Parsing Anhedonia: A reverse-translational strategy for treatment of anhedonia in clinical populations and potential implications of conditioned motivators
Bibliographic record
Abstract
Anhedonia is defined as reduced interest or pleasure in activities previously considered enjoyable and is a cardinal symptom of many neuropsychiatric disorders including major depression, schizophrenia and substance dependence. Pleasurable experiences involve a variety of psychobiological components including learning, memory and motivation that influence engagement with rewarding events and can therefore impact affective responding. Despite the capacity to dissociate these processes in humans and nonhuman animals, contemporary preclinical animal models of anhedonia emphasize responses to immediately pleasurable stimuli including palatable food and drugs of abuse. This limits translatability to the clinic as human patients exhibiting anhedonia largely display normalized responsivity to pleasurable stimuli and instead show deficits in responding for associative cues. Conditioned motivators can serve to bridge the gap between clinical and preclinical knowledge, as they can be dissociated into each independent component process associated with anhedonia. Following several distinct temporally-contingent associations with reward, neutral stimuli acquire meaning and become conditioned motivators, which can be uniquely manipulated to parse several component processes within a variety of tasks. Thus, the properties of conditioned motivators in anhedonia and the neural substrates underlying them will be critical in translating knowledge about these independent neuropsychiatric processes to the clinic. This review emphasizes the utilization of ‘reverse-translation’, integrating patient-based findings with preclinical animal models to experimentally parse component processes of anhedonia and develop holistic experimental models to measure it. Dissociating the independent, measurable component processes of anhedonia is critical for accurate representation in preclinical animal models and for acceleration of treatment strategies to the clinic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".