Dopaminergic mechanisms of individual differences in human effort-based decision-making

Michael T Treadway, Joshua W Buckholtz, Ronald L Cowan, Neil D Woodward, Rui Li, M Sib Ansari, Ronald M Baldwin, Ashley N Schwartzman, Robert M Kessler, David H Zald, Michael T Treadway, Joshua W Buckholtz, Ronald L Cowan, Neil D Woodward, Rui Li, M Sib Ansari, Ronald M Baldwin, Ashley N Schwartzman, Robert M Kessler, David H Zald

Abstract

Preferences for different combinations of costs and benefits are a key source of variability in economic decision-making. However, the neurochemical basis of individual differences in these preferences is poorly understood. Studies in both animals and humans have demonstrated that direct manipulation of the neurotransmitter dopamine (DA) significantly impacts cost/benefit decision-making, but less is known about how naturally occurring variation in DA systems may relate to individual differences in economic behavior. In the present study, 25 healthy volunteers completed a dual-scan PET imaging protocol with [(18)F]fallypride and d-amphetamine to measure DA responsivity and separately completed the effort expenditure for rewards task, a behavioral measure of cost/benefit decision-making in humans. We found that individual differences in DA function in the left striatum and ventromedial prefrontal cortex were correlated with a willingness to expend greater effort for larger rewards, particularly when probability of reward receipt was low. Additionally, variability in DA responses in the bilateral insula was negatively correlated with willingness to expend effort for rewards, consistent with evidence implicating this region in the processing of response costs. These findings highlight the role of DA signaling in striatal, prefrontal, and insular regions as key neurochemical mechanisms underlying individual differences in cost/benefit decision-making.

Figures

Figure 1.
Figure 1.
Schematic diagram of a single trial of the EEfRT. A, Trials begin with a 1 s fixation cue. B, Subjects are then presented with a 5 s choice period in which they are given information regarding the reward magnitude of the high-effort option and the probability of receiving a reward. C, The 1 s “ready” screen. D, Subjects make rapid button presses to complete the chosen task and watch a virtual “bar” on the screen that fills up as they progress to their completion goal. E, Subjects receive feedback on whether they completed the task. F, Subjects receive feedback as to whether they received any money for that trial.
Figure 2.
Figure 2.
Relationship between proportion of high-effort choices during low-probability (12%) trials and DA responses. A, SPM depicting voxels showing significant positive correlation between DA responses in left caudate and vmPFC and proportion of high-effort choices during low-probability trials. B, SPM depicting voxels showing significant positive correlation between DA responses in left vlPFC and temporal cortex and proportion of high-effort choices during low-probability trials. C, Scatter plot of proportion high-effort choices during 12% trials and DA responses in vmPFC D, Scatter plot of proportion high-effort choices during 12% trials and DA responses in left caudate. Visualization threshold reflects correction for multiple comparisons, t > 2.5, k >35.
Figure 3.
Figure 3.
Relationship between total proportion of high-effort choices and DA responses in bilateral insula. A, SPM depicting voxels showing significant inverse correlation between DA responses in bilateral insula and overall proportion of high-effort choices. B, Scatter plot of DA responsivity in left insula and proportion of high-effort choices. C, Scatter plot of DA responsivity in right insula and proportion of high-effort choices. Visualization threshold reflects correction for multiple comparisons, t > 2.5, k >35. Note that regression analyses are still significant when high-influence subject is removed (left, b = −0.64, p = 0.001; right, b = −0.53, p = 0.014). R, right; color bar indicates t value.

Source: PubMed

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