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Seizure-Onset Mapping Based on Time-Variant Multivariate Functional Connectivity Analysis of High-Dimensional Intracranial EEG: A Kalman Filter Approach

Brain Topography volume 30, pages 46–59 (2017)Cite this article

Abstract

The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (<60). The aim of this study was to test two Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73–113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach.

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Acknowledgments

This project has received Funding from the University of Texas Health Science Center at San Antonio School of Medicine/Institute for Integration of Medicine and Science Grant No. 158580 (O.L.); and the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 660230 (P.vM.).

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    Affiliations

    1. Department of Neurology, University of Texas Health Science Center at San Antonio, 8300 Floyd Curl Drive MSC: 7883, San Antonio, TX, 78229-3900, USA

      Octavian V. Lie

    2. Functional Brain Mapping Laboratory, EEG and Epilepsy Unit, University of Geneva, Geneva, Switzerland

      Pieter van Mierlo

    3. iMinds Medical IT Department, Medical Image and Signal Processing Group, Ghent University, Ghent, Belgium

      Pieter van Mierlo

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    1. Octavian V. Lie
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    2. Pieter van Mierlo
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    Correspondence to Octavian V. Lie.

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    Octavian V. Lie and Pieter van Mierlo have contributed equally to this work.

    Electronic Supplementary Material

    Below is the link to the electronic supplementary material.

    10548_2016_527_MOESM1_ESM.eps

    Supplementary material 1 (EPS 43840 kb). Colocalization of the seizure-onset estimates based on outdegree and the resection volume (patient 2). a. preictal baseline and EEG seizure onset (arrow) in the 50-channel dataset (upper), and the corresponding outdegree-over-time maps derived from connectivity analyses using m1/iAWPDC (middle) and m2/iAWPDC (lower). Boxed channels fall within the resection volume; b. EEG recording of the same seizure in the full (n=73) channel dataset (upper), and the corresponding outdegree-over-time map using m2/iAWPDC (lower)

    10548_2016_527_MOESM2_ESM.eps

    Supplementary material 2 (EPS 2370 kb). Dynamic correlation of the outdegree maps estimated based on m1 and m2 (patient 2). Time-matched Pearson correlation of the outdegree values resulting from m1 or m2 application. c- channel, rho- mean Pearson correlation coefficient

    10548_2016_527_MOESM3_ESM.eps

    Supplementary material 3 (EPS 47184 kb). Colocalization of the seizure-onset estimates based on outdegree and the resection volume (patient 3). a. preictal baseline and EEG seizure onset (arrow) in the 50-channel dataset (upper), and the corresponding outdegree-over-time maps derived from connectivity analyses using m1/iAWPDC (middle) and m2/iAWPDC (lower). Boxed channels fall within the resection volume; b. EEG recording of the same seizure in the full (n=91) channel dataset (upper), and the corresponding outdegree-over-time map using m2/iAWPDC (lower)

    10548_2016_527_MOESM4_ESM.eps

    Supplementary material 4 (EPS 2585 kb). Dynamic correlation of the outdegree maps estimated based on m1 and m2 (patient 3). Time-matched Pearson correlation of the outdegree values resulting from m1 or m2 application. c- channel, rho- mean Pearson correlation coefficient

    10548_2016_527_MOESM5_ESM.eps

    Supplementary material 5 (EPS 1675 kb). Functional connectivity analysis of a seizure recorded with high-density iEEG. a. designed signal matrix for a seizure signal involving 80 ictal channels added to random background noise; b. The connectivity matrix of the 192-channel seizure

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    Lie, O.V., van Mierlo, P. Seizure-Onset Mapping Based on Time-Variant Multivariate Functional Connectivity Analysis of High-Dimensional Intracranial EEG: A Kalman Filter Approach. Brain Topogr 30, 46–59 (2017). https://doi.org/10.1007/s10548-016-0527-x

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    • DOI: https://doi.org/10.1007/s10548-016-0527-x

    Keywords

    • Intracranial EEG
    • Seizure
    • Epilepsy surgery
    • Functional connectivity
    • Autoregressive
    • Kalman filter