Independent component analysis as a model-free approach for the detection of BOLD changes related to epileptic spikes: a simulation study.

Human Brain Mapping
Pierre Levan, Jean Gotman

Abstract

EEG-fMRI in epileptic patients is commonly analyzed using the general linear model (GLM), which assumes a known hemodynamic response function (HRF) to epileptic spikes in the EEG. In contrast, independent component analysis (ICA) can extract Blood-Oxygenation Level Dependent (BOLD) responses without imposing constraints on the HRF. This technique was evaluated on data generated by superimposing artificial responses on real background fMRI signals. Simulations were run using a wide range of EEG spiking rates, HRF amplitudes, and activation regions. The data were decomposed by spatial ICA into independent components. A deconvolution method then identified component time courses significantly related to the simulated spikes, without constraining the shape of the HRF. Components matching the simulated activation regions ("concordant components") were found in 84.4% of simulations, while components at discordant locations were found in 12.2% of simulations. These false activations were often related to large artifacts that coincidentally occurred simultaneously with some of the random simulated spikes. The performance of the method depended closely on the simulation parameters; when the number of spikes was low, concordant component...Continue Reading

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Citations

Sep 24, 2011·Biomedical Engineering Online·Arjon TurnipMyung-Yung Jeong
May 27, 2014·Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology·Clara Huishi ZhangBin He
Apr 6, 2016·Frontiers in Neuroscience·Giovanni PellegrinoChristophe Grova
Feb 23, 2018·Frontiers in Human Neuroscience·Rodolfo AbreuPatrícia Figueiredo
Nov 17, 2020·Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences·Kamen A TsvetanovJames B Rowe

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