banner pubs

SCI Publications


M. Raj, M. Mirzargar, R. Kirby, R. Whitaker, J. Preston. “Evaluating Alignment of Shapes by Ensemble Visualization,” In IEEE Computer Graphics and Applications, IEEE, 2015.


The visualization of variability in 3D shapes or surfaces, which is a type of ensemble uncertainty visualization for volume data, provides a means of understanding the underlying distribution for a collection or ensemble of surfaces. While ensemble visualization for surfaces is already described in the literature, we conduct an expert-based evaluation in a particular medical imaging application: the construction of atlases or templates from a population of images. In this work, we extend contour boxplots to 3D, allowing us to evaluate it against an enumeration-style visualization of the ensemble members and also other conventional visualizations used by atlas builders, namely examining the atlas image and the corresponding images/data provided as part of the construction process. We present feedback from domain experts on the efficacy of contour boxplots compared to other modalities when used as part of the atlas construction and analysis stages of their work.

D. Reed, M. Berzins, R. Lucas, S. Matsuoka, R. Pennington, V. Sarkar, V. Taylor. “DOE Advanced Scientific Computing Advisory Committee (ASCAC) Report: Exascale Computing Initiative Review,” Note: DOE Report, 2015.
DOI: DOI 10.2172/1222712

H. J.V. Rutherford, G. Gerig, S. Gouttard, M. N. Potenza, L. C. Mayes. “Investigating maternal brain structure and its relationship to substance use and motivational systems,” In Yale Journal of Biology and Medicine, in print, 2015.


Substance use during pregnancy and the postpartum period may have significant implications for both mother and the developing child. However, the neurobiological basis of the impact of substance use on parenting is less well understood. Here we examined the impact of maternal substance use on cortical gray matter (GM) and white matter volumes, and whether this was associated with individual differences in motivational systems of behavioral activation and inhibition. Mothers were included in the substance-using group if any addictive substance was used during pregnancy and/or in the immediate postpartum period (within 3 months of delivery). GM volume was reduced in substance-using mothers compared to non-substance-using mothers, particularly in frontal brain regions. In substance-using mothers, we also found that frontal GM was negatively correlated with levels of behavioral activation (i.e., the motivation to approach rewarding stimuli). This effect was absent in non-substance-using mothers. Taken together, these findings indicate a reduction in GM volume is associated with substance use, and that frontal GM volumetric differences may be related to approach motivation in substance-using mothers.

N. Sadeghi, J. H. Gilmore , G. Gerig. “Modeling Brain Growth and Development,” In Brain, Vol. 1, pp. 429-436. 2015.
DOI: 10.1016/B978-0-12-397025-1.00314-6


Early brain development is characterized by rapid organization and structuring. Magnetic resonance–diffusion tensor imaging (MR-DTI) provides the possibility of capturing these changes noninvasively by following individuals longitudinally to better understand departures from normal brain development in subjects at risk for mental illness. This article illustrates the modeling of neurodevelopmental trajectories using a recently developed framework. Descriptions include the estimation of normative models for healthy singletons and twins and a statistical framework to predict development at 2 years of age only based on neonatal image data – a capability with excellent potential for preclinical diagnosis and eventual early therapeutic intervention.

A. P. Salzwedel, K. M. Grewen, C. Vachet, G. Gerig, W. Lin,, W. Gao. “Prenatal Drug Exposure Affects Neonatal Brain Functional Connectivity,” In The Journal of Neuroscience, Vol. 35, No. 14, pp. 5860-5869. April, 2015.
DOI: 10.1523/JNEUROSCI.4333-14.2015


