Alterations in cortical 40-Hz auditory steady-state response dynamics in children with mild-to-severe hearing loss are related to hearing aid use
Bibliographic record
Abstract
Children who are hard-of-hearing (CHH) show greater variability in language and academic outcomes than children with normal hearing (CNH) throughout development, despite early detection and intervention. Nonetheless, our understanding of the neural mechanisms underlying sensory and cognitive processing in CHH remains incomplete. The 40-Hz cortical auditory steady-state response (ASSR) has been widely used as a marker of auditory system integrity and shown to relate to cognition. Thus, the current study sought to determine the impact of mild-to-severe hearing loss on cortical ASSR dynamics. Thirty-seven participants ages 7-15 years old heard a broadband click train stimulus presented at 40 Hz binaurally during magnetoencephalography (MEG). Peak sources in the bilateral primary auditory cortices were identified, and the power envelope of the 40 Hz ASSR was extracted as a function of time and submitted to linear mixed modeling. We found a significant three-way interaction, suggesting that CHH exhibit altered ASSR dynamics between hemispheres compared to CNH. Moreover, hearing aid use was significantly correlated with left hemisphere ASSR power in CHH. These data underscore the importance of consistent auditory access in CHH and provide preliminary evidence that neuroimaging may hold promise in determining the mechanisms underlying behavioral variability in this population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".