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  Subdivision of Broca's region based on individual-level functional connectivity

Jakobsen, E., Boettger, J., Bellec, P., Geyer, S., Ruebsamen, R., Petrides, M., et al. (2016). Subdivision of Broca's region based on individual-level functional connectivity. European Journal of Neuroscience, 43(4), 561-571. doi:10.1111/ejn.13140.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0029-22E8-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-19E5-F
Genre: Journal Article

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 Creators:
Jakobsen, Estrid1, Author              
Boettger, Joachim1, Author
Bellec, Pierre2, Author
Geyer, Stefan3, Author              
Ruebsamen, Rudolf4, Author
Petrides, Michael5, Author
Margulies, Daniel S.1, Author              
Affiliations:
1Max Planck Research Group Neuroanatomy and Connectivity, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_1356546              
2Centre de recherche de l'Institut universitaire de gériatrie de Montréal, QC, Canada, ou_persistent22              
3Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_2205649              
4University of Leipzig, Germany, ou_persistent22              
5Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada, ou_persistent22              

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Free keywords: fMRI; Neuroimaging; Cortical; Parcellation; Language
 Abstract: Broca's region is comprised of two adjacent cytoarchitectonic areas, 44 and 45, which have distinct connectivity to superior temporal and inferior parietal regions in both macaque monkeys and humans. The current study aimed to make use of prior knowledge of sulcal anatomy and resting-state functional connectivity, together with a novel visualization technique, to manually parcellate areas 44 and 45 in individual brains in vivo. 101 resting-state functional magnetic resonance imaging datasets were used from the Human Connectome Project. Left-hemisphere surface-based correlation matrices were computed and visualized in brainGL. By observing differences in the connectivity patterns of neighboring nodes, areas 44 and 45 were manually parcellated in individual brains, and then compared at the group-level. Additionally, the manual labeling approach was compared to parcellation results based on several data-driven clustering techniques. Areas 44 and 45 could be clearly distinguished from each other in all individuals and the manual segmentation method demonstrated high test-retest reliability. Group−-level probability maps of the areas 44 and 45 showed spatial consistency across individuals and correspond well to cytoarchitectonic probability maps. Group-level connectivity maps were consistent with previous studies showing distinct connectivity patterns of areas 44 and 45. Data-driven parcellation techniques produced clusters with varying degrees of spatial overlap with the manual labels, indicating the need for further investigation and validation of machine learning cortical segmentation approaches. The current study provides a reliable method for individual-level cortical parcellation that could be applied to regions distinguishable by even the most subtle differences in patterns of functional connectivity.

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Language(s): eng - English
 Dates: 2015-09-082015-11-172016-02-142016-02
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1111/ejn.13140
PMID: 26613367
 Degree: -

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Title: European Journal of Neuroscience
  Other : Eur. J. Neurosci
Source Genre: Journal
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Pages: - Volume / Issue: 43 (4) Sequence Number: - Start / End Page: 561 - 571 Identifier: ISSN: 0953-816X
CoNE: https://pure.mpg.de/cone/journals/resource/954925575988