title: Consistency of EEG source localization and connectivity estimates.
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.
authors.affiliation: Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genova, Genova, Italy; Machine Learning Department, Technische Universität Berlin, Berlin, Germany. Electronic address: mahjoory86@gmail.com.
authors.forename: Keyvan
authors.initials: K
authors.lastname: Mahjoory
authors.affiliation: Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Neurophysics Group, Charité University Medicine Berlin, Berlin, Germany; Center for Cognition and Decision Making, National Research University Higher School of Economics, Russian Federation.
authors.forename: Vadim V
authors.initials: VV
authors.lastname: Nikulin
authors.affiliation: Institute of Psychology, University of Würzburg, Würzburg, Germany.
authors.forename: Loïc
authors.initials: L
authors.lastname: Botrel
authors.affiliation: Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, The Netherlands.
authors.forename: Klaus
authors.initials: K
authors.lastname: Linkenkaer-Hansen
authors.affiliation: Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genova, Genova, Italy.
authors.forename: Marco M
authors.initials: MM
authors.lastname: Fato
authors.affiliation: Machine Learning Department, Technische Universität Berlin, Berlin, Germany. Electronic address: stefan.haufe@tu-berlin.de.
authors.forename: Stefan
authors.initials: S
authors.lastname: Haufe
mesh_terms: D000513:Alpha Rhythm; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008959:Models, Neurological; D009434:Neural Pathways / Q000502:physiology; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio
keywords: Consistency; Electroencephalography (EEG); Forward/inverse modeling; Functional/effective connectivity; Reproducibility; Source localization