Visualization in Connectomics - Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization - page 225

Comparison of multiple FU maps can be done visually when displayed next to
each other, but this method is limited as humans are notoriously weak in spotting
visual differences in images. An alternative, which is more quantitative although it
still involves visual assessment to a certain degree, is to compute a mean FU map,
based upon the concept of graph averaging [ 19 ]. The mean of a set of input FUmaps
is defined in such a way that it not only represents the mean group coherence during
a certain task or condition, but also to some extent displays individual variations
in brain activity. The definition of a mean FU map relies on a graph dissimilarity
measure that takes into account both node positions and node or edge attributes.
A visualization of the mean FU map is used with a visual representation of the
frequency of occurrence of nodes and edges in the input FUs. This makes it possible
to investigate which brain regions are more commonly involved in a certain task, by
analyzing the occurrence of an FU of the mean graph in the input FUs.
In [ 19 ] the graph averaging method was applied to the analysis of EEG coherence
networks in two case studies, one on mental fatigue and one on patients with corti-
cobasal ganglionic degeneration. An extension of the method to resting state fMRI
data was presented in [ 18 ].
21.9 Conclusions
There is currently great scientific interest in connectomics, as it is believed to be
an important prerequisite for understanding brain function. As much of the data for
obtaining neural connectivity is image-based, visualization techniques are indispens-
able. Great effort has been put recently into extraction of connectivity information
from images, integration of multimodal information into reference systems, and
visual analysis of such data and systems at different scales. These efforts will need to
be intensified in the future, as data is being produced at a much larger scale, also by
new imaging modalities. New methods to integrate this data across modalities and
scales to attain the ultimate goal, a description of the human connectome, will be the
main challenge for visualization in connectomics.
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