The article reviews the advances and challenges in analyzing resting-state functional magnetic resonance imaging (fMRI) data. Over the past 15 years, resting-state fMRI has become a key tool in neuroscience for studying brain connectivity. Functional connectivity methods, such as seed-based correlation analysis (SCA) and independent component analysis (ICA), have been applied to various fields, including cognitive psychology and clinical research. However, methodological and interpretative issues remain to be resolved. The article discusses the similarities and differences between SCA and ICA, and highlights the importance of technical optimization and experimental refinement to better understand the complexity of human neural architecture. Resting-state networks (RSNs) are consistent across individuals, stages of cognitive development, levels of consciousness, and even species. They reflect intrinsic neural activity and are associated with various cognitive and behavioral functions. RSNs can be reliably detected across individuals and groups using different analysis techniques. However, the interpretation of RSNs is influenced by factors such as the choice of seed regions, model order, and preprocessing steps. The article also discusses the spectral characteristics of RSNs, noting that they are often characterized as low-frequency BOLD fluctuations, though higher frequencies may also contribute. The use of ICA and other methods can help reduce confounding effects from physiological noise and other artifacts. The article also addresses the challenges of analyzing resting-state fMRI data, including the impact of physiological noise, the need for appropriate preprocessing, and the potential for variability in results due to different analytical approaches. It highlights the importance of using standardized methods and careful interpretation of results to ensure the validity of findings. The article concludes that while resting-state fMRI has made significant contributions to neuroscience, further research is needed to fully understand the complex nature of brain connectivity and its implications for cognitive and behavioral functions.The article reviews the advances and challenges in analyzing resting-state functional magnetic resonance imaging (fMRI) data. Over the past 15 years, resting-state fMRI has become a key tool in neuroscience for studying brain connectivity. Functional connectivity methods, such as seed-based correlation analysis (SCA) and independent component analysis (ICA), have been applied to various fields, including cognitive psychology and clinical research. However, methodological and interpretative issues remain to be resolved. The article discusses the similarities and differences between SCA and ICA, and highlights the importance of technical optimization and experimental refinement to better understand the complexity of human neural architecture. Resting-state networks (RSNs) are consistent across individuals, stages of cognitive development, levels of consciousness, and even species. They reflect intrinsic neural activity and are associated with various cognitive and behavioral functions. RSNs can be reliably detected across individuals and groups using different analysis techniques. However, the interpretation of RSNs is influenced by factors such as the choice of seed regions, model order, and preprocessing steps. The article also discusses the spectral characteristics of RSNs, noting that they are often characterized as low-frequency BOLD fluctuations, though higher frequencies may also contribute. The use of ICA and other methods can help reduce confounding effects from physiological noise and other artifacts. The article also addresses the challenges of analyzing resting-state fMRI data, including the impact of physiological noise, the need for appropriate preprocessing, and the potential for variability in results due to different analytical approaches. It highlights the importance of using standardized methods and careful interpretation of results to ensure the validity of findings. The article concludes that while resting-state fMRI has made significant contributions to neuroscience, further research is needed to fully understand the complex nature of brain connectivity and its implications for cognitive and behavioral functions.