The hemodynamic response of the alpha rhythm: An EEG/fMRI study

Abstract

EEG was recorded during fMRI scanning of 16 normal controls in resting condition with eyes closed. Time variations of the occipital alpha band amplitudes were correlated to the fMRI signal variations to obtain insight into the hemodynamic correlates of the EEG alpha activity. Contrary to earlier studies, no a priori assumptions were made on the expected shape of the alpha band response function (ARF). The ARF of different brain regions and subjects were explored and compared. It was found that: (1) the ARF of the thalamus is mainly positive. (2) The ARFs at the occipital and left and right parietal points are similar in amplitude and timing. (3) The peak time of the thalamus is a few seconds earlier than that of occipital and parietal cortex. (4) No systematic BOLD activity was found preceding the alpha band activity, although in the two subjects with the strongest alpha band power such correlation was present. (5) There is a strong and immediate positive correlation at the eyeball, and a strong negative correlation at the back of the eye. Furthermore, it was found that in one subject the cortical ARF was positive, contrary to the other subjects. Finally, a cluster analysis of the observed ARF, in combination with a Modulated Sine Model (MSM) fit to the estimated ARF, revealed that within the cortex the ARF peak time shows a spatial pattern that may be interpreted as a traveling wave. The spatial pattern of alpha band response function represents the combined effect of local differences in electrical alpha band activity and local differences in the hemodynamic response function (HRF) onto these electrical activities. To disentangle the contributions of both factors, more advanced integration of EEG inverse modeling and hemodynamic response modeling is required in future studies.

Introduction

EEG-signals consist of the potential differences recorded from the scalp, which are caused by small electric currents generated by interacting neurons in the cortex. Typical phenomena that can be observed in the spontaneous EEG are e.g. the alpha-rhythm (10 Hz oscillations), sleep spindles (12 to 16 Hz oscillations), inter-ictal epileptic spikes (Niedermeyer and Lopes da Silva, 2004). By using multiple electrodes, and analyzing the spatial distribution of the EEG with current dipole models, one can estimate the locations of the EEG generators through inverse modeling (Manshanden et al., 2002). However, due to the mixing of signals from multiple sources, there are strong limitations on these dipole localization techniques and therefore detailed knowledge on the generators of EEG phenomena is not easy to obtain from scalp recordings.

With functional Magnetic Resonance Imaging (fMRI) stacks of 2D images are repeatedly acquired in two or more conditions (typically while a subject is performing a task or not) and by a statistical comparison activated brain regions can be detected. These activation maps have a relatively high spatial resolution (1 to 4 mm) but since the contrast is based upon local changes in oxygenation level of the blood (BOLD), the temporal resolution of fMRI is limited to about 2 s. Therefore, transient EEG phenomena are not directly visible in the fMRI, although appearance or disappearance of EEG features might cause part of the fMRI observed signal variations.

Recent technical developments (dedicated EEG hardware and artefact removal, Lemieux et al., 1997, Lemieux et al., 1999, Goldman et al., 2000, Niazy et al., 2005, Gonçalves et al., submitted for publication) have made it possible to record EEG during fMRI scanning. Co-registration of EEG and fMRI has added value in order to investigate neurophysiological mechanisms underlying brain functional states. The general approach of data analysis is to use the EEG to detect the occurrence of a given event (inter-ictal epileptic spike, alpha burst), and to translate this into a reference function that can be correlated with the fMRI. In this way, fMRI may help to localize the generators of EEG phenomena, without the need to rely on ambiguous dipole models. This idea has already been successfully applied to localize inter-ictal spikes, to study the alpha rhythm and sleep phenomena (e.g. Salek-Haddadi et al., 2003).

However, the scientific benefit of co-registered EEG/fMRI is not limited to solving the localization problem. In our view, this technique may offer novel insights to obtain a better understanding of EEG phenomena. Thus the co-registration of EEG/fMRI may reveal additional brain regions the activity of which is related to a given EEG phenomenon, although it may not be detected using scalp EEG alone, because they are too deep or lack the required amount of synchrony to produce a measurable potential difference on the scalp. Furthermore, for each of the detected regions that correlates with the observed EEG, it can be determined whether it displays an increase of BOLD (activation) or a decrease (de-activation). Finally, with more advanced data analysis methods, it can be determined whether and on which time scale activation and de-activation occur. It might even be studied whether there are changes in fMRI BOLD signals that precede the observed EEG. In this paper we propose an analysis method to study these aspects of the spontaneous alpha rhythm.

The alpha rhythm, consisting of oscillations in the frequency range 8–12 Hz, dominates the EEG of an awake subject with eyes closed. It was one of the first phenomena observed in the human EEG by Berger (1929) and has been studied exhaustively. For example, Lippold (1970), based on the observation of a close correlation between eye movements and the EEG alpha band signal put forward the hypothesis that eye movements were the generators of the 10 Hz EEG oscillations. This hypothesis was rejected by Chapman et al. (1971) and Cavonius and Estévez-Uscanga (1974) and currently the cortical origin of the alpha rhythm and its relation to thalamic activity is well established (e.g. Steriade et al., 1990, Lopes da Silva, 1991, Nunez et al., 2001, Hughes and Crunelli, 2005. New insight into the brain regions involved has been obtained in recent EEG/fMRI studies (Goldman et al., 2002, Laufs et al., 2003a, Laufs et al., 2003b, Moosmann et al., 2003, Gonçalves et al., 2006a).

All these studies were based on a fixed pre-defined relation between BOLD signal changes and EEG. This relation was described by the convolution between the alpha band power and a standard hemodynamic response function (HRF). According to this approach, it is implicitly assumed that HRF to local alpha band activity is the same for all sites of the brain. However, since it has been demonstrated before that HRFs tend to vary over subject, stimulus modality and stimulus duration (Boynton et al., 1996, Aguirre et al., 1998, Glover, 1999), regional and inter-subject differences might be masked by the use of a constant HRF model. Another implicit assumption in previous EEG/fMRI studies on the alpha band is that the observed EEG represents the local alpha band activity of each brain region. Also this assumption is questionable, because substantial timing differences between electrical activities of different brain regions might exist, whereas the EEG only represents the instantaneous superposition of all electrical activities within the brain. This assumption hampers the proper interpretation of EEG/fMRI correlation patterns.

The main objective of this investigation is to determine which brain regions, both cortical and sub-cortical, are engaged in the generation of alpha rhythms. In this paper we use co-registered EEG/fMRI to study the correlations between the occipital alpha band variations and changes in the fMRI signals. We estimate the response function corresponding to a linear filter with EEG alpha band input and BOLD as output. This response function is here called “Alpha band Response Function” (ARF) and should be interpreted as the combined effect of local changes in electrical activity and the resulting hemodynamic response. The ARF is determined for thalamus, occipital and parietal lobes because from EEG studies it is known that these regions play an important role in the generation of the alpha band rhythm. Furthermore, we study “artifactual” signals originating from the eye. Based on the data of several subjects, a new parametric hemodynamic response model is proposed to summarize our findings and to study timing effects on a time scale shorter than the fMRI repetition time. Finally, a cluster analysis is applied onto the significant single voxel ARFs to visualize the ARF pattern of the single subject’s alpha rhythm.