Decision-making under risk: a graph-based network analysis using functional MRI

Adaptive behavior requires choosing effectively between options involving risks and potential rewards. Existing studies implicate lateral and medial prefrontal areas, striatum, insula, amygdala and parietal regions in specific aspects of decision-making. However, limited attention is given to how brain networks encode economic parameters in patterns of inter-regional interactions. Here, healthy participants underwent functional MRI while evaluating "mixed" gambles presenting potential gains, losses and associated outcome probabilities. Connectivity graphs were constructed from analyses of psychophysiological interactions across a comprehensive atlas of brain regions. Expected value correlated positively with activity within medial prefrontal and occipital cortices, and modulated effective connectivity across a network that extended substantially beyond these nodes. Value-sensitive effective connections were found to be arranged as a unitary, small world network in which medial and anterior-lateral prefrontal areas featured as hubs, characterized by dense connectivity and high shortest-path centrality. Further analyses revealed that the observed effective connectivity effects were more pertinent to dichotomous gain/loss comparisons than to continuous value determination. Factoring expected value into its constituent components, potential loss modulated connectivity across a subset of the value-sensitive network, whereas potential gain and outcome probability were not significantly embodied in functional interactions. Regional response non-linearity was excluded as an artifactual basis to the observed effects, and directionality inferences were confirmed by comparison of dynamic causal models. Our findings extend current literature demonstrating that the representation of value is dependent on distributed processing taking across a widespread network which feeds information into a limited set of integrative prefrontal nodes. This study also has more general paradigmatic implications for neuroeconomics, demonstrating the value of explicit modeling of inter-regional interactions for understanding the neural substrates of decisional processes.