Publication

Spatial Mapping Of Interictal Epileptic Discharges In Fmri With Total Activation

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

Abstract

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.

Official source

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

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

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

Abstract

Official source

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

About this result

Related concepts (29)

Electroencephalography

Electroencephalography (EEG) is a method to record an electrogram of the spontaneous electrical activity of the brain. The biosignals detected by EEG have been shown to represent the postsynaptic potentials of pyramidal neurons in the neocortex and allocortex. It is typically non-invasive, with the EEG electrodes placed along the scalp (commonly called "scalp EEG") using the International 10–20 system, or variations of it. Electrocorticography, involving surgical placement of electrodes, is sometimes called "intracranial EEG".

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.

Related publications (29)

Random matrix methods for high-dimensional machine learning models

Antoine Philippe Michel Bodin

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

EPFL2024

Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

Mohammad Khaja Nazeeruddin

Herein, machine learning (ML) models using multiple linear regression (MLR), support vector regression (SVR), random forest (RF) and artificial neural network (ANN) are developed and compared to predict the output features viz. specific capacitance (Csp), ...

Elsevier Science Sa2024

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...

Amer Chemical Soc2024