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  How do parcellation size and short-range connectivity affect dynamics in large-scale brain network models?

Proix, T., Spiegler, A., Schirner, M., Rothmeier, S., Ritter, P., & Jirsa, V. K. (2016). How do parcellation size and short-range connectivity affect dynamics in large-scale brain network models? NeuroImage, 142, 135-149. doi:10.1016/j.neuroimage.2016.06.016.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-A737-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-1A6B-9
Genre: Journal Article

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 Creators:
Proix, Timothée1, Author
Spiegler, Andreas1, Author
Schirner, Michael2, Author
Rothmeier, Simon2, Author
Ritter, Petra2, 3, Author              
Jirsa, Viktor K.1, Author
Affiliations:
1Institut de Neurosciences des Systèmes, Aix-Marseille Université, France, ou_persistent22              
2Department of Neurology, Charité University Medicine Berlin, Germany, ou_persistent22              
3Minerva Research Group Brain Modes, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_751546              

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Free keywords: Diffusion; Functional and structural MRI; The virtual brain; Large-scale brain network models; Parcellations; Short-range connectivity; SCRIPTS
 Abstract: Recent efforts to model human brain activity on the scale of the whole brain rest on connectivity estimates of large-scale networks derived from diffusion magnetic resonance imaging (dMRI). This type of connectivity describes white matter fiber tracts. The number of short-range cortico-cortical white-matter connections is, however, underrepresented in such large-scale brain models. It is still unclear on the one hand, which scale of representation of white matter fibers is optimal to describe brain activity on a large-scale such as recorded with magneto- or electroencephalography (M/EEG) or functional magnetic resonance imaging (fMRI), and on the other hand, to which extent short-range connections that are typically local should be taken into account. In this article we quantified the effect of connectivity upon large-scale brain network dynamics by (i) systematically varying the number of brain regions before computing the connectivity matrix, and by (ii) adding generic short-range connections. We used dMRI data from the Human Connectome Project. We developed a suite of preprocessing modules called SCRIPTS to prepare these imaging data for The Virtual Brain, a neuroinformatics platform for large-scale brain modeling and simulations. We performed simulations under different connectivity conditions and quantified the spatiotemporal dynamics in terms of Shannon Entropy, dwell time and Principal Component Analysis. For the reconstructed connectivity, our results show that the major white matter fiber bundles play an important role in shaping slow dynamics in large-scale brain networks (e.g. in fMRI). Faster dynamics such as gamma oscillations (around 40 Hz) are sensitive to the short-range connectivity if transmission delays are considered.

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Language(s): eng - English
 Dates: 2015-11-062016-06-092016-06-302016-11-15
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2016.06.016
PMID: 27480624
Other: Epub 2016
 Degree: -

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Title: NeuroImage
Source Genre: Journal
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Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 142 Sequence Number: - Start / End Page: 135 - 149 Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166