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  Probing neural networks for dynamic switches of communication pathways

Finger, H., Gast, R., Gerloff, C., Engel, A. K., & König, P. (2019). Probing neural networks for dynamic switches of communication pathways. PLoS Computational Biology, 15(12): e1007551. doi:10.1371/journal.pcbi.1007551.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0005-3B3C-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-A359-F
Genre: Journal Article

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 Creators:
Finger, Holger 1, Author
Gast, Richard1, 2, Author              
Gerloff, Christian 1, Author
Engel, Andreas K. 1, Author
König, Peter 1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Methods and Development Group MEG and Cortical Networks, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205650              

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 Abstract: Dynamic communication and routing play important roles in the human brain to facilitate flexibility in task solving and thought processes. Here, we present a network perturbation methodology that allows to investigate dynamic switching between different network pathways based on phase offsets between two external oscillatory drivers. We apply this method in a computational model of the human connectome with delay-coupled neural masses. To analyze dynamic switching of pathways, we define four new metrics that measure dynamic network response properties for pairs of stimulated nodes. Evaluating these metrics for all network pathways, we found a broad spectrum of pathways with distinct dynamic properties and switching behaviors. Specifically, we found that 60.1% of node pairs can switch their communication from one pathway to another depending on their phase offsets. This indicates that phase offsets and coupling delays play an important computational role for the dynamic switching between communication pathways in the brain.

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Language(s): eng - English
 Dates: 2019-03-182019-11-182019-12-16
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1007551
PMID: 31841504
PMC: PMC6936858
Other: eCollection 2019
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Project name : Funktionelle Kopplung neuronaler Aktivität im ZNS / SFB 936
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Funding organization : Deutsche Forschungsgemeinschaft (DFG)
Project name : Crossmodales Lernen: Adaptivität, Prädiktion und Interaktion / SFB/TRR 169
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Funding organization : Deutsche Forschungsgemeinschaft (DFG)
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Funding organization : Studienstiftung des Deutschen Volkes

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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 15 (12) Sequence Number: e1007551 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1