29 Characterization of brain networks Based on the spatial patterns of correlated time series that are quite reliably identified in resting state BOLD signals, several intrinsic brain networks
have been identified such as the default-mode network (DMN), the dorsal attention network (DAN) or the salience network (SN). Within these networks, brain regions show increased functional connectivity on time-scales of seconds to minutes. Alterations in resting state networks are found in several neuropsychiatric conditions such as Alzheimer s disease.30 Early studies with simultaneous EEG-fMRI for Inhibitors,research,lifescience,medical the resting state have tried to identifiy BOLD correlates of specific frequency patterns such as alpha oscillations.31-33 MS-275 concentration However, in order to characterize network dynamics
the idea emerged of relating the EEG signal to the functional connectivity within and between Inhibitors,research,lifescience,medical networks. For example, Inhibitors,research,lifescience,medical Hlinka et al showed that 70% of the DMN variance of functional connectivity is explained by delta and beta oscillations.34 Scheeringa et al demonstrated that when alpha power increases, BOLD connectivity between the primary visual cortex and occipital regions decreases as well as the negative coupling between visual areas and regions of the DMN.35 Chang et al investigated the functional connectivity between the DMN, DAN, and SN. They found an inverse relationship between alpha power and the strength of connectivity between DMN and DAN. Moreover, alpha power correlated with the spatial Inhibitors,research,lifescience,medical extent of anticorrelation between DMN and DAN.36 While Inhibitors,research,lifescience,medical these studies were performed form the perspective of linking established fMRI resting state networks and to investigate the relationship to EEG power of distinct frequency bands, another approach is to relate fMRI patterns with more complex patterns STK38 of EEG organization. For example, the
topographic representation of the EEG remains stable over periods of around 100 ms. These quasistable and unique distributions have been termed “microstates.”37 Microstates reflect the summation of concomitant neuronal activity across brain regions rather than activity specific to any frequency band. Alterations in microstates have been demonstrated in several psychiatric disorders such as schizophrenia.38 Using simultaneous EEG-fMRI, several authors have now described the relationship between EEG microstates and BOLD resting-state networks.39-41 Another very interesting approach will be the investigation of the relationship of EEG coherence patterns and fMRI connectivity.