Causal Interactions Within a Frontal-Cingulate-Parietal Network During Cognitive Control: Convergent Evidence from a Multisite-Multitask Investigation

Abstract

Cognitive control plays an important role in goal-directed behavior, but dynamic brain mechanisms underlying it are poorly understood. Here, using multisite fMRI data from over 100 participants, we investigate causal interactions in three cognitive control tasks within a core Frontal-Cingulate-Parietal network. We found significant causal influences from anterior insula (AI) to dorsal anterior cingulate cortex (dACC) in all three tasks. The AI exhibited greater net causal outflow than any other node in the network. Importantly, a similar pattern of causal interactions was uncovered by two different computational methods for causal analysis. Furthermore, the strength of causal interaction from AI to dACC was greater on high, compared with low, cognitive control trials and was significantly correlated with individual differences in cognitive control abilities. These results emphasize the importance of the AI in cognitive control and highlight its role as a causal hub in the Frontal-Cingulate-Parietal network. Our results further suggest that causal signaling between the AI and dACC plays a fundamental role in implementing cognitive control and are consistent with a two-stage cognitive control model in which the AI first detects events requiring greater access to cognitive control resources and then signals the dACC to execute load-specific cognitive control processes.

Keywords: brain network; connectivity; fMRI; human; temporal dependence.

Figure 1.

Task design. Illustration of the 3 cognitive control tasks. For the purpose of visualization, the figure for each task design was regenerated based on the stimuli and parameters described in the original publication. (A) SST1 (Xue et al. 2008)—participants are required to make left/right button presses (right index/middle finger) in response to letter “T”/”D” and try to stop the response if a beep is played. (B) Flanker (Kelly et al. 2008)—participants are required to make left/right button presses (right index/middle finger) in response to the direction of the central arrow. (C) SST2 (Zhang and Li 2012)—participants are required to make button presses (right index finger) in response to a circle and attempt to stop the response if an “X” is displayed. SST, stop-signal task.

Figure 2.

Anatomical locations of the five ROIs in the right hemisphere. ROIs were selected independently of the cognitive control tasks and were based on the salience and central executive networks, two intrinsic brain networks identified using a resting-state fMRI dataset. ROIs include dACC (x = 7, y = 18, z = 33, MNI coordinates), right anterior insula (rAI; x = 37, y = 16, z = −2), right dorsolateral prefrontal cortex (rDLPFC; x = 50, y = 18, z = 44), right ventrolateral prefrontal cortex (rVLPFC; x = 42, y = 26, z = 14), and right posterior parietal cortex (rPPC; x = 48, y = −52, z = 50).

Figure 3.

Brain areas activated during the three cognitive control tasks. The first 3 columns show Frontal-Cingulate-Parietal activation in the three studies. All contrast maps were thresholded at P < 0.05, FDR corrected for multiple comparisons. The rightmost column shows the overlapped suprathreshold activation in all three studies identified using logical “AND” operation.

Figure 4.

Causal interactions identified by MDS and GCA in the three cognitive control tasks. (A) MDS result: Upper panel shows causal interaction graphs for each task (all Ps < 0.05, FDR corrected). The right AI showed significant causal influence to the dACC and right PPC in all 3 studies. Lower panel shows Net Causal Outflow (outflow degree − inflow degree) in each node in each task. The right AI had greater Net Causal Outflow than all other nodes in the network in all three studies. (B) GCA result: Upper panel shows causal interaction graph for each task (all Ps < 0.05, FDR corrected). The right AI showed significant causal influence to the dACC and right PPC in all three studies. Lower panel shows Net Causal Outflow (outflow degree − inflow degree) in each node in each task. The right AI had greater Net Causal Outflow than all other nodes in the network in all three studies. The rightmost column shows the common causal interaction in all three tasks identified using logical “AND” operation. Green edges indicate significant causal interactions along the direction of the arrow and significantly greater causal interaction along the direction of the arrow against the opposite direction (i.e., MDS_rAI−>dACC − MDS_dACC−>rAI or GCA_rAI−>dACC − GCA_dACC−>rAI). * indicates statistical significance after Bonferroni multiple comparison correction (P < 0.05).

Figure 5.

Trial-specific causal interaction in 3 cognitive control tasks. Trial-specific MDS revealed significant causal interactions in low cognitive control trials (i.e., Congruent trials in Flanker task, Go trials in SST1 and SST2 tasks), in high cognitive control trials (i.e., Incongruent trials in Flanker task, Stop trials in SST1 and SST2 tasks), and significantly greater causal interaction in high cognitive control trials than in low cognitive control trials (all Ps < 0.05, FDR corrected). Green edges indicate significant causal interactions along the direction of the arrow (i.e., MDS_rAI−>dACC); red edges indicate significant bidirectional causal interactions (i.e., MDS_rAI−>dACC and MDS_dACC−>rAI).

Figure 6.

Brain-behavior relations in three cognitive control tasks. The strength of causal interactions between the right AI and dACC was correlated with individual measures of cognitive control ability. Data from SST1 (A), Flanker (B), and SST2 (C) tasks. Congruency Effect = (Incongruent RT − Congruent RT)/Congruent RT. *P < 0.05.