We have shown with this study that fMRI can identify brain regions associated with changes in the alpha rhythm. Signal detected by the scalp EEG most likely comes from cortical areas near the recording electrode; however, the fMRI maps of alpha activity presented here include correlated BOLD signal in areas other than the posterior cortex, even though EEG from only posterior electrodes was used. What, then, is the possible role of these correlated regions? We propose that there are three types of regions highlighted by SITE mapping: (1) generator regions of the EEG rhythms themselves, (2) regions that are part of a generating circuit but that do not themselves directly generate the rhythms detected by scalp EEG (e.g. regions at depth such as the thalamus), and (3) regions where activity is correlated with the EEG but not causally linked to rhythm generation (for example, drowsiness corresponded to the alpha power in our experiment but it may have anatomic bases that are independent to the generators of the alpha rhythm). Clearly, the present experiment cannot distinguish among these three.
Our findings are consistent with the notion that the alpha rhythm is an index of reduced cortical activity, and that the thalamus is likely to play a role in producing this active stand-by state. The three occipital regions that showed negative correlation with the alpha rhythm in our experiment (indicating there was less activity in occipital cortex as indexed by BOLD when alpha power was high) also have been identified by others as separate alpha generator regions by independent component analysis (ICA) [26
Our finding that BOLD response in the insula correlates positively with alpha power replicates Sadato’s findings in PET [13
] as well as a study by Valdez-Sosa using EEG source modeling (personal communication). Indeed, this result may indicate a physiologic association between insular activity and the alpha rhythm instead of a direct role of the insula in alpha generation. Drowsiness did play a role in alpha modulation in our study in some subjects and thus might be a factor; however, Sadato’s experiment was performed in individuals with eyes open, suggesting that the correlation in the insula was not due solely to drowsiness.
We, as well as Sadato, showed a positive correlation with alpha power in the thalamus. However, both Larson and Lindgren found a negative correlation between alpha power and thalamic glucose metabolism. While at first these studies seem contradictory, they actually may highlight different thalamic contributions. Because of the low temporal resolution of FDG-PET (30 min), Larson and Lindgren were not able to look at phasic changes in the EEG on an individual subject level. Therefore, their experimental results may point to trait-like properties of alpha generation across subjects. Our findings with fMRI, as well as those of Sadato (collected in 90 s time points), instead reflect alpha modulation on an individual subject level, and thus may highlight the role of the thalamus in moment-to-moment wave generation.
Alpha power fluctuates relatively rapidly, so that delayed imaging [14
] or interleaved studies [13
] run the risk of decreased sensitivity or accuracy from misidentified periods of increase or decrease in alpha power. Thus, the development of tools for simultaneous collection of EEG and fMRI data is particularly significant. Compared to interleaved methods, it is also notable that the scanner noise in our studies was continuous and therefore unlikely to act as a modulator of alpha energy.
This study may also have important implications for interpretation of resting state brain activity. fMRI is a method of mapping changes in brain hemodynamics, and many studies utilize a resting state as a contrast to the activation task of interest. However, BOLD activity during this resting baseline varies widely. Some groups have investigated the variation present in baseline measures [27
], and have found high correlation among functionally connected regions. As the present study was conducted entirely while the subjects were at rest with their eyes closed, we have shown that alpha rhythm changes may account for at least some of the BOLD changes seen in baseline measures of alpha correlated brain regions.
Although subjects in our study reported being in various states of drowsiness in post-scan interviews, the relationship between depth of alpha rhythm modulation and drowsiness was not consistent. Nonetheless, the alpha rhythm modulation during at least some of the scans may have been due to fluctuating drowsiness levels. Thus, our fMRI maps may show BOLD correlation with drowsiness as well as with the alpha rhythm. Our results, however, were consistent across subject and scan, and are also consistent with existing theory on sources of the alpha rhythm as well as other imaging studies of non-drowsy subjects. Additional SITE experiments that distinguish between waking alpha rhythm and drowsiness are necessary to decipher which anatomical regions are involved in each.