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

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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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References

  • Andrzejak RG, David O, Gnatkovsky V et al (2015) Localization of epileptogenic zone on pre-surgical intracranial EEG recordings: toward a validation of quantitative signal analysis approaches. Brain Topogr 28:832–837

    Article  PubMed  Google Scholar 

  • Arnold M, Miltner WH, Witte H, Bauer R, Braun C (1998) Adaptive AR modeling of nonstationary time series by means of Kalman filtering. IEEE Trans Biomed Eng 45:553–562

    Article  CAS  PubMed  Google Scholar 

  • Astolfi L, Cincotti F, Mattia D et al (2008) Tracking the time-varying cortical connectivity patterns by adaptive multivariate estimators. IEEE Trans Biomed Eng 55:902–913

    Article  CAS  PubMed  Google Scholar 

  • Baccalá LA, Sameshima K (2001) Partial directed coherence: a new concept in neural structure determination. Biol Cybern 84:463–474

    Article  PubMed  Google Scholar 

  • Baccalá LA, de Brito CS, Takahashi DY, Sameshima K (2013) Unified asymptotic theory for all partial directed coherence forms. Philos Trans A Math Phys Eng Sci 371:20120158

    Article  PubMed  Google Scholar 

  • Bishara AJ, Hittner JB (2012) Testing the significance of a correlation with nonnormal data: comparison of pearson, spearman, transformation, and resampling approaches. Psychol Methods 17:399–417

    Article  PubMed  Google Scholar 

  • Blinowska KJ (2011) Review of the methods of determination of directed connectivity from multichannel data. Med Biol Eng Comput 49:521–529

    Article  PubMed  PubMed Central  Google Scholar 

  • Brodbeck V, Spinelli L, Lascano AM et al (2011) Electroencephalographic source imaging: a prospective study of 152 operated epileptic patients. Brain 134:2887–2897

    Article  PubMed  PubMed Central  Google Scholar 

  • Bulacio JC, Jehi L, Wong C et al (2012) Long-term seizure outcome after resective surgery in patients evaluated with intracranial electrodes. Epilepsia 53:1722–1730

    Article  PubMed  Google Scholar 

  • Carrette E, Vonck K, De Herdt V et al (2010) Predictive factors for outcome of invasive video-EEG monitoring and subsequent resective surgery in patients with refractory epilepsy. Clin Neurol Neurosurg 112:118–126

    Article  PubMed  Google Scholar 

  • Ding M, Bressler SL, Yang W, Liang H (2000) Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment. Biol Cybern 83:35–45

    Article  CAS  PubMed  Google Scholar 

  • Enatsu R, Jin K, Elwan S, Kubota Y et al (2012) Correlations between ictal propagation and response to electrical cortical stimulation: a cortico-cortical evoked potential study. Epilepsy Res 101:76–87

    Article  PubMed  Google Scholar 

  • Englot DJ, Modi B, Mishra AM, DeSalvo M, Hyder F, Blumenfeld H (2009) Cortical deactivation induced by subcortical network dysfunction in limbic seizures. J Neurosci 29:13006–13018

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Florin E, Gross J, Pfeifer J, Fink GR, Timmermann L (2011) Reliability of multivariate causality measures for neural data. J Neurosci Methods 198:344–358

    Article  PubMed  Google Scholar 

  • Granger CWJ (1969) Investigating causal relations by econometric models and crossspectral methods. Econometrica 37:424–438

    Article  Google Scholar 

  • He B, Dai Y, Astolfi L, Babiloni F, Yuan H, Yang L (2011) eConnectome: a MATLAB toolbox for mapping and imaging of brain functional connectivity. J Neurosci Methods 195:261–269

    Article  PubMed  Google Scholar 

  • Jiménez-Jiménez D, Nekkare R, Flores L et al (2015) Prognostic value of intracranial seizure onset patterns for surgical outcome of the treatment of epilepsy. Clin Neurophysiol 126:257–267

    Article  PubMed  Google Scholar 

  • Jouny CC, Adamolekun B, Franaszczuk PJ, Bergey GK (2007) Intrinsic ictal dynamics at the seizure focus: effects of secondary generalization revealed by complexity measures. Epilepsia 48:297–304

