Numerous studies have examined the neural substrates of intertemporal decision-making, but few have systematically investigated separate neural representations of the two attributes of future rewards (i.e., the amount of the reward and the delay time). More importantly, no study has used the novel analytical method of representational connectivity analysis (RCA) to map the two dimensions' functional brain networks at the level of multivariate neural representations. This study independently manipulated the amount and delay time of rewards during an intertemporal decision task. Both univariate and multivariate pattern analyses showed that brain activity in the dorsomedial prefrontal cortex (DMPFC) and lateral frontal pole cortex (LFPC) was modulated by the amount of rewards, whereas brain activity in the DMPFC and dorsolateral prefrontal cortex (DLPFC) was modulated by the length of delay. Moreover, representational similarity analysis (RSA) revealed that even for the regions of the DMPFC that overlapped between the two dimensions, they manifested distinct neural activity patterns. In terms of individual differences, those with large delay discounting rates (k) showed greater DMPFC and LFPC activity as the amount of rewards increased but showed lower DMPFC and DLPFC activity as the delay time increased. Lastly, RCA suggested that the topological metrics (i.e., global and local efficiency) of the functional connectome subserving the delay time dimension inversely predicted individual discounting rate. These findings provide novel insights into neural representations of the two attributes in intertemporal decisions, and offer a new approach to construct task-based functional brain networks whose topological properties are related to impulsivity.
Keywords: dorsal medial prefrontal cortex; graph theory; intertemporal decision-making; multivariate pattern analysis; representational similarity analysis.
© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.