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

Metzen, D.; Stammen, C.; Fraenz, C.; Schlüter, C.; Johnson, W.; Güntürkün, O.; DeYoung, C. G.

doi:10.1101/2023.07.18.549314

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bioRxiv · 10.1101/2023.07.18.549314

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

Metzen, D.·Stammen, C.·Fraenz, C.·Schlüter, C.·Johnson, W.·Güntürkün, O.·DeYoung, C. G.

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Abstract

Previous research investigating relations between general intelligence and graph-theoretical properties of the brains 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 between g factor scores and global as well as node-specific graph metrics. On the global level, g showed 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 predict g in 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.

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Metzen, D., Stammen, C., Fraenz, C., Schlüter, C., Johnson, W., Güntürkün, O., DeYoung, C. G.. 2023-07-19. Investigating robust associations between functional connectivity based on graph theory and general intelligence. https://doi.org/10.1101/2023.07.18.549314

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