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  Consistency of EEG source localization and connectivity estimates

Mahjoory, K., Nikulin, V. V., Botrel, L., Linkenkaer-Hansen, K., Fato, M. M., & Haufe, S. (2017). Consistency of EEG source localization and connectivity estimates. NeuroImage, 152, 590-601. doi:10.1016/j.neuroimage.2017.02.076.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002D-2929-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-BB3F-5
Genre: Journal Article

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 Creators:
Mahjoory, Keyvan1, 2, Author
Nikulin, Vadim V.3, 4, 5, Author              
Botrel, Loïc6, Author
Linkenkaer-Hansen, Klaus7, Author
Fato, Marco M.1, Author
Haufe, Stefan2, Author
Affiliations:
1Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genova, Italy, ou_persistent22              
2Department of Machine Learning, TU Berlin, Germany, ou_persistent22              
3Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
4Neurophysics Group, Department of Neurology, Charité University Medicine Berlin, Germany, ou_persistent22              
5Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia, ou_persistent22              
6Department of Psychology, Julius Maximilian University, Würzburg, Germany, ou_persistent22              
7Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), VU University Medical Center, Amsterdam, the Netherlands, ou_persistent22              

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Free keywords: Electroencephalography (EEG); Source localization; Functional/effective connectivity; Forward/inverse modeling; Consistency; Reproducibility
 Abstract: As the EEG inverse problem does not have a unique solution, the sources reconstructed from EEG and their connectivity properties depend on forward and inverse modeling parameters such as the choice of an anatomical template and electrical model, prior assumptions on the sources, and further implementational details. In order to use source connectivity analysis as a reliable research tool, there is a need for stability across a wider range of standard estimation routines. Using resting state EEG recordings of N=65 participants acquired within two studies, we present the first comprehensive assessment of the consistency of EEG source localization and functional/effective connectivity metrics across two anatomical templates (ICBM152 and Colin27), three electrical models (BEM, FEM and spherical harmonics expansions), three inverse methods (WMNE, eLORETA and LCMV), and three software implementations (Brainstorm, Fieldtrip and our own toolbox). Source localizations were found to be more stable across reconstruction pipelines than subsequent estimations of functional connectivity, while effective connectivity estimates where the least consistent. All results were relatively unaffected by the choice of the electrical head model, while the choice of the inverse method and source imaging package induced a considerable variability. In particular, a relatively strong difference was found between LCMV beamformer solutions on one hand and eLORETA/WMNE distributed inverse solutions on the other hand. We also observed a gradual decrease of consistency when results are compared between studies, within individual participants, and between individual participants. In order to provide reliable findings in the face of the observed variability, additional simulations involving interacting brain sources are required. Meanwhile, we encourage verification of the obtained results using more than one source imaging procedure.

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Language(s): eng - English
 Dates: 2017-01-262016-08-312017-02-242017-03-122017-05-15
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2017.02.076
PMID: 28300640
Other: Epub 2017
 Degree: -

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Project name : Hyperscanning 2.0 Analyses of Multimodal Neuroimaging Data: Concept, Methods and Applications / HYPERSCANNING 2.0
Grant ID : 625991
Funding program : Funding Programme 7
Funding organization : European Commission (EC)
Project name : -
Grant ID : -
Funding program : Russian Academic Excellence Project 5–100
Funding organization : Ministry of Science and Higher Education of the Russian Federation

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Title: NeuroImage
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 152 Sequence Number: - Start / End Page: 590 - 601 Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166