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使用Weave和脑部分层设色法进行动态数据可视化

Dynamic Data Visualization with Weave and Brain Choropleths.

作者信息

Patterson Dianne, Hicks Thomas, Dufilie Andrew, Grinstein Georges, Plante Elena

机构信息

The University of Arizona, Speech, Language, and Hearing Sciences Department, Tucson, AZ, United States of America.

The University of Arizona, School of Information: Science, Technology, and Arts, Tucson, AZ, United States of America.

出版信息

PLoS One. 2015 Sep 29;10(9):e0139453. doi: 10.1371/journal.pone.0139453. eCollection 2015.

Abstract

This article introduces the neuroimaging community to the dynamic visualization workbench, Weave (https://www.oicweave.org/), and a set of enhancements to allow the visualization of brain maps. The enhancements comprise a set of brain choropleths and the ability to display these as stacked slices, accessible with a slider. For the first time, this allows the neuroimaging community to take advantage of the advanced tools already available for exploring geographic data. Our brain choropleths are modeled after widely used geographic maps but this mashup of brain choropleths with extant visualization software fills an important neuroinformatic niche. To date, most neuroinformatic tools have provided online databases and atlases of the brain, but not good ways to display the related data (e.g., behavioral, genetic, medical, etc). The extension of the choropleth to brain maps allows us to leverage general-purpose visualization tools for concurrent exploration of brain images and related data. Related data can be represented as a variety of tables, charts and graphs that are dynamically linked to each other and to the brain choropleths. We demonstrate that the simplified region-based analyses that underlay choropleths can provide insights into neuroimaging data comparable to those achieved by using more conventional methods. In addition, the interactive interface facilitates additional insights by allowing the user to filter, compare, and drill down into the visual representations of the data. This enhanced data visualization capability is useful during the initial phases of data analysis and the resulting visualizations provide a compelling way to publish data as an online supplement to journal articles.

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