Investigating neuropsychological and reward-related deficits in a chronic corticosterone-induced model of depression
Bibliographic record
Abstract
Chronic stress is a known risk factor for the development of major depression (MDD) and is commonly used to induce a depression-like phenotype in rodents. Similar phenotypic effects are also observed in rodents when treated chronically with the stress hormone corticosterone. In this study, we investigated the neuropsychological consequences of chronic corticosterone treatment in male rats using two translational rodent assays of affective bias, the judgement bias task (JBT) and affective bias test (ABT). We also used the reward learning assay (RLA) and sucrose preference test (SPT) to quantify reward-related behaviours. Negative biases in decision-making were observed in the chronic corticosterone-treated group but only when the treatment was given shortly before each behavioural session. The same dose of corticosterone, when given daily after completion of the behavioural session had no effects. Chronic corticosterone treatment did not potentiate negative affective biases in the ABT induced by either an acute pharmacological or stress manipulation but both reward learning and reward sensitivity were blunted. Analysis of the brain tissue from animals receiving chronic corticosterone found reduced hippocampal neurogenesis consistent with previous studies suggesting corticosterone-induced neurotrophic deficits. Taken together, these data suggest chronic corticosterone treatment induces neuropsychological effects related to changes in reward learning, memory and negative biases in decision making, but these decision-making biases depend on whether rewarding outcomes were experienced during the acute effects of the drug. These findings suggest an important interaction between psychological and biological factors resulting in negative biases in decision-making in this model.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".