  The formation of transient networks in response to external stimuli or as a
reflection of internal cognitive processes is a hallmark of human brain
function. However, its identification in fMRI data of the human brain is
notoriously difficult. Here we propose a new method of fMRI data analysis that
tackles this problem by considering large-scale, task-related synchronisation
networks. Networks consist of nodes and edges connecting them, where nodes
correspond to voxels in fMRI data, and the weight of an edge is determined via
task-related changes in dynamic synchronisation between their respective times
series. Based on these definitions, we developed a new data analysis algorithm
that identifies edges in a brain network that differentially respond in unison
to a task onset and that occur in dense packs with similar characteristics.
Hence, we call this approach "Task-related Edge Density" (TED). TED proved to
be a very strong marker for dynamic network formation that easily lends itself
to statistical analysis using large scale statistical inference. A major
advantage of TED compared to other methods is that it does not depend on any
specific hemodynamic response model, and it also does not require a
presegmentation of the data for dimensionality reduction as it can handle large
networks consisting of tens of thousands of voxels. We applied TED to fMRI data
of a fingertapping task provided by the Human Connectome Project. TED revealed
network-based involvement of a large number of brain areas that evaded
detection using traditional GLM-based analysis. We show that our proposed
method provides an entirely new window into the immense complexity of human
brain function.
