Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Large scale visualization on the Powerwall.
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

SCI Publications

2015


S. Liu, Bei Wang, J. J. Thiagarajan, P. T. Bremer, V. Pascucci. “Visual Exploration of High-Dimensional Data through Subspace Analysis and Dynamic Projections,” In Computer Graphics Forum, Vol. 34, No. 3, Wiley-Blackwell, pp. 271--280. June, 2015.
DOI: 10.1111/cgf.12639

ABSTRACT

We introduce a novel interactive framework for visualizing and exploring high-dimensional datasets based on subspace analysis and dynamic projections. We assume the high-dimensional dataset can be represented by a mixture of low-dimensional linear subspaces with mixed dimensions, and provide a method to reliably estimate the intrinsic dimension and linear basis of each subspace extracted from the subspace clustering. Subsequently, we use these bases to define unique 2D linear projections as viewpoints from which to visualize the data. To understand the relationships among the different projections and to discover hidden patterns, we connect these projections through dynamic projections that create smooth animated transitions between pairs of projections. We introduce the view transition graph, which provides flexible navigation among these projections to facilitate an intuitive exploration. Finally, we provide detailed comparisons with related systems, and use real-world examples to demonstrate the novelty and usability of our proposed framework.



CIBC. Note: map3d: Interactive scientific visualization tool for bioengineering data. Scientific Computing and Imaging Institute (SCI), Download from: http://www.sci.utah.edu/cibc/software.html, 2015.



K.S. McDowell, S. Zahid, F. Vadakkumpadan, J.J. Blauer, R.S. MacLeod, N.A. Trayanova. “Virtual Electrophysiological Study of Atrial Fibrillation in Fibrotic Remodeling,” In PLoS ONE, Vol. 10, No. 2, pp. e0117110. February, 2015.
DOI: 10.1371/journal.pone.0117110

ABSTRACT

Research has indicated that atrial fibrillation (AF) ablation failure is related to the presence of atrial fibrosis. However it remains unclear whether this information can be successfully used in predicting the optimal ablation targets for AF termination. We aimed to provide a proof-of-concept that patient-specific virtual electrophysiological study that combines i) atrial structure and fibrosis distribution from clinical MRI and ii) modeling of atrial electrophysiology, could be used to predict: (1) how fibrosis distribution determines the locations from which paced beats degrade into AF; (2) the dynamic behavior of persistent AF rotors; and (3) the optimal ablation targets in each patient. Four MRI-based patient-specific models of fibrotic left atria were generated, ranging in fibrosis amount. Virtual electrophysiological studies were performed in these models, and where AF was inducible, the dynamics of AF were used to determine the ablation locations that render AF non-inducible. In 2 of the 4 models patient-specific models AF was induced; in these models the distance between a given pacing location and the closest fibrotic region determined whether AF was inducible from that particular location, with only the mid-range distances resulting in arrhythmia. Phase singularities of persistent rotors were found to move within restricted regions of tissue, which were independent of the pacing location from which AF was induced. Electrophysiological sensitivity analysis demonstrated that these regions changed little with variations in electrophysiological parameters. Patient-specific distribution of fibrosis was thus found to be a critical component of AF initiation and maintenance. When the restricted regions encompassing the meander of the persistent phase singularities were modeled as ablation lesions, AF could no longer be induced. The study demonstrates that a patient-specific modeling approach to identify non-invasively AF ablation targets prior to the clinical procedure is feasible.



S. McKenna, M. Meyer, C. Gregg, S. Gerber. “s-CorrPlot: An Interactive Scatterplot for Exploring Correlation,” In Journal of Computational and Graphical Statistics, 2015.
DOI: 10.1080/10618600.2015.1021926

ABSTRACT

The degree of correlation between variables is used in many data analysis applications as a key measure of interdependence. The most common techniques for exploratory analysis of pairwise correlation in multivariate datasets, like scatterplot matrices and clustered heatmaps, however, do not scale well to large datasets, either computationally or visually. We present a new visualization that is capable of encoding pairwise correlation between hundreds of thousands variables, called the s-CorrPlot. The s-CorrPlot encodes correlation spatially between variables as points on scatterplot using the geometric structure underlying Pearson's correlation. Furthermore, we extend the s-CorrPlot with interactive techniques that enable animation of the scatterplot to new projections of the correlation space, as illustrated in the companion video in Supplemental Materials. We provide the s-CorrPlot as an open-source R-package and validate its effectiveness through a variety of methods including a case study with a biology collaborator.



SCI Institute. Note: NCR Toolset: A collection of software tools for the reconstruction and visualization of neural circuitry from electron microscopy data. Scientific Computing and Imaging Institute (SCI). Download from: http://www.sci.utah.edu/software.html, 2015.



