Distinct brain networks for adaptive and stable task control in humans

Abstract

Control regions in the brain are thought to provide signals that configure the brain's moment-to-moment information processing. Previously, we identified regions that carried signals related to task-control initiation, maintenance, and adjustment. Here we characterize the interactions of these regions by applying graph theory to resting state functional connectivity MRI data. In contrast to previous, more unitary models of control, this approach suggests the presence of two distinct task-control networks. A frontoparietal network included the dorsolateral prefrontal cortex and intraparietal sulcus. This network emphasized start-cue and error-related activity and may initiate and adapt control on a trial-by-trial basis. The second network included dorsal anterior cingulate/medial superior frontal cortex, anterior insula/frontal operculum, and anterior prefrontal cortex. Among other signals, these regions showed activity sustained across the entire task epoch, suggesting that this network may control goal-directed behavior through the stable maintenance of task sets. These two independent networks appear to operate on different time scales and affect downstream processing via dissociable mechanisms.

Fig. 1.

Analyzing interactions between predefined task-control regions using rs-fcMRI. (A) Old hypothetical framework of centralized control system based on multistudy (10 tasks, 183 subjects) analyses. [Reproduced with permission from ref. (Copyright 2006, Elsevier).] Control initiation (yellow), set maintenance (red), and feedback/control adjustment (blue). (B) rs-fcMRI is measured by calculating the correlations in spontaneous BOLD fluctuations between brain regions. Spontaneous resting state BOLD fluctuations for two sample regions (right aI/fO and left aI/fO), measured in a single subject. (C) Voxelwise rs-fcMRI map for a sample seed region, R aI/fO (4, 16, 36).

Fig. 2.

Task-control graphs across different thresholds. 2D pseudoanatomical renderings of task-control graphs. (A) Thresholded at r ≥ 0.2 (P < 1 × 10⁻⁹; two-tailed; Bonferroni corrected; t test). (B) Thresholded at r ≥ 0.175 (P < 1 × 10⁻⁷; two-tailed; Bonferroni corrected; t test). (C) Thresholded at r ≥ 0.15 (P < 5 × 10⁻⁵; two-tailed; Bonferroni corrected; t test).

Fig. 3.

Task-control graph components (r ≥ 0.2) on the brain. Eight separate components that constitute task-control graph (r ≥ 0.2) displayed on inflated surface rendering of the brain. Nodes are color-coded by components in Fig. 2A. Interregional correlations were significant at P < 1 × 10⁻⁹ (two-tailed; Bonferroni corrected; t test).

Fig. 4.

Dual-network hypothesis of task control. Thin arrows schematize strong functional connections, ovals schematize hubs, and thick arrows schematize putative flow of information. (A) Information may flow between the frontoparietal and cinguloopercular networks, such that the stable control network receives control initiation signals from the adaptive control network at the beginning of a task period, as well as adjustment signals during task performance. (B) Alternatively, the frontoparietal and cinguloopercular control networks may be organized in parallel. Both networks might interpret cues, implement top–down control, and process bottom–up feedback. The frontoparietal network may adjust task control on a trial-by-trial basis, whereas the cinguloopercular network might affect downstream processing in a more stable fashion. Frameworks intermediate between A and B are also consistent with the data.