Identifying causal networks of neuronal sources from EEG/MEG data with the phase slope index: a simulation study
| dc.contributor.author | Ewald, Arne | |
| dc.contributor.author | Avarvand, Forooz Shahbazi | |
| dc.contributor.author | Nolte, Guido | |
| dc.date.accessioned | 2017-11-28T08:49:25Z | |
| dc.date.available | 2017-11-28T08:49:25Z | |
| dc.date.issued | 2013 | |
| dc.description | Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich. | de |
| dc.description | This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively. | en |
| dc.description.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. | en |
| dc.identifier.eissn | 0013-5585 | |
| dc.identifier.issn | 1862-278X | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/7196 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-6471 | |
| dc.language.iso | en | |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
| dc.subject.ddc | 610 Medizin und Gesundheit | |
| dc.subject.other | effective connectivity | en |
| dc.subject.other | electroencephalography | en |
| dc.subject.other | imaginary part of coherency | en |
| dc.subject.other | magnetoencephalography | en |
| dc.subject.other | source localization | en |
| dc.subject.other | volume conduction | en |
| dc.title | Identifying causal networks of neuronal sources from EEG/MEG data with the phase slope index: a simulation study | en |
| dc.type | Article | |
| dc.type.version | publishedVersion | |
| dcterms.bibliographicCitation.doi | 10.1515/bmt-2012-0028 | |
| dcterms.bibliographicCitation.issue | 2 | |
| dcterms.bibliographicCitation.journaltitle | Biomedical engineering = Biomedizinische Technik | |
| dcterms.bibliographicCitation.originalpublishername | De Gruyter | |
| dcterms.bibliographicCitation.originalpublisherplace | Berlin [u.a.] | |
| dcterms.bibliographicCitation.pageend | 178 | |
| dcterms.bibliographicCitation.pagestart | 165 | |
| dcterms.bibliographicCitation.volume | 58 | |
| tub.accessrights.dnb | domain | |
| tub.affiliation | Fak. 4 Elektrotechnik und Informatik::Inst. Softwaretechnik und Theoretische Informatik::FG Maschinelles Lernen | de |
| tub.affiliation.faculty | Fak. 4 Elektrotechnik und Informatik | de |
| tub.affiliation.group | FG Maschinelles Lernen | de |
| tub.affiliation.institute | Inst. Softwaretechnik und Theoretische Informatik | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin |
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