Prenatal drug exposure, particularly prenatal cocaine exposure (PCE), incurs great public and scientific interest because of its associated neurodevelopmental consequences. However, the neural underpinnings of PCE remain essentially uncharted, and existing studies in school-aged children and adolescents are confounded greatly by postnatal environmental factors. In this study, leveraging a large neonate sample (N = 152) and non-invasive resting-state functional magnetic resonance imaging, we compared human infants with PCE comorbid with other drugs (such as nicotine, alcohol, marijuana, and antidepressant) with infants with similar non-cocaine poly drug exposure and drug-free controls. We aimed to characterize the neural correlates of PCE based on functional connectivity measurements of the amygdala and insula at the earliest stage of development. Our results revealed common drug exposure-related connectivity disruptions within the amygdala–frontal, insula–frontal, and insula–sensorimotor circuits. Moreover, a cocaine-specific effect was detected within a subregion of the amygdala–frontal network. This pathway is thought to play an important role in arousal regulation, which has been shown to be irregular in PCE infants and adolescents. These novel results provide the earliest human-based functional delineations of the neural-developmental consequences of prenatal drug exposure and thus open a new window for the advancement of effective strategies aimed at early risk identification and intervention.

S. Sankaranarayanan, T.E. Schomay, K.A. Aiello, O. Alter. “Tensor GSVD of patient- and platform-matched tumor and normal DNA copy-number profiles uncovers chromosome arm-wide patterns of tumor-exclusive platform-consistent alterations encoding for cell transformation and predicting ovarian cancer survival,” In PLoS ONE, Vol. 10, No. e121396, 2015.
DOI: 10.1371/journal.pone.0121396

Note: Scientific Computing and Imaging Institute (SCI), University of Utah,, 2015.

SCI Institute. Note: SCIRun: A Scientific Computing Problem Solving Environment, Scientific Computing and Imaging Institute (SCI), Download from:, 2015.

CIBC. Note: Seg3D: Volumetric Image Segmentation and Visualization. Scientific Computing and Imaging Institute (SCI), Download from:, 2015.

SCI Institute. Note: ShapeWorks: An open-source tool for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on specific surface parameterization. Scientific Computing and Imaging Institute (SCI). Download from:, 2015.

P. Skraba, Bei Wang, G. Chen, P. Rosen. “Robustness-Based Simplification of 2D Steady and Unsteady Vector Fields,” In IEEE Transactions on Visualization and Computer Graphics (to appear), 2015.


Vector field simplification aims to reduce the complexity of the flow by removing features in order of their relevance and importance, to reveal prominent behavior and obtain a compact representation for interpretation. Most existing simplification techniques based on the topological skeleton successively remove pairs of critical points connected by separatrices, using distance or area-based relevance measures. These methods rely on the stable extraction of the topological skeleton, which can be difficult due to instability in numerical integration, especially when processing highly rotational flows. In this paper, we propose a novel simplification scheme derived from the recently introduced topological notion of robustness which enables the pruning of sets of critical points according to a quantitative measure of their stability, that is, the minimum amount of vector field perturbation required to remove them. This leads to a hierarchical simplification scheme that encodes flow magnitude in its perturbation metric. Our novel simplification algorithm is based on degree theory and has minimal boundary restrictions. Finally, we provide an implementation under the piecewise-linear setting and apply it to both synthetic and real-world datasets. We show local and complete hierarchical simplifications for steady as well as unsteady vector fields.

SLASH. Note: SLASH: A hybrid system for high-throughput segmentation of large neuropil datasets, SLASH is funded by the National Institute of Neurological Disorders and Stroke (NINDS) grant 5R01NS075314-03., 2015.

H. Strobelt, B. Alsallakh, J. Botros, B. Peterson, M. Borowsky, H. Pfister,, A. Lex. “Vials: Visualizing Alternative Splicing of Genes,” In IEEE Transactions on Visualization and Computer Graphics (InfoVis '15), Vol. 22, No. 1, pp. 399-408. 2015.


Alternative splicing is a process by which the same DNA sequence is used to assemble different proteins, called protein isoforms. Alternative splicing works by selectively omitting some of the coding regions (exons) typically associated with a gene. Detection of alternative splicing is difficult and uses a combination of advanced data acquisition methods and statistical inference. Knowledge about the abundance of isoforms is important for understanding both normal processes and diseases and to eventually improve treatment through targeted therapies. The data, however, is complex and current visualizations for isoforms are neither perceptually efficient nor scalable. To remedy this, we developed Vials, a novel visual analysis tool that enables analysts to explore the various datasets that scientists use to make judgments about isoforms: the abundance of reads associated with the coding regions of the gene, evidence for junctions, i.e., edges connecting the coding regions, and predictions of isoform frequencies. Vials is scalable as it allows for the simultaneous analysis of many samples in multiple groups. Our tool thus enables experts to (a) identify patterns of isoform abundance in groups of samples and (b) evaluate the quality of the data. We demonstrate the value of our tool in case studies using publicly available datasets.

