Multimodal imaging: an evaluation of univariate and multivariate methods for simultaneous EEG/fMRI

De Martino, Federico; Valente, Giancarlo; de Borst, Aline W.; Esposito, Fabrizio; Roebroeck, Alard; Goebel, Rainer; Formisano, Elia

doi:10.1016/j.mri.2009.12.026

« Previous Next »Magnetic Resonance Imaging
Volume 28, Issue 8 , Pages 1104-1112, October 2010

Multimodal imaging: an evaluation of univariate and multivariate methods for simultaneous EEG/fMRI

Federico De Martino
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD Maastricht, The Netherlands
- Corresponding author. Tel.: +31 433884532; fax: +31 433884125.
Giancarlo Valente
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD Maastricht, The Netherlands
Aline W. de Borst
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD Maastricht, The Netherlands
- Department of Neuroscience, University of Pisa, Pisa, Italy
Fabrizio Esposito
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD Maastricht, The Netherlands
- Department of Neuroscience, University of Naples “Federico II”, 80125 Naples, Italy
Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD Maastricht, The Netherlands
Rainer Goebel
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD Maastricht, The Netherlands
Elia Formisano
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD Maastricht, The Netherlands

Received 11 October 2009; received in revised form 17 December 2009; accepted 21 December 2009. published online 25 January 2010.

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. Multimodal imaging techniques rely on the assumption of a common neuronal source for the different recorded signals. In order to maximally exploit the combination of these techniques, one needs to understand the coupling (i.e., the relation) between electroencephalographic (EEG) and fMRI blood oxygen level-dependent (BOLD) signals.

Recently, simultaneous EEG-fMRI measurements have been used to investigate the relation between the two signals. Previous attempts at the analysis of simultaneous EEG-fMRI data reported significant correlations between regional BOLD activations and modulation of both event-related potential (ERP) and oscillatory EEG power, mostly in the alpha but also in other frequency bands.

Beyond the correlation of the two measured brain signals, the relevant issue we address here is the ability of predicting the signal in one modality using information from the other modality. Using multivariate machine learning-based regression, we show how it is possible to predict EEG power oscillations from simultaneously acquired fMRI data during an eyes-open/eyes-closed task using either the original channels or the underlying cortically distributed sources as the relevant EEG signal for the analysis of multimodal data.

Keywords: Multimodal imaging, Univariate and multivariate methods, EEG/fMRI