C. Nobre, A. Lex. “OceanPaths: Visualizing Multivariate Oceanography Data,” In Eurographics Conference on Visualization (EuroVis) - Short Papers, Edited by E. Bertini, J. Kennedy, E. Puppo, The Eurographics Association, 2015.
DOI: 10.2312/eurovisshort.20151124

ABSTRACT

Geographical datasets are ubiquitous in oceanography. While map-based visualizations are useful for many different domains, they can suffer from cluttering and overplotting issues when used for multivariate data sets. As a result, spatial data exploration in oceanography has often been restricted to multiple maps showing various depths or time intervals. This lack of interactive exploration often hinders efforts to expose correlations between properties of oceanographic features, specifically currents. OceanPaths provides powerful interaction and exploration methods for spatial, multivariate oceanography datasets to remedy these situations. Fundamentally, our method allows users to define pathways, typically following currents, along which the variation of the high-dimensional data can be plotted efficiently. We present a case study conducted by domain experts to underscore the usefulness of OceanPaths in uncovering trends and correlations in oceanographic data sets.



I. OguzI, J. Cates, M. Datar, B. Paniagua, T. Fletcher, C. Vachet, M. Styner, R. Whitaker. “Entropy-based particle correspondence for shape populations,” In International Journal of Computer Assisted Radiology and Surgery, Springer, pp. 1-12. December, 2015.

ABSTRACT

Purpose
Statistical shape analysis of anatomical structures plays an important role in many medical image analysis applications such as understanding the structural changes in anatomy in various stages of growth or disease. Establishing accurate correspondence across object populations is essential for such statistical shape analysis studies.

Methods
In this paper, we present an entropy-based correspondence framework for computing point-based correspondence among populations of surfaces in a groupwise manner. This robust framework is parameterization-free and computationally efficient. We review the core principles of this method as well as various extensions to deal effectively with surfaces of complex geometry and application-driven correspondence metrics.

Results
We apply our method to synthetic and biological datasets to illustrate the concepts proposed and compare the performance of our framework to existing techniques.

Conclusions
Through the numerous extensions and variations presented here, we create a very flexible framework that can effectively handle objects of various topologies, multi-object complexes, open surfaces, and objects of complex geometry such as high-curvature regions or extremely thin features.



B.R. Parmar, T.R. Jarrett, E.G. Kholmovski, N. Hu, D. Parker, R.S. MacLeod, N.F. Marrouche, R. Ranjan. “Poor scar formation after ablation is associated with atrial fibrillation recurrence,” In Journal of Interventional Cardiac Electrophysiology, Vol. 44, No. 3, pp. 247-256. December, 2015.

ABSTRACT

Purpose
Patients routinely undergo ablation for atrial fibrillation (AF) but the recurrence rate remains high. We explored in this study whether poor scar formation as seen on late-gadolinium enhancement magnetic resonance imaging (LGE-MRI) correlates with AF recurrence following ablation.

Methods
We retrospectively identified 94 consecutive patients who underwent their initial ablation for AF at our institution and had pre-procedural magnetic resonance angiography (MRA) merged with left atrial (LA) anatomy in an electroanatomic mapping (EAM) system, ablated areas marked intraprocedurally in EAM, 3-month post-ablation LGE-MRI for assessment of scar, and minimum of 3-months of clinical follow-up. Ablated area was quantified retrospectively in EAM and scarred area was quantified in the 3-month post-ablation LGE-MRI.

Results
With the mean follow-up of 336 days, 26 out of 94 patients had AF recurrence. Age, hypertension, and heart failure were not associated with AF recurrence, but LA size and difference between EAM ablated area and LGE-MRI scar area was associated with higher AF recurrence. For each percent higher difference between EAM ablated area and LGE-MRI scar area, there was a 7–9 % higher AF recurrence (p values 0.001–0.003) depending on the multivariate analysis.

Conclusions
In AF ablation, poor scar formation as seen on LGE-MRI was associated with AF recurrence. Improved mapping and ablation techniques are necessary to achieve the desired LA scar and reduce AF recurrence.



B. Peterson, N. Xiao, J. Holmen, S. Chaganti, A. Pakki, J. Schmidt, D. Sunderland, A. Humphrey, M. Berzins. “Developing Uintah’s Runtime System For Forthcoming Architectures,” Subtitled “Refereed paper presented at the RESPA 15 Workshop at SuperComputing 2015 Austin Texas,” SCI Institute, 2015.



B. Peterson, H. K. Dasari, A. Humphrey, J.C. Sutherland, T. Saad, M. Berzins. “Reducing overhead in the Uintah framework to support short-lived tasks on GPU-heterogeneous architectures,” In Proceedings of the 5th International Workshop on Domain-Specific Languages and High-Level Frameworks for High Performance Computing (WOLFHPC'15), ACM, pp. 4:1-4:8. 2015.
DOI: 10.1145/2830018.2830023



J. M. Phillips, Bei Wang, Y. Zheng. “Geometric Inference on Kernel Density Estimates,” In CoRR, Vol. abs/1307.7760, 2015.