B. Summa, A. A. Gooch, G. Scorzelli, V. Pascucci. “Paint and Click: Unified Interactions for Image Boundaries,” In Computer Graphics Forum, Vol. 34, No. 2, Wiley-Blackwell, pp. 385--393. May, 2015.
DOI: 10.1111/cgf.12568


Image boundaries are a fundamental component of many interactive digital photography techniques, enabling applications such as segmentation, panoramas, and seamless image composition. Interactions for image boundaries often rely on two complementary but separate approaches: editing via painting or clicking constraints. In this work, we provide a novel, unified approach for interactive editing of pairwise image boundaries that combines the ease of painting with the direct control of constraints. Rather than a sequential coupling, this new formulation allows full use of both interactions simultaneously, giving users unprecedented flexibility for fast boundary editing. To enable this new approach, we provide technical advancements. In particular, we detail a reformulation of image boundaries as a problem of finding cycles, expanding and correcting limitations of the previous work. Our new formulation provides boundary solutions for painted regions with performance on par with state-of-the-art specialized, paint-only techniques. In addition, we provide instantaneous exploration of the boundary solution space with user constraints. Finally, we provide examples of common graphics applications impacted by our new approach.

M. R. Swanson, J. J. Wolff, J. T. Elison, H. Gu, H. C. Hazlett, K. Botteron, M. Styner, S. Paterson, G. Gerig, J. Constantino, S. Dager, A. Estes, C. Vachet, J. Piven. “Splenium development and early spoken language in human infants,” In Developmental Science, Wiley Online Library, 2015.
ISSN: 1467-7687
DOI: 10.1111/desc.12360


The association between developmental trajectories of language-related white matter fiber pathways from 6 to 24 months of age and individual differences in language production at 24 months of age was investigated. The splenium of the corpus callosum, a fiber pathway projecting through the posterior hub of the default mode network to occipital visual areas, was examined as well as pathways implicated in language function in the mature brain, including the arcuate fasciculi, uncinate fasciculi, and inferior longitudinal fasciculi. The hypothesis that the development of neural circuitry supporting domain-general orienting skills would relate to later language performance was tested in a large sample of typically developing infants. The present study included 77 infants with diffusion weighted MRI scans at 6, 12 and 24 months and language assessment at 24 months. The rate of change in splenium development varied significantly as a function of language production, such that children with greater change in fractional anisotropy (FA) from 6 to 24 months produced more words at 24 months. Contrary to findings from older children and adults, significant associations between language production and FA in the arcuate, uncinate, or left inferior longitudinal fasciculi were not observed. The current study highlights the importance of tracing brain development trajectories from infancy to fully elucidate emerging brain–behavior associations while also emphasizing the role of the splenium as a key node in the structural network that supports the acquisition of spoken language.

Note: VisTrails: A scientific workflow management system. Scientific Computing and Imaging Institute (SCI), Download from:, 2015.

I. Wald, A. Knoll, G. P. Johnson, W. Usher, V. Pascucci, M. E. Papka. “CPU Ray Tracing Large Particle Data with Balanced P-k-d Trees,” In 2015 IEEE Scientific Visualization Conference, IEEE, Oct, 2015.
DOI: 10.1109/scivis.2015.7429492


We present a novel approach to rendering large particle data sets from molecular dynamics, astrophysics and other sources. We employ a new data structure adapted from the original balanced k-d tree, which allows for representation of data with trivial or no overhead. In the OSPRay visualization framework, we have developed an efficient CPU algorithm for traversing, classifying and ray tracing these data. Our approach is able to render up to billions of particles on a typical workstation, purely on the CPU, without any approximations or level-of-detail techniques, and optionally with attribute-based color mapping, dynamic range query, and advanced lighting models such as ambient occlusion and path tracing.

