A Comparative evaluation of voxel-based spatial mapping in diffusion tensor imaging.

This paper presents a comparative evaluation of methods for automated voxel-based spatial mapping in diffusion tensor imaging studies. Such methods are an essential step in computational pipelines and provide anatomically comparable measurements across a population in atlas-based studies. To better understand their strengths and weaknesses, we tested a total of eight methods for voxel-based spatial mapping in two types of diffusion tensor templates. The methods were evaluated with respect to scan-rescan reliability and an application to normal aging. The methods included voxel-based analysis with and without smoothing, two types of region-based analysis, and combinations thereof with skeletonization. The templates included a study-specific template created with DTI-TK and the IIT template serving as a standard template. To control for other factors in the pipeline, the experiments used a common dataset, acquired at 1.5T with a single shell high angular resolution diffusion MR imaging protocol, and tensor-based spatial normalization with DTI-TK. Scan-rescan reliability was assessed using the coefficient of variation (CV) and intraclass correlation (ICC) in eight subjects with three scans each. Sensitivity to normal aging was assessed in a population of 80 subjects aged 25-65 years old, and methods were compared with respect to the anatomical agreement of significant findings and the R2 of the associated models of fractional anisotropy. The results show that reliability depended greatly on the method used for spatial mapping. The largest differences in reliability were found when adding smoothing and comparing voxel-based and region-based analyses. Skeletonization and template type were found to have either a small or negligible effect on reliability. The aging results showed agreement among the methods in nine brain areas, with some methods showing more sensitivity than others. Skeletonization and smoothing were not major factors affecting sensitivity to aging, but the standard template showed higher R2 in several conditions. A structural comparison of the templates showed that large deformations between them may be related to observed differences in patterns of significant voxels. Most areas showed significantly higher R2 with voxel-based analysis, particularly when clusters were smaller than the available regions-of-interest. Looking forward, these results can potentially help to interpret results from existing white matter imaging studies, as well as provide a resource to help in planning future studies to maximize reliability and sensitivity with regard to the scientific goals at hand.

Neuroimaging abnormalities in clade C HIV are independent of Tat genetic diversity.

Controversy remains regarding the neurotoxicity of clade C human immunodeficiency virus (HIV-C). When examined in preclinical studies, a cysteine to serine substitution in the C31 dicysteine motif of the HIV-C Tat protein (C31S) results in less severe brain injury compared to other viral clades. By contrast, patient cohort studies identify significant neuropsychological impairment among HIV-C individuals independent of Tat variability. The present study clarified this discrepancy by examining neuroimaging markers of brain integrity among HIV-C individuals with and without the Tat substitution. Thirty-seven HIV-C individuals with the Tat C31S substitution, 109 HIV-C individuals without the Tat substitution (C31C), and 34 HIV- controls underwent 3T structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). Volumes were determined for the caudate, putamen, thalamus, corpus callosum, total gray matter, and total white matter. DTI metrics included fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD). Tracts of interest included the anterior thalamic radiation (ATR), cingulum bundle (CING), uncinate fasciculus (UNC), and corpus callosum (CC). HIV+ individuals exhibited smaller volumes in subcortical gray matter, total gray matter and total white matter compared to HIV- controls. HIV+ individuals also exhibited DTI abnormalities across multiple tracts compared to HIV- controls. By contrast, neither volumetric nor diffusion indices differed significantly between the Tat C31S and C31C groups. Tat C31S status is not a sufficient biomarker of HIV-related brain integrity in patient populations. Clinical attention directed at brain health is warranted for all HIV+ individuals, independent of Tat C31S or clade C status.

Kernel regression estimation of fiber orientation mixtures in diffusion MRI.

