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Dynamic Data Visualization with Weave and Brain Choropleths.

Patterson D, Hicks T, Dufilie A, Grinstein G, Plante E - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

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

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.

No MeSH data available.


Related in: MedlinePlus

Line charts of Activation Extent and Intensity.Each row represents a component (IC-1 (a-c), IC-2 (d-f), IC-3 (g-i) and IC-4 (j-l)). The first column represents changes in the extent of significant activation for the IC on the left (purple) and right (green) from scan 1 through 4. The second (left hemisphere) and third (right hemisphere) columns compare group average percent signal change for each IC in low performers (red) and high performers (blue) from scans 1 through 4. The figure reveals that there is no clear relationship between volume variation (column 1) and signal intensity (columns 2 and 3). The figure also demonstrates that signal intensity variation in this simplified region-based analysis is comparable to signal intensity variation in the more traditional analysis.
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pone.0139453.g003: Line charts of Activation Extent and Intensity.Each row represents a component (IC-1 (a-c), IC-2 (d-f), IC-3 (g-i) and IC-4 (j-l)). The first column represents changes in the extent of significant activation for the IC on the left (purple) and right (green) from scan 1 through 4. The second (left hemisphere) and third (right hemisphere) columns compare group average percent signal change for each IC in low performers (red) and high performers (blue) from scans 1 through 4. The figure reveals that there is no clear relationship between volume variation (column 1) and signal intensity (columns 2 and 3). The figure also demonstrates that signal intensity variation in this simplified region-based analysis is comparable to signal intensity variation in the more traditional analysis.

Mentions: Whereas the brain maps display regional data for either the scan count or the signal during a scan, the line charts display group average signal change across all four scans. On the lower left the “group average signal” line chart compares high and low performers across the four scans and averaged across regions. This is very similar to the line charts in Fig 2 of the original analysis [19]. Although this division reflects the participant subgroups used in the original study, other participant subgroupings (e.g., normal vs. impaired, +genotype vs.–genotype, young vs. old) could be displayed in this manner. By applying the IC and LR filters, this dynamic line chart can be configured to display lines corresponding to the eight line charts in the original Fig 2 [19] (sans standard error bars) [See Fig 3]. The “regional group average signal” line chart supplements the “group average line chart” by comparing high and low performers across the four scans for each region. We can drill down into this chart to reveal novel patterns. For example, disabling all filters reveals exceptionally high regional signal in the right posterior superior temporal gyrus in low performers. Filtering by IC reveals that this signal is associated with IC-1. Filtering by region (e.g., STG_post) allows us to compare this exceptional signal in high and low performers to the homotopic region in the contralateral hemisphere. Given the role of the right posterior superior temporal gyrus in processing human voice [20], we can reasonably conclude that attention to human voice was an especially active focus of brain resources throughout the experiment.


Dynamic Data Visualization with Weave and Brain Choropleths.

Patterson D, Hicks T, Dufilie A, Grinstein G, Plante E - PLoS ONE (2015)

Line charts of Activation Extent and Intensity.Each row represents a component (IC-1 (a-c), IC-2 (d-f), IC-3 (g-i) and IC-4 (j-l)). The first column represents changes in the extent of significant activation for the IC on the left (purple) and right (green) from scan 1 through 4. The second (left hemisphere) and third (right hemisphere) columns compare group average percent signal change for each IC in low performers (red) and high performers (blue) from scans 1 through 4. The figure reveals that there is no clear relationship between volume variation (column 1) and signal intensity (columns 2 and 3). The figure also demonstrates that signal intensity variation in this simplified region-based analysis is comparable to signal intensity variation in the more traditional analysis.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4587848&req=5

pone.0139453.g003: Line charts of Activation Extent and Intensity.Each row represents a component (IC-1 (a-c), IC-2 (d-f), IC-3 (g-i) and IC-4 (j-l)). The first column represents changes in the extent of significant activation for the IC on the left (purple) and right (green) from scan 1 through 4. The second (left hemisphere) and third (right hemisphere) columns compare group average percent signal change for each IC in low performers (red) and high performers (blue) from scans 1 through 4. The figure reveals that there is no clear relationship between volume variation (column 1) and signal intensity (columns 2 and 3). The figure also demonstrates that signal intensity variation in this simplified region-based analysis is comparable to signal intensity variation in the more traditional analysis.
Mentions: Whereas the brain maps display regional data for either the scan count or the signal during a scan, the line charts display group average signal change across all four scans. On the lower left the “group average signal” line chart compares high and low performers across the four scans and averaged across regions. This is very similar to the line charts in Fig 2 of the original analysis [19]. Although this division reflects the participant subgroups used in the original study, other participant subgroupings (e.g., normal vs. impaired, +genotype vs.–genotype, young vs. old) could be displayed in this manner. By applying the IC and LR filters, this dynamic line chart can be configured to display lines corresponding to the eight line charts in the original Fig 2 [19] (sans standard error bars) [See Fig 3]. The “regional group average signal” line chart supplements the “group average line chart” by comparing high and low performers across the four scans for each region. We can drill down into this chart to reveal novel patterns. For example, disabling all filters reveals exceptionally high regional signal in the right posterior superior temporal gyrus in low performers. Filtering by IC reveals that this signal is associated with IC-1. Filtering by region (e.g., STG_post) allows us to compare this exceptional signal in high and low performers to the homotopic region in the contralateral hemisphere. Given the role of the right posterior superior temporal gyrus in processing human voice [20], we can reasonably conclude that attention to human voice was an especially active focus of brain resources throughout the experiment.

Bottom Line: 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.

View Article: PubMed Central - PubMed

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

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.

No MeSH data available.


Related in: MedlinePlus