Default-mode network streams for coupling to language and control systems

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.

Keywords: Default network; brain networks; fMRI; functional connectivity; individual variability.

Fig. 1.

Internal and external validity of subnetworks are maximized at 0.1% graph density. (A) RSFC homogeneity (y axis) within brain networks (red) was higher than within rotated networks (black) or other subjects’ networks (green), across all network densities (x axis). This is illustrated for subject MSC01. (B) The task-activation variance explained (y axis) by brain networks (blue) was consistently higher than by rotated networks (black) or other subjects’ networks (green), across all network densities (x axis). This is illustrated for the Words > Fixation task in subject MSC01. (C) When averaged across subjects, internal validity (homogeneity compared to null; red) and external validity (task variance explained compared to null, averaged across tasks; blue) peaked at 0.1% network density (black arrows). Error bars represent SEs across subjects. See SI Appendix, Fig. S2 for all subjects and all tasks.

Fig. 2.

DMN subnetworks in subject MSC01. Different colors represent different subnetworks in cortex (Top Left), ventral caudate and MTL (Bottom Left), and cerebellum (Right). ant., anterior; dors., dorsal. See SI Appendix, Fig. S3 for all subjects.

Fig. 3.

(A–I) Spatial distributions of DMN subnetworks across subjects. Color indicates number of subjects with spatial overlap of matched subnetworks at each point in the brain. ant., anterior; dors., dorsal.

Fig. 4.

Subnetworks represent differentially task-activated divisions within large-scale networks. (A) Task heterogeneity within the large-scale DMN in example subject MSC01. Patterns of task activation (Top) driven by a Scene > Face contrast correspond very well with DMN subnetwork divisions (Bottom). (B) Variance in the pattern of Scene > Face task activation (y axis) explained by DMN subnetwork divisions (red) and by rotated DMN subnetworks (black), for each subject (x axis).

Fig. 5.

DMN subnetworks are differentiated by functional connectivity to large-scale networks and by network role. (A) A spring-embedding plot illustrating relationships among DMN subnetworks and other networks in one example subject. Language and Frontoparietal networks are linked to specific DMN subnetworks. Nodes in this network are contiguous subnetwork regions. The Posterior MTL subnetwork is not shown here as it connects only to itself in this subject. (B) The Parietal subnetwork exhibited stronger RSFC to the rest of the DMN than any Lateral subnetwork. (C) The Anterior Lateral subnetworks both exhibited stronger connectivity to the Language network than any other subnetwork, except the Left Dorsal Lateral subnetwork. (D) The Dorsal Lateral subnetworks both exhibited stronger connectivity to the Frontoparietal network than any other subnetwork. (E) The Parietal subnetwork exhibited larger within-network degree Z scores than any Lateral subnetwork except the Right Anterior Lateral subnetwork. (F) The Parietal subnetwork exhibited smaller participation coefficients than any Lateral subnetwork. Error bars indicate SEs across subjects. ant., anterior; dors., dorsal.

Fig. 6.

BOLD signals in Lateral DMN subnetworks are delayed relative to those in the Parietal subnetwork. (A) Average temporal ordering of signals in each subnetwork relative to other subnetworks. Error bars indicate SEs across subjects. (B) TD matrix indicating relative lead/lag of each network pairing. Dark colors indicate the “column” subnetwork is earlier; light colors indicate the “row” subnetwork is earlier. *P < 0.05 corrected; ***P < 0.001 corrected. ant., anterior; dors., dorsal.