We present and evaluate a method for kernel regression estimation of fiber orientations and associated volume fractions for diffusion MR tractography and population-based atlas construction in clinical imaging studies of brain white matter. This is a model-based image processing technique in which representative fiber models are estimated from collections of component fiber models in model-valued image data. This extends prior work in nonparametric image processing and multi-compartment processing to provide computational tools for image interpolation, smoothing, and fusion with fiber orientation mixtures. In contrast to related work on multi-compartment processing, this approach is based on directional measures of divergence and includes data-adaptive extensions for model selection and bilateral filtering. This is useful for reconstructing complex anatomical features in clinical datasets analyzed with the ball-and-sticks model, and our framework's data-adaptive extensions are potentially useful for general multi-compartment image processing. We experimentally evaluate our approach with both synthetic data from computational phantoms and in vivo clinical data from human subjects. With synthetic data experiments, we evaluate performance based on errors in fiber orientation, volume fraction, compartment count, and tractography-based connectivity. With in vivo data experiments, we first show improved scan-rescan reproducibility and reliability of quantitative fiber bundle metrics, including mean length, volume, streamline count, and mean volume fraction. We then demonstrate the creation of a multi-fiber tractography atlas from a population of 80 human subjects. In comparison to single tensor atlasing, our multi-fiber atlas shows more complete features of known fiber bundles and includes reconstructions of the lateral projections of the corpus callosum and complex fronto-parietal connections of the superior longitudinal fasciculus I, II, and III.

Genetic markers of cholesterol transport and gray matter diffusion: a preliminary study of the CETP I405V polymorphism.

Variations of the cholesteryl ester transfer protein polymorphism (CETP I405V/rs5882) have been associated with an increased risk for neurodegeneration, particularly when examined in conjunction with the epsilon 4 isoform of apolipoprotein E (ApoE4). Despite these identified relationships, the impact of I405V on gray matter microstructure remains unknown. The present study examined the impact of the CETP I405V polymorphism on gray matter integrity among 52 healthy adults between ages 51 and 85. Gray matter was measured bilaterally using diffusion tensor imaging (DTI) metrics of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). Participants were grouped according to a dominant statistical model (II genotype vs. IV/VV genotypes) and secondary analyses were completed to examine the interactive effects of CETP and ApoE4 on DTI metrics. Compared to individuals with the IV/VV genotypes, II homozygotes demonstrated significantly higher MD in bilateral temporal, parietal, and occipital gray matter. Secondary analyses revealed higher FA and AD in the left temporal lobe of IV/VV genotypes with an ApoE4 allele. Our results provide preliminary evidence that CETP II homozygosity is a predisposing risk factor for gray matter abnormalities in posterior brain regions in healthy older adults, independent of an ApoE4 allele.

Polar histograms of curvature for quantifying skeletal joint shape and congruence.

The effect of articular joint shape and congruence on kinematics, contact stress, and the natural progression of joint disease continue to be a topic of interest in the orthopedic biomechanics literature. Currently, the most widely used metrics of assessing skeletal joint shape and congruence are based on average principal curvatures across the articular surfaces. Here we propose a method for comparing articular joint shape and quantifying joint congruence based on three-dimensional (3D) histograms of curvature--shape descriptors that preserve spatial information. Illustrated by experimental results from the trapeziometacarpal joint, this method could help unveil the interrelations between joint shape and function and provide much needed insight for the high incidence of osteoarthritis (OA)--a mechanically mediated disease whose onset has been hypothesized to be precipitated by joint incongruity.

Evaluating Text Reading Speed in VR Scenes and 3D Particle Visualizations.

This work reports how text size and other rendering conditions affect reading speeds in a virtual reality environment and a scientific data analysis application. Displaying text legibly yet space-efficiently is a challenging problem in immersive displays. Effective text displays that enable users to read at their maximum speed must consider the variety of virtual reality (VR) display hardware and possible visual exploration tasks. We investigate how text size and display parameters affect reading speed and legibility in three state-of-the-art VR displays: two head-mounted displays and one CAVE. In our perception experiments, we establish limits where reading speed declines as the text size approaches the so-called critical print sizes (CPS) of individual displays, which can inform the design of uniform reading experiences across different VR systems. We observe an inverse correlation between display resolution and CPS. Yet, even in high-fidelity VR systems, the measured CPS was larger than in comparable physical text displays, highlighting the value of increased VR display resolutions in certain visualization scenarios. Our findings indicate that CPS can be an effective metric for evaluating VR display usability. Additionally, we evaluate the effects of text panel placement, orientation, and occlusion-reducing rendering methods on reading speeds in generic volumetric particle visualizations. Our study provides insights into the trade-off between text representation and legibility in cluttered immersive environments with specific suggestions for visualization designers and highlight areas for further research.

