From EEG to BOLD: Brain mapping and estimating transfer functions in simultaneous EEG-fMRI acquisitions

Abstract

Simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) aims to disentangle the description of brain processes by exploiting the advantages of each technique. Most studies in this field focus on exploring the relationships between fMRI signals and the power spectrum at some specific frequency bands (alpha, beta, etc.). On the other hand, brain mapping of EEG signals (e.g., interictal spikes in epileptic patients) usually assumes an haemodynamic response function for a parametric analysis applying the GLM, as a rough approximation. The integration of the information provided by the high spatial resolution of MR images and the high temporal resolution of EEG may be improved by referencing them by transfer functions, which allows the identification of neural driven areas without strong assumptions about haemodynamic response shapes or brain haemodynamic's homogeneity. The difference on sampling rate is the first obstacle for a full integration of EEG and fMRI information. Moreover, a parametric specification of a function representing the commonalities of both signals is not established. In this study, we introduce a new data-driven method for estimating the transfer function from EEG signal to fMRI signal at EEG sampling rate. This approach avoids EEG subsampling to fMRI time resolution and naturally provides a test for EEG predictive power over BOLD signal fluctuations, in a well-established statistical framework. We illustrate this concept in resting state (eyes closed) and visual simultaneous fMRI-EEG experiments. The results point out that it is possible to predict the BOLD fluctuations in occipital cortex by using EEG measurements.

A personalized history of EEG–fMRI integration

“…Recently, based on EEG-fMRI experiments it was proposed that EEG alpha oscillations influence evoked BOLD responses (Becker et al, 2011;Scheeringa et al, 2011b). Finally, groups have tried to establish a more general relationship between EEG oscillations and the BOLD signal on the search for a transfer function between the two (Kilner et al, 2005;Mulert et al, 2010;Rosa et al, 2010b;Sato et al, 2010;Scheeringa et al, 2011a).…”

EEG/fMRI fusion based on independent component analysis: Integration of data-driven and model-driven methods

“…Thus, the imaging of the localizable foci can be enhanced largely even using the canonical HRF. Considering the variability of the HRF context, a method that is independent of a speci¯c shape of BOLD response may be an attractive alternative (Benar et al, 2002;Gotman, 2008;Sturzbecher et al, 2009;Sato et al, 2010). However, the main obstacle for these approaches is the large number of parameters.…”

Spontaneous EEG-Functional MRI in Mesial Temporal Lobe Epilepsy: Implications for the Neural Correlates of Consciousness

“…The result has been a greater preference for the utilization of data-driven or exploratory methods such as ICA and temporal clustering analysis [71, 72]. A major advantage of data-driven analyses is the flexibility to circumvent a priori hypothesized HRF while simultaneously deducing the activated fMRI response [73]. However, it suffers an inherent deficit: the lack of statistical measure to assess the intended hypothesis.…”