Brainstem correlates of temporal auditory processing in children with specific language impairment
Bibliographic record
Abstract
Deficits in identification and discrimination of sounds with short inter-stimulus intervals or short formant transitions in children with specific language impairment (SLI) have been taken to reflect an underlying temporal auditory processing deficit. Using the sustained frequency following response (FFR) and the onset auditory brainstem responses (ABR) we evaluated if children with SLI show abnormalities at the brainstem level consistent with a temporal processing deficit. To this end, the neural encoding of tonal sweeps, as reflected in the FFR, for different rates of frequency change, and the effects of reducing inter-stimulus interval on the ABR components were evaluated in 10 4-11-year-old SLI children and their age-matched controls. Results for the SLI group showed degraded FFR phase-locked neural activity that failed to faithfully track the frequency change presented in the tonal sweeps, particularly at the faster sweep rates. SLI children also showed longer latencies for waves III and V of the ABR and a greater prolongation of wave III at high stimulus rates (>30/sec), suggesting greater susceptibility to neural adaptation. These results taken together appear to suggest a disruption in the temporal pattern of phase-locked neural activity necessary to encode rapid frequency change and an increased susceptibility to desynchronizing factors related to faster rates of stimulus presentation in children with SLI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".