R. Whitaker, W. Thompson, J. Berger, B. Fischhof, M. Goodchild, M. Hegarty, C. Jermaine, K. S. McKinley, A. Pang, J. Wendelberger. “Workshop on Quantification, Communication, and Interpretation of Uncertainty in Simulation and Data Science,” Note: Computing Community Consortium, 2015.


Modern science, technology, and politics are all permeated by data that comes from people, measurements, or computational processes. While this data is often incomplete, corrupt, or lacking in sufficient accuracy and precision, explicit consideration of uncertainty is rarely part of the computational and decision making pipeline. The CCC Workshop on Quantification, Communication, and Interpretation of Uncertainty in Simulation and Data Science explored this problem, identifying significant shortcomings in the ways we currently process, present, and interpret uncertain data. Specific recommendations on a research agenda for the future were made in four areas: uncertainty quantification in large-scale computational simulations, uncertainty quantification in data science, software support for uncertainty computation, and better integration of uncertainty quantification and communication to stakeholders.

J. J. Wolff, G. Gerig, J. D. Lewis, T. Soda, M. A. Styner, C. Vachet, K. N. Botteron, J. T. Elison, S. R. Dager, A. M. Estes, H. C. Hazlett, R. T. Schultz, L. Zwaigenbaum, J. Piven. “Altered corpus callosum morphology associated with autism over the first 2 years of life,” In Brain, 2015.
DOI: 10.1093/brain/awv118


Numerous brain imaging studies indicate that the corpus callosum is smaller in older children and adults with autism spectrum disorder. However, there are no published studies examining the morphological development of this connective pathway in infants at-risk for the disorder. Magnetic resonance imaging data were collected from 270 infants at high familial risk for autism spectrum disorder and 108 low-risk controls at 6, 12 and 24 months of age, with 83% of infants contributing two or more data points. Fifty-seven children met criteria for ASD based on clinical-best estimate diagnosis at age 2 years. Corpora callosa were measured for area, length and thickness by automated segmentation. We found significantly increased corpus callosum area and thickness in children with autism spectrum disorder starting at 6 months of age. These differences were particularly robust in the anterior corpus callosum at the 6 and 12 month time points. Regression analysis indicated that radial diffusivity in this region, measured by diffusion tensor imaging, inversely predicted thickness. Measures of area and thickness in the first year of life were correlated with repetitive behaviours at age 2 years. In contrast to work from older children and adults, our findings suggest that the corpus callosum may be larger in infants who go on to develop autism spectrum disorder. This result was apparent with or without adjustment for total brain volume. Although we did not see a significant interaction between group and age, cross-sectional data indicated that area and thickness differences diminish by age 2 years. Regression data incorporating diffusion tensor imaging suggest that microstructural properties of callosal white matter, which includes myelination and axon composition, may explain group differences in morphology.

M. Zhang, P. T. Fletcher. “Finite-Dimensional Lie Algebras for Fast Diffeomorphic Image Registration,” In Information Processing in Medical Imaging (IPMI), 2015.


This paper presents a fast geodesic shooting algorithm for diffeomorphic image registration. We first introduce a novel finite-dimensional Lie algebra structure on the space of bandlimited velocity fields. We then show that this space can effectively represent initial velocities for diffeomorphic image registration at much lower dimensions than typically used, with little to no loss in registration accuracy. We then leverage the fact that the geodesic evolution equations, as well as the adjoint Jacobi field equations needed for gradient descent methods, can be computed entirely in this finite-dimensional Lie algebra. The result is a geodesic shooting method for large deformation metric mapping (LDDMM) that is dramatically faster and less memory intensive than state-of-the-art methods. We demonstrate the effectiveness of our model to register 3D brain images and compare its registration accuracy, runtime, and memory consumption with leading LDDMM methods. We also show how our algorithm breaks through the prohibitive time and memory requirements of diffeomorphic atlas building.