Higher Intelligence Is Associated with Less Task-Related Brain Network Reconfiguration

Abstract

The human brain is able to exceed modern computers on multiple computational demands (e.g., language, planning) using a small fraction of the energy. The mystery of how the brain can be so efficient is compounded by recent evidence that all brain regions are constantly active as they interact in so-called resting-state networks (RSNs). To investigate the brain's ability to process complex cognitive demands efficiently, we compared functional connectivity (FC) during rest and multiple highly distinct tasks. We found previously that RSNs are present during a wide variety of tasks and that tasks only minimally modify FC patterns throughout the brain. Here, we tested the hypothesis that, although subtle, these task-evoked FC updates from rest nonetheless contribute strongly to behavioral performance. One might expect that larger changes in FC reflect optimization of networks for the task at hand, improving behavioral performance. Alternatively, smaller changes in FC could reflect optimization for efficient (i.e., small) network updates, reducing processing demands to improve behavioral performance. We found across three task domains that high-performing individuals exhibited more efficient brain connectivity updates in the form of smaller changes in functional network architecture between rest and task. These smaller changes suggest that individuals with an optimized intrinsic network configuration for domain-general task performance experience more efficient network updates generally. Confirming this, network update efficiency correlated with general intelligence. The brain's reconfiguration efficiency therefore appears to be a key feature contributing to both its network dynamics and general cognitive ability.

Significance statement: The brain's network configuration varies based on current task demands. For example, functional brain connections are organized in one way when one is resting quietly but in another way if one is asked to make a decision. We found that the efficiency of these updates in brain network organization is positively related to general intelligence, the ability to perform a wide variety of cognitively challenging tasks well. Specifically, we found that brain network configuration at rest was already closer to a wide variety of task configurations in intelligent individuals. This suggests that the ability to modify network connectivity efficiently when task demands change is a hallmark of high intelligence.

Keywords: brain connectivity; cognitive control; fMRI; individual differences; intelligence; task switching.

Figure 1.

Assessing FC network architecture reconfiguration “distance”. A, We hypothesized that high-performing individuals would be characterized by more efficient FC updates, as indicated by a smaller reconfiguration distance and therefore a greater degree of similarity between rest FC and task FC structure. Note that we also used across-task average FC in place of rest FC to better isolate truly intrinsic (context independent) FC. This conceptual figure illustrates our hypotheses on a matrix representing FC strengths between six nodes. B, The mean time series from 264 regions of interest were extracted and all pairwise correlations were calculated for task and rest for each participant. We then calculated the FC reconfiguration efficiency by calculating the similarity of task and rest FC patterns (the upper triangles of the depicted matrices).

Figure 2.

FC reconfiguration efficiency is related to behavioral performance on three different tasks. A, Correlation between accuracy on the language task and similarity between rest FC structure and language task FC structure. B, Correlation between accuracy on the reasoning task and similarity between rest FC structure and reasoning task FC structure. C, Correlation between accuracy on the working memory task and similarity between rest FC structure and working memory task FC structure. Note that these effects were also present when using across-task average FC in place of rest FC, suggesting that effects are driven by intrinsic (i.e., context independent) FC rather than rest FC per se.

Figure 3.

FC reconfiguration efficiency in specific networks is related to task performance across three tasks. We repeated the tests for FC reconfiguration efficiency correlations with task performance, but for each network separately. Colored networks indicate that the degree of efficiency for each node in the network to the rest of the nodes in the brain is correlated with performance on the language task (A), the reasoning task (B), and the working memory task (C).

Figure 4.

Rest FC is “preconfigured” to switch into a task-general structure in high-performing individuals. A, Visualization of the task-general network. The figure depicts changes in FC from rest common to all seven tasks. This matrix would appear to be quite similar to the rest FC matrix if this subtraction was not performed. Note, however, that the task-general FC matrix without the rest FC matrix subtracted was used for analysis. Similarity between rest FC and task-general FC structure is correlated with performance on the language task (B), the reasoning task (C), and the working memory task (D).

Figure 5.

FC reconfiguration efficiency is related to general intelligence. A measure of general intelligence (g) was calculated for each individual based on six different measures of cognition (solid boxes at bottom). A measure of general efficiency was calculated for each individual based on reconfiguration efficiency scores from three tasks (solid boxes in middle). Correlation values inside solid boxes indicate the strength of the relationship between each measure and the first component from PCA. Efficiency measures for each of the three tasks (solid boxes in middle) were correlated with g (dashed boxes in middle). General efficiency is correlated with general intelligence (dashed box at right). Asterisks note significant correlations (p < 0.05) between efficiency and g. Note that results were similar when including all seven tasks (not just the three tasks that had well distributed accuracy scores).