Anterior insular cortex is a bottleneck of cognitive control

Abstract

Cognitive control, with a limited capacity, is a core process in human cognition for the coordination of thoughts and actions. Although the regions involved in cognitive control have been identified as the cognitive control network (CCN), it is still unclear whether a specific region of the CCN serves as a bottleneck limiting the capacity of cognitive control (CCC). Here, we used a perceptual decision-making task with conditions of high cognitive load to challenge the CCN and to assess the CCC in a functional magnetic resonance imaging study. We found that the activation of the right anterior insular cortex (AIC) of the CCN increased monotonically as a function of cognitive load, reached its plateau early, and showed a significant correlation to the CCC. In a subsequent study of patients with unilateral lesions of the AIC, we found that lesions of the AIC were associated with a significant impairment of the CCC. Simulated lesions of the AIC resulted in a reduction of the global efficiency of the CCN in a network analysis. These findings suggest that the AIC, as a critical hub in the CCN, is a bottleneck of cognitive control.

Keywords: Anterior cingulate cortex; Anterior insular cortex; Cognitive control; Cognitive control capacity; Cognitive control network.

Figure 1. The backward masking majority function task (MFT-M).

(a) Schematic of the MFT-M. Participants were required to report the majority of arrow directions in each trial. Upper right panel: possible congruency ratios (majority : minority) of arrow sets. Lower left panel: timeline of the stimuli in one trial under different stimulus exposure time (ET, in ms) conditions. Events in a trial of sequence are indicated by the color-coded blocks. Duration of each stimulus is illustrated by the length of each color bar. Responses had to be made with the 2500 ms response window and the total length of each trial was 5000 ms. (b) Information entropy as an index of information amount in each congruency condition, regardless of the ET. (c) Information rate as an index of cognitive load in each task condition. The information rate increases as a function of both information entropy and the reciprocal of ET (1/ET), and shows a super-additive interaction between information entropy and 1/ET.

Figure 2. Behavioral performance in the fMRI study.

(a) Response accuracy and (b) Reaction time (RT) in each condition. Error bars indicate standard error (SE) for within-subject design.

Figure 3. Main effects of general linear modeling.

Brain regions with significantly increased (red) or decreased (blue) activation as a linear function of information entropy (a) and the reciprocal of ET (b). (c) Regions with significant positive information entropy by the reciprocal of ET interaction (super-additive effect). (d) Regions with a positive effect in the conjunction of the two main effects and the interaction effect.

Figure 4. Activation in the anterior insular cortex (AIC) and the anterior cingulate cortex (ACC).

(a) Illustration of the super-additive activation in these regions. (b) Fitted curves (black lines) for the relationship between information rate and activation in these regions. For the left and right AIC, the horizontal dashed line and the vertical solid lines indicate the estimated plateau and half-time of the fitted logistic function, respectively. L: left, R: right.

Figure 5. Correlation between brain activation and the CCC.

(a) The brain region showing significant positive correlation between the super-additive effect and the CCC. Areas in the pink outlines are the regions showing a significant effect in the conjunction analysis of Fig. 3d. (b) Illustration of the activation in the right AIC in the median-split low and high CCC groups.

Figure 6. AIC as a mediator between the CCC and IQ.

The super-additive activation in right AIC as a mediator (M) between the CCC (X) and Full-Scale IQ (FSIQ) (Y). In the scatter plot for the relationship between CCC and FSIQ, the solid black line and the dashed grey line represent the path c (correlation between X and Y) and path c’ (correlation between X and Y controlling M), respectively.

Figure 7. Impaired CCC in patients with lesions in the AIC.

Lesion reconstruction for patients with unilateral lesion in anterior insular cortex (AIC group) and (b) patients with unilateral lesion in anterior cingulate cortex (ACC group). All lesions were mapped on right hemisphere. Colors indicate the percentage of the overlap of lesions across patients. (c) Estimated CCC of participants in different groups. NIC (dark grey bar): neurological intact control. BDC (light grey bar): brain damage control, referring to patients with lesion in regions outside the cognitive control network. Error bars indicate the standard deviation. Pink bar: each patient in the AIC group (left AIC lesion: 2 and 7; right AIC lesion: AIC 1, 3, 4, 5, 6, and 8). Light pink bar: each patient in the ACC group (left ACC lesion: ACC 3, 5, and 6; right ACC lesion: ACC 1, 2, and 4). *: p < .05.

Figure 8. Network analysis results.

(a) Regions of interest (ROI) definition. CON: cingulo-opercular network. FPN: frontoparietal network. (b) Group-averaged Bayesian network. Circles represent nodes, with their colors indicating the module they belong to. Arrows represent directional connections between nodes with their strength (indicated by the thickness of the arrow shaft) significantly higher than baseline. FEF: frontal eye field. IPS: areas around and along the intra-parietal sulcus. TH: thalamus. CdN: caudate nucleus. V: visual areas. Regions in the left and right hemisphere are presented at the left and right sides, respectively. Gray dashed arrows: intra-module connections. Black arrows: inter-module connections. (c) Participation coefficient of the inward (P in; upper-panel) and outward (P out; lower-panel) connections of the AIC and ACC. (d) Global efficiency (E_global) of networks with simulated unilateral lesion of the AIC and ACC, compared to the non-lesioned network. The grey line represents the mean (solid line) and standard error (dashed lines) of the E_global of the non-lesioned network. *: p < .05, **: p < .01, ***: p < .001. Error bars indicate the standard error for within-subject design.