• DocumentCode
    3715872
  • Title

    Exploring the epileptic network with parallel ICA of interictal EEG-FMRI

  • Author

    B. Hunyadi;M. De Vos;W. Van Paesschen;S. Van Huffel

  • Author_Institution
    STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Belgium
  • fYear
    2015
  • Firstpage
    429
  • Lastpage
    433
  • Abstract
    The ultimate goal of the EEG-fMRI analysis in refractory focal epilepsy is the precise localization of the epileptogenic zone (EZ) to facilitate successful surgery. Many studies have shown that simultaneous GLM-based EEG-correlated fMRI analysis can identify fMRI voxels which covary with the timing of interictal spikes assessed on EEG. However, this type of analysis often does not reveal a single focus but an extensive epileptic network. In this paper we investigate whether parallel independent component analysis (ICA), a data-driven, symmetric integration approach can disentangle this network. We assume that ICA of EEG and ICA of fMRI will reveal different temporal and spatial aspects of this network, respectively. We hypothesize that by matching these different epilepsy-related EEG and fMRI components, we can get a deeper insight in the neural processes this extensive network represents. We tested parallel ICA on 12 refractory epilepsy patients who underwent full presurgical evaluation and showed concordant data (excluding EEG-fMRI) pointing to a single epileptic focus. Our results show that parallel ICA has an added value, as it can help the interpretation of the GLM results and pinpoint the EZ. Furthermore, it might help to understand how the various aspects of epileptic activity are reflected in EEG and fMRI.
  • Keywords
    "Electroencephalography","Epilepsy","Surfaces","Integrated circuits","Signal processing","Surgery","Europe"
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing Conference (EUSIPCO), 2015 23rd European
  • Electronic_ISBN
    2076-1465
  • Type

    conf

  • DOI
    10.1109/EUSIPCO.2015.7362419
  • Filename
    7362419