Anhedonia in depression and schizophrenia: A transdiagnostic challenge
Bibliographic record
Abstract
BACKGROUND: Anhedonia, as a dysregulation of the reward circuit, is present in both Major Depressive Disorder (MDD) and schizophrenia (SZ). AIMS: To elucidate the clinical and neurobiological differences between schizophrenia (SZ) and depression (MDD) in regard to anhedonia, while reconciling the challenges and benefits of assessing anhedonia as a transdiagnostic feature under the Research Domain Criteria (RDoC) framework. METHODS: In this review, we summarize data from publications examining anhedonia or its underlying reward deficits in SZ and MDD. A literature search was conducted in OVID Medline, PsycINFO and EMBASE databases between 2000 and 2017. RESULTS: While certain subgroups share commonalities, there are also important differences. SZ may be characterized by a disorganization, rather than a deficiency, in reward processing and cognitive function, including inappropriate energy expenditure and focus on irrelevant cues. In contrast, MDD has been characterized by deficits in anticipatory pleasure, development of reward associations, and integration of information from past experience. Understanding the roles of neurotransmitters and aberrant brain circuitry is necessary to appreciate differences in reward function in SZ and MDD. CONCLUSION: Anhedonia as a clinical presentation of reward circuit dysregulation is an important and relatively undertreated symptom of both SZ and MDD. In order to improve patient outcomes and quality of life, it is important to consider how anhedonia fits into both diagnoses.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".