ABSTRACT

We show that geometric inference of a point cloud can be calculated by examining its kernel density estimate with a Gaussian kernel. This allows one to consider kernel density estimates, which are robust to spatial noise, subsampling, and approximate computation in comparison to raw point sets. This is achieved by examining the sublevel sets of the kernel distance, which isomorphically map to superlevel sets of the kernel density estimate. We prove new properties about the kernel distance, demonstrating stability results and allowing it to inherit reconstruction results from recent advances in distance-based topological reconstruction. Moreover, we provide an algorithm to estimate its topology using weighted Vietoris-Rips complexes.



J.R. Pruett Jr., S. Kandala, S. Hoertel, A.Z. Snyder, J.T. Elison, T. Nishino, E. Feczko, N.U.F. Dosenbach, B. Nardos, J.D. Power, B. Adeyemo, K.N. Botteron, R.C. McKinstry, A.C. Evans, H.C. Hazlett, S.R. Dager, S. Paterson, R.T. Schultz, D.L. Collins, V.S. Fonov, M. Styner, G. Gerig, S. Das, P. Kostopoulos, J.N. Constantino, A.M. Estes, The IBIS Network, S.E. Petersen, B.L. Schlaggar, J. Piven. “Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data,” In Developmental Cognitive Neuroscience, Vol. 12, pp. 123--133. April, 2015.
DOI: 10.1016/j.dcn.2015.01.003

ABSTRACT

Human large-scale functional brain networks are hypothesized to undergo significant changes over development. Little is known about these functional architectural changes, particularly during the second half of the first year of life. We used multivariate pattern classification of resting-state functional connectivity magnetic resonance imaging (fcMRI) data obtained in an on-going, multi-site, longitudinal study of brain and behavioral development to explore whether fcMRI data contained information sufficient to classify infant age. Analyses carefully account for the effects of fcMRI motion artifact. Support vector machines (SVMs) classified 6 versus 12 month-old infants (128 datasets) above chance based on fcMRI data alone. Results demonstrate significant changes in measures of brain functional organization that coincide with a special period of dramatic change in infant motor, cognitive, and social development. Explorations of the most different correlations used for SVM lead to two different interpretations about functional connections that support 6 versus 12-month age categorization.



S. Pujol, W. Wells, C. Pierpaoli, C. Brun, J. Gee, G. Cheng, B. Vemuri, O. Commowick, S. Prima, A. Stamm, M. Goubran, A. Khan, T. Peters, P. Neher, K. H. Maier-Hein, Y. Shi, A. Tristan-Vega, G. Veni, R. Whitaker, M. Styner, C.F. Westin, S. Gouttard, I. Norton, L. Chauvin, H. Mamata, G. Gerig, A. Nabavi, A. Golby,, R. Kikinis. “The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery,” In Journal of Neuroimaging, Wiley, August, 2015.
DOI: 10.1111/jon.12283

ABSTRACT

BACKGROUND AND PURPOSE
Diffusion tensor imaging (DTI) tractography reconstruction of white matter pathways can help guide brain tumor resection. However, DTI tracts are complex mathematical objects and the validity of tractography-derived information in clinical settings has yet to be fully established. To address this issue, we initiated the DTI Challenge, an international working group of clinicians and scientists whose goal was to provide standardized evaluation of tractography methods for neurosurgery. The purpose of this empirical study was to evaluate different tractography techniques in the first DTI Challenge workshop.

METHODS
Eight international teams from leading institutions reconstructed the pyramidal tract in four neurosurgical cases presenting with a glioma near the motor cortex. Tractography methods included deterministic, probabilistic, filtered, and global approaches. Standardized evaluation of the tracts consisted in the qualitative review of the pyramidal pathways by a panel of neurosurgeons and DTI experts and the quantitative evaluation of the degree of agreement among methods.

RESULTS
The evaluation of tractography reconstructions showed a great interalgorithm variability. Although most methods found projections of the pyramidal tract from the medial portion of the motor strip, only a few algorithms could trace the lateral projections from the hand, face, and tongue area. In addition, the structure of disagreement among methods was similar across hemispheres despite the anatomical distortions caused by pathological tissues.

CONCLUSIONS
The DTI Challenge provides a benchmark for the standardized evaluation of tractography methods on neurosurgical data. This study suggests that there are still limitations to the clinical use of tractography for neurosurgical decision making.



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.

ABSTRACT

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.

ABSTRACT

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

ABSTRACT

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

ABSTRACT

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, www.sci.utah.edu, 2015.