Modularity Measures of Functional Brain Networks Predict Individual Differences in Long-Term Memory

Abstract

Long-term memory (LTM) is crucial to daily functioning, and individuals show a wide range in LTM capacity. In this study, we ask: How does the brain's functional organization explain individual differences in LTM? We focused on two important, widely studied forms of LTM, general recognition and recollection memory. Inspired by recent work on graph theory and modularity of the brain, we explored how modularity measures of brain activity during encoding could predict individual differences in later LTM performance. Specifically, we examined two modularity measures that describe distinct aspects of network functioning: diversity—the extent a node connects with different modules—and locality—the extent a node has more connections within its own modules. Combining modularity measures and connectome-predictive modeling (CPM), a powerful framework for predicting individual differences in behavior from brain functional connectivity, we found that diversity and locality measures together significantly predicted individual differences in both general recognition and recollection memory. Modularity-based predictions were less strong than CPM models using only connectivity features. With regard to predictive neuroanatomy, we found that the default mode network was the most consistently selected brain network across our models. Our findings extend previous work on how the modularity of the brain is related to cognition and demonstrate that successful LTM is supported by critical connector hubs coordinating between and within networks during encoding. More broadly, they demonstrate the utility of a graph-based approach to reveal how modularity of brain networks relates to individual differences in LTM.