Many recent developments surrounding the functional network organization of the human brain have focused on data that have been averaged across groups of individuals. While such group-level approaches have shed considerable light on the brain’s large-scale distributed systems, they conceal individual differences in network organization, which recent work has demonstrated to be common and widespread. Here our goal was to leverage information about individual-level brain organization to identify locations of high inter-subject consensus. We probabilistically mapped 14 functional networks in multiple datasets with relatively high amounts of data. All networks show "core" (high-probability) regions, but differ from one another in the extent of their higher-variability components. These patterns replicate well across datasets with different scanning parameters. We produced a set of high-probability regions of interest (ROIs) from these probabilistic maps; these and the probabilistic maps are made publicly available, allowing researchers to apply information about group consistency to their own work in rest- or task-based studies.
bioRxiv Subject Collection: Neuroscience