Neurometrics of intrinsic connectivity networks at rest using fMRI: Retest reliability and cross-validation using a meta-level method

Functional images of the resting brain can be empirically parsed into intrinsic connectivity networks (ICNs) which closely resemble patterns of evoked task-based brain activity and which have a biological and genetic basis. Recently, ICNs have become popular for investigating brain functioning and brain-behavior relationships. However, the replicability and neurometrics of these networks are only beginning to be reported. Using a meta-level independent component analysis (ICA), we produced ICNs from three data sets collected from two samples of healthy adults. The ICNs from our data sets demonstrated robust and independent replication of 12 intrinsic networks that reflected 17 canonical, task-based, brain networks. We found within-subject reliability of ICNs was modest overall, but ranged from poor to good, and that voxels with the highest measured connectivity rarely had the highest reliability. Networks associated with executive functions, visuospatial reasoning, motor coordination, speech and audition, default mode, vision, and interoception showed moderate to high group-level reproducibility and replicability. However, only the first four of these networks also showed fair or better within-subject reliability over time. Our findings highlight the replicability of ICNs across data sets, the range of within-subject neurometrics across different networks, and the shared characteristics between resting and task-based networks.