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  Task-related edge density (TED): A new method for revealing dynamic network formation in fMRI data of the human brain

Lohmann, G., Stelzer, J., Zuber, V., Buschmann, T., Margulies, D. S., Bartels, A., et al. (2016). Task-related edge density (TED): A new method for revealing dynamic network formation in fMRI data of the human brain. PLoS One, 11(6): e0158185. doi:10.1371/journal.pone.0158185.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-11EB-A Version Permalink: http://hdl.handle.net/21.11116/0000-0003-1E49-B
Genre: Journal Article

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 Creators:
Lohmann, Gabriele1, 2, Author              
Stelzer, Johannes1, 2, Author              
Zuber, Verena3, Author
Buschmann, Tilo4, Author              
Margulies, Daniel S.5, Author              
Bartels, Andreas6, Author
Scheffler, Klaus1, 2, Author
Affiliations:
1Department of Biomedical Magnetic Resonance, University Hospital Tübingen, Germany, ou_persistent22              
2Department of High-field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, ou_persistent22              
3European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom, ou_persistent22              
4Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany, ou_persistent22              
5Max Planck Research Group Neuroanatomy and Connectivity, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_1356546              
6Vision & Cognition Group, Center for Integrative Neuroscience, Eberhard Karls University Tübingen, Germany, ou_persistent22              

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 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.

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Language(s): eng - English
 Dates: 2016-01-182016-06-102016-06-24
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1371/journal.pone.0158185
PMID: 27341204
PMC: PMC4920409
Other: eCollection 2016
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Title: PLoS One
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 11 (6) Sequence Number: e0158185 Start / End Page: - Identifier: ISSN: 1932-6203
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000277850