Visual Cue Effects on a Classification Accuracy Estimation Task in Immersive Scatterplots.

Immersive visualization in virtual reality (VR) allows us to exploit visual cues for perception in 3D space, yet few existing studies have measured the effects of visual cues. Across a desktop monitor and a head-mounted display (HMD), we assessed scatterplot designs which vary their use of visual cues-motion, shading, perspective (graphical projection), and dimensionality-on two sets of data. We conducted a user study with a summary task in which 32 participants estimated the classification accuracy of an artificial neural network from the scatterplots. With Bayesian multilevel modeling, we capture the intricate visual effects and find that no cue alone explains all the variance in estimation error. Visual motion cues generally reduce participants' estimation error; besides this motion, using other cues may increase participants' estimation error. Using an HMD, adding visual motion cues, providing a third data dimension, or showing a more complicated dataset leads to longer response times. We speculate that most visual cues may not strongly affect perception in immersive analytics unless they change people's mental model about data. In summary, by studying participants as they interpret the output from a complicated machine learning model, we advance our understanding of how to use the visual cues in immersive analytics.

Memory-efficient semantic segmentation of large microscopy images using graph-based neural networks.

We present a graph neural network (GNN)-based framework applied to large-scale microscopy image segmentation tasks. While deep learning models, like convolutional neural networks (CNNs), have become common for automating image segmentation tasks, they are limited by the image size that can fit in the memory of computational hardware. In a GNN framework, large-scale images are converted into graphs using superpixels (regions of pixels with similar color/intensity values), allowing us to input information from the entire image into the model. By converting images with hundreds of millions of pixels to graphs with thousands of nodes, we can segment large images using memory-limited computational resources. We compare the performance of GNN- and CNN-based segmentation in terms of accuracy, training time and required graphics processing unit memory. Based on our experiments with microscopy images of biological cells and cell colonies, GNN-based segmentation used one to three orders-of-magnitude fewer computational resources with only a change in accuracy of $-2\;%$ to $+0.3\;%$. Furthermore, errors due to superpixel generation can be reduced by either using better superpixel generation algorithms or increasing the number of superpixels, thereby allowing for improvement in the GNN framework's accuracy. This trade-off between accuracy and computational cost over CNN models makes the GNN framework attractive for many large-scale microscopy image segmentation tasks in biology.

Elongation of the dorsal carpal ligaments: a computational study of in vivo carpal kinematics.

PURPOSE: The dorsal radiocarpal (DRC) and dorsal intercarpal (DIC) ligaments play an important role in scapholunate and lunotriquetral stability. The purpose of this study was to compute changes in ligament elongation as a function of wrist position for the DRC and the scaphoid and trapezoidal insertions of the DIC. METHODS: We developed a computational model that incorporated a digital dataset of ligament origin and insertions, bone surface models, and in vivo 3-dimensional kinematics (n = 28 wrists), as well as an algorithm for computing ligament fiber path. RESULTS: The differences between the maximum length and minimum length of the DRC, DIC scaphoid component, and DIC trapezoidal component over the entire range of motion were 5.1 ± 1.5 mm, 2.7 ± 1.5 mm, and 5.9 ± 2.5 mm, respectively. The DRC elongated as the wrist moved from ulnar extension to radial flexion, and the DIC elongated as the wrist moved from radial deviation to ulnar deviation. CONCLUSIONS: The DRC and DIC lengthened in opposing directions during wrist ulnar and radial deviation. Despite complex carpal bone anatomy and kinematics, computed fiber elongations were found to vary linearly with wrist position. Errors between computed values and model predictions were less than 2.0 mm across all subjects and positions. CLINICAL RELEVANCE: The relationships between ligament elongation and wrist position should further our understanding of ligament function, provide insight into the potential effects of dorsal wrist incisions on specific wrist ranges of motion, and serve as a basis for modeling of the wrist.

