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Band Correlation

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Markus Barth – 1st expert on this subject based on the ideXlab platform

  • corrigendum the quest for eeg power Band Correlation with ica derived fmri resting state networks
    Frontiers in Human Neuroscience, 2014
    Co-Authors: Marlene Meyer, Markus Barth, Ronald J Janssen, Erik S B Van Oort, Christian F Beckmann

    Abstract:

    We have noticed that during the revision process of the original manuscript a modification in the analysis script to enable the parallel processing of more data sets led to incorrect indices for the selection of active dipoles. This mistake in the analysis pipeline affected the results of SFPC, i.e., Figure 5 and the part of Table ​Table11 labeled “SFPC variance for 5 subjects.”

    Table 1

    Correction of Table ​Table11 in the original manuscript for GFPC and SFPC.

    We corrected this mistake in the analysis script and reanalyzed the 5 Subjects. While this affected the individual frequency power time courses, it did not result in a more stable Correlation with the RSN timelines. The corrected Figure 5 of this erratum depicts the corrected rank graphs for SFPC, which show only minor differences to the erroneous graphs in the original Figure 5 of the published manuscript. This reflects a similar inter subject and temporal variance independent of the change in dipole location.

    Figure 1

    Correction of Figure 5 of the original manuscript, showing only minor differences to the erroneous graphs in the original Figure 5. This reflects a similar inter subject and temporal variance independent of the change in dipole location.

    We also noted a lapse in the part of the original Table ​Table1,1, which shows the variance values for SFPC and GFPC for 5 subjects. This was due to an error in the data transfer between Excel and Word in the final version of the manuscript after the revision process. The corrected Table ​Table11 below shows the corrected values of both GFPC and SFPC analysis.

    It is important to note that the corrected results did not impact on our original conclusions of the published manuscript.

  • the quest for eeg power Band Correlation with ica derived fmri resting state networks
    Frontiers in Human Neuroscience, 2013
    Co-Authors: Matthias C Meyer, Markus Barth, Ronald J Janssen, Erik S B Van Oort, Christian F Beckmann

    Abstract:

    The neuronal underpinnings of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) resting state networks (RSNs) are still unclear. To investigate the underlying mechanisms, specifically the relation to the electrophysiological signal, we used simultaneous recordings of electroencephalography (EEG) and fMRI during eyes open resting state (RS). Earlier studies using the EEG signal as independent variable show inconclusive results possibly due to variability in the temporal Correlations between RSNs and power in the low EEG frequency Band, as recently reported (Goncalves et al. 2006 and 2008, Meyer et al. (2013)). In this study we use three different methods, including one that uses RSN timelines as independent variable, to explore the temporal relationship of RSNs and EEG frequency power in eyes open RS in detail. The results of these three distinct analysis approaches support the hypothesis that the Correlation between low EEG frequency power and BOLD RSNs is instable over time, at least in eyes open RS.

Erik S B Van Oort – 2nd expert on this subject based on the ideXlab platform

  • corrigendum the quest for eeg power Band Correlation with ica derived fmri resting state networks
    Frontiers in Human Neuroscience, 2014
    Co-Authors: Marlene Meyer, Markus Barth, Ronald J Janssen, Erik S B Van Oort, Christian F Beckmann

    Abstract:

    We have noticed that during the revision process of the original manuscript a modification in the analysis script to enable the parallel processing of more data sets led to incorrect indices for the selection of active dipoles. This mistake in the analysis pipeline affected the results of SFPC, i.e., Figure 5 and the part of Table ​Table11 labeled “SFPC variance for 5 subjects.”

    Table 1

    Correction of Table ​Table11 in the original manuscript for GFPC and SFPC.

    We corrected this mistake in the analysis script and reanalyzed the 5 Subjects. While this affected the individual frequency power time courses, it did not result in a more stable Correlation with the RSN timelines. The corrected Figure 5 of this erratum depicts the corrected rank graphs for SFPC, which show only minor differences to the erroneous graphs in the original Figure 5 of the published manuscript. This reflects a similar inter subject and temporal variance independent of the change in dipole location.

    Figure 1

    Correction of Figure 5 of the original manuscript, showing only minor differences to the erroneous graphs in the original Figure 5. This reflects a similar inter subject and temporal variance independent of the change in dipole location.

