Brain Modularity Mediates the Relation between Task Complexity and Performance

TL;DR

This study investigates how brain network modularity relates differently to simple and complex task performance, supporting a theory that modularity benefits simple tasks while low modularity benefits complex tasks.

Contribution

It empirically tests and confirms a theoretical model linking brain modularity to task complexity and performance using resting-state fMRI data.

Findings

Higher modularity correlates with better simple task performance.

Lower modularity correlates with better complex task performance.

Results support the modularity-performance relationship predicted by the theory.

Abstract

Recent work in cognitive neuroscience has focused on analyzing the brain as a network, rather than as a collection of independent regions. Prior studies taking this approach have found that individual differences in the degree of modularity of the brain network relate to performance on cognitive tasks. However, inconsistent results concerning the direction of this relationship have been obtained, with some tasks showing better performance as modularity increases and other tasks showing worse performance. A recent theoretical model (Chen & Deem, 2015) suggests that these inconsistencies may be explained on the grounds that high-modularity networks favor performance on simple tasks whereas low-modularity networks favor performance on more complex tasks. The current study tests these predictions by relating modularity from resting-state fMRI to performance on a set of simple and complex behavioral tasks. Complex and simple tasks were defined on the basis of whether they did or did not draw on executive attention. Consistent with predictions, we found a negative correlation between individuals' modularity and their performance on a composite measure combining scores from the complex tasks but a positive correlation with performance on a composite measure combining scores from the simple tasks. These results and theory presented here provide a framework for linking measures of whole brain organization from network neuroscience to cognitive processing.