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  Functional parcellation using time courses of instantaneous connectivity

van Oort, E., Mennes, M., Navarro Schröder, T., Kumar, V., Zaragoza Jimenez, N., Grodd, W., et al. (2018). Functional parcellation using time courses of instantaneous connectivity. NeuroImage, 170, 31-40. doi:10.1016/j.neuroimage.2017.07.027.

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van Oort, ESB, Author
Mennes, M, Author
Navarro Schröder, T, Author
Kumar, VJ1, Author                 
Zaragoza Jimenez, NI, Author
Grodd, W1, Author           
Doeller, CF, Author
Beckmann, CF, Author
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1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              

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 Abstract: Functional neuroimaging studies have led to understanding the brain as a collection of spatially segregated functional networks. It is thought that each of these networks is in turn composed of a set of distinct sub-regions that together support each network's function. Considering the sub-regions to be an essential part of the brain's functional architecture, several strategies have been put forward that aim at identifying the functional sub-units of the brain by means of functional parcellations. Current parcellation strategies typically employ a bottom-up strategy, creating a parcellation by clustering smaller units. We propose a novel top-down parcellation strategy, using time courses of instantaneous connectivity to subdivide an initial region of interest into sub-regions. We use split-half reproducibility to choose the optimal number of sub-regions.
We apply our Instantaneous Connectivity Parcellation (ICP) strategy on high-quality resting-state FMRI data, and demonstrate the ability to generate parcellations for thalamus, entorhinal cortex, motor cortex, and subcortex including brainstem and striatum. We evaluate the subdivisions against available cytoarchitecture maps to show that our parcellation strategy recovers biologically valid subdivisions that adhere to known cytoarchitectural features.

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 Dates: 2017-072018-04
 Publication Status: Issued
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 Identifiers: DOI: 10.1016/j.neuroimage.2017.07.027
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Title: NeuroImage
Source Genre: Journal
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Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 170 Sequence Number: - Start / End Page: 31 - 40 Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166