Development of distinct control networks through segregation and integration

Abstract

Human attentional control is unrivaled. We recently proposed that adults depend on distinct frontoparietal and cinguloopercular networks for adaptive online task control versus more stable set control, respectively. During development, both experience-dependent evoked activity and spontaneous waves of synchronized cortical activity are thought to support the formation and maintenance of neural networks. Such mechanisms may encourage tighter "integration" of some regions into networks over time while "segregating" other sets of regions into separate networks. Here we use resting state functional connectivity MRI, which measures correlations in spontaneous blood oxygenation level-dependent signal fluctuations between brain regions to compare previously identified control networks between children and adults. We find that development of the proposed adult control networks involves both segregation (i.e., decreased short-range connections) and integration (i.e., increased long-range connections) of the brain regions that comprise them. Delay/disruption in the developmental processes of segregation and integration may play a role in disorders of control, such as autism, attention deficit hyperactivity disorder, and Tourette's syndrome.

Fig. 1.

Proposed dual networks for adult human task control. The dual-networks model of control in adults was motivated by results from cross-studies analyses of mixed blocked/event-related fMRI and rs-fcMRI data (2, 6). Both control networks are proposed to interpret cues, implement top-down control, and process bottom-up feedback, but use different mechanisms and over different temporal scales. The frontoparietal network (yellow) is proposed to act for rapid online control on a trial-by-trial basis (i.e., adaptive control) and the cinguloopercular (black) for more sustained task sets (i.e., stable control).

Fig. 2.

Graphs formed from putative task-control regions in children, adolescents, and adults. ROI locations are drawn to correspond to topographic brain locations. Right-sided ROIs are displayed on the right and anterior ROIs at the top of each graph. (A) rs-fcMRI revealed two separate control networks in adults as previously described (6). (B) The top 75 connections in adolescents revealed a similar two-component system as seen in adults; however, the dACC/msFC region was incorporated into the frontoparietal network. (C) The top 75 connections in children revealed a significant deviation from the adult architecture. The two networks were connected by a bridge connection (aPFC–dlPFC). The dACC/msFC region was incorporated into the frontoparietal network. Children lacked connections from the dlPFC to IPS and IPL. (D) Fit LOWESS curves of connection strength (r) versus age. As connection strength between the dACC/msFC region and the dF cortex decreased with age, correlation strength increased between the dACC/msFC and aI/fO regions. The aPFC region also decreased its connection strength with the dlPFC region with age but was already strongly connected to the aI/fO region in children. The strength of the aI/fO–aPFC connection was maintained into adulthood.

Fig. 3.

Increased long-range and decreased short-range connectivity with age. Direct comparisons of all possible connections between adults and children were performed to test the statistical reliability of between-group differences. Both left- and right-hemisphere regions are placed on a transparent brain to aid with visualization. Red and blue lines highlight significant between-group differences for connections with an r ≥ 0.1 in either children or adults (i.e., absolute difference). Light blue and pink lines highlight connections present in both children and adults (r ≥ 0.1) that differed significantly in connection strength between groups (relative difference; P ≤ 0.05). (A) The segregation of the dACC/msFC region from the frontoparietal network (Fig. 2) was statistically significant, as was the disconnection of the aPFC from the dlPFC region (P ≤ 0.05). Most of the connections that “grew down” with age constituted short-range connections. Connections that “grew up” with age are faded to highlight this observation. (B) Connections between the dACC/msFC region and the cinguloopercular network that grew stronger with age were statistically significant (P ≤ 0.05). The connections of left dlPFC to left IPS and left frontal to left IPS were already present in children but significantly increased in strength with age. Most of the connections that “grew up” with age constituted long-range connections. Connections that “grew down” with age are faded to highlight this observation. Selected LOWESS curves are presented in A and B.

Fig. 4.

Euclidean distance as a function of Δr. The Euclidean distance (millimeters) of all connections that differed significantly between children and adults is plotted against the change in correlation strength (Δr) across development. Connections that increased in strength with age are displayed in blue, and those connections that decreased with age are displayed in red. The mean r and Euclidean distance for each group are also plotted (black circles). A t test (P < 0.0001) confirmed the qualitative finding (Fig. 3) that connections between spatially distant regions are more likely to “grow up” with age, whereas connections between closely adjacent regions tend to “grow down” with age.