This study presents a comprehensive functional interpretation of intrinsic connectivity networks (ICNs) using a neuroinformatics approach based on the BrainMap behavioral taxonomy and a stratified, data-driven ordering of cognitive processes. ICNs, identified through independent component analysis (ICA) of resting state and task-based neuroimaging data, are shown to correspond to specific cognitive functions such as emotion, perception, interoception, and action. The BrainMap database, which contains metadata from over 1.4 million experiments, was used to assess the functional properties of ICNs by analyzing their relationships to various cognitive domains and tasks. Hierarchical clustering analysis (HCA) was applied to group ICNs based on their metadata, revealing distinct clusters of networks with similar functional characteristics. The results demonstrate that ICNs can be mapped to specific metadata combinations, indicating their involvement in functionally unique operations. The study also highlights the importance of combining brain imaging data with behavioral metadata to gain a more accurate understanding of the functional organization of the brain. The findings provide a resource for future interpretations of brain networks in resting state studies and contribute to the understanding of functional connections across the entire brain. The study also addresses the limitations of resting state data in capturing the full range of cognitive functions and emphasizes the need for integrating behavioral and neuroimaging data to improve the interpretation of brain networks. The results suggest that ICNs can be used to infer the functional roles of different brain regions and processes, and that their analysis can provide insights into the complex relationships between brain function and behavior. The study also discusses the implications of these findings for future research in cognitive neuroscience and the potential for using ICNs to better understand the organization of the human brain.This study presents a comprehensive functional interpretation of intrinsic connectivity networks (ICNs) using a neuroinformatics approach based on the BrainMap behavioral taxonomy and a stratified, data-driven ordering of cognitive processes. ICNs, identified through independent component analysis (ICA) of resting state and task-based neuroimaging data, are shown to correspond to specific cognitive functions such as emotion, perception, interoception, and action. The BrainMap database, which contains metadata from over 1.4 million experiments, was used to assess the functional properties of ICNs by analyzing their relationships to various cognitive domains and tasks. Hierarchical clustering analysis (HCA) was applied to group ICNs based on their metadata, revealing distinct clusters of networks with similar functional characteristics. The results demonstrate that ICNs can be mapped to specific metadata combinations, indicating their involvement in functionally unique operations. The study also highlights the importance of combining brain imaging data with behavioral metadata to gain a more accurate understanding of the functional organization of the brain. The findings provide a resource for future interpretations of brain networks in resting state studies and contribute to the understanding of functional connections across the entire brain. The study also addresses the limitations of resting state data in capturing the full range of cognitive functions and emphasizes the need for integrating behavioral and neuroimaging data to improve the interpretation of brain networks. The results suggest that ICNs can be used to infer the functional roles of different brain regions and processes, and that their analysis can provide insights into the complex relationships between brain function and behavior. The study also discusses the implications of these findings for future research in cognitive neuroscience and the potential for using ICNs to better understand the organization of the human brain.