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Predicting EEG single trial responses with simultaneous fMRI and Relevance Vector Machine regression

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.neuroimage.2010.07.068 Byline: Federico De Martino, Aline W. de Borst, Giancarlo Valente, Rainer Goebel, Elia Formisano Abstract: The combination of electroencephalography (EEG) and functional Magnetic Resonan... Full description

Journal Title: NeuroImage 15 May 2011, Vol.56(2), pp.826-836
Main Author: De Martino, Federico
Other Authors: de Borst, Aline W , Valente, Giancarlo , Goebel, Rainer , Formisano, Elia
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 1053-8119 ; E-ISSN: 1095-9572 ; DOI: 10.1016/j.neuroimage.2010.07.068
Link: https://www.sciencedirect.com/science/article/pii/S1053811910010608
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recordid: elsevier_sdoi_10_1016_j_neuroimage_2010_07_068
title: Predicting EEG single trial responses with simultaneous fMRI and Relevance Vector Machine regression
format: Article
creator:
  • De Martino, Federico
  • de Borst, Aline W
  • Valente, Giancarlo
  • Goebel, Rainer
  • Formisano, Elia
subjects:
  • Medicine
ispartof: NeuroImage, 15 May 2011, Vol.56(2), pp.826-836
description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.neuroimage.2010.07.068 Byline: Federico De Martino, Aline W. de Borst, Giancarlo Valente, Rainer Goebel, Elia Formisano Abstract: The combination of electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) has been proposed as a tool to study brain dynamics with both high temporal and high spatial resolution. Integration through EEG-fMRI trial-by-trial coupling has been proposed as a method to combine the different data sets and achieve temporal expansion of the fMRI data (Eichele et al., 2005). To fully benefit of this type of analysis simultaneous EEG-fMRI acquisitions are necessary (Debener et al., 2006). Here we address the issue of predicting the signal in one modality using information from the other modality. We use multivariate Relevance Vector Machine (RVM) regression to "learn" the relation between fMRI activation patterns and simultaneously acquired EEG responses in the context of a complex cognitive task entailing an auditory cue, visual mental imagery and a control visual target. We show that multivariate regression is a valuable approach for predicting evoked and induced oscillatory EEG responses from fMRI time series. Prediction of EEG from fMRI is largely influenced by the overall filtering effects of the hemodynamic response function. However, a detailed analysis of the auditory evoked responses shows that there is a small but significant contribution of single trial modulations that can be exploited for linking spatially-distributed patterns of fMRI activation to specific components of the simultaneously-recorded EEG signal. Article History: Received 29 November 2009; Revised 5 July 2010; Accepted 28 July 2010
language: eng
source:
identifier: ISSN: 1053-8119 ; E-ISSN: 1095-9572 ; DOI: 10.1016/j.neuroimage.2010.07.068
fulltext: fulltext
issn:
  • 1053-8119
  • 10538119
  • 1095-9572
  • 10959572
url: Link


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The combination of electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) has been proposed as a tool to study brain dynamics with both high temporal and high spatial resolution. Integration through EEG-fMRI trial-by-trial coupling has been proposed as a method to combine the different data sets and achieve temporal expansion of the fMRI data (Eichele et al., 2005). To fully benefit of this type of analysis simultaneous EEG-fMRI acquisitions are necessary (Debener et al., 2006).

Here we address the issue of predicting the signal in one modality using information from the other modality. We use multivariate Relevance Vector Machine (RVM) regression to “learn” the relation between fMRI activation patterns and simultaneously acquired EEG responses in the context of a complex cognitive task entailing an auditory cue, visual mental imagery and a control visual target. We show that multivariate regression is a valuable approach for predicting...

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The combination of electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) has been proposed as a tool to study brain dynamics with both high temporal and high spatial resolution. Integration through EEG-fMRI trial-by-trial coupling has been proposed as a method to combine the different data sets and achieve temporal expansion of the fMRI data (Eichele et al., 2005). To fully benefit of this type of analysis simultaneous EEG-fMRI acquisitions are necessary (Debener et al., 2006).

Here we address the issue of predicting the signal in one modality using information from the other modality. We use multivariate Relevance Vector Machine (RVM) regression to “learn” the relation between fMRI activation patterns and simultaneously acquired EEG responses in the context of a complex cognitive task entailing an auditory cue, visual mental imagery and a control visual target. We show that multivariate regression is a valuable approach for predicting...

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date2011-05-15