Neural Systems Underlying Individual Differences in Intertemporal Decision-making

Abstract

Excessively choosing immediate over larger future rewards, or delay discounting (DD), associates with multiple clinical conditions. Individual differences in DD likely depend on variations in the activation of and functional interactions between networks, representing possible endophenotypes for associated disorders, including alcohol use disorders (AUDs). Numerous fMRI studies have probed the neural bases of DD, but investigations of large-scale networks remain scant. We addressed this gap by testing whether activation within large-scale networks during Now/Later decision-making predicts individual differences in DD. To do so, we scanned 95 social drinkers (18-40 years old; 50 women) using fMRI during hypothetical choices between small monetary amounts available "today" or larger amounts available later. We identified neural networks engaged during Now/Later choice using independent component analysis and tested the relationship between component activation and degree of DD. The activity of two components during Now/Later choice correlated with individual DD rates: A temporal lobe network positively correlated with DD, whereas a frontoparietal-striatal network negatively correlated with DD. Activation differences between these networks predicted individual differences in DD, and their negative correlation during Now/Later choice suggests functional competition. A generalized psychophysiological interactions analysis confirmed a decrease in their functional connectivity during decision-making. The functional connectivity of these two networks negatively correlates with alcohol-related harm, potentially implicating these networks in AUDs. These findings provide novel insight into the neural underpinnings of individual differences in impulsive decision-making with potential implications for addiction and related disorders in which impulsivity is a defining feature.

Figure 1

Schematic diagram of independent component analysis (ICA) approach. Voxel-wise maps of the WANT>CON contrast from 95 subjects (A) were entered into an ICA solving for 10 independent components (B). (C) Component maps were then regressed on each subject’s WANT>CON contrast maps to obtain estimates of the relationship of each component to the contrast for each subject; these subject-level contrast estimates were entered into group level analyses represented by Table 2 and Figures 3 and 4.

Figure 2

Voxel-wise statistical relationships. A) Results of the voxel-wise test of the main effect of task (WANT>CON contrast) corrected for multiple comparisons (α = 0.05) using permutation testing. B) The voxel-wise relationship between individual impulsive choice ratio (ICR) and activation during subjective intertemporal decision-making (WANT>CON contrast), unthresholded for visualization purposes. C) The voxel-wise relationship between ICR and activation during subjective intertemporal decision-making, as in panel B, but corrected for multiple comparisons (α = 0.05) using permutation testing. W: Wald statistic.

Figure 3

Spatial maps of the seven physiologically relevant networks identified from independent component analysis (ICA) sorted by their relationship to intertemporal decision-making. Numbers at left represent the component number; note that components 1, 2, and 7, were excluded as noise. Components significantly activated during intertemporal decision-making are surrounded by a red bounding box; significantly deactivated components are surrounded by a blue bounding box. Components significantly correlated with the impulsive choice ratio (ICR) are indicated with an asterisk (*). Components are displayed with a threshold of |z|>1.

Figure 4

Scatter plots depicting the relationships between task-related component activation and intertemporal choice. A) Scatter plot of the relationship between the impulsive choice ratio (ICR) and Want>Control (WANT>CON) contrast estimates for Component 3. B) Scatter plot of the relationship between ICR and WANT>CON contrast estimates for Component 9. C) Scatter plot of the relationship between WANT>CON contrast estimates for Components 3 and 9. D) Scatter plot of the relationship between ICR and the difference in WANT>CON contrast estimates for Components 3 and 9. Least squares fit lines are plotted for visualization purposes. Correlation coefficients represent partial correlations controlling for age and sex.

Figure 5

A visual representation of the generalized psychophysiological interactions analysis (gPPI) employed to investigate the subjective decision-making-related modulation of connectivity between Component 3 and Component 9. The dependence of one time series (i.e., TS1) was predicted by the second time series (i.e., TS2), the task conditions, the interaction of the task design and TS2, and covariates of no interest (not pictured). The task conditions modeled included subjective decisions (i.e., WANT), objective (control; CON) decisions (i.e., SOONER, LARGER), as well as DON’T WANT decisions and corresponding cues for each decision type (not pictured). Similarly, the interaction between TS2 and each task condition was modeled. The interaction beta estimates corresponding to WANT, SOONER, and LARGER trials were used to estimate a WANT>CON contrast. Component 3 and Component 9 were tested as dependent variables in two separate models.