Evidence for Two Independent Factors that Modify Brain Networks to Meet Task Goals - PubMed (original) (raw)
Evidence for Two Independent Factors that Modify Brain Networks to Meet Task Goals
Caterina Gratton et al. Cell Rep. 2016.
Abstract
Humans easily and flexibly complete a wide variety of tasks. To accomplish this feat, the brain appears to subtly adjust stable brain networks. Here, we investigate what regional factors underlie these modifications, asking whether networks are either altered at (1) regions activated by a given task or (2) hubs that interconnect different networks. We used fMRI "functional connectivity" (FC) to compare networks during rest and three distinct tasks requiring semantic judgments, mental rotation, and visual coherence. We found that network modifications during these tasks were independently associated with both regional activation and network hubs. Furthermore, active and hub regions were associated with distinct patterns of network modification (differing in their localization, topography of FC changes, and variability across tasks), with activated hubs exhibiting patterns consistent with task control. These findings indicate that task goals modify brain networks through two separate processes linked to local brain function and network hubs.
Keywords: brain networks; fMRI; graph theory; task control.
Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.
Figures
Figure 1. Proposed factors contributing to task FC
Intrinsic network interactions (right) may be modified to accomplish task goals by changing connectivity between regions activated by a task (Hypothesis 1; activated regions shown with red outlines) or by changing connectivity patterns of specialized hub regions (Hypothesis 2; squares) that help connect networks to each other. Regions and connections without changes are faded in the bottom panel to emphasize differences.
Figure 2. A common FC organization is present during task and rest states
(A) FC was calculated via time-series correlations among 264 cortical and subcortical regions of interest (spheres), distributed across 13 networks (Power et al., 2011) (colors; surface colors represent networks used for voxelwise analyses). FC during rest (B, left) and task (C, right) is very similar, dominated by a strong network structure with high correlations within each system (diagonal) compared to between systems (off-diagonals; similar results were seen for individual tasks, Supp. Fig. 1A).
Figure 3. Subtle, but reliable, FC differences were present during task and rest states
Subtle but reliable differences were seen in the direct contrast of task and rest correlation matrices for 264 regions of interest (A) and on average for each voxel to other voxels within its own network (B, left) or voxels in other networks (B, right). FC changed within-system (along the diagonal, e.g., increases within the DMN, decreases within the visual and other sensory/motor systems; red and blue arrows in B) and between-systems (off-diagonal, e.g., increases between visual and subsets of control systems (e.g., CO, FP, DAN); pink and purple arrows in B). These effects were consistent for individual tasks (Supp. Fig. 1B).
Figure 4. FC modulations in activated regions and connector hubs
Active (A) and connector hub nodes (B) show significantly enhanced modulations in between-network FC, but not within network FC – instead, connector hubs show lower changes in within-system FC than non-connectors nodes. Similar effects were seen for individual tasks (Supp. Fig. 5). ***p<0.001, **p<0.01, error bars represent standard error across ROIs.
Figure 5. Regions stratified into classes by activated and connector hub characteristics
Regions were stratified into 4 classes: silent simple (bottom 25% of both activation and PC), activated simple (top 25% activation, bottom 25% PC), silent connector (bottom 25% activation, top 25% PC), and activated connector (top 25% activation and PC) nodes. Node locations are shown as white spheres, overlaid on their systems (colors). Classes were associated with distinct systems.
Figure 6. Classes differ in the magnitude, topography, and flexibility of their FC patterns
Node classes had different FC-related attributes. (A) They differed in the absolute magnitude of within and between network FC changes (measured via one-way ANOVA, ***p<0.001). (C) Classes differed in the topography of FC differences across networks, quantified via the FC task-rest difference for a class of regions (source) to each brain network (target; *p(FDR)<0.05; control = CO, Salience, FP, DAN, VAN; relevant processing = visual, SM; processing = SMlat, auditory). (B) Classes also differed in the flexibility of their topography across tasks, measured as the average correlation among FC difference maps for each class. These attributes, and the figures associated with each, are summarized in (D; absolute magnitudes of FC changes are shown with increasing +/− signs relative to silent simple nodes to denote increasingly large differences). Error bars represent standard error across ROIs.
Figure 7. Nodes within the CO network show distinct FC patterns based on their class
Regions associated with different classes showed distinct patterns of FC modulations, even when they were part of the same network. For example, we contrast the pattern of FC modulations (task - rest) exhibited by activated connectors (N=7, orange) and silent connectors (N=4, green) that are part of the CO network (purple; A). (B) Classes clustered separately from one another based on their FC difference maps. (C) Activated connector CO regions showed increased coupling with FP, DAN, and visual regions relative to silent connector CO regions (quantified in left panel for different types of networks; *p(FDR)<0.05; see Fig. 6 for network groupings). Error bars represent standard error across ROIs.
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