Identifying causal networks of neuronal sources from EEG/MEG data with the phase slope index - a simulation study
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Identifying causal networks of neuronal sources from EEG/MEG data with the phase slope index - a simulation study. / Ewald, Arne; Avarvand, Forooz Shahbazi; Nolte, Guido.
in: BIOMED ENG-BIOMED TE, Jahrgang 58, Nr. 2, 01.04.2013, S. 165-78.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Identifying causal networks of neuronal sources from EEG/MEG data with the phase slope index - a simulation study
AU - Ewald, Arne
AU - Avarvand, Forooz Shahbazi
AU - Nolte, Guido
PY - 2013/4/1
Y1 - 2013/4/1
N2 - The investigation of functional neuronal synchronization has recently become a growing field of research. With high temporal resolution, electroencephalography and magnetoencephalography are well-suited measurement techniques to identify networks of interacting sources underlying the recorded data. The analysis of the data in terms of effective connectivity, nevertheless, contains intrinsic issues such as the problem of volume conduction and the non-uniqueness of the inverse solution. Here, we briefly introduce a series of existing methods assessing these problems. To determine the locations of interacting brain sources robust to volume conduction, all computations are solely based on the imaginary part of the cross-spectrum as a trustworthy source of information. Furthermore, we demonstrate the feasibility of estimating causal relationships of systems of neuronal sources with the phase slope index in realistically simulated data. Finally, advantages and drawbacks of the applied methodology are highlighted and discussed.
AB - The investigation of functional neuronal synchronization has recently become a growing field of research. With high temporal resolution, electroencephalography and magnetoencephalography are well-suited measurement techniques to identify networks of interacting sources underlying the recorded data. The analysis of the data in terms of effective connectivity, nevertheless, contains intrinsic issues such as the problem of volume conduction and the non-uniqueness of the inverse solution. Here, we briefly introduce a series of existing methods assessing these problems. To determine the locations of interacting brain sources robust to volume conduction, all computations are solely based on the imaginary part of the cross-spectrum as a trustworthy source of information. Furthermore, we demonstrate the feasibility of estimating causal relationships of systems of neuronal sources with the phase slope index in realistically simulated data. Finally, advantages and drawbacks of the applied methodology are highlighted and discussed.
KW - Algorithms
KW - Brain
KW - Brain Mapping
KW - Causality
KW - Computer Simulation
KW - Diagnosis, Computer-Assisted
KW - Electroencephalography
KW - Electroencephalography Phase Synchronization
KW - Humans
KW - Magnetoencephalography
KW - Models, Neurological
KW - Nerve Net
KW - Reproducibility of Results
KW - Sensitivity and Specificity
U2 - 10.1515/bmt-2012-0028
DO - 10.1515/bmt-2012-0028
M3 - SCORING: Journal article
C2 - 23435095
VL - 58
SP - 165
EP - 178
JO - BIOMED ENG-BIOMED TE
JF - BIOMED ENG-BIOMED TE
SN - 0013-5585
IS - 2
ER -