Clinical contributors to cerebral white matter integrity in HIV-infected individuals.

HIV-infected people frequently exhibit brain dysfunction characterized by preferential damage to the cerebral white matter. Despite suppressed viral load and reconstituted immune function afforded by combination antiretroviral therapy (CART), brain dysfunction continues to be observed even in medically stable individuals. To provide insight into the etiology of HIV-associated brain dysfunction in the CART era, we examined the effects of HIV disease markers, antiretroviral treatment, hepatitis C (HCV) coinfection, and age on DTI measures of white matter integrity in a cohort of 85 individuals aged 23 to 65 years with chronic HIV infection. Fractional anisotropy and mean diffusivity were derived from 29 cerebral white matter regions, which were segmented on each individual brain using a high-resolution T1-weighted image and registered to diffusion images. Significant effects of clinical variables were found on white matter abnormalities in nearly all brain regions examined. Most notably, HCV coinfection and older age were associated with decreased anisotropy or increased diffusivity in the majority of brain regions. Individuals with higher current CD4 levels exhibited higher anisotropy in parietal lobe regions, while those undergoing antiretroviral treatment exhibited higher anisotropy in temporal lobe regions. The observed diffuse pattern of white matter injury suggests that future neuroimaging studies should employ methodologies that are not limited to circumscribed regions of interest. The current findings underline the multifactorial nature of HIV-associated brain dysfunction in the CART era, and the importance of examining the effects of HIV disease in the context of other comorbidities, in particular HCV coinfection and aging.

A new approach for quantitative phosphoproteomic dissection of signaling pathways applied to T cell receptor activation.

Reversible protein phosphorylation plays a pivotal role in the regulation of cellular signaling pathways. Current approaches in phosphoproteomics focus on analysis of the global phosphoproteome in a single cellular state or of receptor stimulation time course experiments, often with a restricted number of time points. Although these studies have provided some insights into newly discovered phosphorylation sites that may be involved in pathways, they alone do not provide enough information to make precise predictions of the placement of individual phosphorylation events within a signaling pathway. Protein disruption and site-directed mutagenesis are essential to clearly define the precise biological roles of the hundreds of newly discovered phosphorylation sites uncovered in modern proteomics experiments. We have combined genetic analysis with quantitative proteomic methods and recently developed visual analysis tools to dissect the tyrosine phosphoproteome of isogenic Zap-70 tyrosine kinase null and reconstituted Jurkat T cells. In our approach, label-free quantitation using normalization to copurified phosphopeptide standards is applied to assemble high density temporal data within a single cell type, either Zap-70 null or reconstituted cells, providing a list of candidate phosphorylation sites that change in abundance after T cell stimulation. Stable isotopic labeling of amino acids in cell culture (SILAC) ratios are then used to compare Zap-70 null and reconstituted cells across a time course of receptor stimulation, providing direct information about the placement of newly observed phosphorylation sites relative to Zap-70. These methods are adaptable to any cell culture signaling system in which isogenic wild type and mutant cells have been or can be derived using any available phosphopeptide enrichment strategy.

Visualization of 3D Stress Tensor Fields Using Superquadric Glyphs on Displacement Streamlines.

