Changes to Neural Activation Patterns (c-fos Labeling) in Chinchilla Auditory Midbrain following Neonatal Exposure to an Enhanced Sound Environment
Bibliographic record
Abstract
Sensory brain regions show neuroplastic changes following deficits or experimental augmentation of peripheral input during a neonatal period. We have previously shown reorganization of cortical tonotopic maps after neonatal cochlear lesions or exposure to an enhanced acoustic environment. Such experiments probe the cortex and show reorganization, but it is unclear if such changes are intrinsically cortical or reflect projections from modified subcortical regions. Here, we ask whether an enhanced neonatal acoustic environment can induce midbrain (inferior colliculus (IC)) changes. Neonatal chinchillas were chronically exposed to a 70 dB SPL narrowband (2 ± 0.25 kHz) sound stimulus for 4 weeks. In line with previous studies, we hypothesized that such exposure would induce widening of the 2 kHz tonotopic map region in IC. To probe c-fos expression in IC (central nucleus), sound-exposed and nonexposed animals were stimulated with a 2 kHz stimulus for 90 minutes. In sound-exposed subjects, we find no change in the width of the 2 kHz tonotopic region; thus, our hypothesis is not supported. However, we observed a significant increase in the number of c-fos-labeled neurons over a broad region of best frequencies. These data suggest that neonatal sound exposure can modify midbrain regions and thus change the way neurons in IC respond to sound stimulation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".