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

  • Previous 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 between \(\it g\) factor scores and global as well as node-specific graph metrics. On the global level, \(\it g\) showed no significant associations with global efficiency or small-world propensity in any sample, but significant positive associations with global clustering coefficient 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 local clustering and only led to one significant, one-way prediction across data sets for nodal efficiency. 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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Author:Dorothea MetzenORCiDGND, Christina StammenGND, Christoph FraenzGND, Caroline SchlüterORCiDGND, Wendy JohnsonGND, Onur GüntürkünORCiDGND, Colin G. DeYoungGND, Erhan GençGND
Parent Title (English):Scientific reports
Publisher:Macmillan Publishers Limited, part of Springer Nature
Place of publication:London
Document Type:Article
Date of Publication (online):2024/04/08
Date of first Publication:2024/01/16
Publishing Institution:Ruhr-Universität Bochum, Universitätsbibliothek
Issue:Article 1368
First Page:1368-1
Last Page:1368-18
Dieser Beitrag ist auf Grund des DEAL-Springer-Vertrages frei zugänglich.
Institutes/Facilities:Institut für Kognitive Neurowissenschaft, Abteilung Biopsychologie
Dewey Decimal Classification:Philosophie und Psychologie / Psychologie
open_access (DINI-Set):open_access
faculties:Fakultät für Psychologie
Licence (English):License LogoCreative Commons - CC BY 4.0 - Attribution 4.0 International