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

Abstract

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 MRI, 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 optimal decision-making, as well as associate with differences in each participants sensitivity to high frequency rewards. These findings show that aspects of structural brain network organization can constrain how information is used in value-based decision-making.\n\nAbbreviationsMRI - Magnetic Resonance Imaging; IGT - Iowa Gambling Task; DWI - Diffusion Weighted Imaging; QSDR - Q-Space Diffeomorphic Reconstruction; PCA - Principal Components Analysis; GLM - Generalized Linear Models