Abstract
Using graph theory measures common to complex network analyses of neuroimaging data, the objective of this study was to explore the effects of increasing working memory processing load on functional brain network topology in a cohort of young adults. Measures of modularity in complex brain networks quantify how well a network is organized into densely interconnected communities. We investigated changes in both the large-scale modular organization of the functional brain network as a whole and regional changes in modular organization as demands on working memory increased from n = 1 to n = 2 on the standard n-back task. We further investigated the relationship between modular properties across working memory load conditions and behavioral performance. Our results showed that regional modular organization within the default mode and working memory circuits significantly changed from 1-back to 2-back task conditions. However, the regional modular organization was not associated with behavioral performance. Global measures of modular organization did not change with working memory load but were associated with individual variability in behavioral performance. These findings indicate that regional and global network properties are modulated by different aspects of working memory under increasing load conditions. These findings highlight the importance of assessing multiple features of functional brain network topology at both global and regional scales rather than focusing on a single network property.
Highlights
The recent use of graph theory measures in complex network analyses of neuroimaging data has allowed for the identification and classification of global and regional brain network properties as well as the quantification of changes in network properties across different task conditions
This study utilized data-driven methods and graph theory measures to quantify changes in functional brain network topology as a function of working memory load. These analyses were made possible by recent advances in the study of complex brain networks that allow for the systematic investigation of global and regional module properties implicated in working memory
No significant changes in modularity or module hub properties were observed as a large-scale property of the brain network as a whole when working memory load on the n-back task increased from n = 1 to n = 2
Summary
The recent use of graph theory measures in complex network analyses of neuroimaging data has allowed for the identification and classification of global and regional brain network properties as well as the quantification of changes in network properties across different task conditions. In using this mathematical framework, networks are composed of differentiable elements (nodes) and the pairwise relationships between those elements (edges). The current work utilizes a graph theoretic complex network approach to explore a key aspect of functional brain network topology, modularity, and the relationship between module properties and behavioral performance during a working memory task in a cohort of young adults
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