The human brain is organized into large networks. One important brain network is the Default network, which enables cognitive functions such as social thinking, memory, and reward. In group-averaged data, this network emerges as a unitary whole, despite its involvement in multiple cognitive functions. Here, we tested whether Default networks found in individual humans, rather than group-average networks, contain organized substructure. In individuals, we consistently found nine subnetworks within the Default network. These subnetworks matched brain activity patterns during cognitive tasks. Some subnetworks resembled brain circuits involved in specific Default functions. Others linked Default network to other large networks. In summary, this study describes a set of brain circuits within the Default networks of individual humans.Abstract
The human brain is organized into large-scale networks identifiable using resting-state functional connectivity (RSFC). These functional networks correspond with broad cognitive domains; for example, the Default-mode network (DMN) is engaged during internally oriented cognition. However, functional networks may contain hierarchical substructures corresponding with more specific cognitive functions. Here, we used individual-specific precision RSFC to test whether network substructures could be identified in 10 healthy human brains. Across all subjects and networks, individualized network subdivisions were more valid—more internally homogeneous and better matching spatial patterns of task activation—than canonical networks. These measures of validity were maximized at a hierarchical scale that contained ∼83 subnetworks across the brain. At this scale, nine DMN subnetworks exhibited topographical similarity across subjects, suggesting that this approach identifies homologous neurobiological circuits across individuals. Some DMN subnetworks matched known features of brain organization corresponding with cognitive functions. Other subnetworks represented separate streams by which DMN couples with other canonical large-scale networks, including language and control networks. Together, this work provides a detailed organizational framework for studying the DMN in individual humans.
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