Abstract
For the past decade, the detection and quantification of interactions within and between physiological networks has become a priority-in-common between the fields of biomedicine and computer science. Prominent examples are the interaction analysis of brain networks and of the cardiovascular-respiratory system. The aim of the study is to show how and to what extent results from time-variant partial directed coherence analysis are influenced by some basic estimator and data parameters. The impacts of the Kalman filter settings, the order of the autoregressive (AR) model, signal-to-noise ratios, filter procedures and volume conduction were investigated. These systematic investigations are based on data derived from simulated connectivity networks and were performed using a Kalman filter approach for the estimation of the timevariant multivariate AR model. Additionally, the influence of electrooculogram artefact rejection on the significance and dynamics of interactions in 29 channel electroencephalography recordings, derived from a photic driving experiment, is demonstrated. For artefact rejection, independent component analysis was used. The study provides rules to correctly apply particular methods that will aid users to achieve more reliable interpretations of the results.
| Original language | English |
|---|---|
| Article number | 20110616 |
| Journal | Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences |
| Volume | 371 |
| Issue number | 1997 |
| DOIs | |
| Publication status | Published - Aug 28 2013 |
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Keywords
- Effective connectivity
- Electrooculogram artefact rejection
- Independent component analysis
- Partial directed coherence
- Time-variant multivariate autoregressive modelling
ASJC Scopus subject areas
- Mathematics(all)
- Physics and Astronomy(all)
- Engineering(all)
Cite this
Time-variant partial directed coherence for analysing connectivity : A methodological study. / Leistritz, L.; Pester, B.; Doering, A.; Schiecke, K.; Babiloni, F.; Astolfi, L.; Witte, H.
In: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 371, No. 1997, 20110616, 28.08.2013.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Time-variant partial directed coherence for analysing connectivity
T2 - A methodological study
AU - Leistritz, L.
AU - Pester, B.
AU - Doering, A.
AU - Schiecke, K.
AU - Babiloni, F.
AU - Astolfi, L.
AU - Witte, H.
PY - 2013/8/28
Y1 - 2013/8/28
N2 - For the past decade, the detection and quantification of interactions within and between physiological networks has become a priority-in-common between the fields of biomedicine and computer science. Prominent examples are the interaction analysis of brain networks and of the cardiovascular-respiratory system. The aim of the study is to show how and to what extent results from time-variant partial directed coherence analysis are influenced by some basic estimator and data parameters. The impacts of the Kalman filter settings, the order of the autoregressive (AR) model, signal-to-noise ratios, filter procedures and volume conduction were investigated. These systematic investigations are based on data derived from simulated connectivity networks and were performed using a Kalman filter approach for the estimation of the timevariant multivariate AR model. Additionally, the influence of electrooculogram artefact rejection on the significance and dynamics of interactions in 29 channel electroencephalography recordings, derived from a photic driving experiment, is demonstrated. For artefact rejection, independent component analysis was used. The study provides rules to correctly apply particular methods that will aid users to achieve more reliable interpretations of the results.
AB - For the past decade, the detection and quantification of interactions within and between physiological networks has become a priority-in-common between the fields of biomedicine and computer science. Prominent examples are the interaction analysis of brain networks and of the cardiovascular-respiratory system. The aim of the study is to show how and to what extent results from time-variant partial directed coherence analysis are influenced by some basic estimator and data parameters. The impacts of the Kalman filter settings, the order of the autoregressive (AR) model, signal-to-noise ratios, filter procedures and volume conduction were investigated. These systematic investigations are based on data derived from simulated connectivity networks and were performed using a Kalman filter approach for the estimation of the timevariant multivariate AR model. Additionally, the influence of electrooculogram artefact rejection on the significance and dynamics of interactions in 29 channel electroencephalography recordings, derived from a photic driving experiment, is demonstrated. For artefact rejection, independent component analysis was used. The study provides rules to correctly apply particular methods that will aid users to achieve more reliable interpretations of the results.
KW - Effective connectivity
KW - Electrooculogram artefact rejection
KW - Independent component analysis
KW - Partial directed coherence
KW - Time-variant multivariate autoregressive modelling
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UR - http://www.scopus.com/inward/citedby.url?scp=84880564070&partnerID=8YFLogxK
U2 - 10.1098/rsta.2011.0616
DO - 10.1098/rsta.2011.0616
M3 - Article
C2 - 23858483
AN - SCOPUS:84880564070
VL - 371
JO - Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
JF - Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
SN - 0962-8428
IS - 1997
M1 - 20110616
ER -