Mining EEG-fMRI using independent component analysis

Abstract

Independent component analysis (ICA) is a multivariate approach that has become increasingly popular for analyzing brain imaging data. In contrast to the widely used general linear model (GLM) that requires the user to parameterize the brain's response to stimuli, ICA allows the researcher to explore the factors that constitute the data and alleviates the need for explicit spatial and temporal priors about the responses. In this paper, we introduce ICA for hemodynamic (fMRI) and electrophysiological (EEG) data processing, and one of the possible extensions to the population level that is available for both data types. We then selectively review some work employing ICA for the decomposition of EEG and fMRI data to facilitate the integration of the two modalities to provide an overview of what is available and for which purposes ICA has been used. An optimized method for symmetric EEG-fMRI decomposition is proposed and the outstanding challenges in multimodal integration are discussed.

Figure 1

Scatterplot of two independent, mixed signals illustrates the need for higher order statistics as an alternative to orthogonal projection as a means to faithfully un-mix the data.

Figure 2

Illustration of artefact removal from EEG data by means of ICA. A Section of selected channels from a multi-channel EEG recording is shown, with ongoing EEG oscillations in the alpha range evident at occipital electrodes and two eye blinks at fronto-polar channels. B Un-mixing of the EEG data into a set of independent components. Each component can be described on the basis of a spatial pattern (map) and a time course (activation). C Back-projection of all but components 1 and 2 reveals artefact-corrected EEG data.

Figure 3

Prototypical independent components from fMRI data. The figure shows the activation maps of nine independent components from an event related fMRI study (Eichele, et al., 2008b) rendered onto the MNI template at representative transverse slices. The maps are shown in neurological convention (left hemisphere is on the left). Activations are plotted in red, deactivations in blue. To the left of each map, the hemodynamic response functions within the respective ICs as estimated via deconvolution from 1-20 seconds after stimulus onset, in arbitrary (range-scaled) amplitude units are displayed. The group average from the 13 participants is plotted as a solid line, error bars indicate ±1 S.E.M., dotted lines represent all individual estimates. The empirical HRFs were used to estimate single trial amplitudes in the fMRI data.

Figure 4

EEG-fMRI integration with deconvolution. The spatial ICA of the fMRI data results in individual maps and timecourses. The single trial HRF amplitude modulation estimated from the IC timecourses are used for prediction of EEG activity. In order to recover the amplitude modulation (AM), the pseudoinverse of a convolution matrix generated from the stimulus timing and an assumed HRF length is multiplied with the IC timecourse, yielding individually and regionally specific HRFs. These HRFs are then convolved separately with each stimulus onset, yielding a design matrix (X) with predictors for each trial 1..n. The regression of the design onto the IC timecourse (y) yields the single trial amplitude modulation for this IC (β₁.. β_n). In the EEG, a group temporal ICA provides independent source timecourses, whose trial-by-trial modulation are extracted and correlated with the fMRI activity.