The article reviews the methods and challenges in analyzing resting-state functional magnetic resonance imaging (fMRI) data, focusing on seed-based correlation analysis (SCA) and independent component analysis (ICA). It highlights the potential of resting-state fMRI in understanding brain dynamics and connectivity patterns across multiple large-scale networks. The review discusses the strengths and limitations of SCA and ICA, emphasizing the importance of methodological and interpretative issues. Key points include the spatial and spectral characteristics of resting-state networks (RSNs), the impact of physiological noise, and the complexities of group analysis. The authors emphasize the need for further technical optimization and experimental refinement to fully characterize the complexity of human neural functional architecture.The article reviews the methods and challenges in analyzing resting-state functional magnetic resonance imaging (fMRI) data, focusing on seed-based correlation analysis (SCA) and independent component analysis (ICA). It highlights the potential of resting-state fMRI in understanding brain dynamics and connectivity patterns across multiple large-scale networks. The review discusses the strengths and limitations of SCA and ICA, emphasizing the importance of methodological and interpretative issues. Key points include the spatial and spectral characteristics of resting-state networks (RSNs), the impact of physiological noise, and the complexities of group analysis. The authors emphasize the need for further technical optimization and experimental refinement to fully characterize the complexity of human neural functional architecture.