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  Analytical operations relate structural and functional connectivity in the brain

Saggio, M. L., Ritter, P., & Jirsa, V. K. (2016). Analytical operations relate structural and functional connectivity in the brain. PLoS One, 11(8): e0157292. doi:10.1371/journal.pone.0157292.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-A73C-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-1DDD-5
Genre: Journal Article

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 Creators:
Saggio, Maria Luisa1, Author
Ritter, Petra2, 3, 4, 5, Author              
Jirsa, Viktor K.1, Author
Affiliations:
1Institut de Neurosciences des Systèmes, Aix-Marseille Université, France, ou_persistent22              
2Minerva Research Group Brain Modes, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_751546              
3Department of Neurology, Charité University Medicine Berlin, Germany, ou_persistent22              
4Bernstein Center for Computational Neuroscience, Berlin, Germany, ou_persistent22              
5Berlin School of Mind and Brain, Humboldt University Berlin, Germany, ou_persistent22              

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 Abstract: Resting-state large-scale brain models vary in the amount of biological elements they incorporate and in the way they are being tested. One might expect that the more realistic the model is, the closer it should reproduce real functional data. It has been shown, instead, that when linear correlation across long BOLD fMRI time-series is used as a measure for functional connectivity (FC) to compare simulated and real data, a simple model performs just as well, or even better, than more sophisticated ones. The model in question is a simple linear model, which considers the physiological noise that is pervasively present in our brain while it diffuses across the white-matter connections, that is structural connectivity (SC). We deeply investigate this linear model, providing an analytical solution to straightforwardly compute FC from SC without the need of computationally costly simulations of time-series. We provide a few examples how this analytical solution could be used to perform a fast and detailed parameter exploration or to investigate resting-state non-stationarities. Most importantly, by inverting the analytical solution, we propose a method to retrieve information on the anatomical structure directly from functional data. This simple method can be used to complement or guide DTI/DSI and tractography results, especially for a better assessment of inter-hemispheric connections, or to provide an estimate of SC when only functional data are available.

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Language(s): eng - English
 Dates: 2015-05-252016-05-262016-08-18
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1371/journal.pone.0157292
PMID: 27536987
PMC: PMC4990451
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 (8) Sequence Number: e0157292 Start / End Page: - Identifier: ISSN: 1932-6203
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000277850