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  Task-induced edge density as a marker for dynamic network formation in fMRI

Lohmann, G., Stelzer, J., Buschmann, T., Zuber, V., Margulies, D., Bartels, A., et al. (2015). Task-induced edge density as a marker for dynamic network formation in fMRI. Poster presented at 45th Annual Meeting of the Society for Neuroscience (Neuroscience 2015), Chicago, IL, USA.

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http://www.sfn.org/am2015/ (Publisher version)
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Lohmann, G1, Author           
Stelzer, J1, Author           
Buschmann, T, Author
Zuber, V, Author
Margulies, D, Author
Bartels, A2, Author           
Scheffler, K1, Author           
Affiliations:
1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              
2Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              

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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, in the past twenty years, task-based fMRI studies have primarily focused on signal amplitude changes or connectivity related to a few selected nodes. Shifting focus away from signal amplitudes or constraining connectivity patterns of a few selected nodes, we propose an alternative view on fMRI data analysis by considering large-scale, task-induced synchronization networks. Networks consist of nodes and edges connecting them, where nodes in our method correspond to voxels in fMRI data, and the weight of an edge between any two voxels is determined via task-induced changes in dynamic synchronization between their respective times series. Based on these definitions, we developed a new data analysis algorithm that is designed to identify time series of voxels in an fMRI image that collectively synchronize in response to a task. At the heart of our approach is the concept of spatially localized and task-induced edge density motivating us to call this algorithm "TED" (Task induced Edge Density). In short, TED identifies edges in a brain network that differentially respond in unison to a task onset and that occur in dense packs of edges with similar responses to tasks. We found TED to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical approaches such as the local false discovery rate (local fdr). A major advantage of TED compared to other network-based methods is that it does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. Because its conceptual basis is task-induced synchronization it does not depend on a hemodynamic response model. We applied TED to task-based fMRI data provided by the Human Connectome Project focusing on the motor, social recognition and working memory tasks. In all cases, TED identified several task-specific, large-scale patterns of synchronization. We conclude that the new TED method provides us with an entirely new window into the immense complexity of human brain function.

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 Dates: 2015-10-21
 Publication Status: Published in print
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 Identifiers: URI: http://www.sfn.org/am2015/
BibTex Citekey: LohmannSBZMBS2015
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Title: 45th Annual Meeting of the Society for Neuroscience (Neuroscience 2015)
Place of Event: Chicago, IL, USA
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Title: 45th Annual Meeting of the Society for Neuroscience (Neuroscience 2015)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: 830.12 Start / End Page: - Identifier: -