Influence of Testosterone Metabolites on Song-Control System Neuroplasticity during Photostimulation in Adult European Starlings (Sturnus vulgaris)
Bibliographic record
Abstract
The song-control system is a network of discrete nuclei in the songbird brain that controls the production and learning of birdsong and exhibits some of the best-studied neuroplasticity found in the adult brain. Photoperiodic growth of the song-control system during the breeding season is driven, at least in part, by the gonadal steroid testosterone. When acting on neural tissue, however, testosterone can be metabolized into 5α-dihydrotestosterone (DHT) or 17β-estradiol (E2), which activate different hormonal signaling pathways. By treating adult starlings with both testosterone metabolites and metabolite antagonists, we attempted to isolate the effects of androgen and estrogen treatment on neuroplasticity during photostimulation in male and female European starlings (Sturnus vulgaris). Photostimulation resulted in a large HVC volume typical of the breeding season in all treatments independent of hormone treatment. E2 had additional effects on HVC growth by reducing neuron density and enhancing early survival of new neurons recruited to HVC in females but did not significantly affect HVC volume. Conversely, DHT reduced the migration of new neurons, assessed by the expression of doublecortin, to HVC. DHT also increased syrinx mass and maintained RA (robust nucleus of the arcopallium) cytoarchitecture in the presence of aromatase inhibitors. In addition, we document the first evidence of sex-specific neuroplastic responses of the song-control system to androgens and estrogens. These findings suggest that the contributions of DHT and E2 signaling in songbird neuroplasticity may be regulated by photoperiod and that future studies should account for species and sex differences in the brain.
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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) | 0.000 | 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".