    Article  PubMed  Google Scholar 

  • Kalman RE (1960) A new approach to linear filtering and prediction theory. J Basic Eng 82:34–45

    Google Scholar 

  • Kalman RE, Bucy RS (1961) New results on linear filtering and prediction theory. J Basic Eng 83:95–108

    Article  Google Scholar 

  • Kasess CH (2002) Estimation of time-variant multivariate autoregressive models using Kalman filtering, dissertation, Graz University of Technology, Graz

  • Korzeniewska A, Mańczak M, Kamiński M, Blinowska KJ, Kasicki S (2003) Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method. J Neurosci Methods 125:195–207

    Article  PubMed  Google Scholar 

  • Korzeniewska A, Cervenka MC, Jouny CC et al (2014) Ictal propagation of high frequency activity is recapitulated in interictal recordings: effective connectivity of epileptogenic networks recorded with intracranial EEG. Neuroimage 101:96–113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Leonardi N, Van De Ville D (2015) On spurious and real fluctuations of dynamic functional connectivity during rest. Neuroimage 104:430–436

    Article  PubMed  Google Scholar 

  • Lie OV, Papanastassiou AM, Cavazos JE, Szabó ÁC (2015) Influence of intracranial electrode density and spatial configuration on interictal spike localization: a case study. J Clin Neurophysiol 32:e30–e40

    Article  PubMed  Google Scholar 

  • Milde T, Leistritz L, Astolfi L et al (2010) A new Kalman filter approach for the estimation of high-dimensional time-variant multivariate AR models and its application in analysis of laser-evoked brain potentials. Neuroimage 50:960–969

    Article  PubMed  Google Scholar 

  • Moeller F, Muthuraman M, Stephani U, Deuschl G, Raethjen J, Siniatchkin M (2013) Representation and propagation of epileptic activity in absences and generalized photoparoxysmal responses. Hum Brain Mapp 34:1896–1909

    Article  PubMed  Google Scholar 

  • Molenaar PC, Beltz AM, Gates KM, Wilson SJ (2016) State space modeling of time-varying contemporaneous and lagged relations in connectivity maps. Neuroimage 125:791–802

    Article  PubMed  Google Scholar 

  • Morrell MJ, RNS System in Epilepsy Study Group (2011) Responsive cortical stimulation for the treatment of medically intractable partial epilepsy. Neurology 77:1295–1304

    Article  PubMed  Google Scholar 

  • Mullen T, Acar ZA, Worrell G, Makeig S (2011) Modeling cortical source dynamics and interactions during seizure. Conf Proc IEEE Eng Med Biol Soc 2011:1411–1414

    PubMed  PubMed Central  Google Scholar 

  • Omidvarnia AH, Mesbah M, Khlif MS, O’Toole JM, Colditz PB, Boashash B (2011) Kalman filter-based time-varying cortical connectivity analysis of newborn EEG. Conf Proc IEEE Eng Med Biol Soc 2011:1423–1426

    CAS  PubMed  Google Scholar 

  • Plomp G, Quairiaux C, Michel CM, Astolfi L (2014) The physiological plausibility of time-varying Granger-causal modeling: normalization and weighting by spectral power. Neuroimage 97:16–206

    Article  Google Scholar 

  • Plomp G, Astolfi L, Coito A, Michel CM (2015) Spectrally weighted Granger-causal modeling: motivation and applications to data from animal models and epileptic patients. Conf Proc IEEE Eng Med Biol Soc 2015:5392–5395

    PubMed  Google Scholar 

  • Rosenow F, Lüders H (2001) Presurgical evaluation of epilepsy. Brain 124:1683–1700

    Article  CAS  PubMed  Google Scholar 

  • Schlögl A (2000) The electroencephalogram and the adaptive autoregressive model: theory and applications. Shaker Verlag, Aachen

    Google Scholar 

  • Schlögl A, Supp G (2006) Analyzing event-related EEG data with multivariate autoregressive parameters. Prog Brain Res 159:135–147

    Article  PubMed  Google Scholar 

  • Sinha N, Dauwels J, Wang Y, Cash SS, Taylor PN (2014) An in silico approach for pre-surgical evaluation of an epileptic cortex. Conf Proc IEEE Eng Med Biol Soc 2014:4884–4887

