This study investigates the consistency of resting-state functional networks in healthy subjects using tensor probabilistic independent component analysis (tensor-PICA) on resting-state functional MRI (fMRI) data. The research aims to identify coherent spatiotemporal patterns in the brain's resting state and quantify their consistency across subjects and sessions. The study found 10 distinct resting-state networks, including those related to motor function, visual processing, executive functioning, auditory processing, memory, and the default-mode network. These networks showed consistent spatial patterns and significant temporal dynamics, with BOLD signal changes up to 3%. Areas with high mean percentage BOLD signal changes were found to be more consistent and show less variation around the mean. The results suggest that the brain's baseline activity is dynamic and consistent across subjects, with percentage BOLD signal changes comparable to those observed in task-related experiments. The study also highlights the importance of considering both spatial and temporal characteristics of resting-state fluctuations, as well as their magnitude and consistency, in understanding the brain's resting state. The findings support the idea that resting-state networks are functionally relevant and can be used to study brain function and dysfunction. The study also discusses the limitations of the method, including the potential for linear decomposition to miss nonlinear functional connectivity and the challenges of interpreting individual subject-level functional connectivity from group-level findings. Overall, the study provides evidence that resting-state networks are consistent across subjects and show significant temporal dynamics, with implications for understanding brain function and dysfunction.This study investigates the consistency of resting-state functional networks in healthy subjects using tensor probabilistic independent component analysis (tensor-PICA) on resting-state functional MRI (fMRI) data. The research aims to identify coherent spatiotemporal patterns in the brain's resting state and quantify their consistency across subjects and sessions. The study found 10 distinct resting-state networks, including those related to motor function, visual processing, executive functioning, auditory processing, memory, and the default-mode network. These networks showed consistent spatial patterns and significant temporal dynamics, with BOLD signal changes up to 3%. Areas with high mean percentage BOLD signal changes were found to be more consistent and show less variation around the mean. The results suggest that the brain's baseline activity is dynamic and consistent across subjects, with percentage BOLD signal changes comparable to those observed in task-related experiments. The study also highlights the importance of considering both spatial and temporal characteristics of resting-state fluctuations, as well as their magnitude and consistency, in understanding the brain's resting state. The findings support the idea that resting-state networks are functionally relevant and can be used to study brain function and dysfunction. The study also discusses the limitations of the method, including the potential for linear decomposition to miss nonlinear functional connectivity and the challenges of interpreting individual subject-level functional connectivity from group-level findings. Overall, the study provides evidence that resting-state networks are consistent across subjects and show significant temporal dynamics, with implications for understanding brain function and dysfunction.