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  Subject specificity of the correlation between large-scale structural and functional connectivity

Zimmermann, J., Griffiths, J., Schirner, M., Ritter, P., & McIntosh, A. R. (2018). Subject specificity of the correlation between large-scale structural and functional connectivity. Network Neuroscience, 3(1), 90-106. doi:10.1162/netn_a_00055.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-10BD-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-A59F-0
Genre: Journal Article

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 Creators:
Zimmermann, J.1, Author
Griffiths, J.1, Author
Schirner, M.1, Author
Ritter, Petra2, 3, 4, 5, Author              
McIntosh, A. R.1, Author
Affiliations:
1Baycrest Health Sciences, Rotman Research Institute, University of Toronto, ON, Canada, ou_persistent22              
2Department of Neurology, Charité University Medicine Berlin, Germany, ou_persistent22              
3Berlin School of Mind and Brain, Humboldt University Berlin, Germany, ou_persistent22              
4Berlin Institute of Health (BIH), Germany, ou_persistent22              
5Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              

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Free keywords: Functional connectivity; Individual variability; Structural connectivity
 Abstract: Structural connectivity (SC), the physical pathways connecting regions in the brain, and functional connectivity (FC), the temporal coactivations, are known to be tightly linked. However, the nature of this relationship is still not understood. In the present study, we examined this relation more closely in six separate human neuroimaging datasets with different acquisition and preprocessing methods. We show that using simple linear associations, the relation between an individual's SC and FC is not subject specific for five of the datasets. Subject specificity of SC-FC fit is achieved only for one of the six datasets, the multimodal Glasser Human Connectome Project (HCP) parcellated dataset. We show that subject specificity of SC-FC correspondence is limited across datasets due to relatively small variability between subjects in SC compared with the larger variability in FC.

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Language(s): eng - English
 Dates: 2017-10-302018-04-042018-10-23
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1162/netn_a_00055
PMID: 30793075
PMC: PMC6326745
Other: eCollection 2019
 Degree: -

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Project name : -
Grant ID : 8344 ; 10276
Funding program : -
Funding organization : John von Neumann Institute for Computing
Project name : -
Grant ID : RGPIN-2017-06793
Funding program : -
Funding organization : Natural Sciences and Engineering Research Council of Canada (NSERC)
Project name : US-German Collaboration in Computational Neuroscience
Grant ID : 01GQ1504A
Funding program : -
Funding organization : German Federal Ministry of Education and Research (BMBF)
Project name : Bernstein Focus State Dependencies of Learning
Grant ID : 01GQ0971-5
Funding program : -
Funding organization : German Federal Ministry of Education and Research (BMBF)
Project name : Personalized whole brain simulations: linking connectomics and dynamics in the human brain / BrainModes
Grant ID : 683049
Funding program : Horizon 2020
Funding organization : European Commission (EC)
Project name : -
Grant ID : -
Funding program : Private Exzellenzinitiative Johanna Quandt
Funding organization : Stiftung Charité
Project name : -
Grant ID : -
Funding program : Johanna Quandt Professorship for Brain Simulation
Funding organization : Berlin Instititute of Health (BIH)

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Title: Network Neuroscience
Source Genre: Journal
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Publ. Info: Cambridge, MA : MIT Press
Pages: - Volume / Issue: 3 (1) Sequence Number: - Start / End Page: 90 - 106 Identifier: ISSN: 2472-1751
CoNE: https://pure.mpg.de/cone/journals/resource/2472-1751