Scale-Free Dynamics of Resting-State fMRI Microstates - Nurhan Erbil; Gopikrishna Deshpande (Fractal and Fractional)
The functional significance of RSNs is examined via simultaneous EEG-fMRI studies on the basis of the relation of RSNs with different frequency bands of EEG and EEG-based microstate analysis. In this study, we try to identify RSNs from microstates of cortical surface maps of the BOLD signal. In addition, the scale-free dynamics of these map sequences were also examined. The structural and resting state functional MRI images were acquired on a 3T scanner with three different fMRI acquisition protocols from seven subjects. Microstate segmentations from EEG, fMRI, and simulated data were evaluated. Wavelet-based fractal analysis was performed on map sequence time series and the Hurst exponent (H) was calculated. By using HRF-deconvolved fMRI time series, the effect of the HRF (hemodynamic response function) on fMRI-derived microstates was tested. The fMRI map sequence has a system with a memory system smaller than 16 s. When the HRF was deconvolved, the duration of the memory of the system was reduced to 4 s. On the other hand, the results of simulation data indicated that these systems are specific to the resting state BOLD signal. Similar to EEG microstates, fMRI also has microstates and both of them have scale-free dynamics. fMRI microstate dynamics have two different components, one is related to the HRF and the other is independent of the HRF. The significance of fMRI microstates and their relation with RSNs need to be further studied.