Stress tensor fields play a central role in solid mechanics studies, but their visualization in 3D space remains challenging as the information-dense multi-variate tensor needs to be sampled in 3D space while avoiding clutter. Taking cues from current tensor visualizations, we adapted glyph-based visualization for stress tensors in 3D space. We also developed a testing framework and performed user studies to evaluate the various glyph-based tensor visualizations for objective accuracy measures, and subjective user feedback for each visualization method. To represent the stress tensor, we color encoded the original superquadric glyph, and in the user study, we compared it to superquadric glyphs developed for second-order symmetric tensors. We found that color encoding improved the user accuracy measures, while the users also rated our method the highest. We compared our method of placing stress tensor glyphs on displacement streamlines to the glyph placement on a 3D grid. In the visualization, we modified the glyph to show both the stress tensor and the displacement vector at each sample point. The participants preferred our method of glyph placement on displacement streamlines as it highlighted the underlying continuous structure in the tensor field.

Tractography Processing with the Sparse Closest Point Transform.

We propose a novel approach for processing diffusion MRI tractography datasets using the sparse closest point transform (SCPT). Tractography enables the 3D geometry of white matter pathways to be reconstructed; however, algorithms for processing them are often highly customized, and thus, do not leverage the existing wealth of machine learning (ML) algorithms. We investigated a vector-space tractography representation that aims to bridge this gap by using the SCPT, which consists of two steps: first, extracting sparse and representative landmarks from a tractography dataset, and second transforming curves relative to these landmarks with a closest point transform. We explore its use in three typical tasks: fiber bundle clustering, simplification, and selection across a population. The clustering algorithm groups fibers from single whole-brain datasets using a non-parametric k-means clustering algorithm, with performance compared with three alternative methods and across four datasets. The simplification algorithm removes redundant curves to improve interactive visualization, with performance gauged relative to random subsampling. The selection algorithm extracts bundles across a population using a one-class Gaussian classifier derived from an atlas prototype, with performance gauged by scan-rescan reliability and sensitivity to normal aging, as compared to manual mask-based selection. Our results demonstrate how the SCPT enables the novel application of existing vector-space ML algorithms to create effective and efficient tools for tractography processing. Our experimental data is available online, and our software implementation is available in the Quantitative Imaging Toolkit.

A Virtual Reality Memory Palace Variant Aids Knowledge Retrieval from Scholarly Articles.

We present exploratory research of virtual reality techniques and mnemonic devices to assist in retrieving knowledge from scholarly articles. We used abstracts of scientific publications to represent knowledge in scholarly articles; participants were asked to read, remember, and retrieve knowledge from a set of abstracts. We conducted an experiment to compare participants' recall and recognition performance in three different conditions: a control condition without a pre-specified strategy to test baseline individual memory ability, a condition using an image-based variant of a mnemonic called a "memory palace," and a condition using a virtual reality-based variant of a memory palace. Our analyses show that using a virtual reality-based memory palace variant greatly increased the amount of knowledge retrieved and retained over the baseline, and it shows a moderate improvement over the other image-based memory palace variant. Anecdotal feedback from participants suggested that personalizing a memory palace variant would be appreciated. Our results support the value of virtual reality for some high-level cognitive tasks and help improve future applications of virtual reality and visualization.

Virtual and augmented reality: New tools for visualizing, analyzing, and communicating complex morphology.

Virtual and augmented reality (VR/AR) are new technologies with the power to revolutionize the study of morphology. Modern imaging approaches such as computed tomography, laser scanning, and photogrammetry have opened up a new digital world, enabling researchers to share and analyze morphological data electronically and in great detail. Because this digital data exists on a computer screen, however, it can remain difficult to understand and unintuitive to interact with. VR/AR technologies bridge the analog-to-digital divide by presenting 3D data to users in a very similar way to how they would interact with actual anatomy, while also providing a more immersive experience and greater possibilities for exploration. This manuscript describes VR/AR hardware, software, and techniques, and is designed to give practicing morphologists and educators a primer on using these technologies in their research, pedagogy, and communication to a wide variety of audiences. We also include a series of case studies from the presentations and workshop given at the 2019 International Congress of Vertebrate Morphology, and suggest best practices for the use of VR/AR in comparative morphology.

Visual integration of quantitative proteomic data, pathways, and protein interactions.

