Distinct Contributions of Ventromedial and Dorsolateral Subregions of the Human Substantia Nigra to Appetitive and Aversive Learning

Abstract

The role of neurons in the substantia nigra (SN) and ventral tegmental area (VTA) of the midbrain in contributing to the elicitation of reward prediction errors during appetitive learning has been well established. Less is known about the differential contribution of these midbrain regions to appetitive versus aversive learning, especially in humans. Here we scanned human participants with high-resolution fMRI focused on the SN and VTA while they participated in a sequential Pavlovian conditioning paradigm involving an appetitive outcome (a pleasant juice), as well as an aversive outcome (an unpleasant bitter and salty flavor). We found a degree of regional specialization within the SN: Whereas a region of ventromedial SN correlated with a temporal difference reward prediction error during appetitive Pavlovian learning, a dorsolateral area correlated instead with an aversive expected value signal in response to the most distal cue, and to a reward prediction error in response to the most proximal cue to the aversive outcome. Furthermore, participants' affective reactions to both the appetitive and aversive conditioned stimuli more than 1 year after the fMRI experiment was conducted correlated with activation in the ventromedial and dorsolateral SN obtained during the experiment, respectively. These findings suggest that, whereas the human ventromedial SN contributes to long-term learning about rewards, the dorsolateral SN may be particularly important for long-term learning in aversive contexts.

Separate mesocortical and mesolimbic pathways encode effort and reward learning signals

“…Here we demonstrate a segregation between effort and reward learning within SN/VTA across the domains of task activation, functional connectivity, and gray matter density. In SN/VTA, a dorsomedial encoding of reward PEs, and a ventrolateral encoding of effort PEs, extends previous studies on SN/VTA subregions (56,57,67,68) by demonstrating that this segregation has functional implications that are exploited during multiattribute learning. In contrast to previous studies on SN/VTA substructures (56, 67-69), we performed whole-brain imaging, which allowed us to investigate the precise interactions between dopaminergic midbrain and striatal/ cortical areas.…”

“…Although dopaminergic activity cannot be assessed directly using fMRI, both effort and reward PEs were evident in segregated regions of dopaminergic-rich midbrain, and where an effective connectivity analysis indicated a directional influence from SN/VTA toward subcortical (reward PE) and cortical (effort PE) targets via ascending mesolimbic and mesocortical pathways, respectively. Dopaminergic midbrain is thought to comprise several distinct dopaminergic populations that have dissociable functions (54,56,67,68). Here we demonstrate a segregation between effort and reward learning within SN/VTA across the domains of task activation, functional connectivity, and gray matter density.…”

“…In contrast to previous studies on SN/VTA substructures (56, 67-69), we performed whole-brain imaging, which allowed us to investigate the precise interactions between dopaminergic midbrain and striatal/ cortical areas. However, this required a slightly lower spatial SN/ VTA resolution than previous studies (56,(67)(68)(69), restricting our analyses to spatial gradients across the entire SN/VTA rather than subregion analyses. We speculate that the dorsomedial region showing reward PE activity is likely to correspond to a dorsal tier of dopamine neurons known to form mesolimbic connections projecting to VS regions (55) (SI Appendix, Fig.…”

Learning, Reward, and Decision Making

“…Evidence indicates that reward-related prediction errors also play a role in learning in humans. Numerous fMRI studies have reported correlations between RPE signals from RL models and activity in the striatum and midbrain nuclei known to contain dopaminergic neurons during Pavlovian and instrumental learning paradigms (D’Ardenne et al 2008, O’Doherty 2004, O’Doherty et al 2003b,Pauli et al 2015, Wittmann et al 2005). …”

Modeling Trait Anxiety: From Computational Processes to Personality

“…Given a suitable experimental design, this tendency might correspond to a lower fitted temperature parameter in a reinforcement learning model, which increases the probability that reinforced actions are repeated. Since key quantities in reinforcement learning models have well-established neural correlates (46–49), measuring them in neuroimaging experiments could be an effective way to detect anxiety-related differences in neural activation [for a study addressing state anxiety, see Ref. (50)].…”