Functional Characterization of the Cingulo-Opercular Network in the Maintenance of Tonic Alertness - PubMed (original) (raw)

Functional Characterization of the Cingulo-Opercular Network in the Maintenance of Tonic Alertness

Sepideh Sadaghiani et al. Cereb Cortex. 2015 Sep.

Abstract

The complex processing architecture underlying attentional control requires delineation of the functional role of different control-related brain networks. A key component is the cingulo-opercular (CO) network composed of anterior insula/operculum, dorsal anterior cingulate cortex, and thalamus. Its function has been particularly difficult to characterize due to the network's pervasive activity and frequent co-activation with other control-related networks. We previously suggested this network to underlie intrinsically maintained tonic alertness. Here, we tested this hypothesis by separately manipulating the demand for selective attention and for tonic alertness in a two-factorial, continuous pitch discrimination paradigm. The 2 factors had independent behavioral effects. Functional imaging revealed that activity as well as functional connectivity in the CO network increased when the task required more tonic alertness. Conversely, heightened selective attention to pitch increased activity in the dorsal attention (DAT) network but not in the CO network. Across participants, performance accuracy showed dissociable correlation patterns with activity in the CO, DAT, and fronto-parietal (FP) control networks. These results support tonic alertness as a fundamental function of the CO network. They further the characterization of this function as the effortful process of maintaining cognitive faculties available for current processing requirements.

Keywords: alertness; attention; cingulo-opercular network; fMRI; functional connectivity.

© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

PubMed Disclaimer

Figures

Figure 1.

Figure 1.

The experimental design and paradigm. (A) In a 2 × 2 factorial block design, the factors alertness demand and selective attention demand were each presented at 2 levels. (B) Example time courses for stimulus blocks corresponding to the 2 × 2 design in A (real experimental blocks contained 4 times as many stimuli). Each vertical line represents one sound and its height represents the pitch. The target sound (highlighted with arrows ↓) was identical throughout the experiment and had the highest pitch. In a sustained task, participants pressed a response button whenever they heard the target. Stimuli occurred at regular intervals in the low alertness conditions (left) and at jittered irregular intervals in the high alertness condition (right). The pitch of the nontarget sounds was closer to—and thus harder to discriminate from—the target sound in the high attention condition (bottom) when compared with the low attention condition (top).

Figure 2.

Figure 2.

Both experimental factors affected behavior and the effect was dissociable. (A) Accuracy as measured by _d_′ decreases with higher tonic alertness demand and with higher selective attention demand (P < 0.001). No interaction was observed. (B) Conversely, RT increased only as a function of selective attention demand but was unaffected by alertness. There was no interaction.

Figure 3.

Figure 3.

Effects of alertness and attention demands on activation levels. (A) Task-positive ICNs defined using resting-state seed-based functional connectivity. FWE-corrected P < 0.05, extent >20 voxels. Coronal slice shows subcortical areas of CO (y = −10). (B and C) Change in estimated activity levels with experimental conditions for the average signal in each of the 3 ICNs. Only the CO ICN showed higher activity under heightened alertness demand (B). Only the DAT ICN increased activity due to increased selective attention demand (C). Error bars show ± standard error. (D and E) Voxel-wise mapping of the contrasts previously investigated in (B and C). The differential activation is overlaid on the corresponding ICNs. P < 0.005 uncorrected, extent >50 voxels.

Figure 4.

Figure 4.

Effects of overall task, alertness, and selective attention demands on functional connectivity. (A) Spherical VOIs from a functional atlas (Power et al. 2011). (B) Resting-state connectivity confirms strong connectivity between COP and SAL, and (to a lesser extend) between FP and DAT. (C) During task when compared with rest, an increase in connectivity is observed in the FP network as well as between FP and CO networks. DAT network becomes less integrated. (D) Matrix shows differences in pair-wise correlations for high > low alertness demand. The CO network showed a significant connectivity increase while the FP network reduced connectivity under high alertness. The previously strong correlations between FP and DAT networks significantly decreased with heightened alertness. (E) Matrix shows differences in pair-wise correlations for high > low attention. This contrast did not show any differences within or across networks. Thick black divisions indicate network boundaries. Thin black lines subdivide CO into COP and SAL. Significant contrasts in between or within-network correlations (averaged across region-pairs) are marked in gray in the lower triangle of matrices. Note that the scale in B represents correlations, while the scales in C and D) represent differences in correlations.

Figure 5.

Figure 5.

The dependence of overall network activity on overall accuracy. The better the participant performed, the less strongly the CO and FP networks were engaged (A and B) and the more the DAT network was activated (C).

Similar articles

Cited by

References

    1. Chang LJ, Yarkoni T, Khaw MW, Sanfey AG. 2013. Decoding the role of the insula in human cognition: functional parcellation and large-scale reverse inference. Cereb Cortex. 23:739–749. - PMC - PubMed
    1. Chawla D, Rees G, Friston KJ. 1999. The physiological basis of attentional modulation in extrastriate visual areas. Nat Neurosci. 2:671–676. - PubMed
    1. Cohen JR, D'Esposito M. 2011. The comparison of task-related networks and resting state networks during working memory. Quebec City: OHBM.
    1. Corbetta M, Shulman GL. 2002. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci. 3:201–215. - PubMed
    1. Critchley HD. 2005. Neural mechanisms of autonomic, affective, and cognitive integration. J Comp Neurol. 493:154–166. - PubMed

Publication types

MeSH terms

Substances

LinkOut - more resources