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Data-Driven EEG Informed Functional MRI Combined with Network Analysis Successfully Identifies the Seizure Onset Zone

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Bioengineering and Biomedical Signal and Image Processing (BIOMESIP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12940))

Abstract

A data-driven network analysis strategy was developed to apply EEG-informed functional MRI to identify the seizure onset zone in the presurgical work-up of epilepsy patients (n = 10). Instead of voxel-wise general linear model analysis the time series of independent components were correlated with the interictal epileptic discharges density function, yielding the so-called epileptic network. We used eigenvector centrality mapping and a symmetry index to detect the epileptic independent component (ICE) out of the epileptic network. The location of the ICE was for 9 of the 10 patients studied concordant with the clinical hypothesis. Moreover, the clinical evaluation including the outcome of surgery indicated successful localization of the ICE for 6 out of 8 patients who had a resection. The robustness of the methods used to identify the ICE was demonstrated by evaluating the results of the patient study against the results of similar network analysis procedures applied to the functional MRI sequences of 10 healthy controls. In conclusion, the data-driven network analysis strategy successfully identifies the ICE. The concordance of the ICE with the clinical information, including outcome of the resection of the patients, is in support of the usefulness of EEG-fMRI as initial diagnostic tool in the presurgical work-up of epilepsy patients.

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Acknowledgements

This study was part of the Central Nervous System and Imaging (CSI) project and the Devices for NeuroControl and NeuroRehabilitation (DeNeCor) project funded by ENIAC Joint Undertaking (grant numbers 120209, 324257). The authors thank, especially, the Academic Center for Epileptology, Kempenhaeghe for the contribution to the data collection, Kees Hermans for his contribution to the fMRI data analysis and Jan Verwoerd (clinical scientist at Philips Health Care, Best) for his contribution to the EEG-related fMRI sequences development.

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Authors and Affiliations

  1. Department of Research and Development, Academic Center for Epileptology, Kempenhaeghe/MUMC+, Heeze, The Netherlands

    Pauly Ossenblok, Albert Colon, Paul Boon & Petra van Houdt

  2. Department of Mathematics and Computer Sciences, Eindhoven University of Technology, Eindhoven, The Netherlands

    Pauly Ossenblok

  3. MR Clinical Science, Philips Healthcare, Best, The Netherlands

    Liesbeth Geerts

  4. Department of Radiology, University Medical Center, Amsterdam, The Netherlands

    Jan de Munck

Authors
  1. Pauly Ossenblok
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  2. Albert Colon
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  5. Petra van Houdt
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  6. Jan de Munck
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Correspondence to Pauly Ossenblok .

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Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Granada, Granada, Spain

    Daniel Castillo-Secilla

  3. University of Granada, Granada, Spain

    Luis Javier Herrera

  4. Universidad de Granada, Granada, Granada, Spain

    Héctor Pomares

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Ossenblok, P., Colon, A., Geerts, L., Boon, P., van Houdt, P., de Munck, J. (2021). Data-Driven EEG Informed Functional MRI Combined with Network Analysis Successfully Identifies the Seizure Onset Zone. In: Rojas, I., Castillo-Secilla, D., Herrera, L.J., Pomares, H. (eds) Bioengineering and Biomedical Signal and Image Processing. BIOMESIP 2021. Lecture Notes in Computer Science(), vol 12940. Springer, Cham. https://doi.org/10.1007/978-3-030-88163-4_20

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  • DOI: https://doi.org/10.1007/978-3-030-88163-4_20

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