ABSTRACT
Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g., specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain-behavior relationships essential for general intelligence in a large (behavioral, N=805; cortical volume, N=246; fractional anisotropy, N=165), developmental (ages 5-18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade our cognitive, neural, and multilayer networks. Moreover, using community detection (Walktrap algorithm) and calculating node centrality (absolute strength and bridge strength), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role ‘between’ brain and behavior. We discuss implications and possible avenues for future studies.
Competing Interest Statement
EB is a member of the scientific advisory board of Sosei Heptares.
Footnotes
We corrected the absolute strength centrality estimates (single-layer networks), Walktrap community group labels and, therefore, bridge strength estimates (multilayer networks), stability estimates, further clarified the Methods, and expanded the Introduction and Discussion. Lastly, we removed the clique percolation section from the Supplementary Material but added edge-weight comparisons between networks with and without outliers.
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