Dorsolateral prefrontal cortex drives mesolimbic dopaminergic regions to initiate motivated behavior

Abstract

How does the brain translate information signaling potential rewards into motivation to get them? Motivation to obtain reward is thought to depend on the midbrain [particularly the ventral tegmental area (VTA)], the nucleus accumbens (NAcc), and the dorsolateral prefrontal cortex (dlPFC), but it is not clear how the interactions among these regions relate to reward-motivated behavior. To study the influence of motivation on these reward-responsive regions and on their interactions, we used dynamic causal modeling to analyze functional magnetic resonance imaging (fMRI) data from humans performing a simple task designed to isolate reward anticipation. The use of fMRI permitted the simultaneous measurement of multiple brain regions while human participants anticipated and prepared for opportunities to obtain reward, thus allowing characterization of how information about reward changes physiology underlying motivational drive. Furthermore, we modeled the impact of external reward cues on causal relationships within this network, thus elaborating a link between physiology, connectivity, and motivation. Specifically, our results indicated that dlPFC was the exclusive entry point of information about reward in this network, and that anticipated reward availability caused VTA activation only via its effect on the dlPFC. Anticipated reward thus increased dlPFC activation directly, whereas it influenced VTA and NAcc only indirectly, by enhancing intrinsically weak or inactive pathways from the dlPFC. Our findings of a directional prefrontal influence on dopaminergic regions during reward anticipation suggest a model in which the dlPFC integrates and transmits representations of reward to the mesolimbic and mesocortical dopamine systems, thereby initiating motivated behavior.

Figure 1.

The full tested model space. Sixteen models that systematically varied the context sensitivity of the connections represented by blue arrows were constructed. The black arrows represent connections that were allowed to be context sensitive in all models. These 16 models were crossed with all seven possible combinations of driving inputs (red). Driving inputs represent cue information about all reward types (high and low), while modulatory inputs represented only high reward cues.

Figure 2.

Bayesian model selection results for the full model space. The Bayesian model selection indicates the most likely model for the full model space. The top eight models account for 81% of the exceedance probability (the exceedance probabilities for all 112 models sum to 1). All eight of the best models have the driving input solely at the dlPFC. Context-sensitive connections for the top eight models are shown above in the inset.

Figure 3.

Exceedance probabilities for each family of models sharing a driving input configuration. The ratio of exceedance probabilities of the first to second best model is 21, indicating with very high certainty that reward information enters the modeled network solely at the dlPFC.

Figure 4.

Connectivity determined by Bayesian model averaging of models in the winning family, which all shared driving inputs solely to the dlPFC (Occam's window, 11.8 models; SD, 5.3). All solid connections are significant at a posterior probability threshold of 95% that the posterior mean is larger than zero. The dotted connections are not significant. Driving inputs represent cue information about all reward types (high and low), while modulatory inputs represented only high reward cues. Left, The intrinsic (baseline) connectivity for each connection. Right, Modulation of connectivity during reward motivation. Modulation of the blue connections was varied in the model space. Connection strengths are indicated on each arrow (in hertz). Only the connections from dlPFC to VTA and NAcc are significantly modulated by reward motivation.