We introduce several novel visualization and interaction paradigms for visual analysis of published protein-protein interaction networks, canonical signaling pathway models, and quantitative proteomic data. We evaluate them anecdotally with domain scientists to demonstrate their ability to accelerate the proteomic analysis process. Our results suggest that structuring protein interaction networks around canonical signaling pathway models, exploring pathways globally and locally at the same time, and driving the analysis primarily by the experimental data, all accelerate the understanding of protein pathways. Concrete proteomic discoveries within T-cells, mast cells, and the insulin signaling pathway validate the findings. The aim of the paper is to introduce novel protein network visualization paradigms and anecdotally assess the opportunity of incorporating them into established proteomic applications. We also make available a prototype implementation of our methods, to be used and evaluated by the proteomic community.

Gremlin: an interactive visualization model for analyzing genomic rearrangements.

In this work we present, apply, and evaluate a novel, interactive visualization model for comparative analysis of structural variants and rearrangements in human and cancer genomes, with emphasis on data integration and uncertainty visualization. To support both global trend analysis and local feature detection, this model enables explorations continuously scaled from the high-level, complete genome perspective, down to the low-level, structural rearrangement view, while preserving global context at all times. We have implemented these techniques in Gremlin, a genomic rearrangement explorer with multi-scale, linked interactions, which we apply to four human cancer genome data sets for evaluation. Using an insight-based evaluation methodology, we compare Gremlin to Circos, the state-of-the-art in genomic rearrangement visualization, through a small user study with computational biologists working in rearrangement analysis. Results from user study evaluations demonstrate that this visualization model enables more total insights, more insights per minute, and more complex insights than the current state-of-the-art for visual analysis and exploration of genome rearrangements.

Topic-Based Exploration and Embedded Visualizations for Research Idea Generation.

This work analyzes sensemaking frameworks and experiments with an iteratively designed visual analysis tool to identify design implications for facilitating research idea generation using visualizations. Our tool, ThoughtFlow, structures and visualizes literature collections using topic models to bridge the information gap between core activities during research ideation. To help users stay focused on a topic while discovering relevant documents, we designed and analyzed usage patterns for two types of embedded visualization that help determine document relevance while minimizing distraction. We analyzed how research ideation outcomes and processes differ when using ThoughtFlow and conventional search engines by augmenting insight-based evaluation with concept-map analysis. Our results suggest that operations afforded by topic models match well with later ideation stages when coherent topics have emerged, but not with early stages when users are still relying heavily on individual keywords to gather background knowledge. We also present qualitative evidence that citation sparklines encourage more exploration of recommended references, and that a preference for paper thumbnails may depend on the consistency between the evidence and the current mental frame.

Measuring the Effects of Scalar and Spherical Colormaps on Ensembles of DMRI Tubes.

We report empirical study results on the color encoding of ensemble scalar and orientation to visualize diffusion magnetic resonance imaging (DMRI) tubes. The experiment tested six scalar colormaps for average fractional anisotropy (FA) tasks (grayscale, blackbody, diverging, isoluminant-rainbow, extended-blackbody, and coolwarm) and four three-dimensional (3D) spherical colormaps for tract tracing tasks (uniform gray, absolute, eigenmaps, and Boy's surface embedding). We found that extended-blackbody, coolwarm, and blackbody remain the best three approaches for identifying ensemble average in 3D. Isoluminant-rainbow colormap led to the same ensemble mean accuracy as other colormaps. However, more than 50 percent of the answers consistently had higher estimates of the ensemble average, independent of the mean values. The number of hues, not luminance, influences ensemble estimates of mean values. For ensemble orientation-tracing tasks, we found that both Boy's surface embedding (greatest spatial resolution and contrast) and absolute colormaps (lowest spatial resolution and contrast) led to more accurate answers than the eigenmaps scheme (medium resolution and contrast), acting as the uncanny-valley phenomenon of visualization design in terms of accuracy. Absolute colormap broadly used in brain science is a good default spherical colormap. We could conclude from our study that human visual processing of a chunk of colors differs from that of single colors.