Accurate epileptogenic focus localization through time-variant functional connectivity analysis of intracranial electroencephalographic signals

Mierlo, Pieter van; Carrette, Evelien; Hallez, Hans; Vonck, Kristl; Roost, Dirk Van; Boon, Paul; Staelens, Steven

doi:10.1016/j.neuroimage.2011.02.009

Cited by 74 publications

(76 citation statements)

References 41 publications

(51 reference statements)

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“…To adapt the autoregressive model to non-stationary signals it was allowed to the coefficients of the autoregressive model (i.e., matrix H in eq. 2) to vary in time, by means of a Kalman filtering procedure (Arnold et al, 1998) (the interested reader may refer to (Van Mierlo et al, 2013;Van Mierlo et al, 2011) for details on the methodology).…”

Section: Tms/hd-eeg Directed Functional Connectivity Estimation: Specmentioning

confidence: 99%

Tracking Dynamic Interactions Between Structural and Functional Connectivity: A TMS/EEG-dMRI Study

et al. 2017

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Section: Tms/hd-eeg Directed Functional Connectivity Estimation: Specmentioning

confidence: 99%

Tracking Dynamic Interactions Between Structural and Functional Connectivity: A TMS/EEG-dMRI Study

et al. 2017

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“…In order to track the evolution of connectivity over time, adaptive methods have been developed. An extension of DTF, ADTF, and an investigation of different variations of it, is applied in [19]. Another time-varying adaptive method, short-time direct DTF [20], is used in [21].…”

Section: Focus Localizationmentioning

confidence: 99%

Mapping the epileptic brain with EEG dynamical connectivity: Established methods and novel approaches

et al. 2012

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Several algorithms rooted in statistical physics, mathematics and machine learning are used to analyze neuroimaging data from patients affected by epilepsy, with the main goals of localizing the brain region where seizure originates from and of detecting upcoming seizure activity in order to trigger therapeutic neurostimulation devices. Some of these methods explore the dynamical connections between brain regions, exploiting the high temporal resolution of the electroencephalographic signals recorded at the scalp or directly from the cortical surface or in deeper brain areas. In this paper we describe this specific class of algorithms and their clinical application, by reviewing the state of the art and reporting their application on EEG data from an epileptic patient.

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“…It is expected that targeting only the hubs of functional networks would make therapies more effective and potentially even less invasive. In the past, there have been a number of studies aiming at 1) the definition of different types of functional networks -based on various EEG-derived signal interdependencies used to define the networks links (edges), and 2) the investigation of how distinct topological properties of these networks change before, during and after epileptic seizures (Ponten et al, 2007;Schindler et al, 2008;Kramer et al, 2010;Wilke et al, 2011;van Mierlo et al, 2011;Varotto et al, 2012). For example, in (Schindler et al, 2008;Kramer et al, 2010) it was found that initial ictal network dynamics are characterized by strong modularization, i.e.…”

Section: Introductionmentioning

confidence: 99%

Chow–Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series

2015

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Seizure freedom in patients suffering from pharmacoresistant epilepsies is still not achieved in 20 − 30% of all cases. Hence, current therapies need to be improved, based on a more complete understanding of ictogenesis. In this respect, the analysis of functional networks derived from intracranial electroencephalographic (iEEG) data has recently become a standard tool. Functional networks however are purely descriptive models and thus are conceptually unable to predict fundamental features of iEEG time-series, e.g. in the context of therapeutical brain stimulation.In this paper we present some first steps towards overcoming the limitations of functional network analysis, by showing that its results are implied by a simple predictive model of time-sliced iEEG time-series. More specifically, we learn distinct Graphical models (so called Chow-Liu(CL) trees) as models for the spatial dependencies between iEEG signals. Bayesian inference is then applied to the CL trees, allowing for an analytic derivation/prediction of functional networks, based on thresholding of the absolute value Pearson correlation coefficient(CC) matrix. Using various measures, the thus obtained networks are then compared to those which were derived in the classical way from the empirical CC-matrix. In the high threshold limit we find (a) an excellent agreement between the two networks and (b) key features of periictal networks as they have previously been reported in the literature.Apart from functional networks, both matrices are also compared element-wise, showing that the CL approach leads to a sparse representation, by setting small correlations to values close to zero while preserving the larger ones.Overall, this paper shows the validity of CL-trees as simple, spatially predictive models for periictal iEEG data. Moreover, we suggest straightforward generalizations of the CL-approach for modeling also the temporal features of iEEG signals.

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Accurate epileptogenic focus localization through time-variant functional connectivity analysis of intracranial electroencephalographic signals

Cited by 74 publications

References 41 publications

Tracking Dynamic Interactions Between Structural and Functional Connectivity: A TMS/EEG-dMRI Study

Tracking Dynamic Interactions Between Structural and Functional Connectivity: A TMS/EEG-dMRI Study

Mapping the epileptic brain with EEG dynamical connectivity: Established methods and novel approaches

Chow–Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series

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