Investigating robust associations between functional connectivity based on graph theory and general intelligence

Abstract

AbstractPrevious research investigating relations between general intelligence and graph-theoretical properties of the brain’s intrinsic functional network has yielded contradictory results. A promising approach to tackle such mixed findings is multi-center analysis. For this study, we analyzed data from four independent data sets (total N > 2000) to identify robust associations amongst samples betweengfactor scores and global as well as node-specific graph metrics. On the global level,gshowed no significant associations with global efficiency in any sample, but significant positive associations with global clustering coefficient and small-world propensity in two samples. On the node-specific level, elastic-net regressions for nodal efficiency and local clustering yielded no brain areas that exhibited consistent associations amongst data sets. Using the areas identified via elastic-net regression in one sample to predictgin other samples was not successful for nodal efficiency and only led to significant predictions between two data sets for local clustering. Thus, using conventional graph theoretical measures based on resting-state imaging did not result in replicable associations between functional connectivity and general intelligence.