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  The missing role of gray matter in studying brain controllability

Jamalabadi, H., Zuberer, A., Kumar, V., Li, M., Alizadeh, S., Moradi, A., et al. (2021). The missing role of gray matter in studying brain controllability. Network Neuroscience, 5(1), 198-210. doi:10.1162/netn_a_00174.

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Jamalabadi, H, Author              
Zuberer, A, Author
Kumar, VJ1, 2, Author              
Li, M1, 2, Author              
Alizadeh, S, Author              
Moradi, AA, Author
Gaser, C, Author
Esterman, M, Author
Walter, M1, 2, Author              
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              

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 Abstract: Brain controllability properties are normally derived from the white matter fiber tracts in which the neural substrate of the actual energy consumption, namely the gray matter, has been widely ignored. Here, we study the relationship between gray matter volume of regions across the whole cortex and their respective control properties derived from the structural architecture of the white matter fiber tracts. The data suggests that the ability of white fiber tracts to exhibit control at specific nodes not only depends on the connection strength of the structural connectome but additionally depends on gray matter volume at the host nodes. Our data indicate that connectivity strength and gray matter volume interact with respect to the brain's control properties. Disentangling effects of the regional gray matter volume and connectivity strength, we found that frontal and sensory areas play crucial roles in controllability. Together these results suggest that structural and regional properties of the white matter and gray matter provide complementary information in studying the control properties of the intrinsic structural and functional architecture of the brain.

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 Dates: 2020-042021-032021-05
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: DOI: 10.1162/netn_a_00174
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Title: Network Neuroscience
Source Genre: Journal
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Publ. Info: Cambridge, MA : MIT Press
Pages: - Volume / Issue: 5 (1) Sequence Number: - Start / End Page: 198 - 210 Identifier: ISSN: 2472-1751
CoNE: https://pure.mpg.de/cone/journals/resource/2472-1751