title: Task-Related Edge Density (TED)-A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain.
abstract: 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 that show differing levels of synchrony between two distinct task conditions 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 and an emotion processing 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.
authors.affiliation: University Hospital, Department of Biomedical Magnetic Resonance Imaging, University of Tübingen, Tübingen, Germany.|Magnetic Resonance Centre, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany.
authors.forename: Gabriele
authors.initials: G
authors.lastname: Lohmann
authors.affiliation: University Hospital, Department of Biomedical Magnetic Resonance Imaging, University of Tübingen, Tübingen, Germany.|Magnetic Resonance Centre, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany.
authors.forename: Johannes
authors.initials: J
authors.lastname: Stelzer
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.
authors.forename: Verena
authors.initials: V
authors.lastname: Zuber
authors.affiliation: Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany.
authors.forename: Tilo
authors.initials: T
authors.lastname: Buschmann
authors.affiliation: Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
authors.forename: Daniel
authors.initials: D
authors.lastname: Margulies
authors.affiliation: CIN Vision & Cognition Group, Centre for Integrative Neuroscience, Tübingen, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Bartels
authors.affiliation: University Hospital, Department of Biomedical Magnetic Resonance Imaging, University of Tübingen, Tübingen, Germany.|Magnetic Resonance Centre, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany.
authors.forename: Klaus
authors.initials: K
authors.lastname: Scheffler
mesh_terms: D000465:Algorithms; D001921:Brain / Q000098:blood supply / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D004644:Emotions; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D015233:Models, Statistical; D010100:Oxygen / Q000097:blood / Q000378:metabolism
chemical_list: D010100:Oxygen