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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.