This study investigates the correspondence between the brain's functional architecture during activation and rest. Using data from the BrainMap database, which contains over 30,000 human subjects, the researchers identified major activation networks. They also analyzed resting-state functional magnetic resonance imaging (fMRI) data from 36 subjects to identify major resting-state networks. The results showed that the major functional networks observed during activation closely match those observed during rest, suggesting that the brain's functional networks are continuously active even when at rest. The study used independent component analysis (ICA) to decompose the data into independent networks. The ICA analysis of the BrainMap data and resting-state fMRI data revealed that the major functional networks are similar across both conditions. The resting-state networks were found to correspond to known functional networks, such as the default mode network, visual networks, sensorimotor networks, and auditory networks. These networks were also found to correspond to specific behavioral domains, indicating that they are functionally relevant. The study also found that the resting-state networks are composed of subnetworks that correspond to different functional tasks. These subnetworks were identified using ICA and showed a high degree of correspondence between activation and resting-state networks. The results suggest that the brain's functional networks are continuously active and dynamically changing, even when the brain is at rest. The study highlights the importance of resting-state fMRI in understanding the brain's functional architecture. It shows that resting-state networks are not just passive states but are actively involved in the brain's function. The results also suggest that resting-state fMRI can be used to identify functional networks that are relevant to various cognitive and behavioral tasks. The study provides a framework for understanding the brain's functional architecture and its dynamic changes during different states of activity.This study investigates the correspondence between the brain's functional architecture during activation and rest. Using data from the BrainMap database, which contains over 30,000 human subjects, the researchers identified major activation networks. They also analyzed resting-state functional magnetic resonance imaging (fMRI) data from 36 subjects to identify major resting-state networks. The results showed that the major functional networks observed during activation closely match those observed during rest, suggesting that the brain's functional networks are continuously active even when at rest. The study used independent component analysis (ICA) to decompose the data into independent networks. The ICA analysis of the BrainMap data and resting-state fMRI data revealed that the major functional networks are similar across both conditions. The resting-state networks were found to correspond to known functional networks, such as the default mode network, visual networks, sensorimotor networks, and auditory networks. These networks were also found to correspond to specific behavioral domains, indicating that they are functionally relevant. The study also found that the resting-state networks are composed of subnetworks that correspond to different functional tasks. These subnetworks were identified using ICA and showed a high degree of correspondence between activation and resting-state networks. The results suggest that the brain's functional networks are continuously active and dynamically changing, even when the brain is at rest. The study highlights the importance of resting-state fMRI in understanding the brain's functional architecture. It shows that resting-state networks are not just passive states but are actively involved in the brain's function. The results also suggest that resting-state fMRI can be used to identify functional networks that are relevant to various cognitive and behavioral tasks. The study provides a framework for understanding the brain's functional architecture and its dynamic changes during different states of activity.