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| Methods | Purposes | Strengths | Limitations |
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| Seed-based FC analysis | Estimating correlations between the predefined voxel or regions and the rest of the brain voxels | (i) Easy to calculate and understand | (i) Requires a priori selection of ROI, which may lead to potential biases |
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| Regional homogeneity | Using Kendall’s coefficient concordance to measure the similarity of a given voxel with its nearest neighbors based on the BOLD time-series | (i) Easy to calculate and understand | (i) Potential biases attached to prior seed selection |
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| Amplitude of low-frequency fluctuations | Estimating the intensity of regional spontaneous brain activity by calculating the voxel-wise magnitude within a defined low-frequency range | (i) Can serve as a potential confounding variable when investigating functional connectivity and network | (i) Sensitive to physiological noise, which makes fractional ALFF (fALFF) approach a better choice |
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| Principal component analysis | Finding spatial and temporal components that capture as much of the variability of the data based on decorrelation as possible | (i) Can verify the facticity of difference in the activations between conditions or groups without specifying any prior knowledge of the form of BOLD response or the structure of the experimental design | (i) Based on strong assumptions like linearity, orthogonal principal components, and high signal noise ratio |
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| Independent component analysis | Separating distinct resting-state networks that are spatially or temporally independent of each other and identifying noise within the BOLD signal | (i) Can generate spatially or temporally distributed DM functional connectivity patterns with relatively few a priori assumptions | (i) May be less sensitive to interindividual variation in the composition of such networks and may be more likely to produce errors at the group level if a network is presented across multiple components in some subjects. |
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| Graph theory | Describing the topology of the functional brain networks by calculating connectional characteristics of the graph comprised of nodes (voxels) and edges (connections between voxels) | (i) Directly describes and compares different brain networks utilizing topological parameters | (i) Difficult to interpret |
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