    We also noted a lapse in the part of the original Table ​Table1,1, which shows the variance values for SFPC and GFPC for 5 subjects. This was due to an error in the data transfer between Excel and Word in the final version of the manuscript after the revision process. The corrected Table ​Table11 below shows the corrected values of both GFPC and SFPC analysis.

    It is important to note that the corrected results did not impact on our original conclusions of the published manuscript.

  • the quest for eeg power Band Correlation with ica derived fmri resting state networks
    Frontiers in Human Neuroscience, 2013
    Co-Authors: Matthias C Meyer, Markus Barth, Ronald J Janssen, Erik S B Van Oort, Christian F Beckmann

    Abstract:

    The neuronal underpinnings of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) resting state networks (RSNs) are still unclear. To investigate the underlying mechanisms, specifically the relation to the electrophysiological signal, we used simultaneous recordings of electroencephalography (EEG) and fMRI during eyes open resting state (RS). Earlier studies using the EEG signal as independent variable show inconclusive results possibly due to variability in the temporal Correlations between RSNs and power in the low EEG frequency Band, as recently reported (Goncalves et al. 2006 and 2008, Meyer et al. (2013)). In this study we use three different methods, including one that uses RSN timelines as independent variable, to explore the temporal relationship of RSNs and EEG frequency power in eyes open RS in detail. The results of these three distinct analysis approaches support the hypothesis that the Correlation between low EEG frequency power and BOLD RSNs is instable over time, at least in eyes open RS.

Christian F Beckmann – 3rd expert on this subject based on the ideXlab platform

  • corrigendum the quest for eeg power Band Correlation with ica derived fmri resting state networks
    Frontiers in Human Neuroscience, 2014
    Co-Authors: Marlene Meyer, Markus Barth, Ronald J Janssen, Erik S B Van Oort, Christian F Beckmann

    Abstract:

    We have noticed that during the revision process of the original manuscript a modification in the analysis script to enable the parallel processing of more data sets led to incorrect indices for the selection of active dipoles. This mistake in the analysis pipeline affected the results of SFPC, i.e., Figure 5 and the part of Table ​Table11 labeled “SFPC variance for 5 subjects.”

    Table 1

    Correction of Table ​Table11 in the original manuscript for GFPC and SFPC.

    We corrected this mistake in the analysis script and reanalyzed the 5 Subjects. While this affected the individual frequency power time courses, it did not result in a more stable Correlation with the RSN timelines. The corrected Figure 5 of this erratum depicts the corrected rank graphs for SFPC, which show only minor differences to the erroneous graphs in the original Figure 5 of the published manuscript. This reflects a similar inter subject and temporal variance independent of the change in dipole location.

    Figure 1

    Correction of Figure 5 of the original manuscript, showing only minor differences to the erroneous graphs in the original Figure 5. This reflects a similar inter subject and temporal variance independent of the change in dipole location.

    We also noted a lapse in the part of the original Table ​Table1,1, which shows the variance values for SFPC and GFPC for 5 subjects. This was due to an error in the data transfer between Excel and Word in the final version of the manuscript after the revision process. The corrected Table ​Table11 below shows the corrected values of both GFPC and SFPC analysis.

    It is important to note that the corrected results did not impact on our original conclusions of the published manuscript.

  • the quest for eeg power Band Correlation with ica derived fmri resting state networks
    Frontiers in Human Neuroscience, 2013
    Co-Authors: Matthias C Meyer, Markus Barth, Ronald J Janssen, Erik S B Van Oort, Christian F Beckmann

    Abstract:

    The neuronal underpinnings of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) resting state networks (RSNs) are still unclear. To investigate the underlying mechanisms, specifically the relation to the electrophysiological signal, we used simultaneous recordings of electroencephalography (EEG) and fMRI during eyes open resting state (RS). Earlier studies using the EEG signal as independent variable show inconclusive results possibly due to variability in the temporal Correlations between RSNs and power in the low EEG frequency Band, as recently reported (Goncalves et al. 2006 and 2008, Meyer et al. (2013)). In this study we use three different methods, including one that uses RSN timelines as independent variable, to explore the temporal relationship of RSNs and EEG frequency power in eyes open RS in detail. The results of these three distinct analysis approaches support the hypothesis that the Correlation between low EEG frequency power and BOLD RSNs is instable over time, at least in eyes open RS.