SwordPlots: Exploring Neuron Behavior within Dynamic Communities of Brain Networks

Ma, Chihua; Forbes, Angus G.; Llano, Daniel A.; Berger-Wolf, Tanya Y.; Kenyon, Robert V.

doi:10.2352/j.imagingsci.technol.2016.60.1.010405

Cited by 9 publications

(5 citation statements)

References 33 publications

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“…Almryde and Forbes [AF15] introduce an interactive web application to visualize “traces” of dark matter halos as they move in relation to each other over time, and Scherzinger et al [SBD*17] present an innovative application that provides 2D and 3D views to support the analysis of halo substructures and hierarchies. IGM‐Vis also provides a visual analytics dashboard comprised of integrated panels [DMF17, FBL*18, MFL*16, SCB*19], facilitating a workflow supporting IGM/CGM identification, analysis, and presentation tasks. In addition to these standalone software applications, a range of frameworks and platforms have been developed to support astrophysical visualization.…”

Section: Background and Related Workmentioning

confidence: 99%

IGM‐Vis: Analyzing Intergalactic and Circumgalactic Medium Absorption Using Quasar Sightlines in a Cosmic Web Context

Burchett

Abramov

Otto

et al. 2019

Computer Graphics Forum

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We introduce IGM‐Vis, a novel astrophysics visualization and data analysis application for investigating galaxies and the gas that surrounds them in context with their larger scale environment, the Cosmic Web. Environment is an important factor in the evolution of galaxies from actively forming stars to quiescent states with little, if any, discernible star formation activity. The gaseous halos of galaxies (the circumgalactic medium, or CGM) play a critical role in their evolution, because the gas necessary to fuel star formation and any gas expelled from widely observed galactic winds must encounter this interface region between galaxies and the intergalactic medium (IGM). We present a taxonomy of tasks typically employed in IGM/CGM studies informed by a survey of astrophysicists at various career levels, and demonstrate how these tasks are facilitated via the use of our visualization software. Finally, we evaluate the effectiveness of IGM‐Vis through two in‐depth use cases that depict real‐world analysis sessions that use IGM/CGM data.

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Section: Background and Related Workmentioning

confidence: 99%

IGM‐Vis: Analyzing Intergalactic and Circumgalactic Medium Absorption Using Quasar Sightlines in a Cosmic Web Context

Burchett

Abramov

Otto

et al. 2019

Computer Graphics Forum

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“…A number of 2D visualizations have been used to explore dynamic connectome data [2,10,17,26], and Beck et al [4] summarize approaches to visualize 2D dynamic networks. Other tools instead utilize a 3D layout for investigating dynamic connectome data [16,23,25]. For example, Xing et al's Thought Chart [36] presents distinct 3D trajectories for different task conditions and provides a comparative analysis that generates a summary view of how much one dynamic connectome differs in comparison to others.…”

Section: Background and Related Workmentioning

confidence: 99%

TempoCave: Visualizing Dynamic Connectome Datasets to Support Cognitive Behavioral Therapy

Thomas

Leow

et al. 2019

2019 IEEE Visualization Conference (VIS)

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Figure 1: A screenshot of the TempoCave application. Here, a user compares frames from pre-and post-treatment dynamic connectomes for an individual patient with major depression disorder. Using the option panels on either side of the application, a user can choose different visual encodings to accentuate features useful for understanding the activity of particular brain regions. The left and right coloring of the nodes indicates the modular affinity of a brain region for a patient's pre-and post-treatment connectome, respectively. Likewise, the styling of the connections indicates either the connectome they belong to or the strength of the correlation. A user can synchronize playback of the frames of the connectomes or compare selected frames on demand to gain insight into their dynamics, and during an analysis session a user can interactively toggle on or off brain regions of interest, or switch to alternative representations of the connectome defined using layouts based on dimensionality reduction techniques. ABSTRACTWe introduce TempoCave, a novel visualization application for analyzing dynamic brain networks, or connectomes. TempoCave provides a range of functionality to explore metrics related to the activity patterns and modular affiliations of different regions in the brain. These patterns are calculated by processing raw data retrieved functional magnetic resonance imaging (fMRI) scans, which creates a network of weighted edges between each brain region, where the weight indicates how likely these regions are to activate synchronously. In particular, we support the analysis needs of clinical psychologists, who examine these modular affiliations and weighted edges and their temporal dynamics, utilizing them to understand relationships between neurological disorders and brain activity, which could have significant impact on the way in which patients are diagnosed and treated. We summarize the core functionality of Tem-poCave, which supports a range of comparative tasks, and runs both in a desktop mode and in an immersive mode. Furthermore, we present a real world use case that analyzes pre-and post-treatment connectome datasets from 27 subjects in a clinical study investigating the use of cognitive behavior therapy to treat major depression disorder, indicating that TempoCave can provide new insight into the dynamic behavior of the human brain.

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“…3). Techniques by Ma et al [43,44], Purgato et al [55], and Ye et al [66] present multiple synchronized representations of a dynamic brain network to provide additional insight into the community dynamics within the network. Our tool also presents auxiliary representations to support the analysis of dynamic data, providing detail on demand for selected nodes.…”

Section: Dynamic Network Visualizationmentioning

confidence: 99%

Dynamic Influence Networks for Rule-Based Models

Forbes

Burks²,

Lee³

et al. 2018

IEEE Trans. Visual. Comput. Graphics

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We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle.

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SwordPlots: Exploring Neuron Behavior within Dynamic Communities of Brain Networks

Cited by 9 publications

References 33 publications

IGM‐Vis: Analyzing Intergalactic and Circumgalactic Medium Absorption Using Quasar Sightlines in a Cosmic Web Context

IGM‐Vis: Analyzing Intergalactic and Circumgalactic Medium Absorption Using Quasar Sightlines in a Cosmic Web Context

TempoCave: Visualizing Dynamic Connectome Datasets to Support Cognitive Behavioral Therapy

Dynamic Influence Networks for Rule-Based Models

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