subjects less than 20 years of age). Classification scores in subjects with autism were significantly correlated with the Social Responsiveness Scale (P = 0.05), verbal IQ (P = 0.02) and the Autism Diagnostic Observation Schedule-Generic's combined social and communication subscores (P = 0.05). An analysis of informative connections demonstrated that region of interest pairs with strongest correlation values were most abnormal in autism. Negatively correlated region of interest pairs showed higher correlation in autism (less anticorrelation), possibly representing weaker inhibitory connections, particularly for long connections (Euclidean distance greater than 10 cm). Brain regions showing greatest differences included regions of the default mode network, superior parietal lobule, fusiform gyrus and anterior insula. Overall, classification accuracy was better for younger subjects, with differences between autism and control subjects diminishing after 19 years of age. Classification scores of unaffected siblings of individuals with autism were more similar to those of the control subjects than to those of the subjects with autism. These findings indicate feasibility of a functional connectivity magnetic resonance imaging diagnostic assay for autism.

Modern microscope automation permits the collection of vast amounts of continuous anatomical imagery in both two and three dimensions. These large data sets present significant challenges for data storage, access, viewing, annotation and analysis. The cost and overhead of collecting and storing the data can be extremely high. Large data sets quickly exceed an individual's capability for timely analysis and present challenges in efficiently applying transforms, if needed. Finally annotated anatomical data sets can represent a significant investment of resources and should be easily accessible to the scientific community. The Viking application was our solution created to view and annotate a 16.5 TB ultrastructural retinal connectome volume and we demonstrate its utility in reconstructing neural networks for a distinctive retinal amacrine cell class. Viking has several key features. (1) It works over the internet using HTTP and supports many concurrent users limited only by hardware. (2) It supports a multi-user, collaborative annotation strategy. (3) It cleanly demarcates viewing and analysis from data collection and hosting. (4) It is capable of applying transformations in real-time. (5) It has an easily extensible user interface, allowing addition of specialized modules without rewriting the viewer.

Keywords: Annotation, automated electron microscopy, citizen science, computational methods, connectome, networks, visualization

Biological soft tissues and cells may be subjected to mechanical as well as chemical (osmotic) loading under their natural physiological environment or various experimental conditions. The interaction of mechanical and chemical effects may be very significant under some of these conditions, yet the highly nonlinear nature of the set of governing equations describing these mechanisms poses a challenge for the modeling of such phenomena. This study formulated and implemented a finite element algorithm for analyzing mechanochemical events in neutral deformable porous media under finite deformation. The algorithm employed the framework of mixture theory to model the porous permeable solid matrix and interstitial fluid, where the fluid consists of a mixture of solvent and solute. A special emphasis was placed on solute-solid matrix interactions, such as solute exclusion from a fraction of the matrix pore space (solubility) and frictional momentum exchange that produces solute hindrance and pumping under certain dynamic loading conditions. The finite element formulation implemented full coupling of mechanical and chemical effects, providing a framework where material properties and response functions may depend on solid matrix strain as well as solute concentration. The implementation was validated using selected canonical problems for which analytical or alternative numerical solutions exist. This finite element code includes a number of unique features that enhance the modeling of mechanochemical phenomena in biological tissues. The code is available in the public domain, open source finite element program FEBio (http://mrl.sci.utah.edu/software). [DOI: 10.1115/1.4004810]