Modulation of impulsivity and reward sensitivity in intertemporal choice by striatal and midbrain dopamine synthesis in healthy adults

Abstract

Smith CT, Wallace DL, Dang LC, Aarts E, Jagust WJ, D'Esposito M, Boettiger CA. Modulation of impulsivity and reward sensitivity in intertemporal choice by striatal and midbrain dopamine synthesis in healthy adults.

Decline of prefrontal cortical-mediated executive functions but attenuated delay discounting in aged Fischer 344 × brown Norway hybrid rats

“…Such alterations have been linked to impaired working memory and cognitive inflexibility, and could directly account for some proportion of the variance in intertemporal choice inasmuch as working memory and/or cognitive flexibility contribute to the increased ability of aged rats to delay gratification. In addition to these cognitive capacities supported directly by the mPFC, this brain region is highly interconnected with other brain regions implicated in intertemporal choice, cognitive flexibility, and incentive motivation, including the orbitofrontal cortex, ventral striatum, and basolateral amygdala (Bailey et al, 2016; Churchwell et al, 2009; Floresco et al, 2008b; Ghods-Sharifi et al, 2009; Hosking et al, 2014; Ishikawa et al, 2008; Jimura et al, 2013; McClure et al, 2004; Samanez-Larkin et al, 2011; Smith et al, 2016; Stalnaker et al, 2009; Tye and Janak, 2007; Wassum and Izquierdo, 2015; Winstanley et al, 2004). Age-associated shifts in excitatory/inhibitory dynamics within PFC may also influence intertemporal choice by altering interactions with these brain regions that integrate cognitive and motivational variables.…”

Altered structural and functional brain network overall organization predict human intertemporal decision‐making

“…Based on the graphtheoretical literature, modularity organization was considered as a manager to constrain the stream of information processes throughout the whole brain during the resting-state stage, this attribute that generally involved in the rapid reconfigure on responses of behavioral tasks and concomitant changes with past experience (Bassett & Bullmore, 2006;Simon, 1991Simon, , 1995. As an emblematical high-order cognitive task, it was essential to efficiently regulate cognitive resources for complex parallel processes of neural coding (e.g., valuation signals and cognitive control) when participants made decisions in intertemporal choice (Halfmann et al, 2015;Smith et al, 2016). Naturally, the enhanced functional modularization of complex brain system can potently facilitate efficient communicative behaviors in the collectivized information transmission in favor of cognitive control.…”

Neural Systems Underlying Individual Differences in Intertemporal Decision-making

“…Participants in this sample demonstrated the full range of ICR values (i.e., 0 to 1) with a mean value of 0.62 (Q1=0.35; Q2=0.71; Q3=0.92). As a DD index, ICR has the advantage of very strong internal reliability (Smith et al, 2015; Smith et al, 2016), coupled with the fact that ICR avoids the assumptions of model-based metrics. In contrast, indices derived from temporal discounting models, such as temporal discount rates (“ k ”), are influenced by the assumptions of the particular model employed, and some participants’ data may not conform to these assumptions.…”

“…In contrast, the q -exponential discount function can distinctly parameterize both Now bias (impulsivity; k q ) and the inconsistency ( q ) in such Now bias across delay times in intertemporal choice tasks (Smith et al, 2014; Smith et al, 2016; Takahashi, 2009; Takahashi, Oono, & Radford, 2008). Consistent with our prior findings (Smith et al, 2014; Smith et al, 2016), ICR and k q values were very highly correlated ( ρ =0.92, p <0.001), and the relationships between k q and components 3 and 9 were qualitatively similar to the relationships between ICR and these components, despite reduced statistical power (Component 3: ρ =−0.26, p =0.067; Component 9: ρ =0.32, p =0.024). In addition to their relationship with ICR, the WANT>CON contrast estimates for components 3 and 9 demonstrated a negative correlation with each other ( r =−0.39, p <0.001; Figure 4C) based on Pearson partial correlation controlling for age and sex.…”

“…To estimate k q and q , we conducted nonlinear curve fitting with the Levenberg–Marquardt algorithm implemented in R (Team, 2014) with the minipack.lm package (Elzhov TV, 2015). Discounted Value was calculated as the cumulative selected/maximum dollar ratio at each delay, D. (Smith et al, 2014; Smith et al, 2016). Higher values of k q indicate greater discounting of delayed rewards (more impulsivity) and lower values of k indicate less delayed reward discounting (less impulsivity).…”