Intertemporal choice, the tradeoff among outcomes occurring at different points in time, especially the immediately available benefits/costs and future ones, involves not only gains options but also those associated with losses. People tend to discount future benefits/costs when facing decisions involving a smaller immediate gain/loss and a larger future one, and the subjective value of those delayed benefits/costs is less than their real value.Such a preference can be understood in terms of temporal discounting. Intertemporal choice is ubiquitous in daily decision-making. Understanding the neural mechanism of intertemporal choice is one of the central motivating problems in neuroeconomics. Using functional magnetic resonance imaging (fMRI), based on the economic decision theory, this dissertation aims to explore the underlying neural mechanism of intertemporal choice in the gains and losses domains from the aspects of brain activation, functional connectivity and effective connectivity, the effect of individual differences in personality traits, and reveal the specific neural mechanism underlying gains- and losses-related intertemporal choice. Reward and punishment are essential fators that affect decision-making. Investigating the neural basis of intertemporal choice in the gains and losses domains is very important for a better understanding of decision-making behavior. Using decision-making tasks with a symmetric pattern of gains and losses, we comprehensively investigated the neural mechanism of gains- and losses-related temporal discounting task. We found that the cognitive and neural processes during temporal discounting do not function identically for gains and losses, and we demonstrated for the first time that two distinct neural systems are involved in the valuation of immediate and delayed monetary losses. This result indicated that human are more sensitive to future losses than to future gain, and yield insight into the neural mechanism of intertemporal choice. Research on neural mechanisms of decision-making should move beyond the analysis of task-related regional activations and focus more strongly on functional interactions within and between different neural networks. Using psychophysiological interaction and dynamic causal modeling analyses, we goes significantly further toward investigating the dynamic interactions of the brain regions involved in intermporal decision-making in the gains and losses domains. We found two dist...
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