Prepulse Inhibition of Acoustic Startle Reflex as a Function of the Frequency Difference between Prepulse and Background Sounds in Mice
Bibliographic record
Abstract
BACKGROUND: Prepulse inhibition (PPI) depicts the effects of a weak sound preceding strong acoustic stimulus on acoustic startle response (ASR). Previous studies suggest that PPI is influenced by physical parameters of prepulse sound such as intensity and preceding time. The present study characterizes the impact of prepulse tone frequency on PPI. METHODS: Seven female C57BL mice were used in the present study. ASR was induced by a 100 dB SPL white noise burst. After assessing the effect of background sounds (white noise and pure tones) on ASR, PPI was tested by using prepulse pure tones with the background tone of either 10 or 18 kHz. The inhibitory effect was assessed by measuring and analyzing the changes in the first peak-to-peak magnitude, root mean square value, duration and latency of the ASR as the function of frequency difference between prepulse and background tones. RESULTS: Our data showed that ASR magnitude with pure tone background varied with tone frequency and was smaller than that with white noise background. Prepulse tone systematically reduced ASR as the function of the difference in frequency between prepulse and background tone. The 0.5 kHz difference appeared to be a prerequisite for inducing substantial ASR inhibition. The frequency dependence of PPI was similar under either a 10 or 18 kHz background tone. CONCLUSION: PPI is sensitive to frequency information of the prepulse sound. However, the critical factor is not tone frequency itself, but the frequency difference between the prepulse and background tones.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".