Localization of epileptogenic foci using a new signal analytical approach

Pijn, J.P.; Vijn, Peter; Silva, F.H. Lopes da; Boas, W Van Ende; Blanes, W.

doi:10.1016/s0987-7053(05)80165-0

Cited by 37 publications

(17 citation statements)

References 7 publications

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“…We note that an earlier attempt towards quantification of the FC “nonlinearity” was made in (Lahaye et al, 2003), who reported also the explained variances by the higher order (nonlinear up to order 5) terms in the predictor set within a linear regression framework—although these variances were not corrected for the effect of extra number of regressors in the model, and also the considered model could not capture a more general form of dependence. A similar approach as in Lahaye et al (2003) was previously also applied to assessment of the non(linear) character of connectivity in EEG data by Pijn et al (1990). The h 2 coefficient applied in that study expresses the proportion of variance in one variable explained by another variable using a polynomial fit; the aim was to provide more robust detection of interactions than simple linear correlation in case when the linearity assumption is violated.…”

Section: Discussionmentioning

confidence: 99%

Functional connectivity in resting-state fMRI: Is linear correlation sufficient?

et al. 2011

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Functional connectivity (FC) analysis is a prominent approach to analyzing fMRI data, especially acquired under the resting state condition. The commonly used linear correlation FC measure bears an implicit assumption of Gaussianity of the dependence structure. If only the marginals, but not all the bivariate distributions are Gaussian, linear correlation consistently underestimates the strength of the dependence. To assess the suitability of linear correlation and the general potential of nonlinear FC measures, we present a framework for testing and estimating the deviation from Gaussianity by means of comparing mutual information in the data and its Gaussianized counterpart. We apply this method to 24 sessions of human resting state fMRI. For each session, matrix of connectivities between 90 anatomical parcel time series is computed using mutual information and compared to results from its multivariate Gaussian surrogate that conserves the correlations but cancels any nonlinearity. While the group-level tests confirmed non-Gaussianity in the FC, the quantitative assessment revealed that the portion of mutual information neglected by linear correlation is relatively minor—on average only about 5% of the mutual information already captured by the linear correlation. The marginality of the non-Gaussianity was confirmed in comparisons using clustering of the parcels—the disagreement between clustering obtained from mutual information and linear correlation was attributable to random error. We conclude that for this type of data, practical relevance of nonlinear methods trying to improve over linear correlation might be limited by the fact that the data are indeed almost Gaussian.

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Section: Discussionmentioning

confidence: 99%

Functional connectivity in resting-state fMRI: Is linear correlation sufficient?

et al. 2011

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“…In this respect a number of signal processing methods were developed to identify epileptogenic networks and characterize their dynamics from depth-EEG recordings. Some of these methods were based on functional connectivity, typically quantified by association/synchronization measures among signals (Pijn et al, 1990;Schindler et al, 2007; based on the computation of "epileptogenicity" indexes (Andrzejak et al, 2014). From the modeling viewpoint, the analysis and the description of focal seizure dynamics (initiation, propagation and termination) has long been -and is still -a topic of large interest.…”

Section: Models Of Focal Seizures Seizure Propagation and Transitionmentioning

confidence: 99%

Computational models of epileptiform activity

Wendling

Benquet

Bartolomei

et al. 2016

Journal of Neuroscience Methods

160

128

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We reviewed computer models that have been developed to reproduce and explain epileptiform activity. Unlike other already-published reviews on computer models of epilepsy, the proposed overview starts from the various types of epileptiform activity encountered during both interictal and ictal periods. Computational models proposed so far in the context of partial and generalized epilepsies are classified according to the following taxonomy: neural mass, neural field, detailed network and formal mathematical models. Insights gained about interictal epileptic spikes and high-frequency oscillations, about fast oscillations at seizure onset, about seizure initiation and propagation, about spike-wave discharges and about status epilepticus are described. This review shows the richness and complementarity of the various modeling approaches as well as the fruitful contribution of the computational neuroscience community in the field of epilepsy research. It shows that models have progressively gained acceptance and are now considered as an efficient way of integrating structural, functional and pathophysiological data about neural systems into "coherent and interpretable views". The advantages, limitations and future of modeling approaches are discussed. Perspectives in epilepsy research and clinical epileptology indicate that very promising directions are foreseen, like model-guided experiments or model-guided therapeutic strategy, among others.

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“…The non-linear regression is a non-parametric method used to evaluate and quantify the dependence (linear or not) between two time series X(t) and Y(t) (Lopes da Silva et al 1989;Pijn et al 1990). This method applied to electrophysiology look for an association of signals recorded from two neuronal populations without making any assumption on nature (linear or nonlinear) of this relationship.…”

Section: H 2 Computationmentioning

confidence: 99%

In Vivo Tumour Mapping Using Electrocorticography Alterations During Awake Brain Surgery: A Pilot Study

et al. 2016

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During awake brain surgery for tumour resection, in situ EEG recording (ECoG) is used to identify eloquent areas surrounding the tumour. We used the ECoG setup to record the electrical activity of cortical and subcortical tumours and then performed frequency and connectivity analyses in order to identify ECoG impairments and map tumours. We selected 16 patients with cortical (8) and subcortical (8) tumours undergoing awake brain surgery. For each patient, we computed the spectral content of tumoural and healthy areas in each frequency band. We computed connectivity of each electrode using connectivity markers (linear and non-linear correlations, phase-locking and coherence). We performed comparisons between healthy and tumour electrodes. The ECoG alterations were used to implement automated classification of the electrodes using clustering or neural network algorithms. ECoG alterations were used to image cortical tumours.Cortical tumours were found to profoundly alter all frequency contents (normalized and absolute power), with an increase in the δ activity and a decreases for the other bands (P < 0.05). Cortical tumour electrodes showed high level of connectivity compared to surrounding electrodes (all markers, P < 0.05). For subcortical tumours, a relative decrease in the γ1 band and in the alpha band in absolute amplitude (P < 0.05) were the only abnormalities. The neural network algorithm classification had a good performance: 93.6 % of the electrodes were classified adequately on a test subject. We found significant spectral and connectivity ECoG changes for cortical tumours, which allowed tumour recognition. Artificial neural algorithm pattern recognition seems promising for electrode classification in awake tumour surgery.

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Localization of epileptogenic foci using a new signal analytical approach

Cited by 37 publications

References 7 publications

Functional connectivity in resting-state fMRI: Is linear correlation sufficient?

Functional connectivity in resting-state fMRI: Is linear correlation sufficient?

Computational models of epileptiform activity

In Vivo Tumour Mapping Using Electrocorticography Alterations During Awake Brain Surgery: A Pilot Study

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