    PubMed  Google Scholar 

  • Sommerlade L, Thiel M, Platt B et al (2012) Inference of Granger causal time-dependent influences in noisy multivariate time series. J Neurosci Methods 203:173–185

    Article  PubMed  Google Scholar 

  • Stufflebeam SM, Liu H, Sepulcre J, Tanaka N, Buckner RL, Madsen JR (2011) Localization of focal epileptic discharges using functional connectivity magnetic resonance imaging. J Neurosurg 114:1693–1697

    Article  PubMed  PubMed Central  Google Scholar 

  • Toppi J, Babiloni F, Vecchiato G et al (2012) Towards the time varying estimation of complex brain connectivity networks by means of a General Linear Kalman Filter approach. Conf Proc IEEE Eng Med Biol Soc 2012:6192–6195

    CAS  PubMed  Google Scholar 

  • van Dellen E, Douw L, Baayen JC et al (2009) Long-term effects of temporal lobe epilepsy on local neural networks: a graph theoretical analysis of corticography recordings. PLoS One 4:e8081

    Article  PubMed  PubMed Central  Google Scholar 

  • van Mierlo P, Carrette E, Hallez H et al (2011) Accurate epileptogenic focus localization through time-variant functional connectivity analysis of intracranial electroencephalographic signals. Neuroimage 56:1122–1133

    Article  PubMed  Google Scholar 

  • van Mierlo P, Carrette E, Hallez H et al (2013) Ictal-onset localization through connectivity analysis of intracranial EEG signals in patients with refractory epilepsy. Epilepsia 54:1409–1418

    Article  PubMed  Google Scholar 

  • van Mierlo P, Papadopoulou M, Carrette E et al (2014) Functional brain connectivity from EEG in epilepsy: seizure prediction and epileptogenic focus localization. Prog Neurobiol 121:19–35

    Article  PubMed  Google Scholar 

  • Varotto G, Franceschetti S, Spreafico R, Tassi L, Panzica F (2010) Partial directed coherence estimated on stereo-EEG signals in patients with Taylor’s type focal cortical dysplasia. Conf Proc IEEE Eng Med Biol Soc 2010:4646–4649

    PubMed  Google Scholar 

  • Varotto G, Tassi L, Franceschetti S, Spreafico R, Panzica F (2012) Epileptogenic networks of type II focal cortical dysplasia: a stereo-EEG study. Neuroimage 61:591–598

    Article  PubMed  Google Scholar 

  • Vidaurre C, Sander TH, Schlögl A (2011) BioSig: the free and open source software library for biomedical signal processing. Comput Intell Neurosci 2011:935364

    Article  PubMed  PubMed Central  Google Scholar 

  • Wang HE, Bénar CG, Quilichini PP, Friston KJ, Jirsa VK, Bernard C (2014) A systematic framework for functional connectivity measures. Front Neurosci 8:405

    PubMed  PubMed Central  Google Scholar 

  • Wilke C, Ding L, He B (2008) Estimation of time-varying connectivity patterns through the use of an adaptive directed transfer function. IEEE Trans Biomed Eng 55:2557–2564

    Article  PubMed  PubMed Central  Google Scholar 

  • Wilke C, van Drongelen W, Kohrman M, He B (2009) Identification of epileptogenic foci from causal analysis of ECoG interictal spike activity. Clin Neurophysiol 120(8):1449–1456

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wilke C, van Drongelen W, Kohrman M, He B (2010) Neocortical seizure foci localization by means of a directed transfer function method. Epilepsia 51(4):564–572

    Article  PubMed  Google Scholar 

  • Wilke C, Worrell G, He B (2011) Graph analysis of epileptogenic networks in human partial epilepsy. Epilepsia 52(1):84–93

    Article  PubMed  Google Scholar 

  • Yaffe RB, Borger P, Megevand P et al (2015) Physiology of functional and effective networks in epilepsy. Clin Neurophysiol 126:227–236

    Article  PubMed  Google Scholar 

Download references

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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Correspondence to Octavian V. Lie.

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

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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

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