The proposed research addresses a major mental health issue (anhedonia) with a novel computationally-inspired translational technique in both humans and mice. This approach greatly increases the likelihood that a positive animal model result will be successfully translated to humans. This research plan thus offers a novel way to address the NIMH's mission of defining mechanisms of complex behaviors.

Although considered a trans-diagnostic phenotype, anhedonia can emerge from deficits in motivation, valuation, or hedonic appreciation, each of which reflect different neural processes and are differently expressed across individuals. There is a critical need to refine the construct of anhedonia in order to improve treatment. The long-term goal of this project is to combine computational, imaging, and causal manipulations to define a translational biomarker of diminished valuation in anhedonia. This proposal aims to identify how the EEG response known as the Reward Positivity (RewP) is a candidate biomarker specific to value-based deficiencies in anhedonia. The RewP is only elicited by the presentation of a rewarding outcome, it is decreased in depression, and it scales with the central feature of reinforcement learning models, the positive reward prediction error (+RPE). Importantly, this same neural response can be elicited in rodents using the same learning task as in humans. The objective of this proposal is to test whether induced emotion, depressed mood, and learned helplessness (in mice) directly diminish +RPE coding in the RewP. The rationale for this approach is that electrophysiology is a highly promising tool for identifying mechanisms of complex behaviors and translating these mechanisms between species. Aim 1 will determine if induced emotion and +RPE have independent or interactive effects on the RewP. Aim 2 will recruit depressed participants and determine if anhedonia and +RPE have independent or interactive influences on the source-level generators underlying the RewP (using MEG). Aim 3 will use the same task in a mouse model with infralimbic recordings; this will then test the causal diminishment (learned helplessness) and recovery (fluoxetine) of this mechanism. This proposed research is innovative because it identifies a computational function tightly tied to a neural response that directly addresses the disease-specific phenotype in human patients and is capable of being assessed, manipulated, and recovered within a rodent model. This contribution is expected to be significant because it will advance a translational mechanism for deficient valuation in anhedonia. Upon completion of these aims, the expected outcome will validate the RewP as a sensitive and specific mechanism of aberrant valuation in anhedonia. In line with the Research Domain Criteria framework, the use of computational modeling will facilitate algorithmic contrasts between multiple sub-constructs of approach motivation in the positive valence systems domain. The translational computational psychiatry approach advanced here links circuit-level dysfunction, aberrant computations, and trans-diagnostic behavioral phenotype. The successful completion of the aims advanced here will create a highly promising path for combining these strengths into a computationally-inspired, mechanistically tested, translatable model of aberrant valuation in anhedonia. This novel candidate biomarker will be translatable between species and testable in an outpatient clinic.

Inclusion Criteria:

• Men or women aged 18-55
• Free from psychoactive medication for at least 2 weeks

Exclusion Criteria:

• Participants unwilling or unable to give informed consent
• Presence of other known medical or psychiatric comorbidity that in the investigator's opinion would compromise participation in the study
• History of psychosis
• Not fluent in English

Healthy non-depressed individuals 18-55 years old.

Adverse event data were collected on the single day of the study. (1 day period of time).

Randomization will be based on the last digit (odd vs. even) of the participant's research identifier.