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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, Vol. 58, No. 2, 01.04.2013, p. 165-78.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Ewald, A, Avarvand, FS & Nolte, G 2013, 'Identifying causal networks of neuronal sources from EEG/MEG data with the phase slope index - a simulation study', BIOMED ENG-BIOMED TE, vol. 58, no. 2, pp. 165-78. https://doi.org/10.1515/bmt-2012-0028

APA

Ewald, A., Avarvand, F. S., & Nolte, G. (2013). Identifying causal networks of neuronal sources from EEG/MEG data with the phase slope index - a simulation study. BIOMED ENG-BIOMED TE, 58(2), 165-78. https://doi.org/10.1515/bmt-2012-0028

Vancouver

Ewald A, Avarvand FS, Nolte G. Identifying causal networks of neuronal sources from EEG/MEG data with the phase slope index - a simulation study. BIOMED ENG-BIOMED TE. 2013 Apr 1;58(2):165-78. https://doi.org/10.1515/bmt-2012-0028

Bibtex

@article{ea3583bf17fe459c88cf81127c947123,
title = "Identifying causal networks of neuronal sources from EEG/MEG data with the phase slope index - a simulation study",
abstract = "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.",
keywords = "Algorithms, Brain, Brain Mapping, Causality, Computer Simulation, Diagnosis, Computer-Assisted, Electroencephalography, Electroencephalography Phase Synchronization, Humans, Magnetoencephalography, Models, Neurological, Nerve Net, Reproducibility of Results, Sensitivity and Specificity",
author = "Arne Ewald and Avarvand, {Forooz Shahbazi} and Guido Nolte",
year = "2013",
month = apr,
day = "1",
doi = "10.1515/bmt-2012-0028",
language = "English",
volume = "58",
pages = "165--78",
journal = "BIOMED ENG-BIOMED TE",
issn = "0013-5585",
publisher = "Walter de Gruyter GmbH & Co. KG",
number = "2",

}

RIS

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 -