title: Fractal analysis of spontaneous fluctuations of the BOLD signal in the human brain networks.
abstract: OBJECTIVE
  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 to the analyzed results, it was surprisingly found that low frequency fluctuations (LFFs), which belong to the 1/f signal (a signal with power spectrum whose power spectral density is inversely proportional to the frequency), have been classified into groups using ICA; furthermore, the spatial distributions of these groups within the brain were found to resemble the spatial distributions of different networks, which manifests that the signal characteristics of RS LFFs are distinct across networks. In our work, we applied the 1/f model in the fractal analyses to further investigate this distinction.
  
  
  METHODS
  Twenty healthy participants got involved in this study. They were scanned to acquire the RS-fMRI data. The acquired data were first processed with ICA to obtain the networks of the resting brain. Afterward, the blood-oxygenation level dependent (BOLD) signals of these networks were processed with the fractal analyses for obtaining the fractal parameter α.
  
  
  RESULTS
  α was found to significantly vary across networks, which reveals that the fractal characteristic of LFFs differs across networks. According to prior literatures, this difference could be brought by the discrepancy of hemodynamic response amplitude (HRA) between networks. Hence, in our work, we also performed the computational simulation to discover the relationship between α and HRA. Based on the simulation results, HRA is highly linear-correlated with the fractal characteristics of LFFs which is revealed by α.
  
  
  CONCLUSIONS
  Our results support that the origin of RS-fMRI signals contains arterial fluctuations. Hence, in addition to the commonly used method such as synchrony analysis and power spectral analysis, another approach, the fractal analysis, is suggested for acquiring the information of hemodynamic responses by means of RS-fMRI data.
authors.affiliation: Graduate Institute of Biomedical Engineering and Bioinformatics, National Taiwan University, Taipei, Taiwan.
authors.forename: Yi-Chia
authors.initials: YC
authors.lastname: Li
authors.forename: Yun-An
authors.initials: YA
authors.lastname: Huang
mesh_terms: D000328:Adult; D001783:Blood Flow Velocity / Q000502:physiology; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D002560:Cerebrovascular Circulation / Q000502:physiology*; D005260:Female; D017709:Fractals; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009415:Nerve Net / Q000502:physiology*; D012016:Reference Values; D015203:Reproducibility of Results; D012146:Rest / Q000502:physiology; D012680:Sensitivity and Specificity; D055815:Young Adult
keywords: 1/f; fractal analyses; hemodynamic response; low frequency fluctuations (LFFs); resting-state