Weighted graph comparison techniques for brain connectivity analysis

Alper, Başak; Bach, Benjamin; Riche, Nathalie Henry; Isenberg, Tobias; Fekete, Jean-Daniel

doi:10.1145/2470654.2470724

Cited by 159 publications

(157 citation statements)

References 44 publications

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“…With this task, possible causes of errors are losing track of which nodes have been counted and miscounting edges. Adjacency tasks that consist of finding the most connected node have been featured in several previous studies [21,25,54,58], as have variants on this task [4,20], including our DEGREE task [26].…”

Section: S2 Factor: Tasksmentioning

confidence: 99%

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Active Reading of Visualizations

Walny

Huron

Périn

et al. 2018

IEEE Trans. Visual. Comput. Graphics

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-We investigate whether the notion of active reading for text might be usefully applied to visualizations. Through a qualitative study we explored whether people apply observable active reading techniques when reading paper-based node-link visualizations. Participants used a range of physical actions while reading, and from these we synthesized an initial set of active reading techniques for visualizations. To learn more about the potential impact such techniques may have on visualization reading, we implemented support for one type of physical action from our observations (making freeform marks) in an interactive node-link visualization. Results from our quantitative study of this implementation show that interactive support for active reading techniques can improve the accuracy of performing low-level visualization tasks. Together, our studies suggest that the active reading space is ripe for research exploration within visualization and can lead to new interactions that make for a more flexible and effective visualization reading experience. Permanent repository linkIndex Terms-active reading of visualizations, active reading, information visualization, spectrum of physical engagement

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Section: S2 Factor: Tasksmentioning

confidence: 99%

“…Also, these two tasks often reveal differences between visualization techniques when the number of nodes increases [4,26,58].…”

Section: S2 Factor: Tasksmentioning

confidence: 99%

Active Reading of Visualizations

Walny

Huron

Périn

et al. 2018

IEEE Trans. Visual. Comput. Graphics

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“…They introduce a novel visual encoding for comparing different aspects of two graphs and evaluate it by means of a user study [1]. While Alper et al target 2D representations, we use stereoscopic, 3D depictions of graphs.…”

Section: Related Workmentioning

confidence: 99%

VisNEST — Interactive analysis of neural activity data

Nowke

Schmidt²,

Albada³

et al. 2013

2013 IEEE Symposium on Biological Data Visualization (BioVis)

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Figure 1: VisNEST's control view allows users to inspect neural activity linked to a geometric representation of a macaque brain. Textual annotations show anatomical area designators (right). By selecting a brain area, users may access the corresponding raster plot (top left) and connectivity of populations (bottom left). ABSTRACTThe aim of computational neuroscience is to gain insight into the dynamics and functionality of the nervous system by means of modeling and simulation. Current research leverages the power of High Performance Computing facilities to enable multi-scale simulations capturing both low-level neural activity and large-scale interactions between brain regions. In this paper, we describe an interactive analysis tool that enables neuroscientists to explore data from such simulations. One of the driving challenges behind this work is the integration of macroscopic data at the level of brain regions with microscopic simulation results, such as the activity of individual neurons. While researchers validate their findings mainly by visualizing these data in a non-interactive fashion, state-of-the-art visualizations, tailored to the scientific question yet sufficiently general to accommodate different types of models, enable such analyses to be performed more efficiently. This work describes several visualization designs, conceived in close collaboration with domain experts, for the analysis of network models. We primarily focus on the exploration of neural activity data, inspecting connectivity of brain regions and populations, and visualizing activity flux across regions. We demonstrate the effectiveness of our approach in a case study conducted with domain experts.

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“…MatLink was found to be superior to either node-link diagram or matrix. Matrix representations in general are more efficient than weightes node-link diagrams (Alper, Bach, Henry Riche, Isenberg & Fekete, 2013). However, these studies used generic and fairly simple tasks.…”

Section: Related Workmentioning

confidence: 99%

How Analysts Think: Sense-making Strategies in the Analysis of Temporal Evolution and Criminal Network Structures and Activities

Haider

Seidler

Pohl

et al. 2017

Proceedings of the Human Factors and Ergonomics Society Annual

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Copyright and moral rights to this work are retained by the author and/or other copyright owners unless otherwise stated. The work is supplied on the understanding that any use for commercial gain is strictly forbidden. A copy may be downloaded for personal, non-commercial, research or study without prior permission and without charge.Works, including theses and research projects, may not be reproduced in any format or medium, or extensive quotations taken from them, or their content changed in any way, without first obtaining permission in writing from the copyright holder(s). They may not be sold or exploited commercially in any format or medium without the prior written permission of the copyright holder(s).Full bibliographic details must be given when referring to, or quoting from full items including the author's name, the title of the work, publication details where relevant (place, publisher, date), pagination, and for theses or dissertations the awarding institution, the degree type awarded, and the date of the award.If you believe that any material held in the repository infringes copyright law, please contact the Repository Team at Middlesex University via the following email address:eprints@mdx.ac.ukThe item will be removed from the repository while any claim is being investigated.See also repository copyright: re-use policy: http://eprints.mdx.ac.uk/policies.html#copy A E Solutions (BI) Ltd, Badsey, UK Analysis of criminal activity based on offenders' social networks is an established procedure in intelligence analysis. The complexity of the data poses an obstacle for analysts to gauge network developments, e.g. detect emerging problems. Visualization is a powerful tool to achieve this, but it is essential to know how the analysts' sense-making strategies can be supported most efficiently. Based on a think aloud study we identified ten cognitive strategies on a general level to be useful for designers. We also provide some examples how these strategies can be supported through appropriate visualizations. How Analysts Think: Sense-making Strategies in the Analysis of Temporal Evolution and Criminal Network Structures and Activities

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Weighted graph comparison techniques for brain connectivity analysis

Cited by 159 publications

References 44 publications

Active Reading of Visualizations

Active Reading of Visualizations

VisNEST — Interactive analysis of neural activity data

How Analysts Think: Sense-making Strategies in the Analysis of Temporal Evolution and Criminal Network Structures and Activities

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Weighted graph comparison techniques for brain connectivity analysis

Cited by 159 publications

References 44 publications

Active Reading of Visualizations

Active Reading of Visualizations

VisNEST &#x2014; Interactive analysis of neural activity data

How Analysts Think: Sense-making Strategies in the Analysis of Temporal Evolution and Criminal Network Structures and Activities

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Product

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VisNEST — Interactive analysis of neural activity data