Fractal analysis of spontaneous fluctuations of the BOLD signal in the human brain networks.

摘要: Purpose To investigate what extent brain regions are continuously interacting during resting-state, independent component analyses (ICA) was applied to analyze resting-state functional MRI (RS-fMRI) data. According the analyzed results, it surprisingly found that low frequency fluctuations (LFFs), which belong 1/f signal (a with power spectrum whose spectral density is inversely proportional frequency), have been classified into groups using ICA; furthermore, spatial distributions of these within were resemble different networks, manifests characteristics RS LFFs distinct across networks. In our work, we model in fractal further this distinction. Materials and Methods Twenty healthy participants got involved study. They scanned acquire RS-fMRI The acquired data first processed ICA obtain networks resting brain. Afterward, blood-oxygenation level dependent (BOLD) signals for obtaining parameter α. Results α significantly vary reveals characteristic differs prior literatures, difference could be brought by discrepancy hemodynamic response amplitude (HRA) between Hence, also performed computational simulation discover relationship α HRA. Based on HRA highly linear-correlated revealed α. Conclusion Our results support origin contains arterial fluctuations. addition commonly used method such as synchrony analysis analysis, another approach, suggested acquiring information responses means J. Magn. Reson. Imaging 2014;39:1118–1125. © 2013 Wiley Periodicals, Inc.