We treat every analysis result as a functional clustering of voxels mapped into brain space and employ visualization techniques to allow the user to interactively explore the similarity and differences between the different datasets. This can provide valuable insight into the data or the analysis methodologies being studied. Thus, the tool can be used as a visualization interface of a data mining. Documents: Advanced Search Include Citations.
Authors: Advanced Search Include Citations. Abstract fMRI is an imaging technique that is used to understand brain functionality. The usefulness of such a visualization depends on how effective it conveys information compared to standard text, and if the visualization format requires specialized and limited distributed programs for rendering and interaction the impact may be small.
Manual creation of these visualizations is infeasible, — the visualization should be constructed automatically from description of the study, e. In related visualizations, some workflow management systems display the processing flow graphically Dinov et al. Figure 8. Screenshot of a VRML rendering seeking to convey parts of the information surrounding a neuroimaging study: 3D icons for funding, research organization, researchers, software, subjects, and scanner placed in a torus.
When neuroimaging studies get reported in articles the relationships between the articles can be turned in to visualizations. Many types of visualizations exist and many relationships may be revealed: Between terms, concepts, citations to and from articles as well as between authors, cited authors and cited journals.
The visualizations are of course not limited to articles only in neuroimaging, see, e. For an example in neuroscience Naud et al. One of their illustrations visualized the relationship between poster sessions in the Society for Neuroscience meeting together with words from the abstracts in the sessions.
Figure 9. Each yellow dot is a cluster of articles and words in the article. The four words with highest load on each cluster are listed. Coordinate-based meta-analysis and text mining can be combined to form visualizations, see Figure 10 and Nielsen et al.
The workflow for constructing the visualization in the figure involves the setup of a matrix describing the words in the abstract of papers and the construction of another matrix from kernel density estimation with the coordinates in each paper. After non-negative matrix factorization each individual factor may be rendered in 3D and associated with words from the abstract, e. Figure A Corner cube visualization with labeled brain areas. B Automatically generated legend with words from the text mining of abstracts.
Based on a corpus of articles published between and in the journal NeuroImage we could plot cited authors and cited journals in 2D. Here, the workflow involves specialized algorithms that extract citations and the use of matrix computations, particularly singular value decomposition, for multidimensional scaling-like projection of the data onto 2D. Figure 12 shows a larger bullseye plot on coauthors in the NeuroImage corpus.
Authors near the center, such as Friston and Dolan, have high network degrees, which here corresponds to the number of authored articles Nielsen, Visualization of data mining result of journal co-citation analysis with singular value decomposition on citation data from NeuroImage.
Coauthor bullseye plot target diagram with data from NeuroImage — A line between two authors indicates that they co-wrote a paper.
The concentric circles indicate the number of articles written by the author in the corpus. The Brede Toolbox automatically constructs similar, albeit smaller, bullseye visualizations for each author represented in the Brede Database author ontology. These are available on the Web. The well-tested and widely used GraphViz package provides spatial graph layout for a given network Gansner and North, At one point the PubGene Web service used GraphViz in a large-scale application for displaying relations between genes based on literature in PubMed Jenssen et al.
GraphViz layouts graphs for the Web presentation of the Brede Database. These graphs display the brain function and brain region ontologies, e. Our workflow with the Brede Toolbox involves extraction of the ontology from Brede Database XML files, construction of a file with the graph that GraphViz reads, invoking GraphViz for generation of an image file, and then finally construction of the Web page with the image file embedded.
GraphViz can construct HTML image maps so the nodes in the graph image are associated with clickable hyperlinks. On the final Web page a reader may navigate the brain region and brain function ontologies by clicking on the nodes in the graph. The Brede Toolbox can also use GraphViz for layout of other types of data that can be described as a network, e.
A number of journal Web sites use plots called Citation map in the style of GraphViz for visualizing in- and out-going citations of each article, see, e. ISI Web of Knowledge provides a Java applet to render their citation information with a similar topology.
With the Brede Toolbox we are able to build a workflow with extraction of data from the Brede Database, automated data mining and visualizations. The automated procedures generate publicly accessible Web pages with interactive visualizations. An advantage of the automated procedure is that little human intervention is required to update the visualizations as new data is added to the database.
The visualizations can display not only spatial neuroimages, but for example also results from text mining, and visualization can take place across the Internet with data originating on one server and displayed on another.
The Brede Database represents just a small fragment of the results from the published literature Derrfuss and Mar, However, no universal database exist for coordinates from functional neuroimaging. To gain a higher degree of coverage future work may attempt to aggregate data from different databases for combined visualizations.
Since typical meta-analytic data is anonymous and small compared to a typical neuroimaging study , it is easier to share such data and we may see collaborative Internet-based analyses and visualizations. Our wiki for personality genetics Figure 1 is such a collaborative system. Building a collaborative system for neuroimaging data requires more effort, and in the Brede Wiki only simple visualizations are presently available. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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