Abstract
Seed-based cross-correlation analysis (sCCA) and independent component analysis have been widely employed to extract functional networks from the resting state functional magnetic resonance imaging data. However, the results of sCCA, in terms of both connectivity strength and network topology, can be sensitive to seed selection variations. ICA avoids the potential problems due to seed selection, but choosing which component(s) to represent the network of interest could be subjective and problematic. In this study, we proposed a seed-based iterative cross-correlation analysis (siCCA) method for resting state brain network analysis. The method was applied to extract default mode network (DMN) and stable task control network (STCN) in two independent datasets acquired from normal adults. Compared with the networks obtained by traditional sCCA and ICA, the resting state networks produced by siCCA were found to be highly stable and independent on seed selection. siCCA was used to analyze DMN in first-episode major depressive disorder (MDD) patients. It was found that, in the MDD patients, the volume of DMN negatively correlated with the patients' social disability screening schedule scores.
Highlights
Resting state network (RSN) analysis utilizing the low frequency (,0.1 Hz) fluctuations (LFFs) in resting state functional magnetic resonance imaging data has been extensively used for studying the intrinsic functional architectures of the brain [1,2,3,4,5,6]
Results seed-based iterative cross-correlation analysis (siCCA) vs. seed-based cross-correlation analysis (sCCA) Figure 2 shows the results regarding default mode network (DMN) derived from the two datasets
Different aspects of the seed ROI, affected the results differentially (Fig. 2D). For both the two datasets, the DMNs derived from the concentric ROIs (PCC13, PCC1-6 and PCC1-9) had the least difference (VBSnet = 0.855 and 0.849 respectively), while those derived using seeds located in different brain regions (PCC2-6 vs. vmPFC-6) exhibited the largest difference (VBSnet = 0.464 and 0.395 respectively)
Summary
Resting state network (RSN) analysis utilizing the low frequency (,0.1 Hz) fluctuations (LFFs) in resting state functional magnetic resonance imaging (rs-fMRI) data has been extensively used for studying the intrinsic functional architectures of the brain [1,2,3,4,5,6]. There are two commonly used methods for rs-fMRI data analysis: seed-based cross-correlation analysis (sCCA) [1] and independent component analysis [7,8]. The former measures the connectivity strength of brain voxels with a seed region-of-interest (ROI), and the latter mathematically decompose the LFFs into independent components, which are considered to represent different functional networks. Both sCCA and ICA have some shortcomings. The results of sCCA, in terms of both connectivity strength and network topology, may depend on the selection of seed ROI [4,10]
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