Global connectivity of prefrontal cortex predicts cognitive control and intelligence

Abstract

Control of thought and behavior is fundamental to human intelligence. Evidence suggests a frontoparietal brain network implements such cognitive control across diverse contexts. We identify a mechanism--global connectivity--by which components of this network might coordinate control of other networks. A lateral prefrontal cortex (LPFC) region's activity was found to predict performance in a high control demand working memory task and also to exhibit high global connectivity. Critically, global connectivity in this LPFC region, involving connections both within and outside the frontoparietal network, showed a highly selective relationship with individual differences in fluid intelligence. These findings suggest LPFC is a global hub with a brainwide influence that facilitates the ability to implement control processes central to human intelligence.

Figure 1.

Cognitive control regions, as defined by successful cognitive control. A, ROIs were defined based on brain activity during successful N-back task performance. The following highly selective criteria were used: preferential activation for trials requiring flexible control (lures), correct > incorrect trials, positive correlation with accuracy across participants. All 3 of these regions were hubs (in top 10% connectivity in the brain). B, Illustration of effects defining the LPFC ROI. LPFC was more active across correct relative to error trials and more active for lure than other trial types. LPFC activity (correct trials across all trial types) was also correlated with overall response accuracy across individuals (r = 0.39, p = 0.0002).

Figure 2.

GBC correlations with gF. A, Positive GBC was calculated for the 3 regions identified in the task-based analysis, and correlations were assessed with gF. Only LPFC showed a significant correlation. GBC is a graph theoretical measure of resting state fcMRI, calculated here for each seed ROI, by computing the average connectivity strength between the region and every other voxel of the brain. Note that all 3 regions were in the top 10% in terms of GBC (but only LPFC GBC was significantly correlated with gF). B, Correlations were assessed between each brain voxel's positive GBC and gF scores. Of the entire brain, only left LPFC was statistically significant (p < 0.05, corrected for multiple comparisons). The LPFC region strongly overlapped with the LPFC region identified in the task-based analysis.

Figure 3.

Examining the specificity of the left LPFC GBC–gF correlation. A, The LPFC GBC–gF correlation was compared with the GBC–gF correlation of every brain voxel. Significant voxel clusters are shown (p < 0.05, corrected for multiple comparisons). This supports the specificity of the LPFC GBC–gF effect and demonstrates that the contribution of global connectivity to intelligence is not uniform throughout the brain. Potential control regions (based on previous studies) are highlighted. B, The GBC–gF correlation was calculated for a larger set of regions from Burgess et al. (2011) that appeared likely to show GBC–gF correlations. Using the same dataset as here, Burgess et al. (2011) identified 10 regions (the largest 8 are depicted here) with N-back lure fMRI activity correlating with individual differences in cognitive control capacity (e.g., gF). Despite these regions' activities correlating with gF, only left LPFC (substantially overlapping with the previously defined LPFC region) showed a significant GBC–gF correlation. C, The left LPFC GBC–gF correlation was computed before and after removing the variance from all Burgess et al. (2011) regions (excluding left LPFC). The GBC–gF correlation for the left LPFC region (as defined in Fig. 1) was largely unaffected by removing the variance from the Burgess et al. (2011) regions. This demonstrates that the left LPFC GBC–gF correlation was statistically independent of a large set of cognitive control regions. Note that 9 of the 10 regions had voxels in the top 10% GBC, demonstrating that the GBC–gF correlation is not guaranteed simply due to a control region having both gF-correlating activity and high GBC.

Figure 4.

Identifying the origin of the LPFC GBC–gF correlation. A, We systematically restricted the connectivity contributing to the calculation of the GBC–gF correlation to determine the contributing connectivity strength ranges. LPFC's group average connectivity map was split into Fz 0.05 “strength bands” and restricted GBC–gF correlations calculated using those voxels. All strength ranges were significant, except the highest and lowest, likely due to the low number of voxels included in these 2 strength ranges. This likely indicates that the GBC–gF effect is global in origin, such that the more voxels that are included the better the connectivity–gF correlation. This is in contrast to the effect originating from high-strength connectivity, in which restricting to higher strength connections would improve the connectivity–gF correlation. B, The LPFC seed connectivity group map is shown, colored by connectivity strength ranges (corresponding to colors in A).

Figure 5.

Testing for global or system-specific origins of the LPFC GBC–gF effect. A, We separated LPFC connectivity into 3 systems. The cognitive control network was identified using a meta-analysis of cognitive control studies, sensory-motor regions were identified based on probabilistic cytoarchitecture, and the default mode network was identified using a meta-analysis of studies investigating that network. B, LPFC group average connectivity generally had high positive strength for cognitive control regions, low positive and negative strength (combined using absolute values) for sensory-motor regions, and high negative strength for default mode regions. We used the conjunctions between the masks in A along with these group strength ranges to isolate the 3 systems. Individual differences in mean LPFC connectivity with each of the 3 systems correlated with gF, suggesting a truly global origin of the LPFC GBC–gF effect.