Tachykinin 1-expressing neurons in the lateral habenula signal negative reward prediction error

Evaluating outcomes to accurately predict which actions lead to reward is crucial for survival. Discrepancies between expected and realized outcomes, termed reward prediction errors (RPEs), serve as a teaching signal to update subsequent predictions and promote adaptive behavior. Neural correlates of RPEs have been identified in several brain regions, including the lateral habenula (LHb), which contains a subpopulation of neurons encoding negative reward prediction error (nRPE) that are excited by worse-than-expected outcomes and inhibited by better-than-expected outcomes. LHb projections to the midbrain shape firing in dopaminergic neurons and play a well-established role in reward learning and decision-making. However, the LHb engages in a wide variety of behaviors beyond reward processing, and it remains unclear whether these diverse functions are mediated by specific transcriptionally defined cell types. Little is known about the transcriptomic identity of nRPE-encoding neurons, limiting our understanding of the specific role of these signals in outcome valuation. Using cell type-specific recording in mice performing reward-guided tasks, we demonstrate neurons expressing the neuropeptide gene Tachykinin-1 (Tac1) represent a subpopulation of LHb neurons that encode nRPE. We found LHbTac1 activity is sensitive to changes in both the expected value and realized value of rewards, and scales with the magnitude of the difference. Further, LHbTac1 neurons show little modulation to other task-related events, or to innately aversive stimuli that engage a broader population of LHb cell types. Together, these data demonstrate that Tac1 marks a subpopulation of LHb neurons that preferentially encodes nRPE. Our results provide insight into cell type-specific contributions of habenular neurons in nRPE signaling and open avenues for more targeted manipulations of nRPE-encoding neurons to understand their role in reward-guided behavior.