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

Jakobsen, E., Liem, F., Klados, M., Bayrak, S., Petrides, M., & Margulies, D. S. (2018). Automated individual-level parcellation of Broca's region based on functional connectivity. NeuroImage, 170, 41-53. doi:10.1016/j.neuroimage.2016.09.069.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-B114-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-4F85-E
Genre: Journal Article

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 Creators:
Jakobsen, Estrid1, Author              
Liem, Franz1, Author              
Klados, Manousos1, Author              
Bayrak, Seyma1, Author              
Petrides, Michael2, 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              
2Cognitive Neuroscience Unit, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada, ou_persistent22              

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 Abstract: Broca's region can be subdivided into its constituent areas 44 and 45 based on established differences in connectivity to superior temporal and inferior parietal regions. The current study builds on our previous work manually parcellating Broca's area on the individual-level by applying these anatomical criteria to functional connectivity data. Here we present an automated observer-independent and anatomy-informed parcellation pipeline with comparable precision to the manual labels at the individual-level. The method first extracts individualized connectivity templates of areas 44 and 45 by assigning to each surface vertex within the ventrolateral frontal cortex the partial correlation value of its functional connectivity to group-level templates of areas 44 and 45, accounting for other template connectivity patterns. To account for cross-subject variability in connectivity, the partial correlation procedure is then repeated using individual-level network templates, including individual-level connectivity from areas 44 and 45. Each node is finally labeled as area 44, 45, or neither, using a winner-take-all approach. The method also incorporates prior knowledge of anatomical location by weighting the results using spatial probability maps. The resulting area labels show a high degree of spatial overlap with the gold-standard manual labels, and group-average area maps are consistent with cytoarchitectonic probability maps of areas 44 and 45. To facilitate reproducibility and to demonstrate that the method can be applied to resting-state fMRI datasets with varying acquisition and preprocessing parameters, the labeling procedure is applied to two open-source datasets from the Human Connectome Project and the Nathan Kline Institute Rockland Sample. While the current study focuses on Broca's region, the method is adaptable to parcellate other cortical regions with distinct connectivity profiles.

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Language(s): eng - English
 Dates: 2016-09-292016-09-302018-04-15
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2016.09.069
PMID: 27693796
Other: Epub 2016
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Project name : -
Grant ID : 1U54MH091657
Funding program : -
Funding organization : National Institutes of Health (NIH)
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Funding organization : McDonnell Center for Systems Neuroscience at Washington University
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Grant ID : -
Funding program : International Neuroimaging Data-sharing Initiative
Funding organization : Child Mind Institute
Project name : -
Grant ID : P2ZHP1_155200
Funding program : -
Funding organization : Swiss National Science Foundation

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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: 41 - 53 Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166