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Activities and Seminars

Juliane Britz. Rapid resting-state network dynamics
 
Date: Jun 14, 2012

What: Rapid resting-state network dynamics

Where: BCBL auditorium


When: 12 noon


Resting-state functional connectivity studies with fMRI show that the brain is intrinsically organized into large-scale functional networks (RSNs) for which the hemodynamic signature is stable for about 10 s. Spatial analyses of EEG topography at rest also show discrete epochs of stable global brain states (so-called microstates), but they remain quasi-stationary for only about 100 ms. In order to test the relationship between the rapidly fluctuating EEG-defined microstates and the slowly oscillating fMRI-defined resting states, we recorded the EEG from 64 channels in the scanner while subjects rested with their eyes closed. Conventional EEG-microstate analysis determined the typical four EEG topographies that dominated across all subjects. The convolution of the time course of these microstates with the hemodynamic response function allowed to fit a linear model to the fMRI BOLD responses and revealed four distinct distributed networks. These RSNs have previously been attributed to auditory processing, visual processing, attention reorientation, and subjective interoceptive–autonomic processing. Surprisingly, the convolution with the HRF did not remove any information-carrying signal from the microstate sequence. The microstate sequences showed the same relative temporal behavior before and after convolution with the HRF, i.e. at temporal scales that are two orders of magnitude apart, which suggests that their time course is scale-free. We then deployed a wavelet-based fractal analysis that allowed the determination of scale-free behavior. We showed that microstate sequences are scale-free over 6 dyadic scales covering the 256ms–16s range. The degree of long-range dependency was maintained when shuffling the local microstate labels but became indistinguishable from white noise when equalizing microstate durations, which indicates that temporal dynamics are their key characteristic. Taken together, the four typical EEG microstates represent the electrophysiological correlate of four RSNs and their monofractal characteristics show that they are fluctuating much more rapidly than fMRI alone suggests. Furthermore, such scale-free dynamics can only arise when a system operates at a critical state.