How structural brain network topologies associate with cognitive abilities in a value-based decision-making task

Issue: 
2019
Institution: 
Dept. Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania Dept. Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania Carnegie Mellon Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania Dept. Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania

Value-based decision-making relies on effective communication across disparate brain networks. Given the scale of the networks involved in adaptive decision-making, variability in how they communicate should impact behavior; however, precisely how the topological pattern of structural connectivity of individual brain networks influences individual differences in value-based decision-making remains unclear. Using diffusion magnetic resonance imaging, we measured structural connectivity networks in a sample of community dwelling adults (N = 124). We used standard graph theoretic measures to characterize the topology of the networks in each individual and correlated individual differences in these topology measures with differences in the Iowa Gambling Task. A principal components regression approach revealed that individual differences in brain network topology associate with differences in both optimal decision-making, as well as in each participant’s sensitivity to high frequency rewards. These findings show that aspects of structural brain network organization, specifically small-world style topologies, can determine the efficiency with which information is used in value-based decision-making.

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