This study explores the functional interpretations of intrinsic connectivity networks (ICNs) using a neuroinformatics approach based on the BrainMap database. The authors apply independent component analysis (ICA) to resting state fMRI data and hierarchical clustering analysis (HCA) to behavioral metadata to identify and characterize ICNs. The results provide a detailed functional explication of these networks, linking them to specific cognitive processes such as emotion, interoception, motor and visuospatial processing, and vision. The study highlights the importance of combining spatial and behavioral metadata to achieve a more accurate and comprehensive understanding of ICNs, which can serve as a resource for future research in cognitive neuroscience. The findings also suggest potential avenues for developing a more robust neuroinformatics framework to support large-scale data-driven meta-analyses in cognitive neuroscience.This study explores the functional interpretations of intrinsic connectivity networks (ICNs) using a neuroinformatics approach based on the BrainMap database. The authors apply independent component analysis (ICA) to resting state fMRI data and hierarchical clustering analysis (HCA) to behavioral metadata to identify and characterize ICNs. The results provide a detailed functional explication of these networks, linking them to specific cognitive processes such as emotion, interoception, motor and visuospatial processing, and vision. The study highlights the importance of combining spatial and behavioral metadata to achieve a more accurate and comprehensive understanding of ICNs, which can serve as a resource for future research in cognitive neuroscience. The findings also suggest potential avenues for developing a more robust neuroinformatics framework to support large-scale data-driven meta-analyses in cognitive neuroscience.