Fluoxetine and the dentate gyrus: memory, recovery of function, and electrophysiology
Bibliographic record
Abstract
Chronic fluoxetine increases neurogenesis in the dentate gyrus (DG). In view of the widespread clinical use of fluoxetine and the well-established role of the DG in memory, surprisingly few studies have examined the effects of fluoxetine on memory and hippocampal electrophysiology. Additionally, few studies have evaluated the potential for fluoxetine to promote recovery of function after DG damage. Therefore, we studied the effects of long-term administration of fluoxetine on both spatial-reference memory and working memory, recovery of function after intrahippocampal colchicine infusions, which can destroy 50-70% of DG granule cells, and electrophysiological responses in the DG to perforant path stimulation in freely moving rats. Chronic fluoxetine did not affect matching-to-place or reference-memory performance in intact rats in the Morris water-maze task. Surprisingly, in rats with DG damage, recovery of function on both tasks was adversely affected by chronic fluoxetine. Finally, unlike an earlier study that reported fluoxetine-induced increases in hippocampal population spike amplitudes and excitatory postsynaptic potential slopes in urethane-anesthetized rats, electrophysiological measures in DG of freely moving rats were not affected by chronic fluoxetine treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| 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".