Publication

Spatial Mapping Of Interictal Epileptic Discharges In Fmri With Total Activation

Dimitri Nestor Alice Van De Ville, Fikret Isik Karahanoglu
2013
Conference Papers

Résumé

During the monitoring of pharmacoresistant epilepsy patients prior to surgery, interictal epileptic discharges (IEDs) are analyzed to locate possible sources of epileptic activity. In order to compensate low spatial resolution of EEG, simultaneous EEG-fMRI recordings can be used. Conventional methods typically deploy an EEG-informed analysis of the fMRI data; i.e., EEG-derived IED onset timings are used to setup regressors for linear regression. Recently we have proposed a new fMRI analysis method, total activation (TA), which is able to deconvolve the underlying activity-inducing signal without prior information on the onset timing and duration of events. Here we demonstrate that TA can locate the epileptogenic regions from fMRI data. We compare and validate our results with conventional methods performed by experts prior to surgery.

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https://infoscience.epfl.ch/entities/publication/4c599ca7-8b2d-49ed-a7aa-1912febb200f

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Dimitri Nestor Alice Van De Ville, Fikret Isik Karahanoglu
2013
Conference Papers

Résumé

Source officielle

https://infoscience.epfl.ch/entities/publication/4c599ca7-8b2d-49ed-a7aa-1912febb200f

À propos de ce résultat

Concepts associés (29)

Électroencéphalographie

L'électroencéphalographie (EEG) est une méthode d'exploration cérébrale qui mesure l'activité électrique du cerveau par des électrodes placées sur le cuir chevelu souvent représentée sous la forme d'un tracé appelé électroencéphalogramme. Comparable à l'électrocardiogramme qui permet d'étudier le fonctionnement du cœur, l'EEG est un examen indolore et non invasif qui renseigne sur l'activité neurophysiologique du cerveau au cours du temps et en particulier du cortex cérébral soit dans un but diagnostique en neurologie, soit dans la recherche en neurosciences cognitives.

Bayesian linear regression

Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables, with the goal of obtaining the posterior probability of the regression coefficients (as well as other parameters describing the distribution of the regressand) and ultimately allowing the out-of-sample prediction of the regressand (often labelled ) conditional on observed values of the regressors (usually ).

Bayesian multivariate linear regression

In statistics, Bayesian multivariate linear regression is a Bayesian approach to multivariate linear regression, i.e. linear regression where the predicted outcome is a vector of correlated random variables rather than a single scalar random variable. A more general treatment of this approach can be found in the article MMSE estimator. Consider a regression problem where the dependent variable to be predicted is not a single real-valued scalar but an m-length vector of correlated real numbers.

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